James C. Schnable: Curriculum Vitae (2024)

Tenure Package Executive Summary: James C. Schnable

Position Summary

Initial appointment: Assistant Professor at the University of Nebraska-Lincoln. Start date: May 1st, 2014. Nine-month appointment responsibilities: 80% research, 18% teaching, 2% service. Unit Membership: Tenure home in the Department of Agronomy and Horticulture. Hired as part of a cluster for the Quantitative Life Sciences Initiative. Appointment to the Center for Plant Science Innovation after my initial hire.

Research Metrics Summary (80%)

Funding: 5 federal grants (2 USDA; 2 NSF; 1 ARPA-E), two as lead PI (1 NSF; 1 USDA). Sixth grant (NSF) currently recommended for funding. Current support from the North Central Sun Grant pro- gram, the Nebraska Corn Board, the Wheat Innovation Foundation, and the Water for Food Global Institute and the Midwest Big Data Hub. Prior support from a USAID Linkages Grant with ICRISAT (Hyderabad, India), ConAgra’s Popcorn program, and Iowa Corn Board. $2.42M in funding over four years (See page 2). $1.37M in funding currently "recommended" for support by NSF. $4.78M in pro- posals currently under under review at the National Science Foundation, Department of Energy, and Foundation for Food and Agriculture (See page 51). Publications: 56 total peer reviewed publications; 38 since appointment as an assistant professor at UNL; 28 resulting from work conducted at UNL; 14 papers where a member of my lab (or I) was the ﬁrst or co-ﬁrst author (See page 6). These papers have been cited a total of 3,166 times, or 1,930 times if two marker papers for the release of genome sequences for new grass species are excluded. H-index of 21. Invited Presentations: 50 total invited talks or seminars; 37 since I was hired as an assistant profes- sor at the University of Nebraska-Lincoln; 29 excluding seminars and conferences afﬁliated with the University of Nebraska-Lincoln. Three additional invited speaking engagements scheduled before the deadline for the ﬁnal tenure package (December 1st) (See page 15) Notable Recognition: Junior Faculty Research Award (2016); M. Rhoades Early Career Maize Genetics Award (2018) (see page 51)

Teaching and Mentoring Metrics Summary (18%)

Mentoring: 4 Postdoctoral Scholars; 1 Visiting Scientist; 3 PhD students; 1 co-advised PhD student; 2 visiting PhD students; 1 MS student; 2 co-advised MS students; 13 undergraduates – 4 REU (Research Experience for Undergraduates) students; 2 UCARE (Undergraduate Creative Activities and Research Experience) students; and 7 undergraduate students supported by regular research funding – and 2 high school students. (See page 4) Teaching: Developed and taught a new graduate level course: Professional Development for 1st year graduate students (taught Fall ’15 ’16 ’17 and ’18. Taught “Big Question in Complex Biosystems” Fall ’17 and ’18. (See page 24)

Service Metrics Summary (2%)

University Service: Currently an active member of 6 university committees. Prior service on seven additional committees (4 search committees, 3 organizing committees for meetings). (See page 14) Professional Service: Associate Editor for Molecular Plant; MaizeGDB Advisory Committee; Grant Reviewer: NSF, USDA, JGI, Genome British Columbia. Peer reviewer for 21 journals (includes Science, Nature Plants, PNAS, and Plant Cell). (See page 15) James C. Schnable II

Contents

Executive Summary I

Table of Contents II

Curriculum Vitae 1 Employment...... 1 Education...... 1 Honors and Awards ...... 1 Research Support ...... 1 Economic Development ...... 4 Advising and Mentoring ...... 4 Graduate Students ...... 4 Undergraduates and High School Students ...... 4 Postdoctoral Scholars and Visiting Scientists ...... 4 Publication List ...... 6 Faculty Publications ...... 6 Postdoctoral Publications ...... 12 Graduate Publications ...... 12 Professional and University Service ...... 14 Invited Presentations ...... 15

Candidate Statement 18 Program Overview ...... 18 Research Contributions ...... 19 Introduction ...... 19 Emphasis I: Comparative Genomics of Maize, Sorghum, and Allied Species ...... 19 Emphasis II: Phenomics for Breeding and Quantitative Genetics ...... 22 Emphasis III: The Nebraska Food for Health Center ...... 23 Mentoring and Teaching Contributions ...... 23 Service Contributions ...... 25

Appendix A: Supporting Evidence for Mentoring Activity and Outcomes 26 Present Employment of Lab Alumni ...... 26 Partial List of Poster Presentations With Undergraduate Authors Highlighted ...... 26 Letters from Former Mentees ...... 28 Example Syllabi ...... 36

Appendix B: Supporting Evidence for Research Activity and Outcomes 44 Cover Page Summaries of Funded Grants ...... 44 Pending Grants ...... 51 Letters From UNL Administrators ...... 51 Press Released and News Articles ...... 54 Five Recent and Signiﬁcant Manuscripts ...... 67 James C. Schnable 1

Curriculum Vitae James C. Schnable

Quantitative Life Sciences Initiative Ofﬁce: E207 Beadle Center Center for Plant Science Innovation Phone: (402) 472-3192 Nebraska Food for Health Center Email: [emailprotected] Department of Agronomy & Horticulture Web: schnablelab.org University of Nebraska-Lincoln

Employment

Assistant Professor 2014-present Department of Agronomy and Horticulture, University of Nebraska-Lincoln Start Date: May 1st. Appointment: 80% Research, 18% Teaching and Mentoring 2% Service. NSF PGRP Fellowship Supported Visiting Scholar 2014 Chinese Academy of Agricultural Sciences NSF PGRP Fellowship Supported Postdoctoral Researcher 2013 Donald Danforth Plant Science Center

Education

PhD Plant Biology (with Michael Freeling) 2008-2012 University of California-Berkeley BA Biology 2004-2008 Cornell University

Honors and Awards

Marcus Rhoades Early Career Award 2018 Junior Faculty Excellence in Research Award, University of Nebraska-Lincoln 2016 Faculty Fellow, Robert B. Dougherty Water for Food Institute 2016-Present

Research Support

$2.42M in funding over four years. An additional $1.37M in funding currently recommended for support by NSF. $4.78M in proposals currently under under review at the National Science Foundation, Department of Energy, and Foundation for Food and Agriculture (see page 51) James C. Schnable 2

Current 1. “RoL: FELS: EAGER: Genetic Constraints on the Increase of Organismal Complexity Over Time” National Science Foundation - Rules of Life Schnable JC (PI) Award Period: 08/01/2018 - 07/31/2020 Award Amount: $299,801 2. “Identifying mechanisms conferring low temperature tolerance in maize, sorghum, and frost tolerant relatives.” US Department of Agriculture - National Institute of Food and Agriculture - Agriculture and Food Research Initiative Schnable JC (PI), Roston RL (Co-PI) (Department of Biochemistry, UNL) Award Period: 12/15/2015 - 12/14/2019 Award Amount: $455,000 3. “In-plant and in-soil microsensors enabled high-throughput phenotyping of root nitrogen uptake and nitrogen use efﬁciency.’ ARPA-E ROOTS Dong, L (PI) (Department of Electrical and Computer Engineering, Iowa State), Schnable JC (co-PI), Castellano, M (co-PI) (Department of Agronomy, Iowa State) Award Period: 06/12/2017 - 06/11/2019 Award Amount: $1,100,000. Award Amount to UNL: $334,169 4. “Center for Root and Rhizobiome Innovation.” (Investigator & Management Team Member) National Science Foundation - EPSCoR Choobineh F (PI), Cahoon E (co-PI), Alfano J (co-PI). JCS: wrote one of four objectives in the original grant. Manages that the team of 6 PIs working on that objective. Serves on the management team for the overall project. Award Period: 06/15/2016 - 05/31/2021 Award Amount: $20M total. Awarded to UNL: $10M. Funding directly and speciﬁcally to the Schnable Lab: $649,170 5. “PAPM EAGER: Transitioning to the next generation plant phenotyping robots.” (co-PI) USDA/NSF Joint Program Ge, Y (PI) (Department of Biological Systems Engineering (BSE), UNL), Schnable JC (co-PI), Pitla S (co- PI) (BSE, UNL) Award Period: 11/15/2016 - 11/14/2018 Award Amount: $285,000. 6. Nebraska Corn Board “Genomes to Fields (G2F) - Predicting Final Yield Performance in Variable Environments.” Nebraska Corn Board (Three separate and sequential competitively awarded grants of one year each) Award Period: 07/01/2016 - 06/30/2017 Award Amount: $42,620 Team: Schnable JC (PI), Ge, Y (co-PI) (BSE, UNL), Rodriguez, O (co-PI) (Agronomy and Horticulture, UNL) Award Period: 07/01/2017 - 06/30/2018 Award Amount: $47,945 Team: Schnable JC (PI), Ge, Y (co-PI) (BSE, UNL), Rodriguez, O (co-PI) (Agronomy and Horticulture, UNL) Award Period: 07/01/2018 - 06/30/2019 Award Amount: $51,054 Team: Schnable JC (PI), Ge, Y (co-PI) (BSE, UNL), Shi, Y (co-PI) (BSE, UNL) 7. “Optimizing the Water Use Efﬁciency of C4 Grain Crops Using Comparative Phenomics and Crop Models to Guide Breeding Targets.” Daugherty Water for Food Global Institute – University of Nebraska Foundation (Original Award and Accomplishment Based Renewal) Award Period: 07/01/2017 - 06/30/2018 Award Amount: $17,658 Team: Schnable JC (PI) Award Period: 07/01/2018 - 06/30/2019 Award Amount: $12,000 Team: Schnable JC (PI) James C. Schnable 3

8. “High through put phenotyping to accelerate biomass sorghum improvement.” (co-PI) Sun Grant Program - North Central Region (Original Award and Competitive Renewal) Award Period: 09/01/2016 - 12/31/2018 Award Amount: $149,633 Team: Ge, Y (PI) Schnable JC (co-PI), Sibel Irmak (co-PI) (BSE, UNL), Jin, J (Agricultural & Biological Engineering, Purdue) Award Period: 07/01/2018 - 06/30/2019 Award Amount: $44,000 Team: Ge, Y (PI) Schnable JC (co-PI)

9. “A Low-Cost, High-Throughput Cold Stress Perception Assay for Sorghum Breeding.” Wheat Innovation Fund – University of Nebraska Foundation Roston RL (PI) (Biochemisty, UNL), Schnable JC (co-PI) Award Period: 1/1/2019 - 12/31/2021 Award Amount: $205,000 10. “Automatic feature extraction pipeline development for high-throughput plant phenotyping” National Science Foundation Big Data Hub - Competitive Subaward Xu, Z (PI) (Statistics, UNL), Cui J (co-PI) (Computer Science and Engineering (CSE), UNL), Ge, Y (co-PI) (BSE, UNL), Qiu Y (co-PI) (Statistics, UNL), Schnable JC (co-PI) Award Period: 10/01/2017 - 09/30/2018 Award Amount: $5,000

Completed

11. “Application of tGBS And Genomic Selection to a Hybrid Pearl Millet Breeding Program.” USAID Linkages Grant with ICRISAT (A CGAR Center) Schnable JC (PI), Gupta SK (co-PI) (Pearl Millet Breeding Lead, ICRISAT), Schnable PS (co-PI) (Agronomy, Iowa State) Award Period: 7/1/15 - 9/30/17 Award Amount: $45,000 Awarded to UNL: $23,000 12. “Field Deployable Cameras to Quantify Dynamic Whole Plant Phenotypes in the Field.” Iowa Corn Board Schnable JC (PI) Award Period: 7/1/14 - 6/30/16 Award Amount: $45,395 13. “Marker Discovery & Genetic Diversity. (In Popcorn)” ConAgra Foods Lorenz A (original PI), Schnable JC (replacement PI) Award Period: 01/01/2014 - 12/31/2017 Award Amount: $162,284 14. “A High Throughput Phenotyping Reference Dataset for GWAS in Sorghum” Agricultural Research Division - Internal UNL Funding Schnable JC (PI), Ge Y (co-PI) (BSE, UNL), Qiu Y (co-PI) (Statistics, UNL), Samal A (co-PI) (CSE, UNL), Sigmon B (Agronomy & Horticulture, UNL) Award Period: 07/01/2016 - 6/30/2018 Award Amount: $99,159

Recommended for Funding

15. “RII Track-2 FEC: Functional analysis of nitrogen responsive networks in Sorghum” National Science Foundation - EPSCoR Schmutz J (PI) (Hudson Alpha), Lamb N (co-PI) (Hudson Alpha), Schnable JC (co-PI), Swaminathan K (co-PI) (Hudson Alpha), Clemente T (co-PI) (Agronomy and Horticulture, UNL) James C. Schnable 4

Award Period: 01/01/2019 - 12/31/2022 Award Amount: $4M total. Awarded to UNL: $1,337,633. Funding directly and speciﬁcally to the Schnable Lab: $648,710

Economic Development

Co-Founder, EnGeniousAg LLC 2017-Present Designs, manufactures, and deploys low-cost, instant readout, high-performance, ﬁeld-based nutrient sensors for crops, soil, and water, improving agronomic management practices, increasing grower proﬁtability and reducing the environmental footprint of agriculture. Founder, Dryland Genetics LLC 2014-Present Using high throughput quantitative genetics and ﬁeld phenotyping techologies to develop and commericialize higher yielding cultivars of crops already naturally adapted to using little water and growing arid regions where conventional agriculture fails in the absence of irrigation. Co-Founder, Data2Bio LLC (USA) & DATA生物科技（北京）有限公司 (China) 2010-Present Providing patented tGBS genotyping and genomic selection services to public and private sector plant and animal breeders in the USA and China. Scientiﬁc Advisory Council, GeneSeek, Inc 2017-Present External Advisor to the Scientiﬁc Advisory Board, Indigo Agriculture 2017 External Advisor to the Scientiﬁc Advisory Board, Syngenta AG 2016

Mentoring

Table 1: Graduate Students Mentored Student Degree Program Years Zhikai Liang PhD Agronomy & Horticulture 2015-Present Daniel Carvalho PhD Agronomy & Horticulture 2015-Present Chenyong Miao PhD Agronomy & Horticulture 2016-Present Preston Hurst MS Agronomy & Horticulture 2016-Present Nate Korth PhD (co-advised) Food Science and Technology 2017-Present Xiuru Dai PhD (CSC student) Shandong Agriculture University 2017-Present Xianjun Lai PhD (CSC student) Sichuan Agriculture University 2015-2017 Bhush*t Agarwall MS (co-advised) Computer Science & Engineering 2016-2017 Srinidhi Bashyan MS (co-advised) Computer Science & Engineering 2016-2017 James C. Schnable 5

Table 2: Undergraduates and High School Students Engaged In Research Student Program Years Daniel Ngu Startup and Grant Funds 2014-2017 Kyle Johnson Bioenergy REU Program Summer 2016 Taylor Horn QLSI REU Program Summer 2016 Logan Olson Startup and Grant Funds 2016-2017 Xiaoyang Ye Startup and Grant Funds 2016-2017 Holly Podliska UCARE Program 2016-2017 Nicole Hollander HS Student (Young Nebraska Scientist Program) Summer 2017 Connor Pedersen Grant Funds 2016-2018 Tom Hoban Startup and Grant Funds 2016-Present Isabel Sigmon HS Student (Grant Funds) Summer 2018 Christian Butera Bioenergy REU Program Summer 2018 Ashley Foltz CRRI REU Program Summer 2018 Alex Enersen Grant Funds 2018-Present Alexandra Bradley Grant Funds 2018-Present Alejandro Pages UCARE Program 2018-Present

Table 3: Postdoctoral Scholars and Visiting Scientists Name Years Present Position Yang Zhang (Postdoc) 2014-2017 Scientist, St. Jude’s Children Hospital Lang Yan (Visiting Scholar) 2016-2017 Deputy Director, Potato Functional Genomics, XiChang College Jinliang Yang (Postdoc) 2016-2107 Assistant Professor, University of Nebraska-Lincoln Sunil Kumar (Postdoc) 2017-2018 Postdoc, Niederhuth Lab, Michigan State Guangchao Sun (Postdoc) 2017-Present Schnable Lab Xiaoxi Meng (Postdoc) 2018-Present Schnable Lab James C. Schnable 6

Publications

H-Index: 21 Google Scholar ‡ § Lab members in bold, ∗equal contribution, undergraduate, corresponding The quality, importance, and impact of scientiﬁc articles can be assessed in numerous ways. Some methods, such as individual citation counts, are considered to have greater reliability, but take longer to accumulate. Based on department guidelines, I am providing the Impact Factors for each journal in which I have published a manuscript since beginning my position at the University of Nebraska-Lincoln. In addition, I am providing the number of citations for each article retrieved from Google Scholar, and each article’s Altmetric score, an aggregate estimate of the amount of attention an article receives in the press and social media following publication. Altmetric scores have been shown to exhibit a statistically signiﬁcant, albeit imperfect, correlation with future citation counts, and provide a non-impact factor based estimator of individual article signiﬁcance which can be quantiﬁed for rapidly that citation counts which often require several years to accumulate.

