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Related Concept Videos

Downsampling01:20

Downsampling

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When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
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The probability of having two carbon-13 atoms next to each other is negligible because of the low natural abundance of carbon-13. Consequently, peak splitting due to carbon-carbon spin-spin coupling is not observed in spectra. However, protons up to three sigma bonds away split the carbon signal according to the n+1 rule, resulting in complicated spectra.
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Spin systems where the difference in chemical shifts of the coupled nuclei is greater than ten times J are called first-order spin systems. These nuclei are weakly coupled, and their chemical shifts and coupling constant can generally be estimated from the well-separated signals in the spectrum.
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Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
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Chrom-Sig: de-noising 1-dimensional genomic profiles by signal processing methods.

Nandita J Gupta1,2, Zachary Apell3,2, Minji Kim2,1

  • 1Department of Electrical and Computer Engineering, University of Michigan, Ann Arbor, MI, USA.

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|August 20, 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, such as ChIP-seq and ATAC-seq, without needing control samples. This tool helps researchers accurately identify true biological signals in their data.

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Area of Science:

  • Genomics
  • Computational Biology
  • Bioinformatics

Background:

  • Next-generation sequencing (NGS) technologies like ChIP-seq, CUT&Tag, CUT&RUN, and ATAC-seq are crucial for modern genomic research.
  • These experiments generate coverage data for analyzing transcription factor binding and chromatin accessibility.
  • Technical noise inherent in experimental protocols complicates the accurate extraction of biological signals.

Purpose of the Study:

  • To develop a statistically rigorous and computationally efficient method for de-noising 1D genomic coverage tracks.
  • To provide a tool that does not require prior assumptions or experimental controls (e.g., input or spike-in controls).

Main Methods:

  • Development of Chrom-Sig, a Python package for de-noising genomic coverage data.
  • Utilizes computation of the empirical null distribution to distinguish signal from noise.
  • Tested on diverse datasets including ChIP-seq, CUT&RUN, ATAC-seq, and snATAC-seq.

Main Results:

  • Chrom-Sig effectively decomposes genomic data into signal and noise components.
  • The package performs de-noising and peak calling efficiently, typically within 1-2 hours and using ~20 GB of memory.
  • De-noised data from CTCF CUT&RUN, ATAC-seq, and RNA Polymerase II experiments showed biologically meaningful correlations with known regulatory elements and motifs.

Conclusions:

  • Chrom-Sig offers a versatile and general solution for de-noising genomic coverage tracks generated by various NGS technologies.
  • The tool enhances the accuracy of signal detection in genomic data, facilitating downstream biological interpretation.
  • Chrom-Sig is publicly available, promoting its adoption in the research community.