Weed Genomics Data Repository

An online repository of weed genomic information developed and maintained by the International Weed Genome Consortium.


Studying the overlooked weed species.

Who We Are

The IWGC was conceived at the 2016 International Weed Science Congress in Prague, Czech Republic in order to foster greater collaboration among weed science researchers focused genomics of weed species. Dr. Scott McElroy at Auburn University began working on the Weed Genomics Repository in 2014 in order to provide easy public access to weed sequence data. The IWGC and the Weed Genomics Repository officially merged in February 2018 at a meeting of the Weed Science Society of America in Arlington, Virginia, USA.


The purpose of the Weed Genome Data Repository is to provide ease of access to weed genomic information for the weed science research community. While the majority of genomic related research in weed science is focused on herbicide resistance, our goal is to foster greater research in the field of population genomics and weed evolution. Our goal is also to grow the field of weed genomic related research by providing ease of access to genomic information to those not currently working the area.

Data Powered

Large data sets, distilled for searchability.

Megabytes of data
Analyzed Species
Annotated Sequences
Trinotate Records

Development Team


Dr. Scott McElroy

Dr. McElroy is a Professor in the Department of Crop, Soil, and Environmental Sciences in the College of Agriculture at Auburn University. His primary research focus is on weed science with emphasis on herbicide usage in turfgrass and detection and management of herbicide resistant weeds in turfgrass. Dr. McElroy's weed genomics lab primarily focuses on evolution and ecology of Poa annua, Eleusine indica, and Cyperus species. Dr. McElroy also manages an extensive turfgrass science research program and is currently an elected board member to the International Turfgrass Society. Dr. McElroy is a member of the Weed Science Society of America, former board member for the Southern Weed Science Society, and an associate editor for Weed Technology

Mason Wishum

Mason is a former graduate and undergraduate student from Auburn University majoring in Software Engineering. During his graduate studies he studied Data mining and AI. He took over development in 2018 and improved site functionality and usability. He currently develops and maintains the site.

Sunit Sivaraj

Sunit was a graduate student in the department of Computer Science at Auburn University. During his time as a Graduate student and Graduate Research Assistant, he worked in the area of Model Driven Engineering and Artificial Intelligence. He assisted in developing and maintenance of the site. He completed his thesis in 2018.

Sumeet Wilkhu

Sumeet was a former graduate student of Auburn University. As a Graduate Research Assistant in Computer Science department, he worked on many interesting topics such as Artificial Intelligence, Machine Learning, Web Development etc. and completed his thesis in 2017. He worked on WeedGenomics search engine as a Full-Stack Developer with various responsibilities of development as well as deployment of the product. He is currently working as an Automation engineer at Equifax.

Shu Chen

Shu was a former graduate student of Auburn University and worked in Dr. McElroy’s lab from 2012 to 2015. During his graduate study, Shu mainly focused on constructing high-quality, well annotated transcriptomes and genomes for weed species and deciphering molecular mechanisms underlying herbicide resistances, and helped initialize weedgenomics website and database. Currently, Shu is an Associate Professor at South China Agricultural University, working on functional characterization of genes and networks associated with cold tolerance of a tropical forage, Stylosanthes.

Software Used

The software listed below along with Velvet-Oases, SOAPdenovo-Trans, and EvidentialGene were used to create the data presented on this site.


The repository extensively utilizes Biopython to conduct local blast searches and search NCBI databases as well as transcriptome assembly and annotation. For those not familiar with Biopython we highly recommend it for general bioinformatics usage.


Trinity was the primary software used transcriptome assembly. Unless indicated, Trinity was not set to exclude small assembled contigs to allow for future discovery by end-users.


It was the primary software used for annotation. Names assigned to assembled contigs were taken from Swiss-Prot annotation by Trinotate. Unannotated contigs were annotated as "unidentified contig".