Chrom-Sig: de-noising 1D genomic profiles by signal processing methods.

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Due to the inherent technical noise present in the experimental protocols, researchers need statistically rigorous and computationally efficient methods to extract true biological signal from a mixture of signal and noise. However, existing approaches are often computationally demanding or require input or spike-in controls.\nRESULTS: We developed Chrom-Sig, a Python package to quickly de-noise 1D genomic coverage tracks by computing the empirical null distribution without prior assumptions or experimental controls. When tested on 19 ChIP-seq, CUT&RUN, ATAC-seq, and snATAC-seq datasets, Chrom-Sig can effectively decompose the data into signal and noise components. Notably, Chrom-Sig performs de-noising and peak calling in 1-2 h using around 20 GB of memory. The de-noised signal corroborates with biologically meaningful results: CTCF CUT&RUN data retained a high percentage of peaks overlapping CTCF binding motifs, while ATAC-seq and RNA Polymerase II data were enriched in enhancers and promoters. We envision Chrom-Sig to be a versatile and general tool for current and future genomic technologies.\nAVAILABILITY AND IMPLEMENTATION: Chrom-Sig is publicly available on GitHub (https://github.com/minjikimlab/chromsig) and Zenodo (doi: 10.5281/zenodo.17488772) under the MIT licence.", "claims": [{"id": "claim-method", "statement": "By leveraging pyBedGraph, we implemented the enrichment test—assessing the statistical significance of each observed read by comparing the observed signal to the empirical null distribution in a random location—in a Python package Chrom-Sig (Fig. 1, available as supplementary data at Bioinformatics online).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41325270", "locator": "sections[2] \"2 Materials and methods\" paragraph 3", "quote": "By leveraging pyBedGraph, we implemented the enrichment test—assessing the statistical significance of each observed read by comparing the observed signal to the empirical null distribution in a random location—in a Python package Chrom-Sig (Fig. 1, available as supplementary data at Bioinformatics online)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All data and implementation details of the code can be obtained from GitHub (https://github.com/minjikimlab/chromsig).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41325270", "locator": "sections[11] \"Data availability\" paragraph 2", "quote": "All data and implementation details of the code can be obtained from GitHub (https://github.com/minjikimlab/chromsig)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "When tested on 19 ChIP-seq, CUT&RUN, ATAC-seq, and snATAC-seq datasets, Chrom-Sig can effectively decompose the data into signal and noise components.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:41325270", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "When tested on 19 ChIP-seq, CUT&RUN, ATAC-seq, and snATAC-seq datasets, Chrom-Sig can effectively decompose the data into signal and noise components."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "When tested on 19 ChIP-seq, CUT&RUN, ATAC-seq, and snATAC-seq datasets, Chrom-Sig can effectively decompose the data into signal and noise components.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:41325270"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. 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