# Consenrich
[](https://github.com/nolan-h-hamilton/Consenrich/actions/workflows/Tests.yml)
*[Consenrich](https://github.com/nolan-h-hamilton/Consenrich) is a sequential genome-wide state estimator for extraction of reproducible, spatially-resolved, epigenomic signals hidden in noisy multisample HTS data.*
---
* **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)
---
* Uncertainty-moderated signal tracks encompassing multiple samples' epigenomic profiles $\implies$ Convenient, insightful data representation for conventional analyses aiming to profile trait-specific regulatory landscapes (e.g., via [consensus peak calling](docs/consensus_peaks.md))
* Models trends and noise profiles for each sample with scale-invariance $\implies$ [Multi-sample, multi-assay estimation of target molecular states](docs/atac_dnase.png) from related functional genomics assays, e.g., ChIP-seq + CUT-N-RUN, ATAC-seq + DNase-seq.
* [Preservation of spectral content](docs/filter_comparison.png) $\implies$ Comparison and profiling of group-specific structural signatures discarded by traditional enrichment-focused measures for HTS data.
Several technical features of Consenrich are discussed [below](#technical-features).
## Example Command-Line Use
* Run Consenrich on ten heterogeneous ATAC-seq sample alignments in the current directory (`*.bam`).
* Generate BigWig signal track ('Consenrich Signal Track') and IVW residual estimates ('Consenrich IVW Residuals Track')
```bash
consenrich --bam_files *.bam -g hg38 --signal_bigwig demo_signal.bw --residuals_bigwig demo_residuals.bw
```
---
* Use Consenrich for ChIP-seq enrichment analysis with treatment/control sample alignments (POL2RA, six donors' colon tissue samples). Generate separate BigWig output tracks for signal estimates and inverse-variance weighted residuals. Use fixed-width genomic intervals of 25bp:
```bash
consenrich \
--bam_files \
ENCSR322JEO_POL2RA.bam \
ENCSR472VBD_POL2RA.bam \
ENCSR431EHE_POL2RA.bam \
ENCSR724FCJ_POL2RA.bam \
ENCSR974HQI_POL2RA.bam \
ENCSR132XRW_POL2RA.bam \
--control_files \
ENCSR322JEO_CTRL.bam \
ENCSR472VBD_CTRL.bam \
ENCSR431EHE_CTRL.bam \
ENCSR724FCJ_CTRL.bam \
ENCSR974HQI_CTRL.bam \
ENCSR132XRW_CTRL.bam \
-g hg38 --step 25 \
-o Consenrich_POL2RA.tsv \
--signal_bigwig Consenrich_POL2RA_CTRL_Signal.bw \
--residual_bigwig Consenrich_POL2RA_CTRL_IVW_Residuals.bw
```
**Output**
## Download/Install
Consenrich can 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`
Consenrich is also available via [PyPI/pip](https://pypi.org/project/consenrich/):
* `pip install consenrich`
If managing multiple Python environments, use `python -m pip install consenrich`. If lacking administrative privileges, running with flag `--user` may be necessary.
## Technical Features
* Effectively models sample-and-region-varying noise to better integrate data across heterogeneous samples
* Balances biologically-informed *a priori* predictions with observed HTS data to determine final estimates
* Provides interpretable uncertainty quantification with respect to multiple model aspects
* Runs efficiently in linear time with respect to genome size.
Raw data
{
"_id": null,
"home_page": "https://github.com/nolan-h-hamilton/Consenrich",
"name": "consenrich",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.9",
"maintainer_email": null,
"keywords": "genomics, functional genomics, epigenomics, epigenetics, signal processing, data fusion, state estimator, filter",
"author": "Nolan H. Hamilton, Benjamin D. McMichael, Michael I. Love, Terrence S. Furey",
"author_email": "nolan.hamilton@unc.edu, bdmcmi@ad.unc.edu, milove@email.unc.edu, tsfurey@email.unc.edu",
"download_url": "https://files.pythonhosted.org/packages/9b/fd/f422e6a5958be7a06d66b03f332a7a62cbbd021395ec4dc656324963ec03/consenrich-0.1.0b2.tar.gz",
"platform": null,
"description": "# Consenrich\n\n[](https://github.com/nolan-h-hamilton/Consenrich/actions/workflows/Tests.yml)\n\n\n*[Consenrich](https://github.com/nolan-h-hamilton/Consenrich) is a sequential genome-wide state estimator for extraction of reproducible, spatially-resolved, epigenomic signals hidden in noisy multisample HTS data.*\n\n---\n\n* **Input**:\n * $m \\geq 1$ Sequence alignment files `-t/--bam_files` corresponding to each sample in a given HTS experiment\n * (*Optional*): $m_c = m$ control sample alignments, `-c/--control_files`, for each 'control' sample (e.g., ChIP-seq)\n\n* **Output**: Genome-wide 'consensus' epigenomic state estimates and uncertainty metrics (BedGraph/BigWig)\n\n---\n\n* Uncertainty-moderated signal tracks encompassing multiple samples' epigenomic profiles $\\implies$ Convenient, insightful data representation for conventional analyses aiming to profile trait-specific regulatory landscapes (e.g., via [consensus peak calling](docs/consensus_peaks.md))\n* Models trends and noise profiles for each sample with scale-invariance $\\implies$ [Multi-sample, multi-assay estimation of target molecular states](docs/atac_dnase.png) from related functional genomics assays, e.g., ChIP-seq + CUT-N-RUN, ATAC-seq + DNase-seq.