TranslaSeq: Translational control assessment from ribosome footprint and...

In franciscodavid/TranslaSeq: Translational control assessment from ribosome footprint and total RNA libraries

Description

This function compares gene counts from ribosome footprint libraries with gene counts from total RNA libraries for the same samples across different experimental conditions to assess translational efficiency changes for individual transcripts and their statistical significance.

Usage

TranslaSeq(metadata, refname, fafile, gtffile, ctrlabel, condition,
           outdir = 'TranslaSeq.out', preprocess = F, threads = 1,
           Radapt = 'CTGTAGGCACCATCAAT', platform = 'Illumina', verbose = F)

Arguments

metadata	A dataframe with samples metadata (see Details section).
refname	Name given to the genome annotation used in the analysis.
fafile	Filepath or URL address of the genome sequence FASTA file.
gtffile	Filepath or URL address of the genome annotation GTF file.
ctrlabel	Text label for the control condition in metadata dataframe.
condition	Column name containing the condition variable in metadata.
outdir	Path in which output results will be stored.
preprocess	Boolean indicating whether FASTQ input should be preprocessed to remove adaptors.
threads	Number of threads to be used in processing the input data.
Radapt	Adaptor sequence to be removed from the input FASTQ files.
platform	Name of the platform which generated input data (default: Illumina).
verbose	A boolean used for debugging the entire pipeline.

Details

In a metadata dataframe each row represents an input file. This data structure has the following mandatory columns:

1. name: the sample name.

2. file: the path to the input file.

3. type: the library type, either 'rna' or 'rpf'.

4. comment: string describing the input file.

All other wanted data fields must be inserted between type and comment columns. To sum up, the first column in metadata dataframe must be 'name'; the second one 'file'; the third one 'type'; then any number of arbitrary columns with other data fields and the last column must be 'comment'.

Valid file names should contain these suffixes:

• .fastq[.gz] files providing [compressed] FASTQ data.

• .bam|.sam files providing alignment data.

• .tsv|.count files providing count values.

All files from a metadata dataframe must be of the same type.

From the arbitrary columns, at least one should be named as the 'condition' argument. In its field values, at least one sample must has the label 'ctrlabel', which will be the control condition. All other labels different from 'ctrlabel' will be treated as case conditions to be compared against the control one.

Value

A dataframe specifying mean ribosome footprint counts, total RNA counts and translation efficiency ratio for the control samples along with log2 fold changes for the case samples and their translation efficiency p-value and adjusted p-value.

Author(s)

Francisco D. Morón-Duran

See Also

A working example can be found at https://franciscodavid.github.io/TranslaSeq/vignette.html

franciscodavid/TranslaSeq documentation built on May 16, 2019, 7:11 p.m.