consenrich 0.1.9b0

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

Consenrich

Consenrich is a sequential genome-wide state estimator for extraction of reproducible, spatially-resolved, epigenomic signals hidden in noisy multisample HTS data. The corresponding manuscript preprint is available on $$\text{bio}\textcolor{#960018}{R}\chi \text{iv}$$.

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• 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)

• Output:

  • Genome-wide 'consensus' epigenomic state estimates and uncertainty metrics (BedGraph/BigWig)

• Refer to Examples for a variety of detailed usage instances.

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Features

• Consenrich explicitly models dynamic signal trends and noise profiles for each sample with scale-invariance $\implies$ Multi-sample, multi-assay estimation of target molecular states from related functional genomics assays, e.g., ChIP-seq + CUT-N-RUN, ATAC-seq + DNase-seq.

• Consenrich yields uncertainty-moderated signal tracks that effectively encompass multiple samples' epigenomic profiles $\implies$ Insightful data representation for profiling condition-specific regulatory landscapes (e.g., via consensus peak calling, differential analyses, etc.)

• Consenrich resolves legitimate spatial complexity and attenuates noise $\implies$ Enables spectral/wavelet analyses for deeper profiling of signal trends and pattern-matching.

Download/Install

Consenrich is available via PyPI/pip:

• python -m pip install consenrich

If lacking administrative privileges, running with flag --user may be necessary.

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Consenrich can also be easily downloaded and installed 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 .
5. Check installation: consenrich --help

Manuscript Preprint and Citation

Genome-Wide Uncertainty-Moderated Extraction of Signal Annotations from Multi-Sample Functional Genomics Data
Nolan H Hamilton, Benjamin D McMichael, Michael I Love, Terrence S Furey; doi: 10.1101/2025.02.05.636702

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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},
}