[{"content":"Crop wild relatives hold a vast reservoir of untapped genetic variation for traits that matter to agriculture, including resistance to pests and diseases and tolerance to the abiotic stresses that come with climate change.\nDuring my postdoc at UC Davis, I studied structural variation in maize and its wild relative, teosinte. Working with crops alongside their wild relatives offers a window onto the interplay between plant breeding, climate change, genome function, natural selection and long-term evolutionary dynamics.\n","date":"4 January 2018","externalUrl":null,"permalink":"/projects/crop-wild-relative/","section":"Earlier Research","summary":"Crop wild relatives hold a vast reservoir of untapped genetic variation for pest and disease resistance and for tolerance to the abiotic stresses that climate change brings. I used maize and its wild relative teosinte as my study system.","title":"Crop Wild Relatives","type":"projects"},{"content":"Research themes from my PhD at the University of Florida and postdoc at UC Davis (2010–2018).\n","date":"4 January 2018","externalUrl":null,"permalink":"/projects/","section":"Earlier Research","summary":"","title":"Earlier Research","type":"projects"},{"content":"","date":"4 January 2018","externalUrl":null,"permalink":"/tags/earlier-research/","section":"Tags","summary":"","title":"Earlier Research","type":"tags"},{"content":"","date":"4 January 2018","externalUrl":null,"permalink":"/tags/","section":"Tags","summary":"","title":"Tags","type":"tags"},{"content":" Computational biology and AI leader with 15+ years of experience in genomics, machine learning, and genome editing, including 6+ years as a people leader. I build predictive models that turn biological data into decisions, and the teams that deliver them. Download CV Get in touch What I do # I lead a computational biology team that turns models and data into R\u0026amp;D and product decisions. At Inari, I’m the technical lead for the Predictive Design Engine, an agentic AI platform for designing gene edits, and co-lead its strategy and roadmap.\nBuilt teams from the ground up. 6+ years as a people leader. Built a team of 10+ computational biologists and data analysts across the US and Ghent, Belgium, as Platform Manager, then led a cross-functional team of 8–9 computational biologists and ML scientists as Model Capability Lead. Shipped models that matter. Delivered AI models that prioritize gene targets and design multiplex edits, now powering Inari\u0026rsquo;s Predictive Design Engine. Connected science to the business. Partner across R\u0026amp;D, product, and leadership to align computational strategy with business goals. Track record. Inventor on 8 patent applications and author of 19 peer-reviewed papers (2,500+ citations, h-index 18), from the maize W22 genome (Nature Genetics) to the Amborella genome (Science). I am motivated by applying AI in biology to solve problems with real-world impact, from crops to human health.\n","date":"4 January 2018","externalUrl":null,"permalink":"/","section":"Wenbin Mei, PhD · Computational Biology \u0026 AI Leader","summary":"","title":"Wenbin Mei, PhD · Computational Biology \u0026 AI Leader","type":"page"},{"content":"Understanding the functional contribution of non-coding regions is a major challenge in plant genomics. Many non-coding regions have regulatory roles as promoters, enhancers or conserved non-coding sequences, or are altered by structural variation. Identifying these functionally constrained regions helps plant breeders prioritize GWAS hits and can improve the accuracy of genomic selection models by adding biological priors.\nDuring my PhD, I identified intron retention events that are conserved across grass species. For a subset of these events, the retained intron sequences are themselves conserved, suggesting that they are under functional constraint (Genetics, 2017, cover article). As a postdoc, I explored whole-genome alignments across more than 60 plant species in Phytozome to identify conserved regions genome-wide, with the aim of connecting functional constraint to plant breeding, climate adaptation, genome function and natural selection.