ORESTES are enriched in rare exon usage variants affecting the encoded proteins.

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.

Unlocking the Power of Big Data at the National Institutes of Health.

The era of "big data" presents immense opportunities for scientific discovery and technological progress, with the potential to have enormous impact on research and development in the public sector. In order to capitalize on these benefits, there are significant challenges to overcome in data analytics. The National Institute of Allergy and Infectious Diseases held a symposium entitled "Data Science: Unlocking the Power of Big Data" to create a forum for big data experts to present and share some of the creative and innovative methods to gleaning valuable knowledge from an overwhelming flood of biological data. A significant investment in infrastructure and tool development, along with more and better-trained data scientists, may facilitate methods for assimilation of data and machine learning, to overcome obstacles such as data security, data cleaning, and data integration.

In silico comparison of the transcriptome derived from purified normal breast cells and breast tumor cell lines reveals candidate upregulated genes in breast tumor cells.

Genes that are differentially expressed in tumor tissues are potential diagnostic markers and drug targets. The DNA sequence information available in the public databases can be used to identify transcripts differentially expressed in cancer. We report here the combined use of the ORESTES sequences generated in the FAPESP/LICR Human Cancer Genome Project and information available in the UniGene and SAGE databases to characterize the transcriptome of normal and breast tumor cells. We have identified 154 genes as candidates for overexpression in breast tumor cells. Among these, 28 genes have been shown by others to be overexpressed in breast or other tumors. Using RT-PCR, we tested 11 candidate genes and found that 9 were indeed overexpressed in breast tumor cells.

The generation and utilization of a cancer-oriented representation of the human transcriptome by using expressed sequence tags.

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.