Preprints Miao C, Yang, J, Schnable JC.§ Optimizing the identiﬁcation of causal variants across varying ge- netic architectures in crops. bioRxiv doi: 10.1101/310391

Yan L, Raju SKK, Lai X, Zhang Y, Dai X, Rodriguez O, Mahboub S, Roston RL, Schnable JC.§ Parallels between artiﬁcial selection in temperate maize and natural selection in the cold-adapted crop-wild relative Tripsacum. bioRxiv doi: 10.1101/187575

Other Manuscripts in Review Zou C, Miki D, Li D, Tang Q, Xiao L, Rajput S, Deng P, Peng L, Huang R, Zhang M, Sun Y, Hu J, Fu X, Schnable P, Li F, Zhang H, Feng B, Zhu X, Liu R, Schnable JC, Zhu JK, Zhang H.§ The genome of broomcorn millet (Panicum miliaceum L.) (In Review)

Faculty Publications 56. Lai X, Yan L, Lu Y, Schnable JC§ (2018) Largely unlinked gene sets targeted by selection for domes- tication syndrome phenotypes in maize and sorghum. The Plant Journal doi: 10.1111/tpj.13806 bioRxiv doi: 10.1101/184424 Altmetric Score: 22 (87th percentile for papers of similar age (+/- 6 weeks) published in this jour- nal). Times Cited to Date: 1 Journal Impact Factor (2017): 5.8 Schnable Lab Contribution: All analyses and writing conducted by lab members. 55. Liang Z, Gupta SK, Yeh CT, Zhang Y, Ngu DW,‡ Kumar R, Patil HT, Mungra KD, Yadav DV, Rathore A, Srivastava RK, Gupkta R, Yang J, Varshney RK, Schnable PS, Schnable JC§ (2018) Phenotypic data from inbred parents can improve genomic prediction in pearl millet hybrids. G3:Genes Genomes Genetics doi: 10.1534/g3.118.200242 Altmetric Score: 26 (97th percentile for papers of similar age (+/- 6 weeks) published in this journal). Times Cited to Date: 0 Journal Impact Factor (2017): 2.7 Schnable Lab Contribution: Built the libraries, analyzed the SNP data, conducted the GS tests, wrote the paper. Field data and extracted DNA contributed by ICRISAT collaborators. Sequencing and SNP calling contributed by ISU collaborators. James C. Schnable 7

54. Miao C, Fang J, Li D, Liang P, Zhang X, Yang J, Schnable JC, Tang H§ (2018) Genotype-Corrector: improved genotype calls for genetic mapping. Scientific Reports doi: 10.1038/s41598-018-28294-0 Altmetric Score: 29 (91st percentile for papers of similar age (+/- 6 weeks) published in this journal). Times Cited to Date: 0 Journal Impact Factor (2017): 4.1 Schnable Lab Contribution: Improved and documented the core algorithm. Conducted tests of how much the core algorithm improved genotype call accuracy in a RIL and F2 population when using sub-optimal sequencing depth. Wrote the paper collaboratively with Haibao Tang. 53. Raju SKK, Barnes A, Schnable JC, Roston RL§ (2018) Low-temperature tolerance in land plants: Are transcript and membrane responses conserved? Plant Science doi: 10.1016/j.plantsci.2018.08.002 Altmetric Score: 13 (97th percentile for papers of similar age (+/- 6 weeks) published in this journal). Times Cited to Date: 0 Journal Impact Factor (2017): 3.7 Schnable Lab Contribution: Sunil and I wrote the portions of this review focused on conserved patterns transcriptional responses to cold stress across diverse plants, and worked collaboratively with the Roston lab on the combined transcript/lipid analyses. 52. Carvalho DS, Schnable JC, Almeida AMR§ (2018) Integrating phylogenetic and network approaches to study gene family evolution: the case of the AGAMOUS family of ﬂoral genes. Evolutionary Bioinformatics doi: 10.1177/1176934318764683 bioRxiv doi: 10.1101/195669 Altmetric Score: 6 (89th percentile for papers published in this journal). Insufﬁcient papers of similar age to generate a percentile ranking. Times Cited to Date: 0 Journal Impact Factor (2017): 1.9 Schnable Lab Contribution: Conducted the majority of the analyses. Wrote the paper. 51. Xu Y, Qiu Y, Schnable JC§ (2018) Functional modeling of plant growth dynamics. The Plant Phe- nome doi: 10.2135/tppj2017.09.0007 bioRxiv doi: 10.1101/190967 Altmetric Score: 13 (Altmetric score from preprint. 67th percentile for preprints of similar age (+/- 6 weeks) on bioRxiv). Times Cited to Date: 0 Journal Impact Factor (2017): Not Yet Assigned Schnable Lab Contribution: Conceived of the experiment to test subsampling on different days. Wrote the paper 50. Ott A, Schnable JC, Yeh CT, Wu L, Liu C, Hu HC, Dolgard CL, Sarkar S, Schnable PS.§ (2018) Linked read technology for assembling large complex and polyploid genomes. BMC Genomics (Accepted) Altmetric Score: NA. Times Cited to Date: NA Journal Impact Factor (2017): 3.7 Schnable Lab Contribution: Conducted an analysis of a linked read genome assembly of proso millet, a previously unsequenced allotetraploid grass to assess the accuracy with which separate subgenomes were assembled and resolved using this new linked-reads technique.

49. Liu S,∗ Schnable JC,∗ Ott A,∗ Yeh CT, Springer NM, Yu J, Meuhbauer G, Timmermans MCP, Scan- lon MJ, Schnable PS§ (2018) Intragenic Meiotic Crossovers Generate Novel Alleles with Transgressive Expression Levels. Molecular Biology and Evolution (Accepted) Altmetric Score: NA. Times Cited to Date: NA Journal Impact Factor (2017): 10.2 Schnable Lab Contribution: Analysis of relative correlation between recombination frequency per megabase and the relative density of either syntenic or nonsyntenic genes separately was conducted in the Schnable Lab@UNL. James C. Schnable 8

48. Alkhalifah N, Campbell DA, Falcon CM, ... Schnable JC (31 of 44 authors) ... Spalding EP, Edwards J, Lawrence-Dill CJ§ (2018) Maize Genomes to Fields: 2014 and 2015 ﬁeld season genotype, phenotype, environment, and inbred ear image datasets. BMC Research Notes doi: 10.1186/s13104-018-3508-1 Altmetric Score: 12 (89th percentile for papers of similar age (+/- 6 weeks) published in this journal). Times Cited to Date: 0 Journal Impact Factor (2017): Not Yet Assigned Schnable Lab Contribution: Data collection from grow outs of Genomes to Fields hybrids at Nebraska ﬁeld sites, assisted in writing the manuscript itself. 47. Zhang Y, Ngu DW,‡ Carvalho D, Liang Z, Qiu Y, Roston RL, Schnable JC§ (2017) Differentially reg- ulated orthologs in sorghum and the subgenomes of maize. The Plant Cell doi: 10.1105/tpc.17.00354 Selected as an Editor’s Choice by MaizeGDB Editorial Board August 2017 Altmetric Score: 34 (97th percentile for papers published in this journal). Insufﬁcient papers of sim- ilar age to generate a percentile ranking. Times Cited to Date: 6 Journal Impact Factor (2017): 8.2 Schnable Lab Contribution: All analyses and writing conducted by lab members. Qiu lab assisted in developing new analytical approaches to comparing gene expression across species. Roston lab assisted in interpreting biological responses to plant cold stress.

§ 46. Lai X,∗ Behera S,∗ Liang Z, Lu Y, Deogun JS, Schnable JC (2017) STAG-CNS: An order-aware conserved noncoding sequence discovery tool for arbitrary numbers of species. Molecular Plant. doi: 10.1016/j.molp.2017.05.010 Altmetric Score: 8 (62nd percentile for papers of similar age (+/- 6 weeks) published in this journal). Times Cited to Date: 3 Journal Impact Factor (2017): 9.3 Schnable Lab Contribution: I deﬁned the problem, Sairam Behera created the algorithm, Zhikai Liang and Xianjun Lai, both from my group, conducted multiple rounds of biological validation and provided feedback to Sairam, improving the core algorithm in an iterative process. My lab wrote the paper. 45. Liang Z, Pandey P, Stoerger V, Xu Y, Qiu Y, Ge Y, Schnable JC§ (2017) Conventional and hyperspec- tral time-series imaging of maize lines widely used in ﬁeld trials. GigaScience doi: 10.1093/giga- science/gix117 bioRxiv doi: 10.1101/169045 Altmetric Score: 14 (61st percentile for papers of similar age (+/- 6 weeks) published in this jour- nal). Times Cited to Date: 3 Journal Impact Factor (2017): 7.3 Schnable Lab Contribution: All analyses and writing conducted by lab members. Ge, Qiu, and Xu labs each assisted in developing new analytical approaches. Vincent Stoerger assisted with data generation. 44. Liang Z, Schnable JC§ (2017) Functional divergence between subgenomes and gene pairs after whole genome duplications. Molecular Plant doi: 10.1016/j.molp.2017.12.010 Altmetric Score: 8 (65th percentile for papers of similar age (+/- 6 weeks) published in this jour- nal). Times Cited to Date: 2 Journal Impact Factor (2017): 9.3 Schnable Lab Contribution: All analyses writing conducted by lab members. 43. Pandey P, Ge Y§, Stoerger V, Schnable JC (2017) High throughput in vivo analysis of plant leaf chem- ical properties using hyperspectral imaging. Frontiers in Plant Science doi 10.3389/fpls.2017.01348 Altmetric Score: 15 (96th percentile for papers of similar age (+/- 6 weeks) published in this jour- nal). Times Cited to Date: 20 Journal Impact Factor (2017): 3.7 James C. Schnable 9

Schnable Lab Contribution: Designed a different cross validation technique which was implemented by the ﬁrst author. Drafted portions of the introduction and discussion and revised the manuscript. 42. Gage J, Jarquin D, Romay M, ... Schnable JC (29th of 40 authors) .. Yu J, de Leon N§ (2017) The effect of artiﬁcial selection on phenotypic plasticity in maize. Nature Communications doi: 10.1038/s41467-017-01450-2 Selected as an Editor’s Choice by MaizeGDB Editorial Board December 2017 Altmetric Score: 85 (83st percentile for papers of similar age (+/- 6 weeks) published in this journal). Times Cited to Date: 5 Journal Impact Factor (2017): 12.4 Schnable Lab Contribution: Generated and contributed yield and ﬁeld phenotyping data from Nebraska ﬁeld sites of Genomes to Fields project. 41. Washburn JD, Schnable JC, Brutnell TP, Shao Y, Zhang Y, Ludwig M, Davidse G, Pires JC§ (2017) Genome-guided phylo-transcriptomic methods and the nuclear phylogentic tree of the paniceae grasses. Scientific Reports doi: 10.1038/s41598-017-13236-z Altmetric Score: 1 (36th percentile for papers of similar age (+/- 6 weeks) published in this jour- nal). Times Cited to Date: 1 Journal Impact Factor (2017): 4.1 Schnable Lab Contribution: Grew plants, extracted RNA, built and sequenced libraries and shared data. Consulted with the lead author on the syntenic gene analysis.

§ 40. Ott A,∗ Liu S,∗ Schnable JC, Yeh CT, Wang C, Schnable PS (2017) Tunable Genotyping-By-Sequencing (tGBS®) enables reliable genotyping of heterozygous loci. Nucleic Acids Research doi: 10.1093/nar/gkx853 Altmetric Score: 15 (87th percentile for papers of similar age (+/- 6 weeks) published in this jour- nal). Times Cited to Date: 4 Journal Impact Factor (2017): 11.6 Schnable Lab Contribution: Wrote portions of the manuscript, designed additional analyses to validate datasets which were executed by Alina Ott. 39. Lai X, Schnable JC, Liao Z, Xu J, Zhang G, Li C, Hu E, Rong T, Xu Y, Lu Y§ (2017) Genome-wide characterization of non-reference transposable elements insertion polymorphisms reveals genetic di- versity in tropical and temperate maize. BMC Genomics doi: 10.1186/s12864-017-4103-x Altmetric Score: 0 Times Cited to Date: 3 Journal Impact Factor (2017): 3.7

textscSchnable Lab Contribution: textitThe majority of this paper was written by Xianjun Lai during his time in the Schnable lab. I redesigned several analyses for him to carry out and helped to re-write the paper.

38. Mei W, Boatwright L, Feng G, Schnable JC, Barbazuk WB§ (2017) Evolutionarily conserved alterna- tive splicing across monocots. Genetics doi: 10.1534/genetics.117.300189 Cover Article October 2017 Issue Altmetric Score: 24 (88th percentile for papers of similar age (+/- 6 weeks) published in this jour- nal). Times Cited to Date: 6 Journal Impact Factor (2017): 4.1 Schnable Lab Contribution: Conceived and designed a new approach to identifying orthologous plant exons based on a directed acyclic graph which was robust to the insertion or deletion of entire introns. 37. Mei W, Liu S, Schnable JC, Yeh C, Springer NM, Schnable PS, Barbazuk WB§ (2017) A compre- hensive analysis of alternative splicing in paleopolyploid maize. Frontiers in Plant Science doi: James C. Schnable 10

10.3389/fpls.2017.00694 Altmetric Score: 9 (90th percentile for papers of similar age (+/- 6 weeks) published in this jour- nal). Times Cited to Date: 14 Journal Impact Factor (2017): 3.7 Schnable Lab Contribution: Developed approached to identifying orthologous exons across both maize subgenomes and co-orthologous genes in sorghum (an earlier iteration of the algorithm later used for paper # 42). Consulted with the lead author on the best ways to make comparisons across subgenomes.