\n","date":"3 January 2018","externalUrl":null,"permalink":"/projects/functional-constraint/","section":"Earlier Research","summary":"Finding regions shaped by selection and pinpointing the functionally constrained regions that contribute to phenotypes. My work on conserved alternative splicing across grasses was a cover article in GENETICS.","title":"Functional Constraint Regions for Crop Improvement","type":"projects"},{"content":"Structural variation (SV) covers rearrangements and gains or losses of genomic DNA, including deletions, insertions, inversions and translocations; deletions and insertions are often referred to as copy number variants (CNVs). SV shapes genetic diversity and genome complexity and underlies important plant phenotypes. For example, copy number variation at GL7 contributes to grain size diversity in rice, and a retrotransposon insertion upstream of a MYB transcription factor controls the red color of blood oranges.\nBeyond its effects on phenotype, SV plays an important role in adaptation to new environments and in disease resistance, and growing evidence suggests it is a primary driver of metabolite diversity. As a postdoc, I used whole-genome sequencing of a single natural population of teosinte, the wild relative of maize, to investigate the evolutionary consequences and functions of copy number variants.\n","date":"2 January 2018","externalUrl":null,"permalink":"/projects/structural-variation/","section":"Earlier Research","summary":"Cataloguing structural variation and identifying its functional impact and adaptive role for crop improvement and environmental adaptation.","title":"Structural Variation for Crop Improvement","type":"projects"},{"content":"Gene regulation can be viewed as a series of molecular traits that link genotype to phenotype: chromatin structure, DNA methylation, transcription, splicing and translation. I took an integrative approach, combining functional, evolutionary and statistical genomics, to understand the genetic basis of these molecular traits, how they regulate one another, and how they contribute to phenotypes and environmental adaptation.\n","date":"1 January 2018","externalUrl":null,"permalink":"/projects/gene-regulation-for-crop-improvement/","section":"Earlier Research","summary":"How gene regulation, from chromatin and DNA methylation to transcription, splicing and translation, shapes phenotypes and environmental adaptation.","title":"Gene Regulation for Crop Improvement","type":"projects"},{"content":"My PhD focused on genomics and bioinformatics, so when I moved into population genomics as a postdoc in Jeffrey Ross-Ibarra\u0026rsquo;s lab, I spent a lot of time strengthening my foundation in quantitative and population genetics, building on what I had learned at the Summer Institute in Statistical Genetics. This list collects the books, lecture notes and classic papers that helped me most. The two fields share the same roots: in the 1920s and 1930s, Fisher, Wright and Haldane reconciled Mendelian inheritance with Darwin\u0026rsquo;s natural selection, and their ideas still underpin how we map traits, estimate heritability and predict breeding values today. Population genetics explains how mutation, drift, selection and migration shape variation in genomes; quantitative genetics connects that variation to complex traits and is the statistical engine behind modern plant breeding, from mixed models to genomic selection. I have grouped the resources into quantitative genetics, population genetics and the classic texts, with links to free online versions where they exist.