Analysis tools for TrIP-seq data. There are various individual tools inside to perform different tasks. Limited documentation below, contact stephen.floor@ucsf.edu with questions.
Calculate the properties of an input transcriptome (or regions thereof). Input format is BED, output files are .csv files with various properties as specified on the command line.
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] 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 ####Requirements:
- ViennaRNA RNAfold and RNALfold (http://www.tbi.univie.ac.at/RNA)
- HumanCodonTable (this page)
- AnnotationConverter (this page)
- TargetscanScores (this page)
- SNFUtils (this page)
Take two lists of TrIP-seq data (i.e. clusters) and compare them for genes that have the transcripts in each of the two different sets. For each set of gene-linked transcript isoforms, compare input transcriptome features as calculated using transcriptome_properties.py
usage: compare_tripseq_clusters.py [-h] --set1 FNAME ID ... [FNAME ID ... ...] --set2 FNAME ID ... [FNAME ID ... ...] --tx-to-gene TX_TO_GENE [-o OUTPUT] -n NREP [--txome-props TXOME_PROPS [TXOME_PROPS ...]] [--control] --txome-gtf TXOME_GTF optional arguments: -h, --help show this help message and exit --set1 FNAME ID ... [FNAME ID ... ...] Files and IDs containing transcript distributions; compare between set1 and set2 --set2 FNAME ID ... [FNAME ID ... ...] Files and IDs containing transcript distributions; compare between set1 and set2 --tx-to-gene TX_TO_GENE Mapping between transcript ID in input file and gene ID -o OUTPUT, --output OUTPUT Output filename (default is stdout) -n NREP, --nrep NREP Number of replicates of each point --txome-props TXOME_PROPS [TXOME_PROPS ...] List of files with transcriptome properties to correlate among (wildcards ok) --control Perform randomized comparisons of input transcripts as a control. --txome-gtf TXOME_GTF Path to transcriptome GTF - GTF.py (this page)
- Transcript.py (this page)
- SNFUtils.py (this page)
- Two lists of transcripts to compare (i.e. clusters)
- Lists of transcriptome properties to compare between transcript isoforms of the same gene in the two sets (generated by transcriptome_properties.py)
- File containing transcript ID to gene mapping
Plot an individual transcript or all transcripts of a gene. Requires input polysome sequencing data (i.e. TrIPseq) or some other distribution.
Input "tx-to-gene" file should be a file containing four columns: txid, geneid, gene_name, tx_name. This can be downloaded from Ensembl Biomart or other sources.
usage: plot_tripseq_transcript.py [-h] -i INPUT [-o OUTPUT] -n NREP --id ID --tx-to-gene TX_TO_GENE [--text] [--format FORMAT] Plot input transcript ID from input distribution file optional arguments: -h, --help show this help message and exit -i INPUT, --input INPUT File containing transcript distributions -o OUTPUT, --output OUTPUT Output filename (default is stdout) -n NREP, --nrep NREP Number of replicates of each point --id ID Transcript ID(s) to print (can be partial; can be comma-separated list) --tx-to-gene TX_TO_GENE File containing transcript ID to gene name mapping --text Output text data in addition to plots. --format FORMAT Image format to export (png or pdf). - SNFUtils.py (this page)
- Input per-transcript distributions
- File containing transcript ID to gene mapping (if per-gene plotting is desired)
Converts between FPKM and TPM (transcripts per million). Uses the formula TPM_i = FPKM_i * 1e6 / sum(FPKM_g for all genes g) Citation: http://lynchlab.uchicago.edu/publications/Wagner,%20Kin,%20and%20Lynch%20%282012%29.pdf
usage: fpkm_to_tpm.py [-h] -i INPUT [-t SEPARATOR] [-o [OUTPUT]] [--ignore IGNORE] [--filter FILTER] [-u] optional arguments: -h, --help show this help message and exit -i INPUT, --input INPUT File containing identifiers to use for the merge -t SEPARATOR, --separator SEPARATOR Field separator (default comma; "tab" for tabs; "space" for whitespace -o [OUTPUT], --output [OUTPUT] File to output to (default stdout) --ignore IGNORE Number of columns to ignore (one-based; 1 ignores the first column) --filter FILTER Filter genes with TPM below arg -u, --unique Only output lines with unique entries in column 1 - A file with FPKM values to convert to TPM
A class to provide for conversion between two annotation sets.
A class to read GTF files - downloaded from https://gist.github.com/slowkow/8101481 and minimally modified
A file providing various utility functions.
A class harboring information on human codon usage.
A class to read in targetscan scores and provide accessor functions.
A class defining a transcript and structural features associated with it.