36. Walley JW,∗ Sartor RC,∗ Shen Z, Schmitz RJ, Wu KJ, Urich MA, Nery JR, Smith LG, Schnable JC, Ecker JR, Briggs SP§ (2016) Integration of omic networks in a developmental atlas of maize. Science doi: 10.1126/science.aag1125 Selected as an Editor’s Choice by MaizeGDB Editorial Board September 2016 Altmetric Score: 82 (77th percentile for papers of similar age (+/- 6 weeks) published in this journal). Times Cited to Date: 48 Journal Impact Factor (2017): 41.1 Schnable Lab Contribution: Suggested and provided the data which enabled the separate analysis of maize syntenic and non-syntenic genes. This analysis lead to the discovery than non-syntenic maize genes are much less likely to be translated enough protein, even when they are transcribed into mRNAs than genes conserved at syntenic locations across multiple grass species. See Figure 1 in the ﬁnal paper. 35. Ge Y§, Bai G, Stoerger V, Schnable JC (2016) Temporal dynamics of maize plant growth, water use, and plant water content using automated high throughput RGB and hyperspectral imaging. Computers and Electronics in Agriculture doi: 10.1016/j.compag.2016.07.028 Altmetric Score: 29 (99th percentile for papers published in this journal). Single highest altmetric score recorded for this journal. Times Cited to Date: 35 Journal Impact Factor (2017): 2.4 Schnable Lab Contribution: Provided plant material. Interpreted a portion of the resulting trait datasets. Wrote portions of the manuscript particularly those focused on the biological relevance of the measured traits. 34. Liang Z, Schnable JC§ (2016) RNA-seq based analysis of population structure within the maize inbred B73. PLoSOne doi: 10.1371/journal.pone.0157942 Altmetric Score: 16 (90th percentile for papers of similar age (+/- 6 weeks) published in this journal). Times Cited to Date: 4 Journal Impact Factor (2017): 2.8 Schnable Lab Contribution: All analyses and writing conducted by lab members. 33. Rajput SG, Santra DK§, Schnable JC (2016) Mapping QTLs for morpho-agronomic traits in proso millet ( Panicum miliaceum L.). Molecular Breeding doi: 10.1007/s11032-016-0460-4 Altmetric Score: 7 (87th percentile for published in this journal). Times Cited to Date: 4 Journal Impact Factor (2017): 2.1 Schnable Lab Contribution: Taught Santosh Rajput how to analyze GBS data. Conducted analyses to generate a ﬁltered set of dominant markers from homeologous loci collapsed across subgenomes which were ultimately used to generate the genetic map created in this paper. 32. Joyce BL, Huag-Baltzell A, Davey S, Bomhoff M, Schnable JC, Lyons E§ (2016) FractBias: a graph- ical tool for assessing fractionation bias after whole genome duplications. Bioinformatics doi: 10.1093/bioinformatics/btw666 Altmetric Score: 4 (72nd percentile for papers of similar age (+/- 6 weeks) published in this jour- nal). Times Cited to Date: 2 Journal Impact Factor (2017): 5.5 Schnable Lab Contribution: Generated semi-manual subgenome assignments which are used as the basis James C. Schnable 11

for evaluating the accuracy of the automated assignments made by FractBias. Consulted with the lead author on the best ways to make comparisons across subgenomes. 31. Chao S, Wu J, Liang J, Schnable JC, Yang W, Cheng F, Wang X§ (2016) Impacts of whole genome trip- lication on MIRNA evolution in Brassica rapa.Genome Biology and Evolution doi: 10.1093/gbe/evv206 Altmetric Score: 6 (43rd percentile for papers of similar age (+/- 6 weeks) published in this jour- nal). Times Cited to Date: 10 Journal Impact Factor (2017): 3.9 Schnable Lab Contribution: Contributed to the design of comparisons across the Brassica rapa subgenomes. Assisted in drafting portions of the paper. 30. Tang H, Bomhoff MD, Briones E, Schnabe JC, Lyons E§ (2015) SynFind: compiling syntenic regions across any set of genomes on demand. Genome Biology and Evolution doi: 10.1093/gbe/evv219 Altmetric Score: 12 (86th percentile for papers of similar age (+/- 6 weeks) published in this jour- nal). Times Cited to Date: 22 Journal Impact Factor (2017): 3.9 Schnable Lab Contribution: Tested/validated the orthology assignments of the core algorithms and pro- vided feedback to the author to improve the algorithm in an iterative process. Drafted portions of the manuscript related to validation of the core algorithm. 29. Washburn JD, Schnable JC, Davidse G, Pires JC§ (2015) Phylogeny and photosynthesis of the grass tribe Paniceae. American Journal of Botany doi: 10.3732/ajb.1500222 Altmetric Score: 5 (58th percentile for papers of similar age (+/- 6 weeks) published in this jour- nal). Times Cited to Date: 22 Journal Impact Factor (2017): 2.8 Schnable Lab Contribution: Collected germplasm, grew plants and contributed tissue for plastid se- quencing. Revised the research questions to be addressed in the manuscript with the ﬁrst and last authors. 28. Tang H, Zhang X, Miao C, Zhang J, Ming R, Schnable JC, Schnable PS, Lyons E, Lu J§ (2015) ALLMAPS: robust scaffold ordering based on multiple maps. Genome Biology doi: 10.1186/s13059- 014-0573-1 Altmetric Score: 4 (11th percentile for papers of similar age (+/- 6 weeks) published in this jour- nal). Times Cited to Date: 48 Journal Impact Factor (2017): 13.2 Schnable Lab Contribution: Designed analyses for validating the accuracy of consensus ordering pro- duced by the ALLMAPS algorithm. Contributed data for validation of the algorithm. Created visualizations for ﬁgures. Wrote or re-wrote portions of the manuscript text. 27. Schnable JC§ (2015) Genome evolution in maize: from genomes back to genes. Annual Review of Plant Biology doi: 10.1146/annurev-arplant-043014-115604 Altmetric Score: 15 (92nd percentile for papers of similar age (+/- 6 weeks) published in this journal). Times Cited to Date: 18 Journal Impact Factor (2017): 18.7 Schnable Lab Contribution: Wrote the manuscript. 26. Paschold A, Larson NB, Marcon C, Schnable JC, Yeh C, Lanz C, Nettleton D, Piepho H, Schnable PS, Hochholdinger F§ (2014) Nonsyntenic genes drive highly dynamic complementation of gene expression in maize hybrids. Plant Cell doi: 10.1105/tpc.114.130948 Altmetric Score: 18 (93rd percentile for papers of similar age (+/- 6 weeks) published in this journal). Times Cited to Date: 27 James C. Schnable 12

Journal Impact Factor (2017): 8.2 Schnable Lab Contribution: Suggested a key analyses to the remaining authors – the separation of maize genes into syntenically conserved and non-syntenic classes to look for different patterns of gene expression in the F1 hybrid – and provided the datasets and analytical approaches necessary to conduct this analysis.

Postdoctoral Publications

25. Nani TF, Schnable JC, Washburn JD, Albert P, Pereira WA, Sobrinho FS, Birchler JA, Techia VH§ (2018). Location of low copy genes in chromosomes of Brachiaria spp. Molecular Biology Reports doi: 10.1007/s11033-018-4144-5 Times Cited to Date: 0 Journal Impact Factor (2017): 1.9

24. Studer AJ∗, Schnable JC∗, Weissmann S, Kolbe AR, McKain MR, Shao Y, Cousins AB, Kellogg EA, Brutnell TP§ (2016) The draft genome of Dichanthelium oligosanthes:AC3 panicoid grass species. Genome Biology doi: 10.1186/s13059-016-1080-3 Times Cited to Date: 8 Journal Impact Factor (2017): 13.2

23. Huang P, Studer AJ, Schnable JC, Kellogg EA, Brutnell TP§ (2016) Cross species selection scans identify components of C4 photosynthesis in the grasses. Journal of Experimental Botany doi: 10.1093/jxb/erw256 "Insight" highlighting this article by PA Christin also published in JXB doi: 10.1093/jxb/erw390 Times Cited to Date: 17 Journal Impact Factor (2017): 5.4

22. Liu X, Tang S, Jia G, Schnable JC, Su X, Tang C, Zhi H, Diao X§ (2016) The C-terminal motif of SiAGO1b is required for the regulation of growth, development and stress responses in foxtail millet [Setaria italica (L.) P. Beauv]. Journal of Experimental Botany doi: 10.1093/jxb/erw135 Times Cited to Date: 13 Journal Impact Factor (2017): 5.4

21. Jia G, Liu X, Schnable JC, Niu Z, Wang C, Li Y, Wang Sh, Wang Su, Liu J, Gou E, Diao X§ (2015) Microsatellite variations of elite Setaria varieties released during last six decades in China. PLoS One doi: 10.1371/journal.pone.0125688 Times Cited to Date: 11 Journal Impact Factor (2017): 2.8

20. Qie L, Jia G, Zhang W, Schnable JC, Shang Z, Li W, Liu B, Li M, Chai, Y, Zhi H, Diao X§ (2014) Mapping of quantitative trait loci (QTLs) that contribute to germination and early seedling drought tolerance in the interspeciﬁc cross Setaria italica x Setaria viridis. PLoSOne doi: 10.1371/jour- nal.pone.0101868 Times Cited to Date: 33 Journal Impact Factor (2017): 2.8

19. Diao X§, Schnable JC, Bennetzen JL, Li J§ (2014) Initiation of Setaria as a model plant. Frontiers of Agricultural Science and Engineering doi: 10.15302/J-FASE-2014011 Times Cited to Date: 48 Journal Impact Factor (2017): Impact Factor Not Yet Assigned

Graduate Publications

18. Cheng F, Sun C, Wu J, Schnable JC, Woodhouse MR, Liang J, Cai C, Freeling M,§ Wang X§ (2016) Epigenetic regulation of subgenome dominance following whole genome triplication in Brassica rapa. New Phytologist doi: 10.1111/nph.13884 James C. Schnable 13

Times Cited to Date: 19 Journal Impact Factor (2017): 7.4

17. Almeida AMR, Yockteng R, Schnable JC, Alvarez-Buylla ER, Freeling M, Specht CD§ (2014) Co- option of the polarity gene network shapes ﬁlament morphology in angiosperms. Scientific Re- ports doi: 10.1038/srep06194 Times Cited to Date: 8 Journal Impact Factor (2017): 41

16. Martin JA, Johnson NV, Gross SM, Schnable JC, Meng X, Wang M, Coleman-Derr D, Lindquist E, Wei C, Kaeppler S, Chen F, Wang Z§ (2014) A near complete snapshot of the Zea mays seedling transcriptome revealed from ultra-deep sequencing. Scientific Reports doi: 10.1038/srep04519 Selected as an Editor’s Choice by MaizeGDB Editorial Board May 2014 Times Cited to Date: 21 Journal Impact Factor (2017): 4.1

§ § 15. Garsmeur O,∗ Schnable JC,∗ Almeida A, Jourda C, D’Hont A, Freeling M (2014) Two evolution- arily distinct classes of paleopolyploidy. Molecular Biology and Evolution doi: 10.1093/mol- bev/mst230 Times Cited to Date: 84 Journal Impact Factor (2017): 10.2

14. Turco G, Schnable JC, Pedersen B, Freeling M§ (2013) Automated conserved noncoding sequence (CNS) discovery reveals differences in gene content and promoter evolution among the grasses. Frontiers in Plant Sciences doi: 10.3389/fpls.2013.00170 Times Cited to Date: 21 Journal Impact Factor (2017): 3.7

13. Schnable JC, Wang X, Pires JC, Freeling M§ (2012) Escape from preferential retention following re- peated whole genome duplication in plants. Frontiers in Plant Science doi: 10.3389/fpls.2012.00094 Times Cited to Date: 48 Journal Impact Factor (2017): 3.7

12. Freeling M§, Woodhouse MR, Subramaniam S, Turco G, Lisch D, Schnable JC (2012) Fractionation mutagenesis and similar consequences of mechanisms removing dispensable or less-expressed DNA in plants. Current Opinion in Plant Biology doi: 10.1016/j.pbi.2012.01.015 Times Cited to Date: 105 Journal Impact Factor (2017): 7.3

11. Tang H§, Woodhouse MR, Cheng F, Schnable JC, Pedersen BS, Conant GC, Wang X, Freeling M, Pires JC (2012) Altered patterns of fractionation and exon deletions in Brassica rapa support a two- step model of paleohexaploidy. Genetics doi: 10.1534/genetics.111.137349 Times Cited to Date: 111 Journal Impact Factor (2017): 4.1

10. Schnable JC, Freeling M, Lyons E§ (2012) Genome-wide analysis of syntenic gene deletion in the grasses. Genome Biology and Evolution doi: 10.1093/gbe/evs009 Selected as an Editor’s Choice by MaizeGDB Editorial Board Dec 2012 Times Cited to Date: 106 Journal Impact Factor (2017): 3.9

9. Zhang W, Wu Y, Schnable JC, Zeng Z, Freeling M, Crawford GE, and Jiang J§ (2012) High-resolution mapping of open chromatin in the rice genome. Genome Research doi: 10.1101/gr.131342.111 Times Cited to Date: 110 Journal Impact Factor (2017): 4.1 James C. Schnable 14

8. Eichten SR,∗ Swanson-Wagner RA,∗ Schnable JC, Waters AJ, Hermanson PJ, Liu S, Yeh C, Jia Y, Gendler K, Freeling M, Schnable PS, Vaughn MW, Springer NM§ (2011) Heritable epigenetic varia- tion among maize inbreds. PLoSGenetics doi: 10.1371/journal.pgen.1002372 Selected as an Editor’s Choice by MaizeGDB Editorial Board Jan 2012 Times Cited to Date: 115 Journal Impact Factor (2017): 10.1

7. Schnable JC, Lyons E§ (2011) Comparative genomics with maize and other grasses: from genes to genomes. Maydica 56(1763) 77-93 Link directly to PDF Times Cited to Date: 11 Journal Impact Factor (2017): 0.2

6. Tang H, Lyons E, Pedersen B, Schnable JC, Paterson AH, Freeling M. (2011) Screening synteny blocks in pairwise genome comparisons through integer programming. BMC Bioinformatics doi: 10.1186/1471-2105-12-102 Times Cited to Date: 71 Journal Impact Factor (2017): 2.2

5. Schnable JC, Pedersen BS, Subramaniam S, Freeling M§ (2011) Dose-sensitivity, conserved noncod- ing sequences and duplicate gene retention through multiple tetraploidies in the grasses. Frontiers in Plant Science doi: 10.3389/fpls.2011.00002 Commentary by Birchlier and Veitia also published in Frontiers in Plant Science doi: 10.3389/fpls.2011.00064 Times Cited to Date: 29 Journal Impact Factor (2017): 3.7

4. Schnable JC§, Freeling M (2011) Genes identifed by visible mutant phenotypes show increased bias towards one of two maize subgenomes. PLoSOne doi: 10.1371/journal.pone.0017855 Times Cited to Date: 102 Journal Impact Factor (2017): 2.8

3. Schnable JC, Springer NM, Freeling M§ (2011) Differentiation of the maize subgenomes by genome dominance and both ancient and ongoing gene loss. Proceedings of the National Academy of Sciences doi: 10.1073/pnas.1101368108 Selected as an Editor’s Choice by MaizeGDB Editorial Board May 2011 Times Cited to Date: 327 Journal Impact Factor (2017): 9.5

§ 2. Woodhouse MR,∗ Schnable JC,∗ Pedersen BS, Lyons E, Lisch D, Subramaniam S, Freeling M (2010) Following tetraploidy in maize, a short deletion mechanism removed genes preferentially from one of the two homeologs. PLoSBiology doi: 10.1371/journal.pbio.1000409 Selected as an Editor’s Choice by MaizeGDB Editorial Board August 2010 PLoS Biology Cover Article Times Cited to Date: 187 Journal Impact Factor (2017): 9.1 1. The International Brachypodium Initiative (2010) Genome sequencing and analysis of the model grass Brachypodium distachyon. Nature doi: 10.1038/nature08747 Times Cited to Date: 1,226 Journal Impact Factor (2017): 41.6

Service: selected, 2014-Present

University Consortium for Integrated Translational Biology (CITB) 2014-Present James C. Schnable 15

UNL Faculty Greenhouse Committee 2015-Present Department of Agronomy and Horticulture Peer Evaluation Committee 2016-Present Biotech Seminar Series Committee 2017-Present Agronomy and Horticulture Faculty Advisory Committee 2017-Present Nebraska Food for Health Center Faculty Advisory Committee 2017-Present Organizing Committee “International Millet Symposium 2018” 2018 Organizing Committee “Predictive Crop Design, Genome to Phenome” 2017 Search Committee, Director of Phenomic Sciences 2017 Search Committee, Agricultural Research Division 2016 Search Committee, Quantitative Life Sciences Initiative 2016 Search Committee, Department of Agronomy and Horticulture 2016 Organizing Committee “Plant Phenomics: from pixels to traits” 2015

Professional Associate Editor: Molecular Plant 2014-Present Data Management Subcommittee, Maize Genetics Research Collaboration Network 2018-Present MaizeGDB Advisory Committee 2018-Present Grant Reviewer: NSF (panel & ad hoc), USDA (panel), JGI (panel), Genome British Columbia (ad hoc). Peer Reviewer (selected, recent): Bioinformatics, BMC Plant Biology, G3: Genes|Genomes|Genetics, Genome Biology & Evolution, Heredity, Journal of Experimental Botany, Molecular Biology and Evolu- tion, Molecular Plant, Nature Communications, Nature Plants, New Phytologist, PeerJ, Photosynthesis Research, Plant Cell, Plant Cell & Environment, The Plant Genome, The Plant Journal, Plant Methods, Plant Physiology, PLoS Genetics, Science

Invited Talks:

I have delivered a total of 50 invited talks or seminars, including 37 since I was hired as an assistant professor at the University of Nebraska-Lincoln. 29 excluding seminars and conferences afﬁliated with the University of Nebraska-Lincoln.

Talks in italics are scheduled for Fall ’18 or Spring of ’19 but have not yet been delivered and are not including in the count of delivered presentations.

