Genome-wide midrange transcription profiles reveal expression level relationships in human tissue specification

Itai Yanai, Hila Benjamin, Michael Shmoish, Vered Chalifa-Caspi, Maxim Shklar, Ron Ophir, Arren Bar-Even, Shirley Horn-Saban, Marilyn Safran, Eytan Domany, Doron Lancet, Orit Shmueli

Research output: Contribution to journalArticlepeer-review

737 Scopus citations

Abstract

Motivation: Genes are often characterized dichotomously as either housekeeping or single-tissue specific. We conjectured that crucial functional information resides in genes with midrange profiles of expression. Results: To obtain such novel information genome-wide, we have determined the mRNA expression levels for one of the largest hitherto analyzed set of 62839 probesets in 12 representative normal human tissues. Indeed, when using a newly defined graded tissue specificity index τ, valued between 0 for housekeeping genes and 1 for tissue-specific genes, genes with midrange profiles having 0.15 < τ < 0.85 were found to constitute >50% of all expression patterns. We developed a binary classification, indicating for every gene the lB tissues in which it is overly expressed, and the 12 - lB tissues in which it shows low expression. The 85 dominant midrange patterns with lB = 2-11 were found to be bimodally distributed, and to contribute most significantly to the definition of tissue specification dendrograms. Our analyses provide a novel route to infer expression profiles for presumed ancestral nodes in the tissue dendrogram. Such definition has uncovered an unsuspected correlation, whereby de novo enhancement and diminution of gene expression go hand in hand. These findings highlight the importance of gene suppression events, with implications to the course of tissue specification in ontogeny and phylogeny.

Original languageEnglish
Pages (from-to)650-659
Number of pages10
JournalBioinformatics
Volume21
Issue number5
DOIs
StatePublished - 1 Mar 2005
Externally publishedYes

ASJC Scopus subject areas

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

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