Abstract
BackgroundSeveral 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.ResultsTRAM (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.ConclusionsTRAM 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/, along with preconfigured implementations for mapping of human, mouse and zebrafish transcriptomes.
Highlights
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
The transcriptome maps studies mentioned above showed the biological relevance of a global view of gene expression distribution by exploiting the availability of gene expression profile data obtained by the method of serial analysis of gene expression (SAGE) [2,3,5]
The lack of software dedicated to constructing and analyzing transcriptome maps was already pointed out in 2006 [10], emphasizing that up until only algorithms or scripts had been presented and these were often tailored to specific uses
Summary
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. No computational biology tool for the generation and analysis of transcriptome maps was released to perform the algorithms described in these papers, with the exception of the web-based application “Transcriptome Map” [2,8] This only supports a limited number of input data types (derived from a few species, and, for human, only derived from SAGE experiments or from three Affymetrix microchip platforms), normalization methods and visualization options. The tools that are available typically accept only gene lists as input and are not able to represent and analyze the continuous change, along the chromosome, of expression intensity assigned to overlapping regions of desired size, on the basis of the mean expression value calculated across all genes located in that region This representation better reflects the biological reality of the quantitative changes of regional gene activity, rather than a simple count of the enrichment in differentially expressed genes, which is a desirable additional parameter of analysis
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