ISMARA: Completely automated inference of gene regulatory networks from high-throughput data
- Published
- Accepted
- Subject Areas
- Bioinformatics, Computational Biology
- Keywords
- gene regulatory networks, systems biology, transcription regulation, next-generation sequencing, web-service, analysis pipeline
- Copyright
- © 2017 Pachkov et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2017. ISMARA: Completely automated inference of gene regulatory networks from high-throughput data. PeerJ Preprints 5:e3328v1 https://doi.org/10.7287/peerj.preprints.3328v1
Abstract
As the costs of high-throughput measurement technologies continue to fall, experimental approaches in biomedicine are increasingly data intensive and the advent of big data is justifiably seen as holding the promise to transform medicine. However, as data volumes mount, researchers increasingly realize that extracting concrete, reliable, and actionable biological predictions from high-throughput data can be very challenging. Our laboratory has pioneered a number of methods for inferring key gene regulatory interactions from high-throughput data. For example, we developed motif activity response analysis (MARA)[, which models genome-wide gene expression (RNA-Seq, or microarray) and chromatin state (ChIP-Seq) data in terms of comprehensive predictions of regulatory sites for hundreds of mammalian regulators (TFs and micro-RNAs). Using these models, MARA identifies the key regulators driving gene expression and chromatin state changes, the activities of these regulators across the input samples, their target genes, and the sites on the genome through which these regulators act. We recently completely automated MARA in an integrated web-server (ismara.unibas.ch) that allows researchers to analyze their own data by simply uploading RNA-Seq or ChIP-Seq datasets, and provides results in an integrated web interface as well as in downloadable flat form.
Author Comment
This is a poster abstract which has been accepted for the NETTAB 2017 Workshop.