\n* [Preservation of spectral content](docs/filter_comparison.png) $\\implies$ Comparison and profiling of group-specific structural signatures discarded by traditional enrichment-focused measures for HTS data.\n\nSeveral technical features of Consenrich are discussed [below](#technical-features).\n\n## Example Command-Line Use\n\n* Run Consenrich on ten heterogeneous ATAC-seq sample alignments in the current directory (`*.bam`).\n * Generate BigWig signal track ('Consenrich Signal Track') and IVW residual estimates ('Consenrich IVW Residuals Track')\n\n ```bash\n consenrich --bam_files *.bam -g hg38 --signal_bigwig demo_signal.bw --residuals_bigwig demo_residuals.bw\n ```\n\n\n\n---\n\n* Use Consenrich for ChIP-seq enrichment analysis with treatment/control sample alignments (POL2RA, six donors' colon tissue samples). Generate separate BigWig output tracks for signal estimates and inverse-variance weighted residuals. Use fixed-width genomic intervals of 25bp:\n\n ```bash\n consenrich \\\n --bam_files \\\n ENCSR322JEO_POL2RA.bam \\\n ENCSR472VBD_POL2RA.bam \\\n ENCSR431EHE_POL2RA.bam \\\n ENCSR724FCJ_POL2RA.bam \\\n ENCSR974HQI_POL2RA.bam \\\n ENCSR132XRW_POL2RA.bam \\\n --control_files \\\n ENCSR322JEO_CTRL.bam \\\n ENCSR472VBD_CTRL.bam \\\n ENCSR431EHE_CTRL.bam \\\n ENCSR724FCJ_CTRL.bam \\\n ENCSR974HQI_CTRL.bam \\\n ENCSR132XRW_CTRL.bam \\\n -g hg38 --step 25 \\\n -o Consenrich_POL2RA.tsv \\\n --signal_bigwig Consenrich_POL2RA_CTRL_Signal.bw \\\n --residual_bigwig Consenrich_POL2RA_CTRL_IVW_Residuals.bw\n ```\n\n**Output**\n\n\n## Download/Install\n\nConsenrich can be easily downloaded and installed from source:\n\n1. `git clone https://github.com/nolan-h-hamilton/Consenrich.git`\n2. `cd Consenrich`\n3. `python setup.py sdist bdist_wheel`\n4. `python -m pip install .`\n5. Check installation: `consenrich --help`\n\nConsenrich is also available via [PyPI/pip](https://pypi.org/project/consenrich/):\n\n* `pip install consenrich`\n\nIf managing multiple Python environments, use `python -m pip install consenrich`. If lacking administrative privileges, running with flag `--user` may be necessary.\n\n## Technical Features\n\n* Effectively models sample-and-region-varying noise to better integrate data across heterogeneous samples\n* Balances biologically-informed *a priori* predictions with observed HTS data to determine final estimates\n* Provides interpretable uncertainty quantification with respect to multiple model aspects\n* Runs efficiently in linear time with respect to genome size.\n",
"bugtrack_url": null,
"license": "MIT",
"summary": "Genome-wide extraction of reproducible continuous-valued signals hidden in noisy multisample functional genomics data",
"version": "0.1.0b2",
"project_urls": {
"Homepage": "https://github.com/nolan-h-hamilton/Consenrich"
},
"split_keywords": [
"genomics",
" functional genomics",
" epigenomics",
" epigenetics",
" signal processing",
" data fusion",
" state estimator",
" filter"
],
"urls": [
{
"comment_text": null,
"digests": {
"blake2b_256": "30c4a7aedc43ecbeb263ec81e2a811b521629c89b6de8b4e9a2d7a9ec170c323",
"md5": "357f8d45a387edbdfd9195ae5278fcf0",
"sha256": "794f20cc7b6680c6613801d1eb828b8620a8a7a377cae73c7147601e4b1785cd"
},
"downloads": -1,
"filename": "consenrich-0.1.0b2-py3-none-any.whl",
"has_sig": false,
"md5_digest": "357f8d45a387edbdfd9195ae5278fcf0",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.9",
"size": 9372824,
"upload_time": "2025-02-04T18:25:27",
"upload_time_iso_8601": "2025-02-04T18:25:27.774350Z",
"url": "https://files.pythonhosted.org/packages/30/c4/a7aedc43ecbeb263ec81e2a811b521629c89b6de8b4e9a2d7a9ec170c323/consenrich-0.1.0b2-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": null,
"digests": {
"blake2b_256": "9bfdf422e6a5958be7a06d66b03f332a7a62cbbd021395ec4dc656324963ec03",
"md5": "199d66964d452b1cc2687c440cdce69b",
"sha256": "45781b7c60ea8a3f77af22e737a7384cc0148a0445be57862ba30b4682cd8a66"
},
"downloads": -1,
"filename": "consenrich-0.1.0b2.tar.gz",
"has_sig": false,
"md5_digest": "199d66964d452b1cc2687c440cdce69b",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.9",
"size": 9765269,
"upload_time": "2025-02-04T18:25:36",
"upload_time_iso_8601": "2025-02-04T18:25:36.906032Z",
"url": "https://files.pythonhosted.org/packages/9b/fd/f422e6a5958be7a06d66b03f332a7a62cbbd021395ec4dc656324963ec03/consenrich-0.1.0b2.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2025-02-04 18:25:36",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "nolan-h-hamilton",
"github_project": "Consenrich",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"lcname": "consenrich"
}
JSON
