nf-core pipeline · open-chromatin-regions, transcription-factors, transcriptomics

nf-core/tfactivity

Identifies differentially active transcription factors across biological conditions using expression and chromatin data.

Run it with the research agent Documentation Source code

What nf-core/tfactivity is

nf-core/tfactivity is a bioinformatics pipeline designed to detect and rank the most differentially active transcription factors across multiple experimental conditions. It was originally written by Nico Trummer and is based on the previously published TF-Prioritizer workflow.

Understanding which regulatory proteins drive phenotypic changes requires integrating both chromatin accessibility and transcriptomic alterations. nf-core/tfactivity addresses this challenge by coupling gene expression counts with open chromatin measurements, identifying the transcription factors most likely to regulate downstream differential expression.

By bridging chromatin profile analysis with statistical modelling of gene expression, the pipeline provides an automated, reproducible workflow for ranking candidate transcription factors responsible for observed transcriptomic shifts.

What the pipeline does

The pipeline takes called chromatin accessibility peaks (such as from ATAC-seq, DNase-seq, or histone modification ChIP-seq) and expression count matrices. It first determines accessible regions, using the peaks directly or carrying out footprinting between close ChIP-seq peaks. BAM files can also be supplied to predict enhancer regions. The STARE tool then calculates affinity scores between transcription factors and target genes.

In parallel, the workflow identifies differentially expressed genes between conditions while accommodating batch effects specified in the experimental design. A linear regression model evaluates which transcription factors explain the observed expression changes. A combined transcription factor-target gene score is computed from the target genes' differential expression, the transcription factor affinity, and the regression coefficients. Finally, a Mann-Whitney U test ranks the transcription factors across conditions.

Key facts

Pipeline framework: Nextflow (nf-core)
Original author: Nico Trummer
Input data types: Gene expression count matrix, open chromatin peaks (BED format), BAM files for enhancer prediction (optional)
Supported chromatin assays: ATAC-seq, DNase-seq, histone modification ChIP-seq (HM-ChIP-seq)
Key tools: STARE
Statistical tests: Linear regression, Mann-Whitney U test
Primary output: Ranking of differentially active transcription factors

What you can use it for

Comparative regulatory analysis: Prioritise transcription factors driving regulatory variations between distinct biological conditions, disease states, or cell types.
Integrating ATAC-seq and RNA-seq: Combine matched gene expression count matrices with ATAC-seq open chromatin peaks to identify functional transcription factor binding.
Histone modification ChIP-seq integration: Utilise ChIP-seq peaks, such as H3K27ac and H3K4me3, together with signal and control BAM files to predict enhancers and infer active upstream regulators.

Run it here

The research agent runs nf-core/tfactivity with Nextflow on its own machine — up to 32 cores, 256 GiB of memory and 24 hours per run, which covers the test profile and real single-sample runs. It builds the samplesheet, checks it against the pipeline's schema and reads the results back. When a run is too big for one machine, it gives you the exact command for your own cluster instead.

Ask the research agent

Strengths and limits

Good at

Integrates both transcriptomic expression data and epigenomic chromatin accessibility to infer transcription factor activity.
Calculates transcription factor and target gene affinities directly using the specialised tool STARE.
Accounts for experimental batch effects during the differential expression analysis step.
Accepts multiple chromatin assay formats, including ATAC-seq, DNase-seq, and histone modification ChIP-seq.

Watch out for

Requires pre-processed, previously called peak files rather than running primary peak calling from raw sequencing reads.
Reads only the first three columns of input BED peak files.
Requires aligned BAM files with matched controls if enhancer region prediction is desired.

Questions

Can I run nf-core/tfactivity without my own cluster?

What licence is nf-core/tfactivity under?

MIT, like every nf-core pipeline.

What inputs are required to run the pipeline?

The pipeline requires a count matrix of gene expression (with a corresponding design matrix indicating sample conditions) and a samplesheet of open chromatin peak files in BED-compatible format. Optional BAM files and control BAM files can be provided if enhancer prediction is needed.

How should expression data be formatted?

You can supply a single raw count matrix containing gene IDs as rows and samples as columns, alongside a design matrix matching sample IDs to conditions and batches. Alternatively, a gene list and individual count files per sample can be provided.

Which peak file formats does the pipeline accept?

The pipeline accepts peak files that match standard BED formatting, but it only reads and processes the first three columns of each file.

Can batch effects be handled during analysis?

Yes, batch information can be included in the design matrix CSV file and is incorporated during the differential gene expression analysis.

How to cite it

Markus Hoffmann, Nico Trummer, Leon Schwartz, Jakub Jankowski, Hye Kyung Lee, Lina-Liv Willruth, Olga Lazareva, Kevin Yuan, Nina Baumgarten, Florian Schmidt, Jan Baumbach, Marcel H Schulz, David B Blumenthal, Lothar Hennighausen & Markus List. TF-Prioritizer: a Java pipeline to prioritize condition-specific transcription factors. GigaScience, Volume 12, 2023, giad026, https://doi.org/10.1093/gigascience/giad026

Written from nf-core/tfactivity's own README. Statements citing a figure that does not appear there are removed before publication. Licence and running details come from our registry.