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Cis-regulatory Sequences02:02

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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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Gene expression can be regulated at almost every step from gene to protein. Transcription is the step that is most commonly regulated. This involves the binding of proteins to short regulatory sequences on the DNA. This association can either promote or inhibit the transcription of a gene associated with the respective sequence.
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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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DCI: learning causal differences between gene regulatory networks.

Anastasiya Belyaeva1, Chandler Squires1, Caroline Uhler1

  • 1Laboratory for Information and Decision Systems and Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.

Bioinformatics (Oxford, England)
|March 11, 2021
PubMed
Summary
This summary is machine-generated.

We developed a new algorithm, Difference Causal Inference (DCI), to efficiently identify changes in gene regulatory networks between conditions using gene expression data. This method requires fewer samples and computational resources than traditional approaches.

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Area of Science:

  • Computational Biology
  • Genomics
  • Systems Biology

Background:

  • Gene regulatory networks are crucial for understanding cellular mechanisms.
  • Inferring these networks from large-scale gene expression data is computationally intensive and requires high sample sizes.
  • Existing causal inference methods face challenges with current biological datasets.

Purpose of the Study:

  • To develop an efficient algorithm for learning differences in gene regulatory mechanisms between conditions.
  • To infer changes (new, removed, or modified edges) between two causal graphs using gene expression data.
  • To provide a user-friendly Python implementation for broader accessibility.

Main Methods:

  • Difference Causal Inference (DCI) algorithm directly infers changes between causal graphs.
  • Algorithm is sample and computationally efficient by avoiding separate estimation of large graphs.
  • Stability selection is used to identify the most robust difference causal graph.

Main Results:

  • DCI efficiently learns differences in gene regulatory mechanisms between conditions.
  • The algorithm demonstrates efficiency in sample and computational resource usage.
  • Successful application to single-cell RNA-seq data and validation through intervention prediction.

Conclusions:

  • DCI offers an efficient approach to identify dynamic changes in gene regulatory networks.
  • The algorithm is suitable for analyzing large-scale gene expression datasets, including single-cell data.
  • DCI facilitates the design of targeted interventions by revealing condition-specific regulatory changes.