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Related Concept Videos

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Point mutations are genetic alterations involving the change of a single nucleotide base pair in DNA. Depending on how the alteration affects protein synthesis, they can lead to various consequences.Point mutations fall into the following types:Silent mutations occur when a nucleotide change does not alter the amino acid sequence due to the redundancy of the genetic code. For instance, changing ACC to ACA still encodes threonine, leaving the protein function unaffected. This occurs because...
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Related Experiment Video

Updated: Sep 5, 2025

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
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Change point detection for clustered expression data.

Miriam Sieg1, Lina Katrin Sciesielski2, Karin Michaela Kirschner3

  • 1Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Institute of Biometry and Clinical Epidemiology, Charitéplatz 1, Berlin, 10117, Germany. miriam.sieg@charite.de.

BMC Genomics
|July 6, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a method using linear mixed models and simultaneous confidence intervals to detect abrupt changes, or change points, in biological data over time. The Sequen and McDermott contrasts are highlighted for identifying individual or overall progression-related changes.

Keywords:
Change point detectionExpression analysisLinear mixed modelsMultiple contrast testsSimultaneous confidence intervals

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Area of Science:

  • Genomics and Bioinformatics
  • Statistical Modeling
  • Developmental Biology

Background:

  • Biological processes are often studied across multiple time points, generating historical expression data.
  • Data can be clustered within time points, for example, using littermates from the same mother mice.
  • Detecting abrupt changes (change points) in parameters of interest within such historical, clustered data is crucial.

Purpose of the Study:

  • To develop and demonstrate a statistical methodology for identifying change points in time-course biological data.
  • To evaluate the effectiveness of generalized hypothesis testing with linear mixed effects models for this purpose.
  • To compare different contrast matrices for change point detection.

Main Methods:

  • Applied generalized hypothesis testing using a linear mixed effects model.
  • Utilized model coefficients for multiple contrast tests to estimate effect sizes.
  • Visualized effect estimates with simultaneous confidence intervals to determine change points.
  • Conducted simulation studies with various change scenarios and contrast matrices.

Main Results:

  • Simultaneous confidence intervals derived from multiple contrast tests effectively identified change points in clustered expression data.
  • The Sequen contrast proved effective for detecting individual change points.
  • The McDermott contrast was suitable for identifying change points related to overall progression.
  • Provided R code and demonstrated applicability on preclinical in-vivo data.

Conclusions:

  • Linear mixed models and multiple contrast tests with simultaneous confidence intervals are capable of determining change points in clustered time-course data.
  • Confidence intervals offer interpretable effect estimates, allowing scientists to define biologically relevant thresholds.
  • The Sequen and McDermott contrasts are recommended for specific change point detection scenarios.