Quantitating tissue specificity of human genes to facilitate biomarker discovery.

@article{citeulike:1410938, 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 from several human tissues. Our results demonstrate that most human genes ( approximately 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.}, address = {Mayo Clinic Comprehensive Cancer Center and Division of Experimental Pathology, Department of Laboratory Medicine and Pathology, Rochester, MN 55905, USA. vasm@mayo.edu}, author = {Vasmatzis, G. and Klee, E. W. and Kube, D. M. and Therneau, T. M. and Kosari, F. }, citeulike-article-id = {1410938}, issn = {1460-2059}, journal = {Bioinformatics}, month = {June}, number = {11}, pages = {1348--1355}, posted-at = {2007-06-25 16:30:25}, priority = {5}, title = {Quantitating tissue specificity of human genes to facilitate biomarker discovery.}, url = {http://view.ncbi.nlm.nih.gov/pubmed/17384019}, volume = {23}, year = {2007} }

TY - JOUR ID - citeulike:1410938 L3 - citeulike-article-id:1410938 TI - Quantitating tissue specificity of human genes to facilitate biomarker discovery. JF - Bioinformatics VL - 23 IS - 11 SP - 1348 EP - 1355 AD - Mayo Clinic Comprehensive Cancer Center and Division of Experimental Pathology, Department of Laboratory Medicine and Pathology, Rochester, MN 55905, USA. vasm@mayo.edu SN - 1460-2059 N2 - 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 from several human tissues. Our results demonstrate that most human genes ( approximately 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. AU - Vasmatzis, G AU - Klee, EW AU - Kube, DM AU - Therneau, TM AU - Kosari, F PY - 2007/06/01/ UR - http://view.ncbi.nlm.nih.gov/pubmed/17384019 ER -