@@ -1,33 +1,17 @@

-Package: tRanslatome

-Type: Package

-Title: Comparison between multiple levels of gene expression.

-Version: 1.5.0

-Date: 2013-11-28

-Author: Toma Tebaldi, Erik Dassi, Galena Kostoska

-Maintainer: Toma Tebaldi <tebaldi@science.unitn.it>, Erik Dassi

-        <erik.dassi@unitn.it>

-Depends: R (>= 2.15.0), methods, limma, sigPathway, samr, anota, DESeq,

-        edgeR, RankProd, topGO, org.Hs.eg.db, GOSemSim, Heatplus,

-        gplots, plotrix

-Description: Detection of differentially expressed genes (DEGs) from

-        the comparison of two biological conditions (treated vs.

-        untreated, diseased vs. normal, mutant vs. wild-type) among

-        different levels of gene expression (transcriptome

-        ,translatome, proteome), using several statistical methods:

-        Rank Product, Translational Efficiency, t-test, SAM, Limma,

-        ANOTA, DESeq, edgeR. Possibility to plot the results with

-        scatterplots, histograms, MA plots, standard deviation (SD)

-        plots, coefficient of variation (CV) plots. Detection of

-        significantly enriched post-transcriptional regulatory factors

-        (RBPs, miRNAs, etc) and Gene Ontology terms in the lists of

-        DEGs previously identified for the two expression levels.

-        Comparison of GO terms enriched only in one of the levels or in

-        both. Calculation of the semantic similarity score between the

-        lists of enriched GO terms coming from the two expression

-        levels. Visual examination and comparison of the enriched terms

-        with heatmaps, radar plots and barplots.

-License: LGPL

-LazyLoad: yes

-biocViews: CellBiology, GeneRegulation, GeneExpression,

-        DifferentialExpression, Microarray, Sequencing, QualityControl,

-        GO, MultipleComparison

+Package: tRanslatome

+Type: Package

+Title: Comparison between multiple levels of gene expression.

+Version: 1.1.2

+Date: 2015-01-28

+Author: Toma Tebaldi, Erik Dassi, Galena Kostoska

+Maintainer: Toma Tebaldi <tebaldi@science.unitn.it>, Erik Dassi <erik.dassi@unitn.it>

+Depends: R (>= 2.15.0), methods, limma, sigPathway, samr, anota, DESeq,

+        edgeR, RankProd, topGO, org.Hs.eg.db, GOSemSim, Heatplus,

+        gplots, plotrix

+Description: Detection of differentially expressed genes (DEGs) from the comparison of two biological conditions (treated vs. untreated, diseased vs. normal, mutant vs. wild-type) among different levels of gene expression (transcriptome ,translatome, proteome), using several statistical methods:  Rank Product, Translational Efficiency, t-test, SAM, Limma, ANOTA, DESeq, edgeR. Possibility to plot the results with scatterplots, histograms, MA plots, standard deviation (SD) plots, coefficient of variation (CV) plots. Detection of significantly enriched post-transcriptional regulatory factors (RBPs, miRNAs, etc) and Gene Ontology terms in the lists of DEGs previously identified for the two expression levels. Comparison of GO terms enriched only in one of the levels or in both. Calculation of the semantic similarity score between the lists of enriched GO terms coming from the two expression levels. Visual examination and comparison of the enriched terms with heatmaps, radar plots and barplots. 

+License: MIT

+LazyLoad: yes

+biocViews: CellBiology, GeneRegulation, Regulation, GeneExpression,

+        DifferentialExpression, Microarray, HighThroughputSequencing,

+        QualityControl, GO, MultipleComparisons, Bioinformatics

+Packaged: 2015-01-29 13:48:55 UTC; toma

@@ -156,7 +156,7 @@ setMethod("computeDEGs", "TranslatomeDataset",

 						function(x) mean(x[which(cond.2.vector == 1)],na.rm=TRUE) - 

 												mean(x[which(cond.2.vector == 0)],na.rm=TRUE))

 		if (object@data.type  ==  "ngs") 

-      FC2 <- apply(cond.2, 1,

+      FC <- apply(cond.2, 1,

 						function(x) log(mean(x[which(cond.2.vector == 1)],na.rm=TRUE) / 

 														mean(x[which(cond.2.vector == 0)],na.rm=TRUE),base=2))

     avg.trt2 <- apply(cond.2, 1,

@@ -384,7 +384,7 @@ methodLimma <- function(cond, cond.2, cond.vector, cond.2.vector) {

 	cont.secondlevel <- makeContrasts("cond.d - cond.c", levels = design)

 	fitsecondlevel <- contrasts.fit(fit, cont.secondlevel)

 	fitsecondlevel <- eBayes(fitsecondlevel)

-	BH.secondlevel <- p.adjust(fitfirstlevel$F.p.value, method = "BH") 

+	BH.secondlevel <- p.adjust(fitsecondlevel$F.p.value, method = "BH") 

 	# build the significance p-values matrix and return it	

 	return(cbind(fitfirstlevel$F.p.value, BH.firstlevel, 

@@ -41,7 +41,7 @@

 \section{Introduction}

-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/>}.

+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/>}.

 \section{tRanslatome in practice}

 The following code illustrates an analysis pipeline with tRanslatome.

@@ -77,7 +77,7 @@ All the steps contained in the code will be explained in more detail in the foll

 \section{DEGs detection}

 The initial core of the package consists of the class holding input data and results, called \code{TranslatomeDataset}.

-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. 

+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. 

 In our worked example microarray data were previously quantile normalized.

 The function has the following input parameters: