ISyTE 2.0: a database for expression-based gene discovery in the eye. Issue Volume 46:Issue D1(2018) (19th September 2017)

• Record Type: Journal Article
• Authors: Kakrana, Atul; Yang, Andrian; Anand, Deepti; Djordjevic, Djordje; Ramachandruni, Deepti; Singh, Abhyudai; Huang, Hongzhan; Ho, Joshua W K; Lachke, Salil A
• Is Part Of: Nucleic acids research. Volume 46:Issue D1(2018)
• Page Start: D875
• Page End: D885
• Publication Date: 2017-09-19
• Subjects: Nucleic acids -- Periodicals; Molecular biology -- Periodicals; 572.805
• DOI: 10.1093/nar/gkx837
• Languages: English
• ISSNs: 0305-1048

Abstract

Abstract: Although successful in identifying new cataract-linked genes, the previous version of the database iSyTE (i ntegrated Sy stems T ool for E ye gene discovery) was based on expression information on just three mouse lens stages and was functionally limited to visualization by only UCSC-Genome Browser tracks. To increase its efficacy, here we provide an enhanced iSyTE version 2.0 (URL: http://research.bioinformatics.udel.edu/iSyTE ) based on well-curated, comprehensive genome-level lens expression data as a one-stop portal for the effective visualization and analysis of candidate genes in lens development and disease. iSyTE 2.0 includes all publicly available lens Affymetrix and Illumina microarray datasets representing a broad range of embryonic and postnatal stages from wild-type and specific gene-perturbation mouse mutants with eye defects. Further, we developed a new user-friendly web interface for direct access and cogent visualization of the curated expression data, which supports convenient searches and a range of downstream analyses. The utility of these new iSyTE 2.0 features is illustrated through examples of established genes associated with lens development and pathobiology, which serve as tutorials for its application by the end-user. iSyTE 2.0 will facilitate the prioritization of eye development and disease-linked candidate genes in studies involving transcriptomics or next-generation sequencing data, linkage analysis and GWAS approaches.