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\section{Introduction}
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-One way to achieve a comprehensive estimation of the influence of different layers of control on gene expression is to analyze the changes in abundances of molecular intermediates at different levels. For example, comparing changes between abundances of mRNAs in active translation with respect to the corresponding changes in abundances of total mRNAs (by mean of parallel high-throughput profiling) we can estimate the influence of translational controls on each transcript. The tRanslatome package represents a complete platform for comparing data coming from two parallel high-throughput assays, profiling two different levels of gene expression. The package focusses on the comparison between the translatome and the transcriptome, but it can be used to compare any variation monitored at two “-omics” levels (e.g. the transcriptome and the proteome). The package provides a broad variety of statistical methods covering each step of the standard data analysis workflow: detection and comparison of differentially expressed genes (DEGs), detection and comparison of enriched biological themes through Gene Ontology (GO) annotation. The package provides tools to visually compare/contrast the results. An additional feature lies in the possibility to detect enrichment of targets of translational regulators using the experimental annotation contained in the AURA database \url{<http://aura.science.unitn.it/>}.
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+One way to achieve a comprehensive estimation of the influence of different layers of control on gene expression is to analyze the changes in abundances of molecular intermediates at different levels. For example, comparing changes between abundances of mRNAs in active translation with respect to the corresponding changes in abundances of total mRNAs (by mean of parallel high-throughput profiling) we can estimate the influence of translational controls on each transcript. The tRanslatome package represents a complete platform for comparing data coming from two parallel high-throughput assays, profiling two different levels of gene expression. The package focusses on the comparison between the translatome and the transcriptome, but it can be used to compare any variation monitored at two "-omics" levels (e.g. the transcriptome and the proteome). The package provides a broad variety of statistical methods covering each step of the standard data analysis workflow: detection and comparison of differentially expressed genes (DEGs), detection and comparison of enriched biological themes through Gene Ontology (GO) annotation. The package provides tools to visually compare/contrast the results. An additional feature lies in the possibility to detect enrichment of targets of translational regulators using the experimental annotation contained in the AURA database \url{<http://aura.science.unitn.it/>}.
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\section{tRanslatome in practice}
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The following code illustrates an analysis pipeline with tRanslatome.
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\section{DEGs detection}
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The initial core of the package consists of the class holding input data and results, called \code{TranslatomeDataset}.
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-Objects of this class can be created through the \code{newTranslatomeDataset} function. This function takes as input a normalized data matrix coming from the high throughput experiment with entities (genes, transcripts, exons) in rows and samples (normalized signals coming from microarray, next generation sequencing, mass spectrometry) in columns. Since tRanslatome doesn't provide any normalization, signals contained in the data matrix should be normalized before, unless the DEGs selection method doesn’t provide also a normalization step, as in the case of edgeR and DEseq.
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+Objects of this class can be created through the \code{newTranslatomeDataset} function. This function takes as input a normalized data matrix coming from the high throughput experiment with entities (genes, transcripts, exons) in rows and samples (normalized signals coming from microarray, next generation sequencing, mass spectrometry) in columns. Since tRanslatome doesn't provide any normalization, signals contained in the data matrix should be normalized before, unless the DEGs selection method doesn't provide also a normalization step, as in the case of edgeR and DEseq.
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In our worked example microarray data were previously quantile normalized.
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The function has the following input parameters:
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Dan Tenenbaum authored on 22/12/2013 20:43:34