\nQuantitative genetics # Bruce Walsh and Michael Lynch, Evolution and Selection of Quantitative Traits (Oxford University Press, 2018). Draft chapters were first posted on Bruce Walsh\u0026rsquo;s book page (archived copy). Introduction to Plant Quantitative Genetics, Tucson Winter Institute in Plant Breeding, 2013 (archived copy) Advanced Statistical Plant Breeding, Tucson Winter Institute in Plant Breeding, 2013 (archived copy) Mixed Models in Quantitative Genetics, 2012 workshop (archived copy) Rex Bernardo, Essentials of Plant Breeding and Breeding for Quantitative Traits in Plants (Stemma Press) Population genetics # Graham Coop\u0026rsquo;s Population Genetics Notes John Gillespie, Population Genetics: A Concise Guide (2nd edition, Johns Hopkins University Press, 2004) Kent Holsinger\u0026rsquo;s Lecture Notes in Population Genetics Warren Ewens, Mathematical Population Genetics I: Theoretical Introduction (Springer, 2004) Joe Felsenstein, Theoretical Evolutionary Genetics (free online book) Genetic Simulation Resources, a catalogue of population genetics simulation software from the National Cancer Institute Classic books # Charles Darwin, The Variation of Animals and Plants under Domestication (1868) R.A. Fisher, The Genetical Theory of Natural Selection (1930; variorum edition, 1999) Sewall Wright, \u0026ldquo;Evolution in Mendelian Populations\u0026rdquo;, Genetics (1931) J.B.S. Haldane, The Causes of Evolution (1932) Theodosius Dobzhansky, Genetics and the Origin of Species (1937) G. Ledyard Stebbins, Variation and Evolution in Plants (1950) Updated October 2026: links checked, and dead links replaced with publisher pages or archived copies.\n","date":"24 July 2017","externalUrl":null,"permalink":"/posts/population-quantative-genetics-reading/","section":"Blog","summary":"Books, lecture notes and classic texts I have found useful for learning quantitative and population genetics, from the founders of the field to modern plant breeding.","title":"A Reading List for Quantitative and Population Genetics: From Fisher to Modern Plant Breeding","type":"posts"},{"content":"","date":"24 July 2017","externalUrl":null,"permalink":"/posts/","section":"Blog","summary":"","title":"Blog","type":"posts"},{"content":"","date":"24 July 2017","externalUrl":null,"permalink":"/tags/books/","section":"Tags","summary":"","title":"Books","type":"tags"},{"content":"I took a computer class as an undergraduate, but I didn\u0026rsquo;t use programming in research until my master\u0026rsquo;s degree. There I assembled ~100 kb chloroplast genomes from Illumina short reads, which meant learning Linux, Bash and scripting along the way. I enjoyed it so much that I chose bioinformatics for my PhD.\nWhen I started my PhD at the University of Florida, my advisor handed me Sams Teach Yourself Perl in 21 Days. After working through it, I jumped into real projects, and I have believed ever since that learning by doing is the best way into bioinformatics and genomics. BioPerl had many useful examples for processing and analyzing data. The field moves fast, and searching for answers online was my best teacher along the way.\nI picked up more Python and R as my PhD progressed. The choice of language often depends on the lab; my postdoc lab worked mostly in R. Today, Biopython and R\u0026rsquo;s Bioconductor both offer packages for large-scale biological data analysis. For machine learning, Python has scikit-learn, and An Introduction to Statistical Learning is an excellent book with labs in both R and Python.\nHere are a few more resources I found useful.\nPerl # Unix and Perl Primer for Biologists, Korf Lab, UC Davis (archived copy) Bioinformatics in general # Bioinformatics Data Skills by Vince Buffalo R # Posit (RStudio) cheat sheets R for Data Science by Hadley Wickham and colleagues Python # Python Data Science Handbook by Jake VanderPlas The official Python tutorial Community # Capital of Statistics (统计之都), a Chinese-language statistics and data science community Updated October 2026: links checked, and dead links replaced or removed.\n","date":"24 July 2017","externalUrl":null,"permalink":"/posts/coding-resource/","section":"Blog","summary":"How I learned to program for genomics research, and the resources I found most useful for Perl, Python and R.","title":"Learning Bioinformatics by Doing: My Path Through Perl, Python and R","type":"posts"},{"content":"","date":"24 July 2017","externalUrl":null,"permalink":"/tags/python/","section":"Tags","summary":"","title":"Python","type":"tags"},{"content":"","date":"24 July 2017","externalUrl":null,"permalink":"/tags/r/","section":"Tags","summary":"","title":"R","type":"tags"},{"content":"","date":"24 July 