External at Institutions Invited presentations only. Excludes presentations selected based on abstracts or applications. 42 total and 29 during my time at UNL

University of Massachusetts-Amhert, MA, USA (Feb. 2019) Research Triangle, Raleigh, NC, USA (Oct. 2018) James C. Schnable 16

Plant Energy Biology Annual Forum, Perth, Australia (Sept. 2018) Expenses covered by invitation. Washington State University, Pullman, WA, USA (Sept. 2018)

42. The Plant Phenome Journal Webinar Series 2018 41. University of Delaware, Newark, DE, USA 2018 40. Entrepreneurship Panel, USDA FACT: Genomes to Fields, Ames, IA, USA 2018 39. Plant Phenotyping Session, Plant and Animal Genome, San Deigo, CA, USA 2018 38. Chinese Academy of Agricultural Sciences, Beijing, China 2017 37. Beijing Academy of Agricultural and Forestry Sciences, Beijing, China 2017 36. University of Minnesota, St. Paul, MN 2017 35. Plant Genome Evolution, Sitges, Spain 2017 34. Iowa State University, Ames, IA, USA 2017 33. Purdue Plant Science Symposium (Student Organized), West Lafayette, IN, USA 2017 32.P2IRC Annual Symposium, Saskatoon, Saskatchewan, Canada 2017 31. University of Missouri-Columbia, Columbia, MO, USA 2017 30. Maize Tools and Resources (Maize Genetics Conference pre-meeting), St. Louis, MO, USA 2017 29. Phenome, Tucson, AZ, USA 2017 28. Kansas State University, Manhattan, KS, USA 2016 27. University of Georgia-Athens, Athens, GA, USA 2016 26. University of California-San Diego, San Diego, CA, USA 2016 25. Corn Breeding Research Meeting, Jacksonville, FL, USA 2016 24. Chinese Academy of Agricultural Sciences, Beijing, China 2015 23. Beijing Academy of Agricultural and Forestry Sciences, Beijing, China 2015 22. Molecular Plant Symposium: From Model Species to Crops, Shanghai, China 2015 21. Sichuan Agricultural University, Chengdu, China 2015 20. Huazhong Agricultural University, Wuhan, China 2015 19. Shandong Agricultural University, Tai’an, China 2015 18. Monsanto, St. Louis, MO, USA 2015 17. Corn Breeding Research Meeting, St. Charles, IL, USA 2015 16. Life Technologies Session, Plant and Animal Genome, San Diego, CA, USA 2015 15. Maize Session, Plant and Animal Genome, San Diego, CA, USA 2015 14. Millet as Crop: Past and Future, Aohan, Inner Mongolia, China 2014

(May 1st, 2014. Start of my time at UNL) James C. Schnable 17

13. Henan Agricultural University, Zhengzhou, China 2014 12. Chinese Academy of Tropical Agriculture, Haikou, China 2014 11. Cornell University, Ithaca, NY, USA 2014 10. Interdisciplinary Plant Group Seminar Series, University of Missouri, Columbia, MO, USA 2012 9. Donald Danforth Plant Science Center, St. Louis, MO, USA 2012 8. Plant Genomes in China Meeting, Tai’an, China 2012 7. China Agricultural University, Beijing, China 2012 6. Chinese Academy of Agricultural Sciences, Beijing, China 2012 5. American Society of Plant Biology, Austin, TX, USA 2012 4. MaizeGDB, Ames, IA, USA 2012 3. Polyploidy Session, Plant and Animal Genome, San Deigo, CA, USA 2012 2. CSSA Translational Genomics Session, Plant and Animal Genome, San Diego, CA, USA 2012 1. University of Arizona, Tucson, AZ, USA 2011

Internal 8 total and 8 during my time at UNL

8. UNL Plant Phenomics Symposium 2018 7. NeDA 2017: 2nd Nebraska Data Analytics Workshop, UNL 2017 6. Water for Food Global Conference, UNL 2017 5. Complex Biosystems Seminar Series, UNL 2017 4. Food Science Departmental Seminar Series, UNL 2016 3. Animal Science Departmental Seminar Series, UNL 2016 2. Agronomy & Horticulture Departmental Seminar Series, UNL 2015 1. Plant Science Retreat, UNL 2014 James C. Schnable 18

Candidate Statement Program Overview

My research program at the University of Nebraska-Lincoln has had two – sometimes overlapping but logically distinct – themes. The ﬁrst, which I was originally hired to pursue, is to employ comparative genomic approaches, including functional genomic data, to share information across maize, sorghum, and allied domesticated and wild species to develop new ways to link genes to functions and phenotypes. As an extension of this theme, research in my lab has also focused on the ability to distinguish between genes which are likely to play functional roles in determining the phenotype of a plant, and genomic sequences annotated as genes which are extremely unlikely to ever be identiﬁed as playing a consequential role in determining any plant trait. The second research theme within my group is one I was asked to take on by the UNL administration a little over a year after I started at the university: developing and deploying new approaches to utilize high throughput phenotyping in the context of quantitative genetics and crop improvement efforts. This effort currently focuses primarily on maize and sorghum and includes both con- trolled environment and ﬁeld phenotyping efforts. Both programs have been productive bringing in grant dollars and each are produced both publications lead by my own research group with external collabora- tors as co-authors and publications by other research groups with members of my own research group as co-authors. A third, recently developed research focus involves the use to plant quantitative genetics to identify new specialized plant metabolites with the potential the perturb the human gut microbiome, and ultimately use this knowledge to develop new functional foods. During my time at the University of Nebraska I have also been active in economic development. I have founded or co-founded three startups, two during my time at UNL. The second of the three, Dryland Genetics, has also invested over $100,000 funding work in the lab of one of my colleagues in the department (Dipak Santra). I serve on the scientiﬁc advisory council of a local genotyping company (GeneSeek) which is a notable employer of UNL graduates, and I have been a guest member of scientiﬁc advisory board meetings for Syngenta and Indigo Agriculture. My appointment at the University of Nebraska-Lincoln coincided with the launch of the new Complex Biosystems graduate program. Complex Biosystems is one of the ﬁrst, if not the ﬁrst, interde- partmental graduate programs at UNL. I was involved in the original design of this requirements for this program and volunteered to teach Professional Development (LIFE 843) to ﬁrst year graduate students entering the program in its ﬁrst year (Fall 2015). I have continued to teach this course in each subsequent year, and in 2017 picked up Big Questions in Complex Biosystems (LIFE 841), a second required course for ﬁrst year graduate students in Complex Biosystems, when the original instructor was hired away to an- other university. This year I am teaching Big Questions for the second time and Professional Development for the fourth time. However, I believe my greatest educations contributions have come in the context of involving undergraduates in the research process. In the past four years, 12 undergraduates and 2 high school students have had a chance to work on research projects in my lab, supported by a combination of Research Experience for Undergraduate (NSF), Undergraduate Creative Activities & Research Experiences (UNL), and startup or grant research funds (see details below). Both my research focuses necessitate working in team-based environments with statisticians, engineers, computer scientists, and applied plant breeders. I have been extremely pleased with the oppor- tunity this provides for both the undergraduate and graduate students working in my lab to experience working as part of an interdisciplinary team and to develop the necessary skills and vocabulary to com- municate across disciplinary silos. This experience, more than any of the speciﬁc skills I can teach them – with the possible exception of learning how to apply the highly successful hypothesis-testing mindset of geneticists to genomics and big data research questions – is the way I can best prepare students in my research group to be successful in private industry, in academic research, or as entrepreneurs. James C. Schnable 19

Research Contributions

Introduction The grasses are one of the single most evolutionarily, ecologically, and economically successful clades of plants on the planet today. This group of species has reshaped whole ecosystems and adapted to grow and thrive everywhere from salt soaked tropical beaches to frozen tundras. Multiple grass clades have made the jump to using C4 photosynthesis and have evolved multiple distinct enzymatic pathways to carry out the C4 pathway. More than half of all calories consumed by humans around the world come, directly or indirectly, from only three highly productive domesticated grasses: maize, wheat, and rice. Yet, in addition to these three species, at least 27 other grass species have also been domesticated by humans as grain crops (see Glemin & Bataillon 2009 doi: 10.1111/j.1469-8137.2009.02884.x). Yet, at the genomic level, the genomes of grasses remain surprisingly similar with many of the same genes, conserved in the same order, present across all of these species. Research in my group focuses on ways to generate genomic and phenotypic data from sets of related grass species and both developing and using comparative, functional, and quantitative genomic approaches to link genes to functions. In many cases we are able to leverage the fact that most phenotypic and evolutionary transitions observed in the grasses have occurred multiple times independently in related lineages. Since my hire, I have invested a substantial fraction of my total time and effort in building and sustaining research teams which cross disciplinary expertise. I believe that my success in that endeavor is reﬂected both in the breadth of disciplines represented by my co-PIs (Computer Science, Statistics, Engineering, Biochemistry, Food Science, Genetics, and Applied Plant Breeding), but more importantly in the fact that many of my co-PIs are found on multiple projects, including both those I have lead, and those I have contributed to as a team member. Putting teams together is, in some ways, a lot easier than managing and contributing to teams in such a way that the people continue to want to work together in the future. Similarly, you will see a number of names who both show up as middle authors on papers lead by my lab, and who show up as the lead or anchor authors of papers where I and my lab members are middle authors in turn. The sections below describe some of the most productive areas of research in my lab over the past four years. Through these and other projects I have published a total of 38 peer reviewed papers since my appointment as an assistant professor at the University of Nebraska-Lincoln (56 total peer reviewed publications), including 28 resulting from work conducted here at the University. I have been fortunate enough to be quite successful at securing external funding, including two federal grants as PI (one from the USDA and one from NSF), three federal grants as a co-PI (USDA, ARPA-E, and NSF) (see page 44 for the cover page summaries of each funded federal grant) and a large number of nonfederal grants as both a PI and co-PI.

Research Highlights Emphasis I: Comparative Genomics of Maize, Sorghum, and Allied Species The comparative genomics work in my lab focuses primarily on the panicoid grasses, a group of species which includes maize, sorghum, and sugar cane, miscanthus, switchgrass, and many of the domesticated species collectively referred to as millets. As part of an effort to improve the resources for comparative genomics in this group of species, I have worked with JGI and HudsonAlpha to sequence the genomes of additional grass species within this clade, including Dichanthelium oligosanthes (published), Paspalum vagin*tum (completed), and Urochloa fusca (initial draft). All the comparative genomics work performed in my lab depends on a set of high quality syntenic ortholog calls across different grass species which is generated and updated in house. We have distributed updated versions of these lists of syntenic orthologs across different grass genomes online using FigShare prior to their use in lab publications, and these datasets have been widely downloaded and employed in published research by other groups across the USA, EU, and China. and using examples of parallel evolution to generate hypotheses about the functions of speciﬁc genomic sequences. James C. Schnable 20

Emphasis IA: Separating Functional and Functionless or Function Mimicking Parts of the Genome A combination of hom*ology-based, mRNA-sequencing based, and ab initio prediction based approaches now make it straightforward to identify protein coding exons within any existing or newly sequenced plant genome. However, two signiﬁcant challenges remain. The ﬁrst is the identiﬁcation of the regulatory sequences – which are predominantly noncoding – and control where, when, and in response to what stimuli those exonic sequences are transcribed into mRNA remain far more challenging to identify in sil- ico. The second is that recent work, including from my own research group, is beginning to demonstrate that a nontrivial number of gene models present in plant genomes may not be true genes according to the classic requirement that a gene “contribute in any detected way to plant morphology, physiology or development” (Bennetzen et al. 2004 doi: 10.1016/j.pbi.2004.09.003). Comparing orthologous regions of the genome across related species can be used to address both of these questions, at least in part.

Identifying regulatory regions in noncoding DNA: One successful approach for identifying regulatory sequences has been to use comparisons between the noncoding sequence surrounding orthol- ogous genes in related species to identify islands of conserved sequence evolving more slowly than the surrounding noncoding DNA. This slower evolutionary rate is seen as a marker for functional constraint, and regions identiﬁed as conserved noncoding sequences have been experimentally validated as regula- tors of the expression pattern of adjacent protein coding genes. However, previous approaches employed for identifying these functionally constrained functional regulatory regions within noncoding sequence were based on pairwise comparisons between related genes and as a result of multiple testing – analo- gous to the birthday problem – could not conﬁdently identify sequences shorter than 15 base pairs long as exhibiting statistically signiﬁcant functional constraint. This represented a major limitation of existing methods as many transcription factor binding sites can be as short as 4-8 base pairs long. Alignments of more than two sequences have the potential to reduce sequence matches resulting from coincidental matches rather than conserved hom*ology, however conventional multiple sequence alignment algorithms cannot be effectively employed to compare plant promoters as the high rates of sequence divergence, in- sertion, and deletion mean that the majority of intergenic sequence associated with orthologous genes is truly non-hom*ologous, and much of the remainder has diverged to the point where hom*ology is no longer detectable through sequence similarity. Working with Sairam Behera, a computer science student in Jiten- der Deogun’s lab I and two of my students – Xianjun Lai and Zhikai Liang – developed a new approach to identifying small conserved sequences in the noncoding sequence associated with orthologous genes in multiple species. Unlike previous approaches, this algorithm – STAG-CNS – is inherently scalable to incorporate data from orthologous genes in >2 species at once. Using data from genes in six different grass species, we demonstrated that unlike previous pairwise approaches, STAG-CNS could conﬁdently identify sequences as short as 9 base pairs long as showing functional constraint, and that these sequences showed greater overlap with chromatin marks known to be associated with regulatory sequences – such as DNase1 hypersensitive sites – than conserved noncoding sequences identiﬁed through pairwise compar- isons. As additional grass genome have become available since our original paper was published in 2017, STAG-CNS has continued to scale, providing greater and greater resolution in identifying functionally constrained regulatory sequences (Lai et al. 2017 doi: 10.1016/j.molp.2017.05.010).

Separating genes and gene mimics: Maize is rapidly becoming one of the best plant models to study what is, in fact, a gene. This is because of both its long history of forward genetic investigation which provides a signiﬁcant set of veriﬁed "true positive" genes to use as ground truth, as well as grow- ing evidence that different maize haplotypes vary signiﬁcantly in their number of gene-like sequences, including the number of transcribed sequences with valid open reading frames which show hom*ology to genes in other plant species. These sequences present a major challenge to efforts to annotated the genome, and, in fact, the ﬁrst version of the maize genome included multiple sets of gene model anno- tations, based on differing degrees of supporting evidence which identiﬁed between 32,000 and 110,000 putative genes. The conservation of a gene at the same position in the genome between related species (synteny), rather than solely the conservation of gene sequence itself, appears to be a far better mark for function and contribution to plant morphology, physiology or development. Genes conserved at syntenic locations are 9x as likely to be identiﬁed as the causal loci responsible for phenotypic variation mapped James C. Schnable 21 through forward genetics, and 4x as likely to be harbor SNPs identiﬁed in GWAS studies (as reviewed Schnable 2015 doi: 10.1146/annurev-arplant-043014-115604). Since joining UNL I have worked to uncover the reason for this dramatic difference in the functional relevance of nonsyntenic and syntenically con- served genes. Working with a group in Germany interested in allele speciﬁc expression in hybrids, we demonstrated that nonsyntenic genes show much less conservation of expression between alleles in maize hybrids (Paschold et al, published in The Plant Cell in 2014). Working with researchers at Iowa State and Kansas State, we have demonstrated at, at least in maize, the long known correlation between gene density and recombination frequency across the genome is entirely explained by syntenic gene density, and that nonsyntenic gene density shows no correlation with recombination rate (Liu et al 2018, Molecular Biology and Evolution). Working with scientists at the University of California-San Diego, we demonstrated that not only on non-syntenic genes less likely to be transcribed into mRNA, but when mRNAs are detected from nonsyntenic genes these mRNAs are less likely to be translated into proteins, suggesting potential mechanistic explanation for why nonsyntenic genes are much less likely to play a role in determining plant traits (Walley et al. published in Science in 2016).

Emphasis IB: Using parallel evolution to identify genes involved in complex changes in phenotype As mentioned, the grasses have been successful enough as a clade that many complex changes have happened multiple times in parallel in different lineages. These cases of parallel evolution provide an opportunity to address several questions. Firstly, do parallel phenotypic changes in related lineages result from changes in the function of the orthologous genes in each lineage? Secondly, if the answer to the ﬁrst question is yes, can we use cases of parallel evolution to identify the speciﬁc genetic loci involved in complex changes in plant traits?

Domestication Syndrome: In order to test the ﬁrst question, my lab ﬁrst worked with parallel artiﬁcial selection for "domestication syndrome" traits in maize and sorghum. Domestication of different grain crops from different wild grasses involved conscious or unconscious selection for an array of traits including a loss of seed dormancy and shattering, decreasing tillering, increases in seed size, decreases in shade avoidance responses, etc. Reanalyzing published data from separate studies on maize and sorghum and their respective wild relatives using a common pipeline members of my lab discovered that there was no statistically signiﬁcant overlap in the relatively large sets of genes which show population genetic signatures of selection between wild and domesticated accessions. However, at the same time, we found that genes with speciﬁc and known phenotypic roles in producing domestication syndrome traits in one species were disproportionately likely to show population genetic signatures of selection in the other (Lai et al. published in The Plant Journal in 2018).

