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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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Probing High-density Functional Protein Microarrays to Detect Protein-protein Interactions
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Chrom-Lasso: a lasso regression-based model to detect functional interactions using Hi-C data.

Jingzhe Lu1, Xu Wang2, Keyong Sun2

  • 1School of Medicine, Tsinghua University, Beijing, China.

Briefings in Bioinformatics
|May 20, 2021
PubMed
Summary
This summary is machine-generated.

Chrom-Lasso, a new computational model, accurately identifies functional interactions in 3D genome organization (Hi-C data). It overcomes biases and detects weak interactions, improving our understanding of gene regulation.

Keywords:
3D genomicsHi-C data analysisfunctional chromatin interactionslasso regression

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Area of Science:

  • Genomics
  • Molecular Biology
  • Computational Biology

Background:

  • Chromosome Conformation Capture (Hi-C) reveals 3D genome organization.
  • Computational analysis of Hi-C data faces challenges in bias correction and detecting low-count interactions.

Purpose of the Study:

  • To develop a robust computational method for identifying functional interactions from Hi-C data.
  • To improve the detection of weak cis-regulatory element interactions.

Main Methods:

  • Development of Chrom-Lasso, a lasso linear regression model for bias correction and interaction detection.
  • Application of Chrom-Lasso to Hi-C data, including time-series experiments during T cell activation.
  • Validation using 5C data, Genome-Wide Association Studies (GWAS) hits, and experimental confirmation.

Main Results:

  • Chrom-Lasso effectively removes complex biases without assumptions.
  • Identified interactions show higher enrichment for 5C validated interactions and functional GWAS hits compared to existing methods.
  • Time-series analysis revealed concordant dynamic changes in gene expression and chromatin interactions.
  • Experimental validation confirmed Chrom-Lasso's findings on gene co-regulation.

Conclusions:

  • Chrom-Lasso is a powerful tool for analyzing Hi-C data, enhancing the detection of functional chromatin interactions.
  • The model improves the identification of weak interactions between cis-regulatory elements like promoters and enhancers.
  • Chrom-Lasso facilitates a deeper understanding of dynamic gene regulation and its relationship with 3D genome structure.