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Data_Sheet_1_MaizeMine: A Data Mining Warehouse for the Maize Genetics and Genomics Database.PDF (5 MB)

Data_Sheet_1_MaizeMine: A Data Mining Warehouse for the Maize Genetics and Genomics Database.PDF

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posted on 2020-10-22, 04:32 authored by Md Shamimuzzaman, Jack M. Gardiner, Amy T. Walsh, Deborah A. Triant, Justin J. Le Tourneau, Aditi Tayal, Deepak R. Unni, Hung N. Nguyen, John L. Portwood, Ethalinda K. S. Cannon, Carson M. Andorf, Christine G. Elsik

MaizeMine is the data mining resource of the Maize Genetics and Genome Database (MaizeGDB; http://maizemine.maizegdb.org). It enables researchers to create and export customized annotation datasets that can be merged with their own research data for use in downstream analyses. MaizeMine uses the InterMine data warehousing system to integrate genomic sequences and gene annotations from the Zea mays B73 RefGen_v3 and B73 RefGen_v4 genome assemblies, Gene Ontology annotations, single nucleotide polymorphisms, protein annotations, homologs, pathways, and precomputed gene expression levels based on RNA-seq data from the Z. mays B73 Gene Expression Atlas. MaizeMine also provides database cross references between genes of alternative gene sets from Gramene and NCBI RefSeq. MaizeMine includes several search tools, including a keyword search, built-in template queries with intuitive search menus, and a QueryBuilder tool for creating custom queries. The Genomic Regions search tool executes queries based on lists of genome coordinates, and supports both the B73 RefGen_v3 and B73 RefGen_v4 assemblies. The List tool allows you to upload identifiers to create custom lists, perform set operations such as unions and intersections, and execute template queries with lists. When used with gene identifiers, the List tool automatically provides gene set enrichment for Gene Ontology (GO) and pathways, with a choice of statistical parameters and background gene sets. With the ability to save query outputs as lists that can be input to new queries, MaizeMine provides limitless possibilities for data integration and meta-analysis.

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