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Open Access Highly Accessed Research article

TRAM (Transcriptome Mapper): database-driven creation and analysis of transcriptome maps from multiple sources

Luca Lenzi1, Federica Facchin1, Francesco Piva2, Matteo Giulietti2, Maria Chiara Pelleri1, Flavia Frabetti1, Lorenza Vitale1, Raffaella Casadei1, Silvia Canaider1, Stefania Bortoluzzi3, Alessandro Coppe3, Gian Antonio Danieli3, Giovanni Principato2, Sergio Ferrari4 and Pierluigi Strippoli1*

Author affiliations

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

2 Institute of Biology and Genetics, Marche Polytechnic University - Via Brecce Bianche, Monte D'Ago - 60131 - Ancona, Italy

3 Department of Biology, University of Padova - Via G. Colombo, 3 - 35131 - Padova, Italy

4 Department of Biomedical Sciences, University of Modena and Reggio Emilia - Via G. Campi, 287 - 41100 - Modena, Italy

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Citation and License

BMC Genomics 2011, 12:121  doi:10.1186/1471-2164-12-121

Published: 18 February 2011

Abstract

Background

Several tools have been developed to perform global gene expression profile data analysis, to search for specific chromosomal regions whose features meet defined criteria as well as to study neighbouring gene expression. However, most of these tools are tailored for a specific use in a particular context (e.g. they are species-specific, or limited to a particular data format) and they typically accept only gene lists as input.

Results

TRAM (Transcriptome Mapper) is a new general tool that allows the simple generation and analysis of quantitative transcriptome maps, starting from any source listing gene expression values for a given gene set (e.g. expression microarrays), implemented as a relational database. It includes a parser able to assign univocal and updated gene symbols to gene identifiers from different data sources. Moreover, TRAM is able to perform intra-sample and inter-sample data normalization, including an original variant of quantile normalization (scaled quantile), useful to normalize data from platforms with highly different numbers of investigated genes. When in 'Map' mode, the software generates a quantitative representation of the transcriptome of a sample (or of a pool of samples) and identifies if segments of defined lengths are over/under-expressed compared to the desired threshold. When in 'Cluster' mode, the software searches for a set of over/under-expressed consecutive genes. Statistical significance for all results is calculated with respect to genes localized on the same chromosome or to all genome genes. Transcriptome maps, showing differential expression between two sample groups, relative to two different biological conditions, may be easily generated. We present the results of a biological model test, based on a meta-analysis comparison between a sample pool of human CD34+ hematopoietic progenitor cells and a sample pool of megakaryocytic cells. Biologically relevant chromosomal segments and gene clusters with differential expression during the differentiation toward megakaryocyte were identified.

Conclusions

TRAM is designed to create, and statistically analyze, quantitative transcriptome maps, based on gene expression data from multiple sources. The release includes FileMaker Pro database management runtime application and it is freely available at http://apollo11.isto.unibo.it/software/ webcite, along with preconfigured implementations for mapping of human, mouse and zebrafish transcriptomes.