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

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ID: 296174
2004
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Abstract
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.To obtain such novel information genome-wide, we have determined the mRNA expression levels for one of the largest hitherto analyzed set of 62 839 probesets in 12 representative normal human tissues. Indeed, when using a newly defined graded tissue specificity index tau, valued between 0 for housekeeping genes and 1 for tissue-specific genes, genes with midrange profiles having 0.15< tau<0.85 were found to constitute >50% of all expression patterns. We developed a binary classification, indicating for every gene the I(B) tissues in which it is overly expressed, and the 12-I(B) tissues in which it shows low expression. The 85 dominant midrange patterns with I(B)=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.All data and analyses are publically available at the GeneNote website, http://genecards.weizmann.ac.il/genenote/ and, GEO accession GSE803.doron.lancet@weizmann.ac.ilFour tables available at the above site.
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openalex_W2130925862 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors 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
Journal BMC Bioinformatics
Year 2004
DOI
10.1093/bioinformatics/bti042
URL
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