Low temperature tolerance and temperate latitude adaptation: The ﬁrst federally funded project in my research group – a USDA NIFA AFRI grant with Rebecca Roston, a collaborator from the Biochemistry department with a background in cold and freezing stress biology as a co-PI – sought to employ the parallel adaption of different groups of panicoid grass species to temperate climates to identify genes and pathways involved in conferring low temperature tolerance. Both maize and sorghum were domesticated from wild species native to tropical latitudes and are extremely sensitive to cold and freezing temperatures. As a ﬁrst step in this project, we set out to develop robust methods for comparing patterns of transcriptional responses to stimuli across related species using time course gene expression data collected from maize and sorghum exposed to cold stress and grown in a common experiment. Working with a collaborator from the statistics department – Yumou Qiu – we validated an approach to speciﬁcally compare the pattern of transcriptional response to stimulus across orthologous genes in related species which may exhibit different baseline levels of expression under control conditions. In the speciﬁc maize- sorghum comparison, we demonstrated that genes with conserved patterns of expression in response to cold stress across the two species experienced stronger purifying selection and were enriched in genes with plausible mechanistic links to cold acclimation/tolerance while genes with dissimilar patterns of response to cold across the two species were more similar to a random sample of expressed genes (Zhang et al. Published in The Plant Cell in 2017). This ﬁnding lead to proposing a model that gene regulation, like noncoding sequence as a James C. Schnable 22 whole, is a somewhat fast evolving trait, with many transcriptional responses being selectively neutral or nearly neutral. This model is also consistent with allele speciﬁc expression studies in maize which indicated that transcriptional responses to cold stress are frequently not conserved between different al- leles of the same gene. If ultimately proven to be correct, one conclusion is that parallel whole genome transcriptional studies in several related species can provide a way to separate the signal of functionally constrained transcriptional responses from the noise of rapidly evolving but largely selectively neutral re- sponses which are currently confounded in many studies. A review paper which summarized some of the potential implications of this model, and performed a meta analysis of published cold stress experiments across multiple species was written and published with our collaborators in the Roston Lab and came out earlier this year (Raju et al 2018, published in Plant Science). Leveraging support from our $20M NSF EPSCoR grant (The Center for Root and Rhizobiome Innovation), members of my lab have also generated a library of full length cDNA sequences from Trip- sacum dactyloides using PacBio IsoSeq sequencing, a member of a genus sister to Zea which is indigenous to Nebraska and much of the lower 48 United States east of the rocky mountains. Using an analyti- cal method which identiﬁed genes with elevated ratios of non-synonymous substitutions to synonymous substitutions in either T. dactyloides or maize relative to the ratios observed for orthologs of that speciﬁc gene in other related grasses. Using maize as a control, we found that genes with elevated rates of protein sequence evolution speciﬁcally in T. dactyloides were clustered in a lipid biosynthesis pathway known to be involved in conferring freezing tolerance in eudicts, and identiﬁed a statistically signiﬁcant overlap between genes showing elevated rates of protein sequence evolution in T. dactyloides and genes showing signatures of artiﬁcial selection between tropical and temperate latitude adapted maize accessions (Yan et al, Preprint).

Emphasis II: Phenomics for Breeding and Quantitative Genetics of Maize, Sorghum, and Allied Species My interest in high throughput phenotyping of plants dates back to my postdoc at the Danforth Center where I used cameras connected to a raspberry pi computer to measure leaf rolling and leaf dropping of different maize genotypes in response to drought stress. I did not originally anticipate continuing to work on plant phenotyping after my hire at UNL. However about a year into my time at the University I was asked by several administrators to resume this program in addition to my multi-species genomics work. Ultimately I envision being able to connect these two areas of emphasis through the development of phe- notyping methodologies which can be applied across maize, sorghum, foxtail millet and pearl millet for effective identiﬁcation of hom*ologous traits and cross species quantitative genomics. In the interregnum, this research emphasis has a strong component of service to my institution, as well as beneﬁting student training by ensuring members of my lab regularly come into contact with and collaborate with faculty and students from statistics, computer science, and engineering, disciplines that plant biology students often lack sufﬁcient exposure to. Controlled Environment Phenotyping: Employing high throughput phenotyping methodologies in the context of a breeding program or quantitative genetic research requires two very different types of data analysis. The ﬁrst is simply to convert raw sensor or image data into some sort of biologically mean- ingful numerical measurement. The second is to take those sets of numerical data and convert them into biological insight. The latter problem can be addressed with existing tools for genomic prediction and QTL mapping/GWAS, although potentially intriguing alternative methods may also become practi- cal as high throughput phenotyping datasets tends to have much higher dimensionality than conventional phenotyping datasets. However, addressing the ﬁrst problem absolutely requires the development and deployment of new algorithms, and early work in the lab focused on collaborating with computer sci- entists, statisticians, and engineers simply to develop algorithms to extract meaningful measurements of plant traits from RGB and hyperspectral images. This work, supported by startup funds, internal grants, and an USDA/NSF joint EAGER award with my colleague Yufeng Ge as lead PI, resulted in a number of publications, including Ge et al 2016 doi: 10.1016/j.compag.2016.07.028 (published in Computers and Science in Agriculture and already cited 35 times), Pandey et al 2017 doi: 10.3389/fpls.2017.01348 (pub- lished in Frontiers in Plant Science), and Liang et al 2018 doi: 10.1093/gigascience/gix117 (published in Gigascience). One key ﬁnding of Liang et al 2018 was that the amount error in measurements of biological James C. Schnable 23 traits (such as biomass) using image data was, itself, subject to genetic control. After presenting this work at the AGU conference in December of 2017, A colleague has contacted me to tell me that, after seeing this work presented at the AGU conference in December, they have begun running separate GWAS for error between manual and high throughput measurements of the same traits and are able to identify speciﬁc genetic loci involved in controlling error. My own lab is just starting to do similar work, using multiple replicates of the the Sorghum Association Panel which were grown and phenotyped through reproductive maturity on the greenhouse system (completed grant #14) and multiple replicates of the Buckler Goodman 292 maize association panel which are currently being grown and phenotyped on the system as part of the NSF CRRI project (active grant #4). Going forward I anticipate we have hit an inﬂection point where papers published by the lab in this area will start to shift back to addressing biological questions, rather than focusing primarily on methods development and validation. Field Phenotyping: Field phenotyping efforts in the lab have been enabled by my participation in Genomes to Fields Initiative (https://www.genomes2ﬁelds.org/). This participation has in turn been supported by research funding from Nebraska Corn Growers – one of the key stakeholders of my own research program – who have now supported my G2F and ﬁeld phenotyping work for three years run- ning, each year evaluating a new research proposal as part of their competitive funding process. The overall goal of Genomes to Fields is to enable plant science (particularly in quantitative genetics and high throughput phenotyping) on a geographic scale beyond what any individual academic research group (or small company) could manage individually. This in turn should create datasets that make it possible to train students with the big data skillsets that big ag companies are having trouble hiring, catalyze the development and validation of new plant phenotyping technologies, and provide a platform for startup to medium sized ag companies to conduct geographically broad experiments on a scale that would only oth- erwise be feasible for big ag companies. In addition to these initiative-wide goals, I have found that simply having plots in the ﬁeld, combined with the commitment to manually collect ground truth data from our plots, and access to data on the performance of the same lines at dozens of sites in states across the corn belt and beyond is excellent collaborator bait, particularly for statisticians and engineers interested in new ways to analyze and collect plant phenotypic data respectively. My participation in Genomes to Fields has resulted in three publications thus far: Gage et al 2017 doi: 10.1038/s41467-017-01450-2 (published in Nature Communications), Liang et al 2018 (also references in the previous section), and Alkhalifah et al 2018 doi: 10.1186/s13104-018-3508-1 (published in BMC Research Notes).

Emphasis III: The Nebraska Food for Health Center Several years ago I was recruited to join a group of six faculty members who created the original concept for the Nebraska Food for Health Center (NFHC). This team pitched the concept of a center which could unite plant quantitative genetics with studies of the human microbiome to identify new dietary molecules which can perturb the gut microbiome to the Bill and Melinda Gates Foundation and the Raikes Foun- dation, resulting in a $5M charitable give to the University of Nebraska from these two foundations to establish the center, and additional fundraising by the University of Nebraska Foundation with a target of $40M in total investment over coming years. The Nebraska Food for Health center seeks to develop new approaches to perturb the human gut microbiome – both as a tool for basic research and to improve human health – through the application of plant quantitative genetics. Essentially we will identify sets of special- ized plant metabolites present in food with microbially active properties by conducting GWAS to identify genetic loci in plants which are associated with changes in the population structure and composition of human gut microbiomes feed grain derived from speciﬁc plant accessions. Because these microbially active compounds are, by deﬁnition, already produced by existing food crops in varying concentrates, conventional breeding work could be used to develop new varieties of functional foods enriched in com- pounds with beneﬁcial effects or depleted in compounds with detrimental effects, potentially providing new valued-added crop variety options to Nebraska farms – key stakeholders of my research program and the Department of Agronomy and Horticulture as a whole. Work in this area commenced in earnest only in 2017 with the recruitment of the lab’s most recent PhD student, supported by the NFHC graduate fellowship program and co-mentored with Andrew Benson in Food Science and has, as of yet, not resulted in any peer reviewed publications. James C. Schnable 24

Mentoring and Teaching Contributions

My goal as a teacher and mentor is to train students who are equally comfortable in the ﬁeld, at the lab bench, or working at the command line at both the undergraduate and graduate level. Within my lab, I design each graduate student’s project to require signiﬁcant direct interaction with at least one faculty member in statistics, computer science, or engineering, as well as at least one UNL faculty member (besides me) in either plant breeding, genetics, or biochemistry. I have also been working to make sure as many of the graduate and undergraduate students in my lab have interactions with private sector companies prior to graduation. In recent years this has included presenting to groups of visiting scientists from Pioneer Hi-Bred and directly interacting with employees at Indigo Agriculture as part of a collaborative project. Mentoring: I am currently advising three PhD students and one masters student from the Agronomy and Horticulture department. In addition I serve as the co-advisor for one PhD student in food science and one visiting PhD student from Shandong Agriculture University. During my time at the University of Nebraska-Lincoln I have also served as the co-advisor for a second visiting PhD student from Sichuan Agriculture University, and two masters students from the Department of Computer Science and Engineering (see page 4. Students in the lab are making excellent progress towards graduation. My ﬁrst PhD student has published six papers during his three years in the lab (four as ﬁrst author), the second has published three (one as ﬁrst author) in three years, and the third has published one (as ﬁrst author) in two years, with a second ﬁrst author paper in review. Xianjun Lai, a CSC supported graduate student published three ﬁrst author papers – plus one perspective/review piece – in the two years he was part of the lab, allowing him to be hired as an Associate Professor when he returned to China. I also currently mentor two postdoctoral scholars, and have previously mentored three post- docs and one visiting scholar during my time at UNL. Of those four lab alumni, one is now an assistant professor, a second is a staff scientist, and a third the deputy director of a functional genomics center in China, while the fourth did a short term postdoc in my lab while waiting for his spouse to graduate and has now transferred to a new institution for his primary postdoc project. For a complete list of nonunder- graduate lab alumni and their present positions please see the mentoring and teaching appendix (page 26). Teaching: I have placed a high priority on involving undergraduates in the academic research process since the start of my time at UNL. Over the past four years my lab has hosted 13 undergraduate researchers and 2 high school interns. Four students were supported through the Research Experience for Undergraduates summer program, two as part of an internal UNL program called UCARE (Undergrad- uate Creative Activities and Research Experience), one high school intern through the Young Nebraska Scientist program, and the remaining eight were supported by startup funds, and when those were ex- hausted, from research grant funding (see page 4). In recent years undergraduate and high school student research projects in my research group include a statistical reconstruction of ancestral character states for grass phenotypes, identifying computer vision phenotypes which correlate with ﬁeld performance in ex- PVP maize lines and training neural networks to count corn leaves using computer generated corn plants. The preceding examples were all projects conducted by biology or agronomy students, not students with backgrounds in Computer Science or Statistics. For a list of poster presentations including those by un- dergraduate researchers see page 26. The ﬁrst undergraduate in my lab – Daniel Ngu – has already been a co-author on two published papers, and several more recent undergraduates are co-authors on projects currently being written up for submissions. A sample of written feedback from former mentees within the lab is provided as part of Appendix A (see page 28). I joined UNL as a new Complex Biosystems graduate major was being developed and de- ployed. Complex Biosystems is one of the ﬁrst, if not the ﬁrst, interdepartmental graduate program in biology at the University of Nebraska. After being involved in the initial curriculum and program design, I began teaching one of the required ﬁrst year graduate courses for the new program (LIFE 843), which I am currently teaching for the fourth time this fall. This course incorporates training on both oral and written scientiﬁc communication, scientiﬁc misconduct, and professional development. The most success- ful aspect of the course has been a module in which students draft a research statement following the guidelines for the National Science Foundation Graduate Research Fellowship Program. Two years ago I updated this module so that, prior to the drafting their own research statements, students are divided into groups and conduct a mock peer review and stack rank of research statements from successful applicants James C. Schnable 25 in prior years, which drew both positive feedback from students and a notable improvement in the quality of the ﬁnal research statements turned in by students. In addition, after another of the original founding faculty for the Complex Biosystems program left the University, I have taken over coordinating the Fall semester of Life 841, which is another required ﬁrst year course for students in the Complex Biosystems program. Life 841 focuses on the big open questions across a number of ﬁelds including quantitative genetics, plant biology, and microbiome host interactions. Example syllabi for LIFE 843 and LIFE 841 (formerly LIFE 891) are provided in Appendix A (see page 36) I am currently involved in an effort lead by George Graef to revise and revitalize the Plant Breeding curriculum within the Department of Agronomy and Horticulture and hopefully to revive the teaching of Plant Breeding at the undergraduate level. In addition to formal teaching I have been active in conducting outreach to both early learners (Sunday with a Scientist) and high school students (Fascination with Plants Day), as well as to the broader scientiﬁc and research community (both through twitter and podcast interviews). Details of outreach activities are provided as part of Appendix A (see page 34).

