ArticleNucleic acids research2010
agriGO: a GO analysis toolkit for the agricultural community.
Article in Nucleic acids research, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1,400 papers, 2 of them syntheses that pooled it.
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Who cites it
1,400 citing papers in PubMed, 2 syntheses or guidelines pooled it.
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- Unveiling key genetic loci and candidate genes for brown spot disease resistance in rice based on QTL analysis.Scientific reports · 2026Article
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- Elucidation of theFrontiers in plant science · 2026Article
- Genome-wide identification and multi-dimensional functional characterization of the SIR2 family in Brassica napus L.PloS one · 2026Article
- Genome-wide identification and expression analysis of the Small Ubiquitin-like Modifier (SUMO) gene family in Triticum aestivum L.BMC genomics · 2025Article
- A comprehensive evaluation of metabolically engineered Cynara cardunculus calli as platform for valuable fatty acid derivatives production.Scientific reports · 2025Article
- High-resolution time-series transcriptomic and metabolomic profiling reveals the regulatory mechanism underlying salt tolerance in maize.Genome biology · 2025Article
- Identification of key candidate genes regulating hundred-grain weight in the maize inbred line Ye107 from Southern China.Plant cell reports · 2025Article
1,340 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Gene Ontology (GO), the de facto standard in gene functionality description, is used widely in functional annotation and enrichment analysis. Here, we introduce agriGO, an integrated web-based GO analysis toolkit for the agricultural community, using the advantages of our previous GO enrichment tool (EasyGO), to meet analysis demands from new technologies and research objectives. EasyGO is valuable for its proficiency, and has proved useful in uncovering biological knowledge in massive data sets from high-throughput experiments. For agriGO, the system architecture and website interface were redesigned to improve performance and accessibility. The supported organisms and gene identifiers were substantially expanded (including 38 agricultural species composed of 274 data types). The requirement on user input is more flexible, in that user-defined reference and annotation are accepted. Moreover, a new analysis approach using Gene Set Enrichment Analysis strategy and customizable features is provided. Four tools, SEA (Singular enrichment analysis), PAGE (Parametric Analysis of Gene set Enrichment), BLAST4ID (Transfer IDs by BLAST) and SEACOMPARE (Cross comparison of SEA), are integrated as a toolkit to meet different demands. We also provide a cross-comparison service so that different data sets can be compared and explored in a visualized way. Lastly, agriGO functions as a GO data repository with search and download functions; agriGO is publicly accessible at http://bioinfo.cau.edu.cn/agriGO/.
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