2017","externalUrl":null,"permalink":"/tags/resource/","section":"Tags","summary":"","title":"Resource","type":"tags"},{"content":"","date":"23 July 2017","externalUrl":null,"permalink":"/tags/regulatory-genomics/","section":"Tags","summary":"","title":"Regulatory Genomics","type":"tags"},{"content":"","date":"23 July 2017","externalUrl":null,"permalink":"/tags/splicing/","section":"Tags","summary":"","title":"Splicing","type":"tags"},{"content":"The Chinese Genomics Online Meet-up is a self-organized group of young Chinese researchers, mainly PhD students, postdocs and assistant professors, who meet online every two weeks to discuss plant genomics, evolution, statistics, bioinformatics, genetics and more.\nI gave a research talk there, The Cause and Consequence of Alternative Splicing in Maize and Across Species, covering the first two chapters of my PhD dissertation.\nThe first part of the talk presents a genome-wide analysis of alternative splicing in maize, published as A Comprehensive Analysis of Alternative Splicing in Paleopolyploid Maize in Frontiers in Plant Science (2017).\nThe second part asks which alternative splicing events are evolutionarily conserved, and therefore likely under functional constraint, across monocots, focusing on five grass species: maize, sorghum, rice, foxtail millet and Brachypodium. Many alternative splicing variants are probably neutral; alongside classic mutant studies, our approach offers a way to identify potentially functional splicing events. This work was published as Evolutionarily Conserved Alternative Splicing Across Monocots in Genetics (2017), where it was the October 2017 cover article.\nBoth papers are listed as outputs of NSF Plant Genome Research Program award #1547787, for which this dissertation work provided the preliminary data.\nWatch the talk below, or on YouTube.\n","date":"23 July 2017","externalUrl":null,"permalink":"/posts/splicing-talk/","section":"Blog","summary":"A recorded talk on the first two chapters of my PhD dissertation, given at the Chinese Genomics Online Meet-up.","title":"The Causes and Consequences of Alternative Splicing in Maize and Across Grasses (Talk)","type":"posts"},{"content":"","externalUrl":null,"permalink":"/authors/","section":"Authors","summary":"","title":"Authors","type":"authors"},{"content":"","externalUrl":null,"permalink":"/categories/","section":"Categories","summary":"","title":"Categories","type":"categories"},{"content":" Industry # Predictive Design Engine Tech Lead Jan 2026 – Present Inari Agriculture Technical lead for the Predictive Design Engine, Inari\u0026rsquo;s agentic AI platform for designing gene edits Co-lead strategic planning and roadmap development for the platform Advance the platform\u0026rsquo;s scientific capabilities through model architecture, training data, data quality, and benchmarking Lead model capability development across multiple product concepts, aligning model priorities with product goals Model Capability Lead Jan 2024 – Dec 2025 Inari Agriculture Led development of strategic predictive modeling solutions for R\u0026amp;D needs Managed a cross-functional team of 8–9 computational biologists and machine learning scientists building AI-enabled models Mentored junior colleagues in computational biology and machine learning Platform Manager, Computational Biology Jan 2023 – Jan 2024 Inari Agriculture Built a team of 10+ computational biologists and data analysts across the US and Ghent, Belgium, in the Computational Biology platform Developed strategic initiatives to expand computational biology capabilities for R\u0026amp;D Mentored junior scientists to foster growth and innovation within the team Data Analytics \u0026amp; Automation Lead, Science Platform Oct 2020 – Jan 2023 Inari Agriculture Led and managed the Data Analytics \u0026amp; Automation team in Inari\u0026rsquo;s Science Platform Developed team members to strengthen bioinformatics support across R\u0026amp;D Partnered with cross-functional teams to expand data analytics and high-throughput automation capabilities Scientist II, Computational Biology Jan 2019 – Sep 2020 Inari Agriculture Major contributor to the Inari maize HapMap, now used