Seten: a tool for systematic identification and comparison of processes, phenotypes, and diseases associated with RNA-binding proteins from condition-specific CLIP-seq profiles.
RNA-binding proteins (RBPs) control the regulation of gene expression in eukaryotic genomes at post-transcriptional level by binding to their cognate RNAs. Although several variants of CLIP (crosslinking and immunoprecipitation) protocols are currently available to study the global protein-RNA interaction landscape at single-nucleotide resolution in a cell, currently there are very few tools that can facilitate understanding and dissecting the functional associations of RBPs from the resulting binding maps. Here, we present Seten, a web-based and command line tool, which can identify and compare processes, phenotypes, and diseases associated with RBPs from condition-specific CLIP-seq profiles. Seten uses BED files resulting from most peak calling algorithms, which include scores reflecting the extent of binding of an RBP on the target transcript, to provide both traditional functional enrichment as well as gene set enrichment results for a number of gene set collections including BioCarta, KEGG, Reactome, Gene Ontology (GO), Human Phenotype Ontology (HPO), and MalaCards Disease Ontology for several organisms including fruit fly, human, mouse, rat, worm, and yeast. It also provides an option to dynamically compare the associated gene sets across data sets as bubble charts, to facilitate comparative analysis. Benchmarking of Seten using eCLIP data for IGF2BP1, SRSF7, and PTBP1 against their corresponding CRISPR RNA-seq in K562 cells as well as randomized negative controls, demonstrated that its gene set enrichment method outperforms functional enrichment, with scores significantly contributing to the discovery of true annotations. Comparative performance analysis using these CRISPR control data sets revealed significantly higher precision and comparable recall to that observed using ChIP-Enrich. Seten's web interface currently provides precomputed results for about 200 CLIP-seq data sets and both command line as well as web interfaces can be used to analyze CLIP-seq data sets. We highlight several examples to show the utility of Seten for rapid profiling of various CLIP-seq data sets. Seten is available on http://www.iupui.edu/∼sysbio/seten/.
Department of Biohealth Informatics, School of Informatics and Computing, Indiana University Purdue University Indianapolis (IUPUI), Indianapolis, Indiana 46202, USA.
Gungor
0000-0003-1641-3304
G
Budak
Department of Biohealth Informatics, School of Informatics and Computing, Indiana University Purdue University Indianapolis (IUPUI), Indianapolis, Indiana 46202, USA.
Rajneesh
R
Srivastava
Department of Biohealth Informatics, School of Informatics and Computing, Indiana University Purdue University Indianapolis (IUPUI), Indianapolis, Indiana 46202, USA.|Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana 46202, USA.|Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana 46202, USA.
Sarath Chandra
0000-0001-7351-6268
SC
Janga
D001665:Binding Sites; D002460:Cell Line; D047369:Chromatin Immunoprecipitation; D016000:Cluster Analysis; D064112:Clustered Regularly Interspaced Short Palindromic Repeats; D019295:Computational Biology / Q000379:methods*; D063990:Gene Ontology; D056726:Genetic Association Studies / Q000379:methods; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D058977:Molecular Sequence Annotation; D010641:Phenotype; D016601:RNA-Binding Proteins / Q000378:metabolism*; D017423:Sequence Analysis, RNA; D012984:Software; D057188:Workflow
D016601:RNA-Binding Proteins
CLIP (crosslinking and immunoprecipitation); RNA-binding proteins; functional enrichment; gene set enrichment; genotype–phenotype; post-transcriptional networks