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

Comp. Struc. Biotech. J. 18, 1330-1341 (2020)
Publ. Version/Full Text Research data DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag

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.

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Publication type Article: Journal article
Document type Review
Keywords ChIP-seq; RNA-seq; NGS; Data integration; Multi-omics; Transcriptional regulation; Differential Expression Analysis; Factor-binding Sites; False Discovery Rate; Gene-expression; Rna-seq; Chromatin States; Sequence; Dna; Quantification; Single
Language english
Publication Year 2020
HGF-reported in Year 2020
ISSN (print) / ISBN 2001-0370
e-ISSN 2001-0370
Quellenangaben Volume: 18, Issue: , Pages: 1330-1341 Article Number: , Supplement: ,
Publisher Research Network of Computational and Structural Biotechnology (RNCSB)
Publishing Place Radarweg 29, 1043 Nx Amsterdam, Netherlands
Reviewing status Peer reviewed
POF-Topic(s) 90000 - German Center for Diabetes Research
30205 - Bioengineering and Digital Health
30505 - New Technologies for Biomedical Discoveries
Research field(s) Helmholtz Diabetes Center
Enabling and Novel Technologies
PSP Element(s) G-501900-227
G-553500-001
G-503800-001
G-503890-001
G-503800-004
Grants Helmholtz Association ICEMED
ERC StG SILENCE
Scopus ID 85086596025
PubMed ID 32612756
Erfassungsdatum 2020-06-24