COTTONOMICS: a comprehensive cotton multi-omics database. (12th September 2022)

• Record Type: Journal Article
• Authors: Dai, Fan; Chen, Jiedan; Zhang, Ziqian; Liu, Fengjun; Li, Jun; Zhao, Ting; Hu, Yan; Zhang, Tianzhen; Fang, Lei
• Is Part Of: Database. Volume 2022(2022)
• Publication Date: 2022-09-12
• Subjects: Biology -- Databases -- Periodicals; Bioinformatics -- Periodicals; 570.285
• DOI: 10.1093/database/baac080
• Languages: English
• ISSNs: 1758-0463

Abstract

Abstract: The rapid advancement of sequencing technology, including next-generation sequencing (NGS), has greatly improved sequencing efficiency and decreased cost. Consequently, huge amounts of genomic, transcriptomic and epigenetic data concerning cotton species have been generated and released. These large-scale data provide immense opportunities for the study of cotton genomic structure and evolution, population genetic diversity and genome-wide mining of excellent genes for important traits. However, the complexity of NGS data also causes distress, as it cannot be utilized easily. Here, we presented the cotton omics data platform COTTONOMICS (http://cotton.zju.edu.cn/ ), an easily accessible web database that integrates 32.5 TB of omics data including seven assembled genomes, resequencing data from 1180 allotetraploid cotton accessions and RNA-sequencing (RNA-seq), small RNA-sequencing (smRNA-seq), Chromatin Immunoprecipitation sequencing (ChIP-seq), DNase hypersensitive sites sequencing (DNase-seq) and Bisulfite sequencing (BS-seq). COTTONOMICS allows users to employ various search scenarios and retrieve information concerning the cotton genomes, genomic variation (Single nucleotide polymorphisms (SNPs) and Insertion and Deletion (InDels)), gene expression, smRNA expression, epigenetic regulation and quantitative trait locus (QTLs). The user-friendly web interface offers a variety of modules for storing, retrieving, analyzing and visualizing cotton multi-omics data to diverse ends, thereby enabling users to decipher cotton population genetics and identify potential novel genes that influence agronomically beneficial traits. Database URL : http://cotton.zju.edu.cn