TF-Prioritizer: a Java pipeline to prioritize condition-specific transcription factors

Hoffmann M, Trummer N, Schwartz L, Jankowski J, Lee HK, Willruth LL, Lazareva O, Yuan K, Baumgarten N, Schmidt F, Baumbach J, Schulz MH, Blumenthal DB, Hennighausen L, List M (2023)

Publication Status: Published

Publication Type: Journal article

Publication year: 2023

Journal

GigaScience BioMed Central

Book Volume: 12

Article Number: giad026

DOI: 10.1093/gigascience/giad026

Abstract

Background: Eukaryotic gene expression is controlled by cis-regulatory elements (CREs), including promoters and enhancers, which are bound by transcription factors (TFs). Differential expression of TFs and their binding affinity at putative CREs determine tissue- and developmental-specific transcriptional activity. Consolidating genomic datasets can offer further insights into the accessibility of CREs, TF activity, and, thus, gene regulation. However, the integration and analysis of multimodal datasets are hampered by considerable technical challenges. While methods for highlighting differential TF activity from combined chromatin state data (e.g., chromatin immunoprecipitation [ChIP], ATAC, or DNase sequencing) and RNA sequencing data exist, they do not offer convenient usability, have limited support for large-scale data processing, and provide only minimal functionality for visually interpreting results.

Results: We developed TF-Prioritizer, an automated pipeline that prioritizes condition-specific TFs from multimodal data and generates an interactive web report. We demonstrated its potential by identifying known TFs along with their target genes, as well as previously unreported TFs active in lactating mouse mammary glands. Additionally, we studied a variety of ENCODE datasets for cell lines K562 and MCF-7, including 12 histone modification ChIP sequencing as well as ATAC and DNase sequencing datasets, where we observe and discuss assay-specific differences.

Conclusion: TF-Prioritizer accepts ATAC, DNase, or ChIP sequencing and RNA sequencing data as input and identifies TFs with differential activity, thus offering an understanding of genome-wide gene regulation, potential pathogenesis, and therapeutic targets in biomedical research.

Authors with CRIS profile

David Blumenthal Professur für Biomedical Network Science

Related research project(s)

Dimensionality reduction for molecular data based on explanatory power of differential regulatory networks (NetMap) March 1, 2023 - Feb. 28, 2026

Involved external institutions

Technische Universität München (TUM) DE Germany (DE) Universität Hamburg (UHH) DE Germany (DE) Genome Institute of Singapore SG Singapore (SG) US National Institutes of Health (NIH) US United States (USA) (US) University of Oxford GB United Kingdom (GB) Goethe-Universität Frankfurt am Main DE Germany (DE) Universitätsklinikum Frankfurt am Main (KGU) DE Germany (DE) Deutsches Krebsforschungszentrum (DKFZ) DE Germany (DE)

How to cite

APA:

Hoffmann, M., Trummer, N., Schwartz, L., Jankowski, J., Lee, H.K., Willruth, L.-L.,... List, M. (2023). TF-Prioritizer: a Java pipeline to prioritize condition-specific transcription factors. GigaScience, 12. https://dx.doi.org/10.1093/gigascience/giad026

MLA:

Hoffmann, Markus, et al. "TF-Prioritizer: a Java pipeline to prioritize condition-specific transcription factors." GigaScience 12 (2023).

BibTeX: Download