Service Contributions

Scholarly Service I serve as a member of the advisory committee for MaizeGDB, the USDA funded genetics and genomics database for maize research. For folks more familiar with Arabidopsis, MaizeGDB is the equivalent of TAIR. In addition, I was recently recruited to become a member of the Data Management Subcommittee of the NSF-funded Maize Genetics Research Collaboration Network. I am also conscious of the fact that high publication frequency brings with it a responsibility to be active in the peer review process. I have reviewed an average of 2-3 manuscripts per month during my time as an assistant professor for journals ranging from Science to G3. Finally, I have served as an associate editor for Molecular Plant since 2014. University and Department Service During my time as the University of Nebraska I have served on four search committees and the organizing committees for two conferences based at the University of Nebraska (Predictive Crop Design: Genome to Phenome, Plant Phenomics: From Pixels to Traits) and one based at a neighboring school (International Millet Symposium at Colorado State University). I have also served on a number of internal committees with varying degrees of both time commitment required and exposure to political consequences and fallout. The most controversial committee I serve on is the UNL Faculty Greenhouse Committee. On one notable occasion, my faculty mentor within the department ﬁred me as a mentee because she was unhappy that I had brought a concern of hers to the greenhouse committee, but was unable to sway the committee as a whole to adopt the remedy she had requested I seek. I was elected by my peers within the Department of Agronomy and Horticulture to serve a two year on the Faculty Advisory Committee in 2017. Over this period the department has had two chairs, and a third is expected to assume the ofﬁce before the end of my current term on the committee so, in addition to providing a service back to the department, this role as served as a fascinating learning experience into differences in management style and the challenges facing any faculty member who ﬁnds themselves in charge of a department including 67 faculty members with home bases spread across four hundred miles and two time zones. I also serve as a member of the faculty advisory committee for the Raikes and Gates Foundation funded Nebraska Food for Health Center – the only assistant professor to be asked to serve in this role. Within the department I also service on the Peer Evaluation Committee which reviews the annual progress reports submitted by faculty members and makes recommendations and provides text to the department chair for use in the annual evaluation process. James C. Schnable 26

Appendix A: Supporting Evidence for Mentoring Activity and Outcomes

Present Employment of Lab Alumni

Name Schnable Lab Posi- Tenure Current Position tion Yang Zhang Postdoctoral 2014-2017 Research Scientist, St. Jude Children’s Research Scholar Hospital Jinliang Postdoctoral 2016-2017 Assistant Professor, University of Nebraska-Lincoln Yang Scholar Sunil Kumar Postdoctoral 2017-2018 Postdoc, Niederhuth Lab, Michigan State Univer- Scholar sity Lang Yan Visiting Scientist 2016-2017 Deputy Director, Potato Functional Genomics, Xi- Chang College Xianjun Lai CSC PhD Student 2015-2017 Associate Professor, XiChang College Bhush*t Masters Student 2015-2016 Software Engineer, Mode.ai Agarwal Srinidhi Masters Student 2015-2016 Systems Software Developer, University of Bashyam Nebraska-Lincoln

Partial List of Poster Presentations With Undergraduate Authors High- lighted

Lab Members in bold. Undergraduates from the Schnable lab in red

Carvalho DS, Liang Z, Butera C, Stoerger V, Schnable JC.(2018) High-throughput imaging and phe- notyping of panicoid grain crops. 3rd International Millet Symposium. Fort Collins, Colorado. Butera C, Carvalho DS, Liang Z, Miao C, Sun G, Schnable JC.(2018) Automated phenotyping of maize and pearl millet growth patterns and drought stress responses. Summer Research Fair - Univer- sity of Nebraska-Lincoln. Lincoln, Nebraska. Pages AD, Miao C, Clarke J, Schnable JC.(2018) Automated trait extraction from images of Sorghum. Summer Research Fair - University of Nebraska-Lincoln. Lincoln, Nebraska. Foltz A, Sun G, Schnable JC.(2018) Differences in the responses to nutrient stress of the root systems of maize and its domesticated and wild relatives. Summer Research Fair - University of Nebraska- Lincoln. Lincoln, Nebraska. Miao C, Pandey P, Liang Z, ... Schnable JC.(2018) Analysis of sorghum time-series phenotype data using nonparametric curve ﬁtting and machine learning. Phenome 2018. Tucson, Arizona. Pedersen C, Schnable JC, Liang Z.(2018) Analyzing phenotypic correlations in large scale studies combining ﬁeld and greenhouse datasets. Nebraska Plant Breeding Symposium. Lincoln, Nebraska Connor Pedersen was awarded the ﬁrst prize in the undergraduate poster competition at the Nebraska Plant Breeding Symposium. Hoban T, Schnable JC, Miao C, Xu Z, Liang Z.(2018) Using machine learning to count leaves in maize. Nebraska Plant Breeding Symposium. Lincoln, Nebraska Liang Z, Pandey P, Stoerger V, Xu Y, Qiu Y, Ge Y, Schnable JC.(2018) High-throughput imaging of maize lines from public and private sectors employed in ﬁeld trials. Supercomputing and Life Sciences Symposium. Lincoln, Nebraska. James C. Schnable 27

Podliska H, Schnable JC, Carvalho DS.(2018) Cold tolerance in PACMAD grasses. Spring Research Fair - University of Nebraska-Lincoln. Lincoln, Nebraska.

Miao C, Yang J, Schnable JC.(2018) Large-scale simulation studies enabled by HPC reveal the powers of GWAS approaches in dissecting highly polygenic traits in crop species. Supercomputing and Life Sciences Symposium. Lincoln, Nebraska.

Miao C, Pandey P, Liang Z, Carvalho DS, Ye X, Stoerger V, Xu Y, Ge Y, Schnable JC.(2018) Analysis of sorghum time-series phenotype data using functional ANOVA and machine learning. Phenome 2018. Tuscon, Arizona.

Liang Z, Bai G, Ge Y, Rodriguez O, Schnable JC.(2018) Field phenotype prediction on maize using novel phenomic tools and environmental information. 2018 NIFA FACT G2F Workshop. Ames, Iowa.

Schnable JC, Pandey P, Ge Y, Xu Y, Qiu Y, Liang Z.(2017) Lessons From Paired Data From exPVP Maize Lines in Agronomic Field Trials and RGB And Hyperspectral Time-Series Imaging In Controlled Environments. AGU 2017 Fall Meeting. New Orleans, Louisiana.

Shi Y, Veeranampalayam-Sivakumar AN, Li J, Ge Y, Schnable JC, Rodriguez O, Liang Z, Miao C. (2017) Breeding for Increased Water Use Efﬁciency in Corn (Maize) Using a Low-altitude Unmanned Aircraft System. AGU Fall Meeting. New Orleans, Louisiana.

Hoban T, Liang Z, Schnable JC.(2017) Identifying sorghum root hair mutants as a ﬁrst step in compar- ative genetic analysis of maize and sorghum. Spring Research Fair - University of Nebraska-Lincoln. Lincoln, Nebraska.

Carvalho DS, Zhang Y, Schnable JC.(2017) Identifying common and unique enzymatic changesas- sociated with three C4 biochemical pathways in related grasses. Predictive Crop Design: Genome-to- Phenome. Lincoln, Nebraska.

Zhang Y, Ngu DW, Carvalho DS, Liang Z, Qiu Y, Roston RL, Schnable JC.(2017) Statistical ap- proaches to identifying differentially regulated orthologs (DROs) across related grass species. 59th Maize Genetics Conference. St. Louis, Missouri.

Miao C, Yang J, Schnable JC.(2017) Comparative GWAS in Sorghum bicolor and Setaria italica. 59th Maize Genetics Conference. St. Louis, Missouri.

Yan L, Lai X, Rodriguez O, Schnable JC.(2017) Developing transcriptomic resources for Tripsacum to study the adaptation of a maize relative to temperate climates. 59th Maize Genetics Conference. St. Louis, Missouri.

Lai X, Yan L, Lu Y, Schnable JC.(2017) Searching for parallel signatures of selection during domesti- cation in maize and sorghum. 59th Maize Genetics Conference. St. Louis, Missouri.

Liang Z, Bai G, Ge Y, Rodriguez O, Schnable JC.(2017) Field phenotype prediction on maize using novel phenomic tools and environmental information. 59th Maize Genetics Conference. St. Louis, Missouri.

Liang Z, Pandey P, Stoerger V, Xu Y, Qiu Y, Ge Y, Schnable JC.(2017) Conventional and hyperspectral time-series image data sets of maize inbred lines widely used in North American ﬁeld trials. Nebraska EPSCoR RII Track 1 Grant External Review Panel Visit. Lincoln, Nebraska.

Liang Z, Bashyam S, Agarwal B, Samal A, Bai G, Chaudhury SD, Rodriguez O, Qiu Y, Ge Y, Schn- able JC.(2016) Maize Phenomap1 and Phenomap2 datasets: Integration with genomes to ﬁelds. 4th International Plant Phenotyping Symposium. Texcoco, Mexico.

Liang Z, Bashyam S, Samal A, Choudhury SD, Geng B, Ge Y, Rodriguez O, Schnable JC.(2016) Computer vision based phenotyping of panicoid crops. 2016 Purdue Plant Science Symposium. West Lafaytte, Indiana. James C. Schnable 28

Johnson K, Liang Z, Schnable JC.(2016) Maize association studies with high throughput image based phenotyping. Summer Research Fair - University of Nebraska-Lincoln. Lincoln, Nebraska.

Horn T, Zhang Y, Schnable JC.(2016) Cold sensitivity and genetic regulatory responses in panicoid grasses. Summer Research Fair - University of Nebraska-Lincoln. Lincoln, Nebraska.

Liang Z, Schnable JC.(2016)B73 maize population structure analysis by RNA-seq data. 2016 UNL Plant Breeding and Genetics Symposium. Lincoln, Nebraska.

Zhang Y, Ngu DW, Mahboub S, Qiu Y, Roston RL, Schnable JC.(2016) Conservation and divergence of synthetic gene regulation in response to stress in maize and relatives. 58th Maize Genetics Conference. Jacksonville, Florida

Ngu DW, Zhang Y, Schnable JC. (2016). Updates to qTeller: A tool for visualizing published gene expression data. 58th Maize Genetics Conference. Jacksonville, Florida

Carvalho DS, Zhang Y, Schnable JC.(2016) Comparative analysis of C4 photosynthesis genes in two independent origins of C4 in grasses. 58th Maize Genetics Conference. Jacksonville, Florida.

Lai XJ, Bendix C, Zhang Y, Ngu DW, Lu YL, Harmon FG, Schnable JC.(2016) Conserved and lineage- speciﬁc alternative splicing of orthologous genes in maize, sorghum, and setaria. 58th Maize Genetics Conference. Jacksonville, Florida.

Carvalho DS, Zhang Y, Schnable JC.(2015) Comparative transcriptomic analysis of Danthoniopsis dinteri, a novel C4 grass species. Plant Phenomics Symposium. Lincoln, Nebraska. Zhang Y, Ngu DW, Roston RL, Schnable JC.(2015) Core cold responsive genes in the panicoid grasses. Plant Science Symposium "Plant Phenomics: from pixels to traits". Lincoln, Nebraska

Letters from Former Mentees: James C. Schnable 29

Figure 1: Ashley Foltz was an REU student from the University of Wyoming who worked on comparisons of how the root morphology and gene expression of eight different grasses (maize, sorghum, setaria and wild relatives) responded to different nutrient deﬁcits as part of the CRRI EPSCoR project. James C. Schnable 30

Figure 2: Taylor Horn was an REU student from Baylor University who worked on a project linking vari- ation in the cold stress tolerance of wild grasses to their native ranges, conducting phenotypic screening, and mining data from the Global Biodiversity Information Facility (GBIF) for a GIS based analysis that she learned to conduct herself using python. James C. Schnable 31

Figure 3: Kyle Johnson was an REU student from BYU who worked on kernel phenotyping project, imaging kernels from the maize Buckler/Goodman 282 association panel and conducting GWAS for traits measured from those images using computer vision techniques related to size and shape. James C. Schnable 32

Figure 4: Xianjun Lai was a "sandwich" PhD student from Sichuan Agricultural University who was supported for two years of his graduate research in my lab by the Chinese Scholarship Council from 2015-2017. Lang Yan was a visiting scholar in the lab from 2016-2017. James C. Schnable 33

Figure 5: Sunil Kumar came to the lab as a molecular biologist, having just ﬁnished a PhD at UNL, and spent a semester as a postdoc learning both bioinformatic and comparative genomic techniques prior to starting his primary postdoc at Michigan State where he is now working on comparative epigenomics in maize and Tripsacum dactyloides. James C. Schnable 34

Outreach Activities Sunday with a Scientist: Working with members of both the Herr lab (Plant Pathology) and the Roston Lab (Biochemistry), I and other members of my lab designed and delivered in a module for the successful "Sunday with a Scientist" program titled "Why Plants Don’t Wear Sweaters" which is run by the Nebraska State Museum and held in Morrill Hall on the third Sunday of each month. http://museum.unl.edu/sundaywithascientist/may2016.html

Figure 6: Schnable and Roston lab members at a Sunday with a Scientist event held in 2016. Total atten- dance at the 2016 "Why Plants Don’t Wear Sweaters" Sunday with a Scientist event was 110 individuals: Adults 61, Youth 45 and UNL students 4.

Fascination of Plants: As part of the 2017 celebration of "Fascination of Plants" day, the Schnable Lab de- signed and taught a module to introduce high school students from a local magnet school to comparative genomics techniques used the web interface/database CoGe. As a result of limited dry-lab space available to the Schnable Lab, enrollment in this module was capped at two shifts of 20 students each and every spot was ﬁlled.

Figure 7: One shift of 20 high school students crowded into the dry lab space of the Schnable lab to learn and practice basic comparative genomics techniques.

Peggy Smedley Interview: Invited guest interviewed on the Peggy Smedley show – a weekly podcast focused on the Internet of Things – about developments in plant phenotyping. Reported audience size of 100,000 downloads per week. James C. Schnable 35

https://peggysmedleyshow.com/portfolio-items/05-22-18-episode-564-segment-2-iot-measures- and-manages-plants/

Twitter: In order to for the work I do to have impact, it is important to work to ensure people actually hear about it. As the number of scientiﬁc papers published each year continues to accelerate at a rate of 5-10% year, depending on which estimates you believe, it is less and less practical to simply publish papers in journals and sit back and wait for impact to happen – whether that impact is measured in terms of changes in real world outcomes, economic activity, changes in the broad scientiﬁc consensus within a ﬁeld, or simply citations to the paper in question. Some of this necessary evil of self promotion can be achieved through one on one conversations and attendance at scientiﬁc conferences. However, in addition to these approaches, I have developed and maintained a twitter account which I use to disseminate and promote the research and achievements of lab members and scientiﬁc collaborators. The account currently has 1,832 unique followers and generates 100,000-150,000 unique impressions per month. Generally it achieves 1,000-5,000 per tweet, although my record is >54,000 im- pressions for a twitter showcasing time lapse imaging of corn plant development. A modiﬁed version of this small time lapse dataset was later incorporated into the wikipedia page on "Heterosis" by a third party, with proper citation and attribution back to my lab and the student involved. Water of Food: Interview by Daniel Carvalho (a PhD student in the lab) about his work studying the genetic basis of the convert evolution of C4 photosynthesis as part of the mission of the Daugherty Water for Food Global Institute. https://www.youtube.com/watch?v=uF02kuE4Qn0 James C. Schnable 36

Example Syllabi Biosystems Research I Big Questions LIFE891: 3 credits, Fall 2017

Instructional coordinator: Dr. James Schnable E207 Beadle Center Phone: 472-3192 Email: [emailprotected] Office hours: Normally, I will be available for office hours the hour after class. If you need to talk with me outside that time, please email for an appointment time. Additional office hours will be set by each team of instructors.

Meeting place and time: Tuesday/Thursday, 9:30-10:45 AM, 110 Othmer Hall.

Course Objectives: The course will provide an overview of major research questions in the life sciences focused on understanding complex biological systems. Emphasis will be on developing confidence in reading and critically evaluating seminal and current primary literature in a broad range of research on living systems. This will include an introduction to key techniques in biochemical, molecular, organismal, quantitative and computational biology relevant to the life sciences. Students will develop the following set of skills: 1) Conversant in major concepts and issues underlying significant ongoing research questions in the life sciences, including the state of our understanding, and the array of approaches that have been used to address the questions 2) Familiar with contemporary research methods in the life sciences 3) Able to articulate the strengths and limitations of discovery-based versus hypothesis- driven research, understand how use of methodologies differs with these objectives, and appreciate what research questions are best addressed by which approaches 4) Competent in critically reading and interpreting primary literature in diverse systems 5) Strong written communication and scientific presentation in life sciences

Prerequisites: No formal prerequisites, but basic knowledge of math, biology, biochemistry, and/or chemistry will be assumed.

Recommended Reference Text: You do not need to purchase a textbook exclusively for this course. Instructor teams will provide recommendations for resources to consult for reference.

Primary literature: The overall schedule of topics and due dates is [will be] attached to this syllabus. Reading assignments for each class period’s lecture topic will typically consist of a review article and/or 1-2 primary papers. These will be posted in pdf format on the Blackboard site, typically no less than a week before the material will be discussed in class. It will be essential to complete reading assignments prior to the class period so that you are prepared for the discussion. You may also need to consult reference texts or look up additional review materials as needed for background. Big questions in current research will be examined in five modules. Examples of such questions include the following: 1. How do computational approaches advance the pace of life sciences research? 2. Cancer: why don’t we have a cure? 3. How does the gut microbiome impact obesity and disease susceptibility? 4. Feeding the world: can water use be optimized with drought tolerant crops? 5. What are we doing to the soil (or water): impact of industry on environment?

1 Assignments: The course will be assessed on the basis of class readings, discussions, one written project or collection of assignments per modular topic, and an integrative final. Dates and instructors for each module are in the schedule attached. Projects/assignments and respective due dates will be defined by each team. No late submission of written assignments will receive credit.

Grades will be based on the following criteria: grading scale Five written projects 50% total Project 1 10% Project 2 10% Project 3 10% Project 4 10% Project 5 10% Overall participation and preparation* 30% Final 20% *Attendance is a component of class participation and preparation, and is MANDATORY. If you will be absent, you must notify the instructor of the current topic before the class meeting with a detailed reason for absence and the date of absence (acceptable reasons for absence: at scientific meeting; surgery or similar significant medical issue (not just a cold); major experiment that cannot be done another day and time). This is for your benefit.

