DocumentCode :
3200622
Title :
Soybean knowledge base (SoyKB): Bridging the gap between soybean translational genomics and breeding
Author :
Joshi, Titiksha ; Fitzpatrick, Michael R. ; Shiyuan Chen ; Yang Liu ; Hongxin Zhang ; Endacott, Ryan Z. ; Gaudiello, Eric C. ; Stacey, Geoff ; Nguyen, Hung T. ; Dong Xu
Author_Institution :
Dept. of Comput. Sci., Univ. of Missouri, Columbia, MO, USA
fYear :
2013
fDate :
18-21 Dec. 2013
Firstpage :
42
Lastpage :
44
Abstract :
Many genome-scale data are available in soybean including genomic sequence, transcriptomics (microarray, RNA-seq), proteomics and metabolomics datasets, together with growing knowledge of soybean in gene, microRNAs, pathways, and phenotypes. This represents rich and resourceful information which can provide valuable insights, if mined in an innovative and integrative manner and thus, the need for informatics resources to achieve that. Towards this we have developed Soybean Knowledge Base (SoyKB), a comprehensive all-inclusive web resource for soybean translational genomics and breeding. SoyKB handles the management and integration of soybean genomics and multi-omics data along with gene function annotations, biological pathway and trait information. It has many useful tools including Affymetrix probelD search, gene family search, multiple gene/metabolite analysis, motif analysis tool, protein 3D structure viewer and download/upload capacity for experimental data and annotations. It has a user-friendly web interface together with genome browser and pathway viewer, which display data in an intuitive manner to the soybean researchers, breeders and consumers. SoyKB has new innovative tools for soybean breeding including a graphical chromosome visualizer targeted towards ease of navigation for breeders. It integrates QTLs, traits, germplasm information along with genomic variation data such as single nucleotide polymorphisms (SNPs) and genome-wide association studies (GWAS) data from multiple genotypes, cultivars and G. soja. QTLs for multiple traits can be queried and visualized in the chromosome visualizer simultaneously and overlaid on top of the genes and other molecular markers as well as multi-omics experimental data for meaningful inferences.
Keywords :
RNA; bioinformatics; cellular biophysics; genomics; knowledge based systems; molecular configurations; polymorphism; proteins; proteomics; Affymetrix probe ID search; GWAS data; RNA-seq; SoyKB; gene function annotations; genome browser; genome-scale data; genome-wide association studies data; genomic sequence; genomic variation data; germplasm information; graphical chromosome; informatics resources; metabolomics datasets; microarray; multiomics experimental data; multiple gene-metabolite analysis; protein 3D structure; proteomics; single nucleotide polymorphisms; soybean genomics integration; soybean knowledge base; soybean translational breeding; soybean translational genomics; transcriptomics; user-friendly web interface; web resource; Bioinformatics; Browsers; Data visualization; Genomics; Knowledge based systems; Proteomics; Database; SNP; genomics; glycine max; metabolomics; phenotype; proteomics; soybean; traits; transcriptomics;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Bioinformatics and Biomedicine (BIBM), 2013 IEEE International Conference on
Conference_Location :
Shanghai
Type :
conf
DOI :
10.1109/BIBM.2013.6732755
Filename :
6732755
Link To Document :
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