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TIPMaP: a web server to establish transcript isoform profiles from reliable microarray probes.

Neelima Chitturi, Govindkumar Balagannavar, Darshan S Chandrashekar

  • 1Institute of Bioinformatics and Applied Biotechnology (IBAB), Biotech Park, Electronic City, Bengaluru (Bangalore) 560 100, Karnataka state, India. kshitish@ibab.ac.in.

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|December 31, 2013
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Summary
This summary is machine-generated.

This study introduces TIPMaP, a web server for analyzing alternatively spliced transcript expression from existing Affymetrix microarray data. TIPMaP reliably identifies transcript isoforms and their expression patterns, reducing the need for new experiments.

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Area of Science:

  • Bioinformatics
  • Genomics
  • Molecular Biology

Background:

  • Standard 3' Affymetrix gene expression arrays generate vast amounts of data.
  • These arrays were not designed to identify alternatively spliced transcript forms.
  • A gap exists in analyzing specific mRNA form expression from existing microarray data.

Purpose of the Study:

  • To develop a web server for expression profiling of alternatively spliced transcripts using standard 3' Affymetrix microarray data.
  • To validate the tool's performance in identifying transcript expression in a human disease condition.

Main Methods:

  • Downloaded and processed ~4000 gene expression datasets from a public repository.
  • Identified "good probes" and derived "Transcript specific probe-clusters" for each platform.
  • Developed a web server (TIPMaP) accessible at http://resource.ibab.ac.in/TIPMaP.

Main Results:

  • The web server analyzes data from human, mouse, and rat Affymetrix arrays.
  • Experimentally validated for mRNAs in a human male infertility condition (non-obstructive azoospermia).
  • Identifies ~40% of known transcripts as 'transcribed', 'not-detected', or 'differentially regulated' in specific tissues/conditions.

Conclusions:

  • TIPMaP provides a reliable method for identifying differentially expressed alternatively spliced transcript isoforms.
  • The tool enhances the utility of existing microarray data, potentially reducing the need for new tissue sample experiments.