Academic Dishonesty: Academic dishonesty includes fabrication, falsification and plagiarism. Acts of academic dishonesty are not acceptable or tolerated by the scientific community. Thus, these acts should not be tolerated by students. Falsification of research data or its deliberate misinterpretation is a serious offense with consequences ranging from reprimand to dismissal to professional ruin. Plagiarism is more complex and may be the result of carelessness or ignorance, rather than an intentional attempt to deceive. Plagiarism is defined as passing off someone else's ideas, words or writings as your own. Inclusion of a sentence in a paper that is copied from any source without quotation marks and citation is an obvious example of plagiarism. However, paraphrasing another person’s writing or organization of ideas is also regarded as plagiarism. Anything you present that is under your name should be entirely your own work unless so indicated and appropriately cited. This includes ideas, artwork and figures, as well as specific sentence, paragraph or word use. Note that in scientific writing, quotations are typically not used and it is therefore expected that all phraseology is your own, whether cited or not.

This is the University’s policy. We uphold it implicitly and we are committed to making sure you understand the reprehensible practice of plagiarism in all its nuances.

It is that important to your career, starting now.

If you do not fully understand what constitutes plagiarism, please ask for clarification immediately.

We expect and encourage you to discuss assignments and work together as you process the material, but that your individual compositions will be your own original writing. Assignments will be submitted electronically and automatically subjected to plagiarism screening by academic software. If you plagiarize, you will receive an F for the course. UNL's policies concerning grade appeals will be followed.

Students with disabilities are encouraged to contact me for a confidential discussion of their

2 individual needs for academic accommodation. It is the policy of the University of Nebraska to provide flexible and individualized accommodation to students with documented disabilities that may affect their ability to fully participate in course activities or to meet course requirements. To receive accommodation services, students must be registered with the Services for Students with Disabilities (SSD) office, 132 Canfield Administration, 472-3787 voice or TTY.

Grades of Incomplete: According to UNL policy, an Incomplete will be given only in the event of acute illness, military service, hardship, or death in the immediate family (i.e.; parents, children, spouses or siblings). Students are eligible for an Incomplete only if coursework is substantially completed. For this course, at least 50% of the class discussions and coursework must have been completed with a minimum overall grade of C to be considered for the Incomplete option. Resolution of the Incomplete grade would entail retaking the entire course within two years.

Classroom Emergency Preparedness and Response Information • Fire Alarm (or other evacuation): In the event of a fire alarm: Gather belongings (Purse, keys, cellphone, N-Card, etc.) and use the nearest exit to leave the building. Do not use the elevators. After exiting notify emergency personnel of the location of persons unable to exit the building. Do not return to building unless told to do so by emergency personnel. • Tornado Warning: When sirens sound, move to the lowest interior area of building or designated shelter. Stay away from windows and stay near an inside wall when possible. • Active Shooter o Evacuate: if there is a safe escape path, leave belongings behind, keep hands visible and follow police officer instructions. o Hide out: If evacuation is impossible secure yourself in your space by turning out lights, closing blinds and barricading doors if possible. o Take action: As a last resort, and only when your life is in imminent danger, attempt to disrupt and/or incapacitate the active shooter.

UNL Alert: Notifications about serious incidents on campus are sent via text message, email, unl.edu website, and social media. For more information go to: http://unlalert.unl.edu.

Additional Emergency Procedures can be found here: http://emergency.unl.edu/doc/Emergency_Procedures_Quicklist.pdf

JClarke 7/30/17

3 Schedule of lectures and assignments

SA Tues Aug 22 Overview and history TBD Thurs Aug 24 Systems analysis Jean-Jack Riethoven Tues Aug 29 Thurs Aug 31 PBS Tues Sept 5 Pathobiology and Rodrigo Franco Cruz Thurs Sept 7 Biomedical science Tues Sept 12 Thurs Sept 14 Tues Sept 19 Thurs Sept 21 MI/IPB Tues Sept 26 Microbiome and Metagenomics Joshua Herr Thurs Sept 28 Tues Oct 3 MI Amanda Ramer-Tait, Andrew Benson, Thurs Oct 5 Microbial interactions Jacques Izard Tues Oct 10 Gut microbiome Thurs Oct 12 Tues Oct 17 Fall break, no class Thurs Oct 19 Tues Oct 24 Thurs Oct 26 IPB Soil microbes and interactions Tues Oct 31 Daniel Schachtman with roots Thurs Nov 2 TBD Tues Nov 7 Integrative plant biology Tom Clemente COB Thurs Nov 9 Computational organismal Colin Meiklejohn EE Tues Nov 14 Biology, Ecology, Evolution TBD Thurs Nov 16 Drew Tyre Tues Nov 21 Clay Cressler Thurs Nov 23 Thanksgiving break, no class Tues Nov 28 John DeLong and Kristi Montooth Thurs Nov 30 Steven Thomas SA Tues Dec 5 Systems analysis James Schnable Thurs Dec 7 Wed Dec 13 10am-Noon official final time SA, Systems Analysis; PBS, Pathobiology and Biomedical Science; MI, Microbial Interactions; IPB, Integrative Plant Biology; COBEE, Computational Organismal Biology, Ecology and Evolution

4 LIFE 843 – Professional Development

1 Credit

Instructor: James Schnable E207 Beadle Center City Campus [emailprotected]

No required text book

Course Information: This course will meet once per week and is intended for first year graduate students in the life sciences. Course Objectives: At the conclusion of this course, students should be able to: 1. Present scientific information in both oral and written formats in ways accessible to others with scientific backgrounds outside of their own specialization 2. Effectively read the scientific literature, and for individual papers be capable of assessing what was known previously, the hypotheses put forward, and critically assess the whether the results put forward by the authors represent an effective test by the authors. 3. Understand what constitutes scientific misconduct, how to avoid committing misconduct yourself, and how to deal with observing others committing apparent misconduct 4. Describe their career goals post graduate school and describe how they are selecting a lab and research project which will enable them to work towards those goals. Assignments and Assessment: Throughout this course you will give two presentations, write one two page research statement (and associated preliminary documents), and participate in a mock peer review, as well as discussions of scientific misconduct. Five minute presentation (10% of final grade): This presentation should include no more than three slides, take no more than four minutes (leaving one minute for questions). The first slide should introduce the topic for those with no background in the topic (what does the audience need to know to understand your presentation, why is it relevant to their lives), the second slide should present more detailed information, and the third should explain the significance of the information presented on the first two. Presentations can be on any topic other than your past or current research. Previous topics have included: Strategies for conserving water, the origins and future threat of killer bees, and how to cook thai curry. To receive full credit your presentation cannot exceed 240 seconds and must be intelligible to both the instructor and other students. Academic paper presentation (30% of final grade): This presentation should be approximately 15 minutes in length. At a minimum you should include a title slide, a slide listing the hypothesis you believe the authors tested in this paper, a bullet point list summarizing the introduction, slides for each figure in the paper you are presenting (supplementary figures can be optionally included if you think they help describe the paper), a slide summarizing the conclusions the authors drew from their results, and a final slide describing your assessment of whether the authors results A) support their conclusions B) represent a valid test of their hypothesis. When you present each figure you say what it is, what it is showing, and why you think the authors included in their manuscript. Two page research statement (40% of final grade): This research statement will track the form of the statement required by the NSF GRFP. Students will submit a list of 3-5 potential research topics, a research statement outline, and rough draft, and a final draft. All documents should be submitted electronically and are due the midnight before class on the week the assignment is due. Point can be lost for failure to turn in research topics, outline, and rough draft on time, however quality of each student's work will be assessed only from the final paper.

Class participation (Mock peer review 10% Academic misconduct discussion 10%). Attendance and Participation: Attendance is mandatory. If you need to miss a class for an outside event, illness, or other event, contact me prior to the start of class. Weekly schedule: Week 1 (August 21nd): Introduction to the course. Anatomy of a good scientific presentation. Sign up for presentation dates. Week 2 (August 28th): Five minute/three slide presentations for all students Week 3 (September 4th): Receive and read three example research statements. Conduct mock peer review and stack rank during class. Week 4 (September 11th): No class. Students should submit 3-5 ideas for research projects (as little as one sentence each) by e-mail. Week 5 (September 18nd): Three student presentations on academic papers Week 6 (September 25th): Three student presentations on academic papers. One-on-one meetings to go over research proposal ideas should be scheduled for this week. Research proposal outlines due Week 7 (October 2th): Draft research statements due. Peer review of draft research statements. Class will divide into two groups, evaluate anonymous research statements, and provide a summaries of strengths and weaknesses of each proposal to the instructor. Students should submit draft research statements electronically to the instructor no later than midnight the night before class. Week 8 (October 9th): No class. Final research statements, including “response to reviewers” due. October 16th: Fall break, no class Students will received instructor feedback and anonymized peer feedback on final research statements. (Reminder for those planning to submit NSF-GRFP proposals that they will be due by October 22nd (Monday). Week 9 (October 23th): Class discussion of examples of recently retracted scientific papers taken from “Retraction Watch” Week 10 (October 30th): TBD. Week 11 (November 6th): Group discussions of academic misconduct take home examples; Short responses to academic misconduct examples due Week 12 (November 13th): Plagiarism and dual publication. Week 13: (November 20nd) Academic Life Skills: Choosing an advisor, choosing a research project. Week 14: (November 27th) Academic Life Skills: Life after grad school (industry research, academic research, academic teaching, science related non-research fields). Week 15: (December 4th): Open discussion Additional required information:

Fire Alarm (or other evacuation): In the event of a fire alarm: Gather belongings (Purse, keys, cellphone, N-Card, etc.) and use the nearest exit to leave the building. Do not use the elevators. After exiting notify emergency personnel of the location of persons unable to exit the building. Do not return to building unless told to do so by emergency personnel. Tornado Warning: When sirens sound, move to the lowest interior area of building or designated shelter. Stay away from windows and stay near an inside wall when possible. Active Shooter o Evacuate: if there is a safe escape path, leave belongings behind, keep hands visible and follow police officer instructions. o Hide out: If evacuation is impossible secure yourself in your space by turning out lights, closing blinds and barricading doors if possible. o Take action: As a last resort, and only when your life is in imminent danger, attempt to disrupt and/or incapacitate the active shooter. UNL Alert: Notifications about serious incidents on campus are sent via text message, email, unl.edu website, and social media. For more information go to:http://unlalert.unl.edu. Additional Emergency Procedures can be found here: http://emergency.unl.edu/doc/Emergency_Procedures_Quicklist.pdf Students with disabilities are encouraged to contact the instructor for a confidential discussion of their individual needs for academic accommodation. It is the policy of the University of Nebraska-Lincoln to provide flexible and individualized accommodation to students with documented disabilities that may affect their ability to fully participate in course activities or to meet course requirements. To receive accommodation services, students must be registered with the Services for Students with Disabilities (SSD) office, 132 Canfield Administration, 472- 3787 voice or TTY. Academic Integrity: Academic integrity is an essential indicator of the student’s ethical standards. For this reason students are expected to adhere to guidelines concerning academic honesty outlined in Section 4.2 of University’s Student Code of Conduct which can be found at http://stuafs.unl.edu/ja/code/three.shtml. Students are encouraged to contact the instructor to seek clarification of these guidelines whenever they have questions and/or potential concerns. a. Breaches of academic integrity and their consequences vary considerably, so it is not possible to outline one set of absolute chain of consequences for every situation b. Each instructor may impose a consequence(s) for a breach of academic integrity in his/her own course, consistent with the magnitude of the breach. The consequences may range from reduced credit for a test or assignment to failure in the course. c. If the student feels that the consequence(s) imposed are inappropriate, the student should discuss the matter first with the instructor within 7 days of the incident. d. If the student is still dissatisfied with the consequences imposed, he/she may appeal to the Department Head or his/her designee within 14 days of the incident. e. If the student is dissatisfied with the results of his/her appeal to the Department Head, then he/she may appeal to the Dean of the College of Agricultural Sciences and Natural Resources within 21 days of the incident. f. Further appeal may be pursued with the University Judicial Officer as described in http://stuafs.unl.edu/ja/code/three.shtml. g. The course instructor will inform the student’s academic advisor of the final disposition of the breach of academic integrity immediately after the final decision James C. Schnable 44

Appendix B: Supporting Evidence for Research Activity and Outcomes Cover Page Summaries of Funded Grants James C. Schnable 45

Figure 8: RoL: FELS: EAGER: Genetic Constraints on the Increase of Organismal Complexity Over Time. (Schnable is PI)

Overview: The research included as part of this project is aimed at testing a proposed rule of life: that in- creases in organismal complexity are constrained by the availability of certain types of genes which are recalcitrant to most forms of gene duplication, but can be duplicated as part of poly- ploidy (whole genome duplication) (Freeling and Thomas, 2006; Freeling, 2009). Most apparent links between whole genome duplication and an increase in the number of separately deﬁned body parts – for example the evolution of early tetrapods or the emergence of ﬂowering plants – occurred tens or hundreds of millions of years ago. However, we will use a much more recent model to test this hypothesis. The model system Zea mays (maize; corn) produces two special- ized types of inﬂorescences for male and female reproduction which have been shown to be con- trolled by distinct genetic architectures, while all the other genera in the grass tribe to which it belongs – Andropogoneae – produce only a single type of inﬂorescence. This proposal seeks to test the link between a whole genome duplication in the maize lineage and the evolution of its developmentally distinct inﬂorescences. These tests will be conducted using both conventional comparative genomics and novel comparative genetics techniques enabled by reverse genetics re- sources in both maize, and a closely related species Sorghum bicolor, which lacks both the maize whole genome duplication and the specialized and genetically differentiated inﬂorescences found in maize. Intellectual Merit: In both plants and animals, the emergence of new specialized body parts – for example the devel- opment of ﬂoral organs in plants or the multiple specialized types of teeth found in heterodont animals – is a rare process. Generally these specialized organs appear to originate as specialized versions of existing organs, yet their specialization requires a divergence in regulation between different copies of the same organ. This EAGER proposal seeks to test the hypothesis that, in the case of the specialized reproductive organs of maize, the separate regulation and evolution of what were, initially, duplicate copies of the same organs were enabled by a whole genome duplication, which created duplicate copies of many transcription factors which rarely duplicate through other processes. In addition, through the generation and phenotypic characterization of knockouts of syntenic orthologous genes in both maize and sorghum, this proposal provides one of the ﬁrst systematic tests of the Ortholog Conjecture in plants (Koonin, 2005; Nehrt et al., 2011; Chen and Zhang, 2012). Broader Impacts: If successful, this proposal will have deﬁned a potential rule of life that may also explain why the emergence of new specialized body parts is so rare, can predict when the emergence of new specialized body parts is more likely, and provide initial insights into how specialized body parts could be engineered in future synthetic biology efforts. Research in this area also has the potential to guide the development of new engineered varieties of crop plants with multiple specialized leaf types deployed in different parts of their canopies, okafor example – engineering top layer leaves to have fewer chloroplasts, allowing more light to penetrate deeper into the canopy where lower wind speeds and higher humidity reduce the transpirational water cost of photosynthesis (Ort et al., 2011, 2015) or engineering bottom leaves to express modiﬁed forms of chlorophyll such as chlorophylls D & F which can harvest energy from abundant far red light present lower in crop canopies (Chen et al., 2010; Croce and Van Amerongen, 2014). In addition this proposal will provide valuable training on conducting science at the intersection of genomics, genetics, and phenomics to one postdoc, and has the potential to provide spin off projects characterizing individual orthologous gene triplets in more detail to multiple undergraduates, supported by UNL’s internally funded UCARE program as well as an existing REU program in which Schnable participates at UNL.

B–1 James C. Schnable 46

Figure 9: USDA NIFA AFRI Foundational: Identifying mechanisms conferring low temperature tolerance in maize, sorghum, and frost tolerant relatives. (Schnable is PI)

PROJECT SUMMARY

Instructions: The summary is limited to 250 words. The names and affiliated organizations of all Project Directors/Principal Investigators (PD/PI) should be listed in addition to the title of the project. The summary should be a self-contained, specific description of the activity to be undertaken and should focus on: overall project goal(s) and supporting objectives; plans to accomplish project goal(s); and relevance of the project to the goals of the program. The importance of a concise, informative Project Summary cannot be overemphasized.

