Seq-ing answers: Current data integration approaches to uncover mechanisms of transcriptional regulation

Barbara Höllbacher; Kinga Balázs; Matthias Heinig; N Henriette Uhlenhaut

doi:10.1016/j.csbj.2020.05.018

Seq-ing answers: Current data integration approaches to uncover mechanisms of transcriptional regulation

Comput Struct Biotechnol J. 2020 May 31:18:1330-1341. doi: 10.1016/j.csbj.2020.05.018. eCollection 2020.

Authors

Barbara Höllbacher^{1

2

3}, Kinga Balázs¹, Matthias Heinig^{2

3}, N Henriette Uhlenhaut^{1

4}

Affiliations

¹ Institute for Diabetes and Cancer IDC, Helmholtz Zentrum Muenchen (HMGU) and German Center for Diabetes Research (DZD), Munich 85764, Neuherberg, Germany.
² Institute of Computational Biology ICB, Helmholtz Zentrum Muenchen (HMGU) and German Center for Diabetes Research (DZD), Munich 85764, Neuherberg, Germany.
³ Department of Informatics, TUM, Munich 85748, Garching, Germany.
⁴ Metabolic Programming, TUM School of Life Sciences Weihenstephan, Munich 85354, Freising, Germany.

Abstract

Advancements in the field of next generation sequencing lead to the generation of ever-more data, with the challenge often being how to combine and reconcile results from different OMICs studies such as genome, epigenome and transcriptome. Here we provide an overview of the standard processing pipelines for ChIP-seq and RNA-seq as well as common downstream analyses. We describe popular multi-omics data integration approaches used to identify target genes and co-factors, and we discuss how machine learning techniques may predict transcriptional regulators and gene expression.

Keywords: ChIP-seq; Data integration; Multi-omics; NGS; RNA-seq; Transcriptional regulation.

Publication types

Review