consenrich 0.1.13b1

Genome-wide extraction of reproducible continuous-valued signals hidden in noisy multisample functional genomics data

Consenrich

Consenrich is a sequential state estimator for extraction of genome-wide epigenetic signals and uncertainty quantification inferred from multi-sample high-throughput functional genomics datasets.

Consenrich sequentially estimates epigenomic states from multisample HTS data--ATAC-seq, ChIP-seq, etc. By modeling both (i) local and global spatial dependencies and (ii) noise due to regional artifacts and individual samples, Consenrich yields a genome-wide track of 'consensus' signal estimates with variance propagation and elucidated spatial features.

Usage

• Input:

  • $m \geq 1$ Sequence alignment files -t/--bam_files corresponding to each sample in a given HTS experiment
  • (Optional): $m_c = m$ control sample alignments, -c/--control_files, for each 'control' sample (e.g., ChIP-seq)
  • (Optional): wavelet-based template(s) to match for genome-wide pattern matching (--match_wavelet db<2,3,...>, sym<2,3,...>, haar, coif<1,2,...>, dmey)

• Output:

  • Genome-wide 'consensus' epigenomic state estimates and uncertainty metrics
  • (Optional): BED-like output(s) of localized enrichment patterns across multiple resolutions, obtained with a genomics-oriented matched filtering variant, e.g., ConsenrichMatchedResult(Het10, <template_name>)

Example: Consenrich-estimated signal tracks and uncertainty metrics given an input dataset consisting of $m=10$ ATAC-seq alignments of varying data quality (lymphoblastoid) consenrich --bam_files ENCFF*.bam -g hg38 --match_wavelet haar,db2,db4

---

Download/Install

Consenrich is available via PyPI/pip:

• python -m pip install consenrich

---

Consenrich can also be cloned and built from source:

1. git clone https://github.com/nolan-h-hamilton/Consenrich.git
2. cd Consenrich
3. python setup.py sdist bdist_wheel
4. python -m pip install .

Check installation: consenrich -h

Manuscript Preprint and Citation

A manuscript preprint is available on bioRxiv. A revised, up-to-date manuscript is forthcoming.

BibTeX

@article {Hamilton2025
	author = {Hamilton, Nolan H and McMichael, Benjamin D and Love, Michael I and Furey, Terrence S},
	title = {Genome-Wide Uncertainty-Moderated Extraction of Signal Annotations from Multi-Sample Functional Genomics Data},
	year = {2025},
	doi = {10.1101/2025.02.05.636702},
	url = {https://www.biorxiv.org/content/10.1101/2025.02.05.636702v1},
}