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Related Concept Videos

Variance01:15

Variance

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The deviations show how spread out the data are about the mean. A positive deviation occurs when the data value exceeds the mean, whereas a negative deviation occurs when the data value is less than the mean. If the deviations are added, the sum is always zero. So one cannot simply add the deviations to get the data spread. By squaring the deviations, the numbers are made positive; thus, their sum will also be positive.
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Transcription Factors02:16

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Tissue-specific transcription factors contribute to diverse cellular functions in mammals. For example, the gene for beta globin, a major component of hemoglobin, is present in all cells of the body. However, it is only expressed in red blood cells because the transcription factors that can bind to the promoter sequences of the beta globin gene are only expressed in these cells. Tissue-specific transcription factors also ensure that mutations in these factors may impair only the function of...
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Epigenetic Regulation01:46

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Epigenetic mechanisms play an essential role in healthy development. Conversely, precisely regulated epigenetic mechanisms are disrupted in diseases like cancer.
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Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
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GTPases and their Regulation02:14

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Guanine nucleotide-binding proteins (G-proteins), also known as GTPases, are a superfamily of proteins that regulate many cellular processes, such as cell signaling, vesicular transport, and the regulation of cell shape and motility. Mutation or dysfunction of these proteins can lead to disease. There are around 40,000 known G-proteins that can broadly be classified into two groups ‒  small G-proteins consisting of a single domain and large multi-domain G-proteins.
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Master transcription regulators are regulatory proteins that are predominantly responsible for regulating the expression of multiple genes. Often these genes work in concert to drive a  complex process. Activation of a master transcription regulator can lead to a cascade of transcriptional activation necessary for that outcome. These regulators can directly bind to the regulatory sequences of the various genes involved, or they can indirectly regulate transcription by binding to regulatory...
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Related Experiment Video

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Reusable Single Cell for Iterative Epigenomic Analyses
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BROCKMAN: deciphering variance in epigenomic regulators by k-mer factorization.

Carl G de Boer1, Aviv Regev2,3,4

  • 1Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA.

BMC Bioinformatics
|July 5, 2018
PubMed
Summary
This summary is machine-generated.

A new method, BROCKMAN, analyzes DNA sequences linked to epigenomic marks to reveal transcription factor (TF) activity variations. This approach helps understand chromatin organization and gene expression across single cells.

Keywords:
ChromatinClusteringDecompositionEpigenomeFactorizationK-merN-gramSingle-cellTranscription factorscATAC-seq

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Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • Single-cell chromatin organization is key to gene expression but challenging to interpret due to scale, noise, and sparsity.
  • Existing methods struggle with the complexity of single-cell epigenomic data.

Purpose of the Study:

  • To develop a novel computational approach for inferring transcription factor (TF) activity variation from single-cell chromatin data.
  • To overcome the challenges of scale, noise, and sparsity in interpreting single-cell chromatin organization.

Main Methods:

  • Developed BROCKMAN (Brockman Representation Of Chromatin by K-mers in Mark-Associated Nucleotides).
  • Represents samples as vectors of DNA word frequencies associated with epigenomic marks.
  • Employs unsupervised matrix decomposition for hidden structure discovery, sample grouping, and TF identification.

Main Results:

  • BROCKMAN successfully distinguished cell types, treatments, batch effects, and cell cycle status in single-cell ATAC-seq data.
  • Identified variable k-mer components reflecting sets of co-varying, interacting TFs.
  • Demonstrated AP-1 TFs as key determinants of chromatin accessibility variability in K562 cells.
  • Provided a theoretical framework for increased variability in cooperative TF binding.

Conclusions:

  • BROCKMAN offers a powerful tool for analyzing TF activity variation across diverse biological contexts.
  • Facilitates a mechanistic understanding of chromatin variability determinants in single cells, treatments, and individuals.