Various 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 an input. TRAM (Transcriptome Mapper) is a new general software 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, as well as to perform intra-sample and inter-sample data normalization methods, 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 chromosomal segments of defined length are over/under-expressed compared to the desired threshold. When in 'Cluster' mode, the software searches the genome 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 test biological model, based on a meta-analysis comparison between a human CD34+ hematopoietic progenitor cells sample pool and a megakaryocytic cells sample pool, identifying biologically relevant chromosomal segments and gene clusters with differential expression during the differentiation toward megakaryocyte. The large agreement with classical biological knowledge about megakaryocytopoiesis of TRAM results, obtained without any a priori specific assumption, shows that TRAM can perform integrated analysis of expression data from multiple platforms producing high confidence lists of over/under-expressed chromosomal segments and clustered genes. TRAM is the first complete software usable in a personal computer (Macintosh and Windows environments) designed to create, and statistically analyze, quantitative transcriptome maps, based on gene expression data from multiple sources. In conjunction with our previous implementation of a GenBank [1, 2] and UniGene [3] formats full parsing systems, TRAM may also contribute to the building of a novel, relational, multi-purpose, user-friendly and modular platform for the large-scale integrated analysis of genomic and post-genomic data.

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

STRIPPOLI, PIERLUIGI;LENZI, LUCA;FACCHIN, FEDERICA;PELLERI, MARIA CHIARA;VITALE, LORENZA;CASADEI, RAFFAELLA;CANAIDER, SILVIA;FRABETTI, FLAVIA
2010

Abstract

Various 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 an input. TRAM (Transcriptome Mapper) is a new general software 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, as well as to perform intra-sample and inter-sample data normalization methods, 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 chromosomal segments of defined length are over/under-expressed compared to the desired threshold. When in 'Cluster' mode, the software searches the genome 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 test biological model, based on a meta-analysis comparison between a human CD34+ hematopoietic progenitor cells sample pool and a megakaryocytic cells sample pool, identifying biologically relevant chromosomal segments and gene clusters with differential expression during the differentiation toward megakaryocyte. The large agreement with classical biological knowledge about megakaryocytopoiesis of TRAM results, obtained without any a priori specific assumption, shows that TRAM can perform integrated analysis of expression data from multiple platforms producing high confidence lists of over/under-expressed chromosomal segments and clustered genes. TRAM is the first complete software usable in a personal computer (Macintosh and Windows environments) designed to create, and statistically analyze, quantitative transcriptome maps, based on gene expression data from multiple sources. In conjunction with our previous implementation of a GenBank [1, 2] and UniGene [3] formats full parsing systems, TRAM may also contribute to the building of a novel, relational, multi-purpose, user-friendly and modular platform for the large-scale integrated analysis of genomic and post-genomic data.
Atti del XII Congresso della Associazione Italiana di Biologia e Genetica generale e molecolare (A.I.B.G.)
14
14
Strippoli P.; Lenzi L.; Facchin F.; Pelleri M.C.; Vitale L.; Casadei R.; Canaider S.; Frabetti F.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11585/121361
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