Human microRNAs co-silence in well-separated groups and have different predicted essentialities

Gábor Boross, Katalin Orosz, Illés J. Farkas

Research output: Contribution to journalArticle

30 Citations (Scopus)

Abstract

Background: Short regulating RNAs guide many cellular processes. Compared with transcription factor proteins they appear to provide more specialized control and their deletions are less frequently lethal. Results: We find large differences between computationally predicted lists of human microRNA (miRNA)-target pairs. Instead of integrating these lists we use the two most accurate of them. Next, we construct the co-regulation network of human miRNAs as nodes by computing the correlation (link weight) between the gene silencing scores of individual miRNAs. In this network, we locate groups of tightly co-regulating nodes (modules). Despite explicitly allowing overlaps the co-regulation modules of miRNAs are well separated. We use the modules and miRNA co-expression data to define and compute miRNA essentiality. Instead of focusing on particular biological functions we identify a miRNA as essential, if it has a low co-expression with the miRNAs in its module. This may be thought of as having many workers performing the same tasks together in one place (non-essential miRNAs) as opposed to a single worker performing those tasks alone (essential miRNA). Conclusions: On the system level, we quantitatively confirm previous findings about the specialized control provided by miRNAs. For knock-out tests we list the groups of our predicted most and least essential miRNAs. In addition, we provide possible explanations for (i) the low number of individually essential miRNAs in Caenorhabdtits elegans and (ii) the high number of ubiquitous miRNAs influencing cell and tissue-specific miRNA expression patterns in mouse and human.

Original languageEnglish
Pages (from-to)1063-1069
Number of pages7
JournalBioinformatics
Volume25
Issue number8
DOIs
Publication statusPublished - Apr 20 2009

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ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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