across science and product teams (Inari Game Changer Award, 2020) Built population-genetics methods to annotate deleterious SNPs and prioritize variants for CRISPR-Cas editing, leading to a patent application Characterized Inari maize germplasm and improved genomic selection accuracy using biological priors Built sequence-capture and de novo assembly pipelines to validate edit targets in germplasm without reference genomes Organized Inari\u0026rsquo;s Bioinformatics Guild (2020) and co-organized the Plant Breeding \u0026amp; Population Genetics Guild (2019) Academia # Member, Biology Leadership Council Aug 2023 – Present University of Florida Advise Department of Biology leadership, faculty, and students. Postdoctoral Researcher (Ross-Ibarra Lab) Jun 2016 – Dec 2018 University of California, Davis Population genomics of structural variation in the maize wild relative teosinte Studied adaptation in plant genomes and functional constraint regions to inform crop improvement Collaborated on the NSF-funded Panzea project linking genotype and phenotype in maize PhD Research Assistant (Barbazuk Lab) Sep 2010 – May 2016 University of Florida Characterized the genome-wide alternative splicing landscape of maize and other monocots, including how splicing diverged after whole-genome duplication (first-author papers in Genetics and Frontiers in Plant Science, 2017) Integrated epigenomic and transcriptomic data to study how epigenetic marks relate to splicing and expression Contributed to the NSF-funded Amborella trichopoda and maize W22 reference genome projects Designed a sequence-capture probe set for white clover (Trifolium repens) Education # PhD in Botany (Computational Biology) 2016 University of Florida Dissertation on the alternative splicing landscape of maize and other monocots. This work provided the preliminary data for NSF Plant Genome Research Program award #1547787. MS in Biology 2010 Central Washington University BS in Biology 2007 Nanjing Normal University Skills # Leadership \u0026amp; Strategy # Computational biology strategy and multi-year model roadmaps Building and scaling cross-functional teams (computational biologists, ML scientists, data analysts) across sites Aligning model priorities with R\u0026amp;D and product goals Partnering with R\u0026amp;D, product and executive leadership People development and mentoring AI \u0026amp; Modeling # Agentic AI systems for biological design Large language models and biological foundation models Sequence-expression-function-phenotype predictive models Model benchmarking, training-data and data-quality strategy Machine learning and statistical genomics AI-Enabled Computational Biology # Target discovery and prioritization CRISPR-Cas multiplex edit design Population, functional and comparative genomics Systems biology and multi-omics Service, Teaching \u0026amp; Talks # Peer review. 36 verified reviews for 18 journals, including G3 (8), Molecular Plant (5), BMC Plant Biology (3), Applications in Plant Sciences (3), The Plant Cell, Genetics, Molecular Biology and Evolution, Molecular Ecology, and Heredity (Web of Science reviewer record).\nLeadership \u0026amp; community. Biology Leadership Council, University of Florida (2023–present) · Organizer, Bioinformatics Guild, Inari (2020) · Co-organizer, Plant Breeding \u0026amp; Population Genetics Guild, Inari (2019) · Committee member, Chinese Genomics online Meet-up (2017) · Department award, curriculum, and faculty search committees (UF and Central Washington University).\nTeaching \u0026amp; mentoring. Mentor to scientists and research associates at Inari · Guest lecturer on linkage disequilibrium and GWAS, Applied Bioinformatics, UC Davis · Teaching assistant for Genetics, the HHMI Phage Genomics course, and introductory biology (UF and CWU) · Supervised undergraduate (HHMI, NSF STEP) and PhD rotation students.\nSelected talks. Invited speaker, MAS-ASPB \u0026amp; University of Maryland Plant Symposium (2019) · Plant and Animal Genome XXV, San Diego (2017) · Department of Plant Sciences Seminar, UC Davis (2017) · Department of Plant Biology, University of Georgia (2015) · Annual Maize Genetics Conference (talk 2013; posters 2012–2017).