The Transcriptogramer

A software for genome wide expression analysis.

The Transcriptogramer is a bioinformatics application that produces genome wide gene expression profiles over gene arrangements, where gene localization strongly correlates with the probability that their products participate in the same metabolic pathways. Averages of expression levels over neighboring genes on these arrangements allows a significant signal to noise enhancement in transcriptomic data. This software is licensed under GPL open source license and is completely free for any use. The motivation for this software comes from the following publications:

1. da Silva, S.R.M., Perrone, G.C., Dinis, J.M. and de Almeida, R.M.C., Transcriptograms: Reproducibility enhancement and differential expression of non predefined functional gene sets in human genome. BMC Genomics, 15, 1181 (2014).

2. Kuentzer, F.A., (In Portuguese) Otimização e análise de algoritmos de ordenamento de redes proteicas. Master thesis. 2014.Hardware Design Support Group , Pontifícia Universidade Católica do Rio Grande do Sul, Porto Alegre, RS,Brazil.

3. da Silva, S.R.M., (In Portuguese) A eficiência do transcriptograma. Master thesis. 2013.Instituto de Física, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS,Brazil.

4. Perrone, G.C., (In Portuguese) Transcriptograma em duas dimensões. Master thesis. 2013.Instituto de Física, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS,Brazil.

5. Rybarczyk-Filho, J.L., Castro, M.A.A., Dalmolin, R.J, Moreira, J.C.F., Brunnet, L.G. and de Almeida, R.M.C., Towards a genome-wide transcriptogram: the Saccharomyces cerevisiae case. Nucleic Acids Res., 39, 3005-3016 (2011). PMID:21169199

The paper number four introduces transcriptograms as a method to analyze transcriptomic data, with expression levels projected on a 1D gene list, arranged such that the probability that gene products participate in the same metabolic pathway exponentially decreases with distance between the genes on the list. Averages over expression levels of neighboring genes reduce the relevance of the typical noise in microarray measurements. Transcriptograms are hence genome wide gene expression profiles that provide a global view for the cellular metabolism, facilitating the biological interpretation of microarray data. The paper number one provides a study on different orderings with the effects of the transcriptogram method on signal to noise ratio and reproducibility. The paper also discuss case studies, suggesting possible uses for the transcriptogram and the appropriate statistical analyses.

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