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Elucidating tissue specific genes using the Benford distribution.

Deepak Karthik1, Gil Stelzer1, Sivan Gershanov1

  • 1Department of Molecular Biology, Ariel University, Ariel, 40700, Israel.

BMC Genomics
|August 11, 2016
PubMed
Summary
This summary is machine-generated.

Benford

Keywords:
Benford lawGene expressionRNA-seq

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Area of Science:

  • Transcriptomics
  • Bioinformatics
  • Genomics

Background:

  • RNA-sequencing (RNA-seq) provides vast gene expression data.
  • Benford's Law, a mathematical principle, can now be applied to this data.
  • The law describes non-uniform distribution of first digits in count-rich datasets.

Purpose of the Study:

  • To investigate the applicability of Benford's Law to gene expression data.
  • To explore potential biological insights derivable from Benford's Law analysis.
  • To assess the predictability of tissue specificity using Benford's Law.

Main Methods:

  • Analysis of digital gene expression datasets using RNA-seq.
  • Calculation of adherence to Benford's Law for gene sets and individual genes.
  • Supervised learning approach to predict tissue specificity based on Benford behavior.

Main Results:

  • Gene expression datasets consistently exhibit Benford-like distributions.
  • Adherence to Benford's Law correlates with gene expression levels and tissue specificity.
  • Genes least adhering to Benford's Law are linked to cell maintenance, while best adhering genes are tissue-specific.

Conclusions:

  • Benford's Law is applicable to gene expression count data.
  • This mathematical principle offers potential for gleaning biological insights.
  • Benford's Law behavior can predict tissue specificity in gene expression.