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Related Concept Videos

Cis-regulatory Sequences02:02

Cis-regulatory Sequences

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Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...
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Combinatorial Gene Control02:33

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Combinatorial gene control is the synergistic action of several transcriptional factors to regulate the expression of a single gene. The absence of one or more of these factors may lead to a significant difference in the level of gene expression or repression.
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Synthetic Biology02:55

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Synthetic biology is an interdisciplinary science that involves using principles from disciplines such as engineering, molecular biology, cell biology, and systems biology. It involves remodeling existing organisms from nature or constructing completely new synthetic organisms for applications such as protein or enzyme production, bioremediation, value-added macromolecule production, and the addition of desirable traits to crops, to name a few.
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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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Genome-wide Association Studies-GWAS01:11

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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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Fast Bayesian inference for gene regulatory networks using ScanBMA.

William Chad Young, Adrian E Raftery, Ka Yee Yeung1

  • 1Department of Microbiology, University of Washington, Box 357735, 98195-7735, Seattle WA, USA. kayee@uw.edu.

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|April 19, 2014
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Summary

ScanBMA, a Bayesian inference method, efficiently infers gene regulatory networks from genome-wide time-series data. It generates more accurate and compact networks compared to existing methods, improving gene discovery.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • Genome-wide time-series data offer insights into gene regulatory relationships.
  • Developing efficient network inference methods is crucial for handling large mammalian datasets.
  • Robust, accurate, and compact gene-to-gene relationships are needed for biological network analysis.

Purpose of the Study:

  • To develop a scalable and accurate Bayesian inference method for gene regulatory network construction.
  • To integrate external information for enhanced network inference accuracy.
  • To provide a systematic framework for analyzing complex biological networks.

Main Methods:

  • Developed ScanBMA, a Bayesian inference method utilizing external information.
  • Implemented efficient model space searching strategies and data transformations.
  • Employed g-prior for guiding the identification of candidate regulators.

Main Results:

  • ScanBMA demonstrates high computational efficiency, addressing scalability in network inference.
  • The method produces more compact gene regulatory networks with a higher true positive rate.
  • ScanBMA outperforms other methods in key performance metrics like ROC and Precision-Recall curves.

Conclusions:

  • ScanBMA offers a robust and accurate approach for gene regulatory network inference.
  • The method is computationally efficient and scalable for large-scale biological data.
  • ScanBMA provides a valuable tool for systems biology research and gene discovery.