Shobhit Gupta | PubMed Health

PubMed2005

T-STAG: resource and web-interface for tissue-specific transcripts and genes.

T-STAG (tissue-specific transcripts and genes) is a resource and web-interface, designated to analyze tissue/tumor-specific expression patterns in human and mouse transcriptomes. It integrates our refined prediction of specific expression patterns both in genes as well as in individual isoforms with man-mouse orthology data. In combination with the features for combining/contrasting the genes expressed in different tissues, T-STAG implicates important biological applications, such as the detection of differentially expressed genes in tumors, the retrieval of orthologs with significant expression in the same tissue etc. Additionally, our refined categorization of expressed sequence tags (ESTs) according to the normalization of cDNA libraries allows searching for putative low-abundant transcripts. The results are tightly linked to our visualization tools, GeneNest (expression patterns of genes) and SpliceNest (gene structure and alternative splicing). The user-friendly interface of T-STAG offers a platform for comprehensive analysis of tissue and/or tumor-specific expression patterns revealed by the EST data. T-STAG is freely accessible at http://tstag.molgen.mpg.de.

Animals↗

PubMed2004

Strengths and weaknesses of EST-based prediction of tissue-specific alternative splicing.

BACKGROUND: Alternative splicing contributes significantly to the complexity of the human transcriptome and proteome. Computational prediction of alternative splice isoforms are usually based on EST sequences that also allow to approximate the expression pattern of the related transcripts. However, the limited number of tissues represented in the EST data as well as the different cDNA construction protocols may influence the predictive capacity of ESTs to unravel tissue-specifically expressed transcripts. METHODS: We predict tissue and tumor specific splice isoforms based on the genomic mapping (SpliceNest) of the EST consensus sequences and library annotation provided in the GeneNest database. We further ascertain the potentially rare tissue specific transcripts as the ones represented only by ESTs derived from normalized libraries. A subset of the predicted tissue and tumor specific isoforms are then validated via RT-PCR experiments over a spectrum of 40 tissue types. RESULTS: Our strategy revealed 427 genes with at least one tissue specific transcript as well as 1120 genes showing tumor specific isoforms. While our experimental evaluation of computationally predicted tissue-specific isoforms revealed a high success rate in confirming the expression of these isoforms in the respective tissue, the strategy frequently failed to detect the expected restricted expression pattern. The analysis of putative lowly expressed transcripts using normalized cDNA libraries suggests that our ability to detect tissue-specific isoforms strongly depends on the expression level of the respective transcript as well as on the sensitivity of the experimental methods. Especially splice isoforms predicted to be disease-specific tend to represent transcripts that are expressed in a set of healthy tissues rather than novel isoforms. CONCLUSIONS: We propose to combine the computational prediction of alternative splice isoforms with experimental validation for efficient delineation of an accurate set of tissue-specific transcripts.

Alternative Splicing↗