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Size distribution of function-based human gene sets and the split-merge model.

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Gene family sizes, typically power-law distributed, deviate in human gene sets. A beta rank function better fits these distributions, explained by gene set splitting and merging operations.

Keywords:
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Area of Science:

  • Genomics
  • Bioinformatics
  • Systems Biology

Background:

  • Gene family sizes, arising from ancestral duplications, often follow a power-law distribution.
  • Gene sets group genes by function or shared properties, and their size distributions are crucial for understanding biological organization.

Purpose of the Study:

  • To investigate the size distribution of gene sets within the Human Gene Nomenclature Committee (HGNC) database.
  • To identify deviations from the power-law distribution and explore alternative models, such as the beta rank function.
  • To propose and simulate a mechanism that explains these deviations through gene set splitting and merging.

Main Methods:

  • Analysis of HGNC gene set sizes to identify deviations from power-law distributions.
  • Fitting the observed distributions using a beta rank function.
  • Simulation of gene set splitting and merging operations to model changes in distribution.

Main Results:

  • HGNC gene set sizes significantly deviate from a power-law distribution.
  • A beta rank function provides a superior fit to the observed gene set size distributions.
  • Simulations demonstrate that splitting and merging operations can transform a power-law distribution into one that fits the beta rank function.

Conclusions:

  • The distribution of human gene set sizes is better described by a beta rank function than a power-law.
  • Gene set curation processes involving splitting and merging can explain this deviation.
  • The beta rank function offers a more accurate model for gene set sizes, applicable to areas like transcription factor and drug target gene distribution.