\nAwards \u0026amp; Fellowships # Game Changer Award, Inari Agriculture (2020) College of Liberal Arts and Sciences Dissertation Fellowship, University of Florida (2015) Summer Institute in Statistical Genetics Scholarship, University of Washington (2014) Graduate School Preeminence Award (Alumni Graduate Fellowship), University of Florida (2010) International Student Tuition Scholarship, Central Washington University (2009) Graduate Student Summer Research Fellowship, Central Washington University (2008) Master\u0026rsquo;s Research / Creative Activity Fellowship, Central Washington University (2008) ","externalUrl":null,"permalink":"/experience/","section":"Wenbin Mei, PhD · Computational Biology \u0026 AI Leader","summary":"","title":"Experience","type":"page"},{"content":"Patent applications from my work at Inari Agriculture.\nRoss Altman, Wenbin Mei, Thomas Dubois, Jeremy Pardo, Erica Sung, Thomas Kolarik (2026). Model for selecting guide RNA molecules. Patent application WO2026055608A1 · Inari Agriculture. Frederic Van Ex, Hannes Bart Claeys, Michael Lee Nuccio, Rachel Anne Mertz, Christine Marie Gault, Daniel Rodriguez Leal, Arne Verstichele, Wenbin Mei (2025). Promoter edited soybean plants. Patent application WO2025264908A1 · Inari Agriculture. Rachel Anne Mertz, Christine Marie Gault, Daniel Rodriguez Leal, Wenbin Mei, Arne Verstichele, Frederic Van Ex, Hannes Bart Claeys, Michael Lee Nuccio (2025). Enhanced nodulation in soybeans. Patent application WO2025259666A1 · Inari Agriculture. Wenbin Mei, Hannes Bart Claeys, Michael Lee Nuccio, Rachel Anne Mertz, Christine Marie Gault, Daniel Rodriguez Leal, Arne Verstichele, Frederic Van Ex (2025). Soybean with improved nodulation. Patent application WO2025193889A1 · Inari Agriculture. Ross Everett Altman, Adam Patrick Joyce, Wenbin Mei, Jeremy David Pardo (2025). In-frame deletions for modulating protein activity in plants. Patent application WO2025128748A1 · Inari Agriculture. Zhanyuan Jon Zhang, Rhoda Araba Tawiah Brew-appiah, Dongjin Kim, Christopher Alexander Bagley, Jeffrey R. Beringer, Tejinder Kumar Mall, Wenbin Mei, Jeremy David Pardo, Manmeet Singh, Iqbal Singh (2025). Morphogenic regulator combinations for improved plant transformation. Patent application WO2025122750A1 · Inari Agriculture. Zhanyuan Jon Zhang, Wenbin Mei, Jeffrey R. Beringer, Manmeet Singh, Rhoda Araba Tawiah Brew-appiah (2023). Morphoregulators for regeneration of maize somatic embryos. Patent application WO2023178300A2 · Inari Agriculture. Karl Anton Grothe Kremling, Stephen Goff, Wenbin Mei, Ruijuan Li, Ross Everett Altman, Zachary Hartford Lemmon (2021). Methods and systems for assessing genetic variants. Patent application WO2021035164A1 · Inari Agriculture. ","externalUrl":null,"permalink":"/patents/","section":"Wenbin Mei, PhD · Computational Biology \u0026 AI Leader","summary":"","title":"Patents","type":"page"},{"content":"19 peer-reviewed papers, 2,500+ citations, h-index 18 (Google Scholar).\nWenbin Mei, Markus G Stetter, Daniel J Gates, Michelle C Stitzer, Jeffrey Ross-Ibarra (2018). Adaptation in plant genomes: bigger is different. American Journal of Botany. Nathan M. Springer, Sarah N. Anderson, \u0026hellip;, Wenbin Mei, \u0026hellip;, Thomas P. Brutnell (2018). The maize W22 genome provides a foundation for functional genomics and transposon biology. Nature Genetics. Luis Inostroza, Mehul Bhakta, Hernan Acuña, Catalina Vasquez, Joel Ibañez, Gerardo Tapia, Wenbin Mei, Matias Kirst, Marcio Resende, Patricio R Munoz (2018). Disentangling the complexity of cold tolerance in an allotetraploid white clover population using a next generation sequencing-GWAS approach. The Plant Genome. Andrew B. Leslie, Jeremy Beaulieu, Garth Holman, Christopher S. Campbell, Wenbin Mei, Linda R. Raubeson, Sarah Mathews (2018). An overview of extant conifer phylogeny from the perspective of the fossil record. American Journal of Botany. Caitrin McDonough, Ana Caroline C. Sa, Nihal M. El Rouby, Wenbin Mei, \u0026hellip;, Julie Johnson (2017). Genetic variants influencing plasma renin activity in hypertensive patients from the pharmacogenomic