Shotgun proteomics has a number of bioinformatic tools available for identification and quantification of peptides, and the subsequent protein inference. A problem which remains is that to generate a full end-user compatible output table one often has to resort to a full suite of tools. Suites can be very well made, reliable and accepted by the field, but they are “large” monoliths.
These scripts are written to scratch an itch felt some years ago when combining existing tools, and act as small command-line runnable programs that do small things such as adding values to a PSM table, manipulating percolator results or grouping proteins. They are capable of combining multiple different output formats into complete output.
Small things take short time to update, are easy to parallelize, and tools that generate input data for these scripts can usually be upgraded without having to wait for a full tool suite update (unless large output format changes apply). While the tools are not per definition user-friendly in that there is no GUI or chaining/integration, they are easy to implement in frameworks such as Galaxy or Taverna.
We currently support the tools we run ourselves, but these could easily be extended to include more tool output formats.
Generates SQLite database files of various MS data. Can e.g. be used to store statistical or quant data of multiple experiment sample sets, whereafter these can be merged. But it also does protein grouping and sequence filtering thanks to the power of the DB engine.
Example: Store a multi-set tab-separated PSM table:
msslookup psms -i psmtable.txt --spectracol 2 --fasta ENSEMBL80.fa --map ENS80_biomart.txt
Performs various operations on percolator output XML, e.g. splitting into target and decoy, merging, filtering peptides, runs qvality and reassigns qvality output statistics to existing percolator output.
Example: filter unique peptides on best score of a merged percolator file
msspercolator filteruni -i percolator.xml
Use this for modifications of tab-separated PSM tables generated by MSGF+ (supported) or other tools.
Example: add MS2 quant data to PSM table from SQLite lookup (resulting from mslookup)
msspsmtable quanttsv -i psmtable.txt --dbfile db.sqlite --isobaric
Example 2: Split PSM table into multiple tables on column “Biological set”
msspsmtable splittsv -i psmtable.txt --bioset
Creates and modifies peptide tables
Example: create a peptide table by filtering best peptides from PSM table and removing isobaric quant data. Retains MS1 quant data by taking the highest MS1 quant for a given peptide sequence.
msspeptable psm2pep -i psmtable.txt --spectracol 2 --scorecolpattern svm --ms1quantcolpattern area --isobquantcolpattern tmt10plex
Example: Create column in peptide table with linear modeled q-values
msspeptable modelqvals -i peptides.txt --qcolpattern "^q-value" --scorecolpattern svm
Creates and modifies protein tables, also runs qvality on these for FDR calculation
Example: Add best-scoring peptide to protein table (Q-score by Savitsky et al 2014)
mssprottable bestpeptide -i proteins.txt --peptable peptides.txt --scorecolpattern svm --logscore
Example: Add FDR from qvality result to protein table using Q-scores as keys to look up corresponding q-values and PEPs
mssprottable fdr -i proteins.txt --qvality qvals.txt --scorecolpattern "^Q-score"
TODO: Figure out how to actually get changelog content.
Changelog content for this version goes here.
TODO: Brief introduction on what you do with files - including link to relevant help section.
|File Name & Checksum SHA256 Checksum Help||Version||File Type||Upload Date|
|msstitch-1.0-py3-none-any.whl (112.0 kB) Copy SHA256 Checksum SHA256||py3||Wheel||Mar 24, 2016|
|msstitch-1.0.tar.gz (5.2 MB) Copy SHA256 Checksum SHA256||–||Source||Mar 24, 2016|