Calculate various properties of an input transcriptome (GTF/knownGenes)
This program calculates various biological paramters regarding a set of transcript regions. Some properties are universal (length, GC content, etc) and some are specific to certain transcript regions (e.g. rare codons are only defined for a coding sequence).
Two inputs are mandatory:
-i: A .bed file describing transcript regions over which to calculate properties
-g: A FASTA file of the genome from which the transcriptome is derived.
Further inputs determine the properties to compute of the input transcriptome. This program should work with any BED file but is made to operate on region-specific bed files generated by extract-transcript-regions.
The -o flag specifies the output basename, and a series of CSV files will be generated with this basename for each property supplied on the command line.
./transcriptome_properties.py -i grch38_exons.bed -g genome.fa -o grch38_tx --nt 4\
--exonct --length --gc
==> grch38_tx_exonct.csv <==
transcriptID,exonct
ENST00000618880_exon,4
==> grch38_tx_gc_content.csv <==
transcriptID,gc_content
ENST00000618880_exon,0.64
==> grch38_tx_length.csv <==
transcriptID,length
ENST00000618880_exon,582
The non-python dependencies are variable depending on the properties one wishes to compute.
ViennaRNA - https://www.tbi.univie.ac.at/RNA/index.html
Transcriptome region BED files - generated from https://github.com/stephenfloor/extract-transcript-regions
Whole genome fasta file - downloaded from UCSC/Ensembl or similar
TargetScan miRNA scores - downloaded from http://www.targetscan.org/cgi-bin/targetscan/data_download.cgi?db=vert_70
- download the Summary_Counts file
- can extract human-only with: awk '$4 == 9606 {print $0}' Summary_Counts.txt > human_mir_targets.txt
Ensembl/UCSC-to-refseq mapping - required for calculation of TargetScan scores per transcript region
Multithreading is currently only used during the calculation of RNA folding energies, which is by far the most time consuming step. Using RNALfold is considerably faster than the sliding window RNAfold procedure implemented in this program, but RNALfold only returns the minimum free energy and as such it may be desirable to use RNAfold depending on the application.
./transcriptome_properties.py -h
usage: transcriptome_properties.py [-h] -i INPUT -g GENOME [--gc] [--length]
[--exonct] [--nt NT] [-o OUTPUT]
[--window WINDOW]
[--convtorefseq CONVTOREFSEQ]
[--targetscanfile TARGETSCANFILE]
[--deltag] [--lfold] [--cap-structure]
[--kozak] [--uorf-count] [--uorf-overlap]
[--start-codon] [--rare-codons]
[--mirna-sites] [--au-elements]
Calculate transcriptome-wide properties
optional arguments:
-h, --help show this help message and exit
Global arguments:
-i INPUT, --input INPUT
The transcriptome region (BED format)
-g GENOME, --genome GENOME
The genome for the input transcriptome
--gc Calculate GC content
--length Calculate length
--exonct Count # of exons
--nt NT Number of threads (default is 8 or 4 for lfold)
-o OUTPUT, --output OUTPUT
Output basename (e.g. CDS)
--window WINDOW Window size for sliding window calculations (default
75)
--convtorefseq CONVTOREFSEQ
Filename to convert input annotations to refseq (for
targetscan; e.g. knownToRefSeq.txt)
--targetscanfile TARGETSCANFILE
Filename of targetscan scores (e.g.
Summary_Counts.txt)
--deltag Calculate min deltaG in sliding window of size
--window over region
--lfold Use RNALfold to calculate MFE rather than RNAfold
(faster but does not compute centroid,MEA)
5' UTR specific arguments:
--cap-structure Calculate structure at the 5' end
Start-codon-specific arguments:
--kozak Calculate Kozak context score
--uorf-count Calculate number of 5' UTR uORFs (starting with
[ACT]TG)
--uorf-overlap Overlap of uORF with start codon (implies --uorf-
count)
--start-codon Record the start codon used (ATG or other)
CDS-specific arguments:
--rare-codons Calculate codon usage properties
3' UTR specific arguments:
--mirna-sites Compile miRNA binding site info from targetscan
--au-elements Count number of AU-rich elements in the 3' UTR