evaluation of antihypertensive responses (PEAR) study. Circulation: Genomic and Precision Medicine. Wenbin Mei, Jon Lucas Boatwright, Guanqiao Feng, James C Schnable, William Brad Barbazuk (2017). Evolutionarily conserved alternative splicing across monocots. Genetics. Cover article, GENETICS (October 2017) Markus G Stetter, Daniel J Gates, Wenbin Mei, Jeffrey Ross-Ibarra (2017). How to make a domesticate. Current Biology. Xiaoxian Liu, Wenbin Mei, Pamela S Soltis, Douglas E Soltis, W Brad Barbazuk (2017). Detecting alternatively spliced transcript isoforms from single-molecule long-read sequences without a reference genome. Molecular Ecology Resources. Wenbin Mei, Sanzhen Liu, James C Schnable, Cheng-Ting Yeh, Nathan M Springer, Patrick S Schnable, William B Barbazuk (2017). A comprehensive analysis of alternative splicing in paleopolyploid maize. Frontiers in Plant Science. Guanqiao Feng, J Gordon Burleigh, Edward L Braun, Wenbin Mei, W Brad Barbazuk (2017). Evolution of the 3R-MYB gene family in plants. Genome Biology and Evolution. Christine M Gault, Federico Martin, Wenbin Mei, Fang Bai, Joseph B Black, W Brad Barbazuk, A Mark Settles (2017). Aberrant splicing in maize rough endosperm3 reveals a conserved role for U12 splicing in eukaryotic multicellular development. Proceedings of the National Academy of Sciences. MaizeGDB Editor's Choice (April 2017) Wenbin Mei (2016). Alternative splicing landscape in monocots insights from genetic, epigenetic and evolutionary perspectives. Ph.D. dissertation, University of Florida. Markus Ruhsam, Hardeep S Rai, Sarah Mathews, T Gregory Ross, Sean W Graham, Linda A Raubeson, Wenbin Mei, Philip I Thomas, Martin F Gardner, Richard A Ennos, Peter M Hollingsworth (2015). Does complete plastid genome sequencing improve species discrimination and phylogenetic resolution in Araucaria?. Molecular Ecology Resources. Xiefan Fang, Wenbin Mei, William B Barbazuk, Scott A Rivkees, Christopher C Wendler (2014). Caffeine exposure alters cardiac gene expression in embryonic cardiomyocytes. American Journal of Physiology - Regulatory, Integrative and Comparative Physiology. Yuqing Xiong, Wenbin Mei, Eun-Deok Kim, Krishanu Mukherjee, Hatem Hassanein, William Brad Barbazuk, Sibum Sung, Bryan Kolaczkowski, Byung-Ho Kang (2014). Adaptive expansion of the maize maternally expressed gene (Meg) family involves changes in expression patterns and protein secondary structures of its members. BMC Plant Biology. Douglas E Soltis, María Claudia Segovia‐Salcedo, Ingrid Jordon‐Thaden, Lucas Majure, Nicolas M Miles, Evgeny V Mavrodiev, Wenbin Mei, María Beatriz Cortez, Pamela S Soltis, Matthew A Gitzendanner (2014). Are polyploids really evolutionary dead-ends (again)? A critical reappraisal of Mayrose et al. (2011). New Phytologist. J Corrales, X Fang, C Thornton, Wenbin Mei, WB Barbazuk, M Duke, BE Scheffler, KL Willett (2014). Effects on specific promoter DNA methylation in zebrafish embryos and larvae following benzopyrene exposure. Comparative Biochemistry and Physiology Part C: Toxicology \u0026amp; Pharmacology. William Brad Barbazuk, Wenbin Mei (2014). Genome sequencing past and present. Compendium of Bioenergy Plants: Corn. Amborella Genome Project, Wenbin Mei (2013). The Amborella genome and the evolution of flowering plants. Science. Hypaitia B Rauch, Tara L Patrick, Katarina M Klusman, Fabia U Battistuzzi, Wenbin Mei, Volker P Brendel, Shailesh K Lal (2013). Discovery and expression analysis of alternative splicing events conserved among plant SR proteins. Molecular Biology and Evolution. Wenbin Mei (2010). A whole plastome approach to inferring relationships in the Araucariaceae. M.S. thesis, Central Washington University. Hong Li, Zheng Wang, Wenbin Mei, Xiang Ji (2009). Temperature acclimation affects thermal preference and tolerance in three Eremias lizards (Lacertidae). Current Zoology. ","externalUrl":null,"permalink":"/publications/","section":"Wenbin Mei, PhD · Computational Biology \u0026 AI Leader","summary":"","title":"Publications","type":"page"},{"content":"","externalUrl":null,"permalink":"/series/","section":"Series","summary":"","title":"Series","type":"series"}]