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Related Concept Videos

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Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
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Single Molecule Fluorescence In Situ Hybridization smFISH Analysis in Budding Yeast Vegetative Growth and Meiosis
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SwitchFinder - a novel method and query facility for discovering dynamic gene expression patterns.

Svetlana Bulashevska1, Colin Priest2, Daniel Speicher3,4

  • 1B-IT Bonn-Aachen International Center for Information Technology, University of Bonn, Dahlmannstr. 2, Bonn, 53113, Germany. sbulashevska@gmail.com.

BMC Bioinformatics
|December 17, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces SwitchFinder, a novel statistical method for analyzing dynamic gene expression patterns by identifying critical switch-points. SwitchFinder aids in understanding biological processes and facilitates gene regulatory system discovery.

Keywords:
ATRA-induced differentiationBayesian modelingChange-point modelingChange-point problemDynamic patterns of gene expressionGibbs samplingMCMCNeuroblastomaTime-series analysis

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • Biological systems exhibit dynamic behavior requiring time-resolved measurements for comprehensive understanding.
  • Time-resolved gene expression data offers genome-wide insights into gene activity under various conditions.
  • Identifying change-points in gene expression dynamics is crucial for uncovering regulatory mechanisms.

Purpose of the Study:

  • To develop a statistical method for analyzing time-series gene expression data.
  • To identify switch-points and dynamic patterns in gene activity.
  • To facilitate the discovery of biological insights from complex temporal data.

Main Methods:

  • Developed SwitchFinder, a change-point statistical model for time-series analysis.
  • Utilized Bayesian inference with Markov chain Monte Carlo (MCMC) Gibbs sampling for model fitting.
  • Incorporated features like growth, decay, spike, and cleft to characterize gene expression dynamics.
  • Created a web application for interactive querying and pattern deduction.

Main Results:

  • SwitchFinder successfully identifies switch-points in gene expression time-courses.
  • The method characterizes gene expression profiles by dynamic features and onset-times.
  • Application to neuroblastoma cell line data revealed eight distinct gene expression response patterns to ATRA.
  • Identified involvement of signaling pathways (BMP, WNT, Notch, FGF, NTRK) in cell differentiation and repression of cell-cycle genes.

Conclusions:

  • SwitchFinder offers a novel approach for analyzing biological time-series data.
  • The method supports inference and interactive exploration of dynamic patterns, aiding biological discovery.
  • SwitchFinder is freely accessible for researchers to explore gene expression dynamics.