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Bioinformatics Advance Access first published online on March 23, 2007
This version published online on March 26, 2007
Bioinformatics, doi:10.1093/bioinformatics/btm102
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© The Author (2007). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Quantitating Tissue Specificity of Human Genes to Facilitate Biomarker Discovery

George Vasmatzis 1,*, Eric Klee 1, Dagmar Marie Kube 1, Terry Therneau 2 and Farhad Kosari 1

1Mayo Clinic Comprehensive Cancer Center and Division of Experimental Pathology, Department of Laboratory Medicine and Pathology, 200 First St SW, Rochester, MN 55905; Tel. 507-266-4617; Fax 507-266-5193.
2Division of Biostatistics, Health Sciences Research, Mayo Clinic

*To whom correspondence should be addressed. George Vasmatzis, E-mail: vasm{at}mayo.edu


  Abstract

We describe a method to identify candidate cancer biomarkers by analyzing numeric approximations of tissue specificity of human genes. These approximations were calculated by analyzing predicted tissue expression distributions of genes derived from mapping Expressed Sequence Tags (ESTs) to the human genome sequence using a binary indexing algorithm. Tissue specificity values facilitated high throughput analysis of the human genes and enabled the identification of genes highly specific to different tissues. Tissue expression distributions for several genes were compared to estimates obtained from other public gene expression datasets and experimentally validated using quantitative RT-PCR on RNA isolated form several human tissues. Our results demonstrate that most human genes (~98%) are expressed in many tissues (low specificity) and only a small number of genes possess very specific tissue expression profiles. These genes comprise a rich dataset from which novel therapeutic targets and novel diagnostic serum biomarkers may be selected

Associate Editor: Prof. Alfonso Valencia

Received on July 11, 2006; revised on January 16, 2007; accepted on March 10, 2007

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