Title: Identifying mechanisms conferring low temperature tolerance in maize, sorghum, and frost tolerant relatives. PD: Schnable, James, C Institution: University of Nebraska-Lincoln CO-PD: Roston, Rebecca Institution: University of Nebraska-Lincoln CO-PD: Institution: CO-PD: Institution: CO-PD: Institution: CO-PD: Institution: CO-PD: Institution:

Key crops in the US – maize and sorghum – are quite sensitive to both cold and freezing temperatures resulting in crop losses from unexpected cold snaps, and limiting growing seasons at more northern lattitudes, resulting in lower yields. Maize and sorghum show extremely limited genetic variation for survival under freezing temperatures, limiting the effectiveness of conventional intraspecific quantitative genetic approaches. The central premise of this proposal is that interspecific comparisons between low temperate tolerant and low temperature sensitive species within the same grass subfamily will make it possible to link changes in gene regulation to changes in membrane lipid composition and metabolite accumulation known to be two central components freeze and cold tolerances. To this end, changes in membrane lipid composition and gene expression in response to cold and subsequent freezing stress will be profiled for 10 panicoid grasses in objective one. Synteny-based comparative genomic approaches will be employed to enable the comparisons of the transcriptional response of the same genes across multiple species to the same changes in environmental condition. A broader set of 180 grass species will be assayed for cold and freezing tolerance in objective two. And in objective three patterns of metabolic change in response to cold stress will be assayed in maize, sorghum, and two close relatives in which freeze tolerance has evolved independently. Integrating multiple types of data from multiple species in response to different degrees of low temperature stress will permit the identification of mechanisms which convey low temperature tolerance in panicoid grasses. James C. Schnable 47

Figure 10: ARPA-E Roots: In-plant and in-soil microsensors enabled high-throughput phenotyping of root nitrogen uptake and nitrogen use efﬁciency. (Schnable is co-PI)

1. INNOVATION AND IMPACT 1.1 Overall Description. Nitrogen (N) is a key nutrient for crop plants, and improved nitrogen use efficiency (NUE) can significantly reduce fertilizer applications, increase crop yields, and reduce the environmental footprint of agriculture. By examining plant N uptake and NUE on appropriate populations, the genetic control of physiological phenotypes can be defined. Although progress has been made in collecting in-field trait data, high-throughput and high-accuracy measurements of below-ground phenotypes and in planta phenotyping of N status have so far not been possible. Sensing of plant- soil processes that control NUE is currently limited to methods that are time-intensive, laborious, and destructive and have low information content with respect to spatiotemporal characteristics of N soil N supply and plant N uptake. We propose to develop Micro-Electro-Mechanical Systems (MEMS)-based in planta and soil N sensors that will extend rapidly measureable plant phenotypes from yield, number of leaves, and flowering time to deep physiological traits directly related to root N uptake and NUE. The in planta and soil nitrate measurements will be coupled to create a direct measurement of fertilizer NUE that is not currently possible, and thereby enable rapid identification of genotypes with N-uptake- proficient root systems. The core concept is to create a low cost sensors-enable high-throughput, high-accuracy, large-scale NUE phenotyping platform combining minimally invasive, in planta nitrate sensors and microfluidic soil nitrate sensors, to enable integrated sensing of plant and soil processes that influence NUE. The unique in planta sensors, in the form of a microscale needle, are inserted into multiple sites of the plant to prove frequent and accurate monitoring of nitrate uptake. The sensors will create an unprecedented ability to describe the spatial distribution and temporal variations of in planta nitrate status. In addition, we will develop microfluidic soil nitrate sensors capable of monitoring soil nitrate concentrations with full automation from sampling to quantification to signal processing. We will validate the proposed sensors and NUE phenotyping platform, by deploying hundreds of low-cost nitrate in planta and soil sensors within yield trials of corn with known genotypes across multiple, well-defined environments. Together, in planta and soil nitrate sensors will create tremendous synergy from which the integrated data could efficiently identify interactions between genotype and environment that drive N-uptake-proficient root systems, potentially reducing the need for costly and laborious root phenotyping. At present, a plant’s ability to access soil N requires destructive, labor-intensive measurements. With coupled in planta and soil nitrate sensing, the amount of nitrate uptake per amount of soil nitrate can be directly measured, creating a continuous measurement of N uptake per amount of available soil N. Objectives This proposal has two objectives that will enable next-generation field-based, high-throughput phenotyping to accurately assess NUE. (1) Develop, calibrate and optimize low cost, efficient MEMS-based in planta sensors and integrated microfluidic soil sensors for accurate measurements of plant and soil nitrate levels. (2) Establish and validate a field-based, high-throughput phenotyping platform with coupled in planta and soil sensors to generate data from yield trials of known genotypes in multiple, well- defined environments. James C. Schnable 48

Figure 11: NSF EPSCoR Track I: Center for Root and Rhizobiome Innovation. (Schnable is a member of the management)

PROJECT SUMMARY Overview: The Center for Root and Rhizobiome Innovation (CRRI) will be established to develop tools and technologies for more rapid, precise, and predictable crop genetic improvement that complement and transcend methods currently used by biotechnologists and plant breeders. These innovations are needed because of the urgency and enormity of challenges facing global agriculture, including the need to feed a rapidly growing population in the face of extreme climate variations and limitations in water and soil vitality. CRRI research will be structured around a systems and synthetic biology core to generate and iteratively improve network models of plant metabolism for predictable outcomes from genetic modifications. CRRI’s systems and synthetic biology research will be applied to the study of root metabolism and its influence on root-interactions with soil microbes for improved plant health. Research will focus on root metabolism in maize, a plant genetic model and important crop species, but findings will be broadly applicable to other plants and crop species. CRRI will develop and use fundamental knowledge to create translational products with far-reaching impact on plant and microbial biology and global agriculture. Intellectual Merit : CRRI research will be based on generation of large omics datasets from analyses and measurements of root gene expression and metabolites, soil microbiota, and plant phenotypes. Computational innovations for extraction and integration of these datasets will drive the creation of algorithms for predictive model construction and databases for more efficient data mining. CRRI researchers will also devise next-generation synthetic biology tools for precise delivery and expression of large numbers of trait genes in crop plants. By combining computationally-derived models and synthetic biology tools, CRRI will advance the predictive engineering of plant metabolism to accelerate crop improvement. Systems and synthetic biology-enabled research on root-microbiome interactions also lead to innovative solutions for agroecosystem sustainability. Furthermore, collaborations among CRRI engineers and biologists will result in new fiber optic technology for real-time, non-destructive sampling of root-associated microbes and root exuded chemicals. This technology will advance rhizosphere and root-microbiome studies, emerging research areas of agricultural and ecological significance. Broader Impacts : Innovations arising from CRRI will enable strategies for increasing the rate and precision of crop genetic improvement and will generate maize germplasm with altered root metabolism and soil microbe interactions for improved tolerance to drought and low soil fertility. These products will be important for the sustainability of Nebraska’s agriculture-based economy and for meeting the global grand challenges to agricultural production. CRRI will implement an innovative and comprehensive research-based STEM portfolio of education and workforce development activities along the continuum of the STEM pipeline. CRRI will implement: 1) secondary education programs to build the pipeline of students choosing STEM studies and careers; 2) undergraduate and graduate training programs that prepare the next generation of researchers and industry leaders and a rigorous postdoctoral scientist mentoring program; 3) faculty development programs including early career faculty and small colleges that increase research capabilities and provide new opportunities for students; and 4) STEM events that educate the public in the science and ethics of new technologies for informed consumer choices. Over the entire award period, CRRI will provide 55 person-years of postdoctoral scientist training and will support 35 graduate students and 120 undergraduates (cumulative) as participants in research-based activities. CRRI will further impact 15 small college faculty, 15 small college undergraduates, 950 undergraduates in CRRI enhanced courses, 20 students in internships, and 4,680 grade 7-12 students (cumulative). The CRRI diversity plan, through targeted strategies and support of successful programs, will accelerate the pace of diversification among students, postdoctoral scientists, and faculty while facilitating the participation of a broadly-defined group of individuals in CRRI activities.

Page A James C. Schnable 49

Figure 12: NSF EPSCoR Track-2 FEC: Functional analysis of nitrogen responsive networks in Sorghum. (Schnable is co-PI)

Project Summary

Overview We propose to establish a partnership for cutting edge plant genomic research between two EPSCoR regions of Alabama and Nebraska. The team at University of Nebraska-Lincoln will contribute their expertise in plant transformation and automated phenotyping using their new state-of-the-art LemnaTeC high-throughput system for imaging large plants. The team at HudsonAlpha Institute for Biotechnology in Huntsville, Alabama will contribute dedicated outreach for agricultural biotechnology education and genomic and molecular analysis of plant networks. We will combine these advanced tools to better understand the regulation of a complex agronomic trait of agricultural, economic, and environmental importance: how nitrogen affects plant growth and development.

Intellectual Merit We will collect baseline information of plant response to nitrogen levels through combined transcriptomics, automated phenotyping, and molecular function methods in the widely used grain and biomass crop sorghum. We will modify the function and regulation of key hubs and transcription factors using CRISPR/Cas9 transformation methods targeting the promoter regions of these genes. We will then characterize plants with the resulting modified genotypes that show nitrogen response deploying new nitrogen uptake sensors, automated phenotyping through the full cycle of seasonal plant development, and genomics of expression based network reconstruction to identify key modifiers of nitrogen use efficiency. We anticipate that the model and pipelines established through the combined efforts of these two institutions will be extendable to understanding the biology underlying other complex agronomic traits in addition to nitrogen use efficiency.

Broader Impacts In addition to a deep understanding of the molecular basis of plant nitrogen uptake throughout the growing season and the creation of many sorghum lines that can be used for additional nitrogen experiments, this proposal also seeks to train and motivate students to become involved in genetic and biotechnology based research for agriculture. As part of the proposed research, two graduate students will develop methods for automated phenotyping image analysis, a postdoctoral researcher will learn new skills and develop pipelines to go from transcriptomic analysis to empirical determination of gene function, and an early career faculty member will further develop her program in crop functional genomics. HudsonAlpha will develop and deploy a 3-week summer course, ‘AgriGenomics Academy’, for advanced high school students that will be offered for 3 years to 16-18 students each year to excite students to go on to genomics based research efforts in plants. Additional impacts include the recruitment of three undergraduate students who will complete summer internships at both HudsonAlpha and University of Nebraska-Lincoln to learn advanced techniques, and support for the Launching Aspiring Biotechnology Students (LABS), which introduces low and moderate-income students to biotechnology.

Project Summary 1 James C. Schnable 50

Figure 13: USDA/NSF Joint Program PAPM EAGER: Transitioning to the next generation plant pheno- typing robots. (Schnable is co-PI)

1 Project Overview, Goal, and Objectives One of the grand challenges facing agriculture today is to produce enough food and energy for a world population likely exceeding 9.7 billion by 2050. To achieve this goal, the overall production of all major food and energy crops will have to double, which has to occur in the context of climate change (Tilman et al., 2011). Crop yield improvement in the past few decades has been mainly attributed to green revolution, inorganic fertilizer and mechanization; but these technologies are unlikely to sustain the needed yield increase for another 35 years (Ray et al., 2013). Plant phenomics, the use of holistic large scale approaches to collect plant phenotypic information, has the potential to spark a new green revolution (Houle et al., 2010). It would fill the gap between the low cost of generating large scale datasets of plant genotypes and the time consuming and expensive processes of collecting large scale phenotypic datasets. Advancement in plant phenomics would enable more effective utilization of genetic data, and ultimately lead to novel gene discovery and improved crop yield in the field (Furbank and Tester 2011). In recent years there has been a rapid expansion of high throughput plant phenotyping using digital image analysis (Golzarian et al., 2011; Chen et al., 2014). As the current state of the art, digital imaging has proven to be very useful in obtaining plant traits such as size and growth (Neilson et al., 2015; Neumann et al., 2015). An intrinsic limitation of image-based phenotyping, however, lies in the fact that images are indirect measurements. The results are in terms of pixel count or pixel intensity, which by its own convey little information regarding the traits of biological importance. This is particularly true when plant chemical and physiological traits such as water content and photosynthesis are to be measured. To maximize the use of images, researchers are required to collect ground truth trait measurements to establish correlations between the ground truth data and images. Because ground truthing is performed by humans, it is slow and expensive, and represents a major limiting factor for image-based plant phenotyping. There are many specialized plant sensors designed to measure a wide array of plant physiological or chemical traits. Several examples are the fiber-optic sensing head coupled with a NIR spectrometer for leaf reflectance, handheld leaf porometers for stomatal conductance and gas exchange, anthocyanin meters, and portable photosynthesis system for leaf fluorescence and Photosystem II analysis (Figure 1). All these sensors are designed to be operated by human for in vivo plant sensing. Conceivably, robotic systems can be developed to integrate these sensors for autonomous trait measurements. The goal of this project is to develop automated robotic systems that can realize in vivo, human- Figure 1. Specialized plant leaf sensors like plant phenotyping in the greenhouse. Our for in vivo plant sensing central hypothesis is that the throughput, capacity and accuracy of phenotyping by the automated robots will be much better than human, whereas the cost will be substantially lower. Toward that end, there are three specific research aims listed in Research Tasks section (see section 4).

James C. Schnable 51

Pending Grants

1. “Hybrid Pearl Millet as a Biomass Crop for the Arid Great Plains” Department of Energy - Affordable and Sustainable Energy Crops Schnable JC (PI), Ge, Yufeng (co-PI) (Biological and Systems Engineering, UNL), Dweikat, I (Agron- omy and Horticulture, UNL), Wilkins M (Biological and Systems Engineering, UNL), Yang J (Agron- omy and Horticulture, UNL), Cheng X (Mathematics, University of Nebraska-Omaha), Serba, D (Agricultural Research Center, Kansas State University) Award Period: 7/1/2019 - 6/30/2024 Award Amount: $3.9M total. 2. “Crops in silico: Increasing crop production by connecting models from the microscale to the macroscale” Foundation for Food and Agricultural Research Amy Marshal-Colon (University of Illinois- Urbana Champaign, Schnable JC (co-PI) (One of eight total co-PIs, including faculty at UIUC, Purdue, and Penn State) Award Period: 12/1/2018 - 12/31/2022 Award Amount: $5M total. Funding directly and speciﬁcally to the Schnable Lab: $493,823 3. “Ultra-Low Power Sensor Network” ARPA-E - Open Call Full proposal submitted after encouragement from ARPA-E based on evaluation of a preproposal. Kim H (PI) (Department of Electrical and Computer Engineering, University of Utah), Schnable JC (co-PI) (one of four total co-PIs, sole plant biologist) Award Period: 1/1/2019 - 12/31/2020 Award Amount: $1.1M total. Funding directly and speciﬁcally to the Schnable Lab: $198,845 4. “BTT EAGER: A wearable plant sensor for real-time monitoring of sap ﬂow and stem diameter to accelerate breeding for water use efﬁciency” National Science Foundation - Breakthrough Technology Call Full proposal submitted based on an invitation from NSF after evaluation of a preproposal. Schnable JC (PI), Dong L (co-PI) (Electrical and Computer Engineering, ISU), Castellano M (co-PI) (Agronomy, ISU), Schnable P (co-PI) (Agronomy, ISU) Award Period: 1/1/2019 - 12/31/2020 Award Amount: $300k total. Funding directly and speciﬁcally to the Schnable Lab: $99,299 5. “EAGER-SitS: Ultra-Low-Power, Event-based-Wake-Up, Wireless Chemical Sensor Networks for Long- Term Underground Soil Monitoring” Full proposal submitted based on an invitation from NSF after evaluation of a preproposal. National Science Foundation - Signals in the Soil Kim H (PI) (Department of Electrical and Computer Engineering, University of Utah, Schnable JC (co-PI) (one of two total co-PIs) Award Period: 9/1/2018 - 8/31/2020 Award Amount: $300k total. Funding directly and speciﬁcally to the Schnable Lab: $100,000 6. “EAGER SitS: High-resolution Measurement of N Dynamics Using a Miniature in-Soil Lab” Full proposal submitted based on an invitation from NSF after evaluation of a preproposal. National Science Foundation - Signals in the Soil Dong L (PI) (Electrical and Computer Engineering, ISU) Schnable JC (co-PI) (one of four total co-PIs Award Period: 1/1/2019 - 12/31/2020 Award Amount: $300k total. Funding directly and speciﬁcally to the Schnable Lab: $15,273

Letters From UNL Administrators

James C. Schnable 54

Press Releases and News Articles 8/27/2018 Study in contrasts: System advances analysis of corn | Nebraska Today | University of Nebraska–Lincoln

https://news.unl.edu/newsrooms/today/article/study-in-contrasts-system-advances-analysis-of-corn/ 1/7 8/27/2018 Study in contrasts: System advances analysis of corn | Nebraska Today | University of Nebraska–Lincoln

https://news.unl.edu/newsrooms/today/article/study-in-contrasts-system-advances-analysis-of-corn/ 2/7 8/27/2018 Study in contrasts: System advances analysis of corn | Nebraska Today | University of Nebraska–Lincoln