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Motivation: Statistical methods usually used to perform Serial Analysis of Gene Expression (SAGE) analysis are based on hypothesis testing. They answer the biologist's question: 'what are the genes with differential expression greater than r with P-value smaller than P?'. Another useful and not yet explored question is: 'what is the uncertainty in differential expression ratio of a gene?'., Results: We have used Bayesian model for SAGE differential gene expression ratios as a more informative alternative to hypothesis tests since it provides credibility intervals., Availability: The model is implemented in R statistical language script and is available under GNU/GLP copyleft at supplemental web site., Supplementary Information: http://www.ime.usp.br/~rvencio/SAGEci/

Whereas genome sequencing defines the genetic potential of an organism, transcript sequencing defines the utilization of this potential and links the genome with most areas of biology. To exploit the information within the human genome in the fight against cancer, we have deposited some two million expressed sequence tags (ESTs) from human tumors and their corresponding normal tissues in the public databases. The data currently define approximately 23,500 genes, of which only approximately 1,250 are still represented only by ESTs. Examination of the EST coverage of known cancer-related (CR) genes reveals that <1% do not have corresponding ESTs, indicating that the representation of genes associated with commonly studied tumors is high. The careful recording of the origin of all ESTs we have produced has enabled detailed definition of where the genes they represent are expressed in the human body. More than 100,000 ESTs are available for seven tissues, indicating a surprising variability of gene usage that has led to the discovery of a significant number of genes with restricted expression, and that may thus be therapeutically useful. The ESTs also reveal novel nonsynonymous germline variants (although the one-pass nature of the data necessitates careful validation) and many alternatively spliced transcripts. Although widely exploited by the scientific community, vindicating our totally open source policy, the EST data generated still provide extensive information that remains to be systematically explored, and that may further facilitate progress toward both the understanding and treatment of human cancers.

A significant fraction of the variability found in the human transcriptome is due to alternative splicing, including alternative exon usage (AEU), intron retention and use of cryptic splice sites. We present a comparison of a large-scale analysis of AEU in the human transcriptome through genome mapping of Open Reading Frame ESTs (ORESTES) and conventional ESTs. It is shown here that ORESTES probe low abundant messages more efficiently. In addition, most of the variants detected by ORESTES affect the structure of the corresponding proteins.