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

Nandita J Gupta1,2, Zachary Apell2,3, Minji Kim1,2

  • 1Department of Electrical and Computer Engineering, University of Michigan, Ann Arbor, MI 48109, United States.

Bioinformatics (Oxford, England)
|December 1, 2025
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Summary
This summary is machine-generated.

Chrom-Sig is a new Python package that efficiently removes technical noise from genomic sequencing data, like ChIP-seq and ATAC-seq. It accurately identifies true biological signals without needing extra controls, making genomic analysis faster and more reliable.

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Area of Science:

  • Genomics
  • Computational Biology
  • Bioinformatics

Background:

  • Next-generation sequencing (NGS) experiments like ChIP-seq, CUT&Tag, CUT&RUN, and ATAC-seq are crucial for studying transcription factor binding and chromatin accessibility.
  • These NGS methods generate coverage data that often contains technical noise, necessitating robust methods for signal extraction.
  • Existing noise reduction techniques can be computationally intensive or require specific experimental controls, posing limitations for researchers.

Purpose of the Study:

  • To develop a statistically rigorous and computationally efficient method for de-noising 1D genomic coverage tracks.
  • To create a tool that can extract true biological signals from noisy NGS data without prior assumptions or experimental controls.
  • To provide a versatile Python package applicable to various genomic sequencing technologies.

Main Methods:

  • Developed Chrom-Sig, a Python package that computes the empirical null distribution to de-noise genomic coverage tracks.
  • Implemented a method that decomposes NGS data into distinct signal and noise components.
  • Validated the approach on diverse datasets including ChIP-seq, CUT&RUN, ATAC-seq, and snATAC-seq.

Main Results:

  • Chrom-Sig effectively de-noises 1D genomic coverage tracks, distinguishing signal from noise.
  • The package performs de-noising and peak calling rapidly, typically within 1-2 hours and using approximately 20 GB of memory.
  • De-noised data from CTCF CUT&RUN experiments showed high overlap with CTCF binding motifs, and ATAC-seq/RNA Polymerase II data were enriched in regulatory regions like enhancers and promoters.

Conclusions:

  • Chrom-Sig offers a computationally efficient and statistically sound solution for de-noising genomic coverage data.
  • The tool accurately identifies biologically relevant signals, enhancing the interpretation of NGS experiments.
  • Chrom-Sig is a versatile and generalizable tool poised for broad application in current and future genomic research.