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

Shobhit Gupta1, Martin Vingron, Stefan A Haas

  • 1Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Ihnestrasse 63-73, D-14195 Berlin, Germany. gupta@molgen.mpg.de

Nucleic Acids Research
|June 28, 2005
PubMed
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T-STAG is a web resource for analyzing tissue-specific gene and transcript expression in humans and mice. It aids in identifying differentially expressed genes in tumors and finding orthologs with similar expression patterns.

Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • Analyzing tissue-specific gene expression is crucial for understanding biological functions and disease mechanisms.
  • Existing resources often lack comprehensive integration of gene and isoform-level expression data with cross-species orthology.
  • Accurate identification of tissue-specific transcripts and genes can reveal novel biomarkers and therapeutic targets.

Purpose of the Study:

  • To introduce T-STAG, a novel resource and web-interface for analyzing tissue- and tumor-specific expression patterns.
  • To integrate refined predictions of gene and isoform expression with human-mouse orthology data.
  • To provide tools for comparative analysis of gene expression across different tissues and identification of low-abundant transcripts.

Main Methods:

Related Experiment Videos

  • Development of T-STAG, a web-interface integrating transcriptomic data.
  • Refined prediction algorithms for tissue-specific gene and isoform expression.
  • Incorporation of human-mouse orthology data for cross-species comparisons.
  • Categorization of expressed sequence tags (ESTs) based on cDNA library normalization.

Main Results:

  • T-STAG provides a platform for analyzing tissue/tumor-specific expression patterns in human and mouse transcriptomes.
  • The resource facilitates the detection of differentially expressed genes in tumors and retrieval of tissue-specific orthologs.
  • T-STAG enables searching for low-abundant transcripts through refined EST categorization.
  • Integrated visualization tools (GeneNest and SpliceNest) enhance data interpretation.

Conclusions:

  • T-STAG offers a user-friendly interface for comprehensive analysis of tissue-specific expression data.
  • The resource supports critical biological applications in cancer research and comparative genomics.
  • T-STAG advances the study of transcriptomes by integrating diverse data types and analytical features.