Polysome or ribosome profiling allows the user to distinguish between three regulatory modes of gene expression: changes mRNA abundance or translational efficiency leading to altered protein levels or buffering (see references). Following a complete analysis, this function categorizes each identifier into one of these regulatory patterns.
An Anota2seqDataSet containing the output of the anota2seqAnalyze and anota2seqSelSigGenes function for all analyzes.
A logical vector with length 2 that specifies how mRNA abundance genes will be identified. The first position of the vector indicates whether translated mRNA output (e.g. polysome-associated mRNA or RPFs) should be taken into consideration when selecting mRNA abundance genes. The second position specifies whether the total mRNA output should be taken into consideration when selecting mRNA abundance genes. (See details) Default is c(TRUE,TRUE), i.e. abundance identifiers need to pass thresholds for both changes in translated mRNA and total mRNA.
The regulatory modes are given a priority order. First, changes in translational efficiency leading to altered protein levels is considered such that if an identifier passed filtering for this analysis it will be allocated to the translation group. Identifiers that did not pass those thresholds are then considered for belonging to the mRNA abundance group. For this step, the user can decide to base inclusion into the abundance group based on results from analysis of translated mRNA (e.g. polysome-associated mRNA or RPF), total mRNA or both (see below). Finally, the remaining identifiers can be assessed for belonging to the translational buffering group.
An mRNA abundance identifier can be defined based on analysis of translated mRNA (e.g. polysome associated mRNA or or RPF) and total mRNA (and also assuring that fold changes follow the same direction). To perform such analysis, mRNASelect should be set to c(TRUE, TRUE). Alternatively one can identify mRNA abundance identifiers based on analysis of only translated mRNA (e.g. polysome-associated mRNA or RPFs; mRNASelect = c(TRUE, FALSE)) or only total mRNA (mRNASelect = c(FALSE, TRUE)).
An Anota2seqDataSet. anota2seqRegModes adds a column specifying
the regulatory modes of each significant identifier to the dataframe in
the 'selectedTranslatedmRNA', 'selectedTotalmRNA',
'selectedTranslation' and 'selectedBuffering' slots of the
Anota2seqDataSet. It also saves the output of anota2seqSelSigGenes for
identifier regulated by mRNA abundance in the mRNAAbundance slot of the
anota2seqGetOutput for a detailed
description of these outputs.
Oertlin C. et al. Genome-wide analysis of differential translation and differential translational buffering using anota2seq, bioRxiv, 2017.
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# Initialize the Anota2seqDataSet data(anota2seq_data) Anota2seqDataSet <- anota2seqDataSetFromMatrix( dataP = anota2seq_data_P[1:100,], dataT = anota2seq_data_T[1:100,], phenoVec = anota2seq_pheno_vec, dataType = "RNAseq", normalize = TRUE) # Perform analysis of changes in translational efficiency leading to altered # protein levels or buffering; and differential expression of translated mRNA # and total mRNA Anota2seqDataSet <- anota2seqAnalyze(Anota2seqDataSet, analysis = c("translated mRNA","total mRNA", "translation","buffering")) # Select as significant genes of all analyzes, genes with a FDR < 0.15 Anota2seqDataSet <- anota2seqSelSigGenes(Anota2seqDataSet, maxPAdj = .15, analysis = c("translated mRNA", "total mRNA", "translation", "buffering"), selContrast = 1) # Determine which identifiers belong to which regulatory modes. Anota2seqDataSet <- anota2seqRegModes(Anota2seqDataSet)
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