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High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions
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TRAM (Transcriptome Mapper): database-driven creation and analysis of transcriptome maps from multiple sources.

Luca Lenzi1, Federica Facchin, Francesco Piva

  • 1Center for Research in Molecular Genetics Fondazione CARISBO, Department of Histology, Embryology and Applied Biology, University of Bologna - Via Belmeloro, 8 - 40126 - Bologna, Italy.

BMC Genomics
|February 22, 2011
PubMed
Summary

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This summary is machine-generated.

TRAM (Transcriptome Mapper) is a versatile tool for analyzing gene expression data from various sources. It generates quantitative transcriptome maps to identify differentially expressed genes and chromosomal segments, aiding biological discovery.

Area of Science:

  • Bioinformatics
  • Genomics
  • Transcriptomics

Background:

  • Existing gene expression analysis tools are often species-specific or limited to particular data formats.
  • Most tools require gene lists as input, restricting comprehensive analysis.
  • There is a need for a general-purpose tool for global gene expression profile analysis.

Purpose of the Study:

  • To introduce TRAM (Transcriptome Mapper), a novel, general-purpose tool for creating and analyzing quantitative transcriptome maps.
  • To enable analysis of gene expression data from diverse sources and formats.
  • To facilitate the identification of differentially expressed chromosomal segments and gene clusters.

Main Methods:

  • TRAM utilizes a relational database to store and analyze gene expression data.

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  • It incorporates a parser for univocal gene symbol assignment and data normalization (including scaled quantile normalization).
  • The software operates in 'Map' mode for segment analysis and 'Cluster' mode for gene cluster identification, with statistical significance calculated against chromosomal or genomic backgrounds.

    • Main Results:

    • TRAM successfully generated quantitative transcriptome maps and identified over/under-expressed chromosomal segments and gene clusters.
    • A biological model test comparing human hematopoietic progenitor cells and megakaryocytic cells identified relevant differentially expressed regions.
    • The tool demonstrated its capability to handle diverse data sources and perform inter-sample normalization.

    Conclusions:

    • TRAM provides a robust platform for statistically analyzing quantitative transcriptome maps from multiple data sources.
    • The tool is freely available with preconfigured implementations for human, mouse, and zebrafish.
    • TRAM facilitates the discovery of biologically relevant gene expression patterns and chromosomal regions.