SEQing: web-based visualization of iCLIP and RNA-seq data in an interactive python framework

• Published in: BMC bioinformatics Vol. 21; no. 1; pp. 113 - 6
• Main Authors: Lewinski, Martin, Bramkamp, Yannik, Köster, Tino, Staiger, Dorothee
• Format: Journal Article
• Language: English
• Published: London BioMed Central 18.03.2020; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Algorithms; Alternative splicing; Annotations; Binding proteins; Bioinformatics; Biomedical and Life Sciences; Circadian rhythm; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Data visualization; Datasets; Genes; Genomes; Genomic tracks; Genomics; iCLIP; Interactive visualization; Life Sciences; Microarrays; Mobile devices; Mutants; Polymer crosslinking; Protein binding; Proteins; Python; Ribonucleic acid; RNA; RNA sequencing; RNA-Seq; Scientific visualization; Software; Visualization; Visualization (Computer); Web sites (World Wide Web); Alternative splicing; Interactive visualization; Web-based; RNA-binding protein; RNA-Seq; Genomic tracks; Data safety; iCLIP; Binding sites; Python
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-020-3434-9

Background RNA-binding proteins interact with their target RNAs at specific sites. These binding sites can be determined genome-wide through individual nucleotide resolution crosslinking immunoprecipitation (iCLIP). Subsequently, the binding sites have to be visualized. So far, no visualization tool exists that is easily accessible but also supports restricted access so that data can be shared among collaborators. Results Here we present SEQing, a customizable interactive dashboard to visualize crosslink sites on target genes of RNA-binding proteins that have been obtained by iCLIP. Moreover, SEQing supports RNA-seq data that can be displayed in a different window tab. This allows, e.g. crossreferencing the iCLIP data with genes differentially expressed in mutants of the RBP and thus obtain some insights into a potential functional relevance of the binding sites. Additionally, detailed information on the target genes can be incorporated in another tab. Conclusion SEQing is written in Python3 and runs on Linux. The web-based access makes iCLIP data easily accessible, even with mobile devices. SEQing is customizable in many ways and has also the option to be secured by a password. The source code is available at https://github.com/malewins/SEQing .