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Related Concept Videos

Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

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The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
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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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Related Experiment Video

Updated: Feb 22, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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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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Accelerated parallel algorithm for gene network reverse engineering.

Jing He1,2, Zhou Zhou3, Michael Reed3

  • 1Department of Biomedical Informatics, Columbia University, 168th Street, New York, 10032, NY, USA.

BMC Systems Biology
|September 28, 2017
PubMed
Summary
This summary is machine-generated.

We developed GPU-ARACNE, a faster and more accessible tool for reconstructing gene regulatory networks. This parallel implementation of the Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNE) significantly reduces computational demands for researchers.

Keywords:
CUDAGPU-ARACNEGene expression datasetMutual informationParallel computingRegulatory networks

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Area of Science:

  • Computational biology
  • Systems biology
  • Bioinformatics

Background:

  • The Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNE) is effective for gene regulatory network reconstruction.
  • Previous ARACNE implementations are computationally intensive and sample-limited.

Purpose of the Study:

  • To develop an accelerated, parallelized version of ARACNE using GPU computing.
  • To simplify the ARACNE workflow and reduce computational resource requirements.

Main Methods:

  • Implemented ARACNE using multi-level parallelism and Compute Unified Device Architecture (CUDA).
  • Optimized adaptive partitioning for Mutual Information (MI) estimation on GPUs.

Main Results:

  • GPU-ARACNE achieves significant speedups compared to previous implementations.
  • The new implementation reconstructs equally valid gene regulatory networks.
  • User experience is simplified from multi-step to a single operation.

Conclusions:

  • GPU-ARACNE offers a valuable, resource-efficient solution for reconstructing complex gene regulatory networks from large datasets.
  • The GPU-centered optimization techniques are applicable to other computational domains.