msstitch 3.0

MS proteomics post processing utilities

msstitch - MS proteomics post-processing utilities

Shotgun proteomics has a number of bioinformatic tools available for identification and quantification of peptides, and the subsequent protein inference. These are a collection of scripts to integrate a number of these tools, generating ready to use result files.

If you need support for a specific tool, there is limited time but infinite gratitude :)

Usage

An example command flow would first store mzML spectra data in an SQLite file:

msstitch storespectra --spectra file1.mzML file2.mzML \
  --setnames sampleset1 sampleset2 -o db.sqlite

Or, in case the lookup with some other spectra already exists:

msstitch storespectra --dbfile lookup.sqlite --spectra file1.mzML file2.mzML \
  --setnames sampleset1 sampleset2

Then store quantification data from kronik (MS1 precursor quant) and isobaric quantification from OpenMS together with the spectra:

msstitch storequant --dbfile db.sqlite --spectra file1.mzML file2.mzML \
  --kronik file1.kronik file2.kronik \
  --isobaric file1.consensusXML file2.consensusXML

Create a decoy database where peptides are reversed between tryptic residues:

msstitch makedecoy uniprot.fasta -o decoy.fasta --scramble tryp_rev --maxshuffle 10

Or without removing peptide sequences that match to the target DB:

msstitch makedecoy uniprot.fasta -o decoy.fasta --scramble tryp_rev --ignore-target-hits

After running two samples of MSGF and percolator, we can start making a more proper set of PSM tables:

# Add percolator data, filter 0.01 FDR
msstitch perco2psm -i psms1.txt \
  --perco percolator1.xml --mzid psms1.mzIdentML \
  --filtpsm 0.01 --filtpep 0.01
msstitch perco2psm -i psms2.txt \
  --perco percolator2.xml --mzid psms2.mzIdentML \
  --filtpsm 0.01 --filtpep 0.01
# Combine the two sets and split to a target and decoy file
msstitch concat -i psms1.txt psms2.txt -o allpsms.txt
msstitch split -i allpsms.txt --splitcol TD

Now refine the PSM tables, using the earlier created SQLite DB, adding more information (sample name, MS1 precursor quant, isobaric quant, proteingroups, genes):

cp db.sqlite decoy_db.sqlite
msstitch psmtable -i target.tsv -o target_psmtable.txt --fasta uniprot.fasta \
  --dbfile db.sqlite --addmiscleav --addbioset --ms1quant --isobaric \
  --proteingroup --genes
msstitch psmtable -i decoy.tsv -o decoy_psmtable.txt --fasta decoy.fasta \
  --dbfile decoy_db.sqlite --proteingroup --genes --addbioset

If necessary (e.g. multiple TMT sample sets), split the table before making protein/peptide tables:

msstitch split -i target_psmtable.txt --splitcol bioset

Create a peptide table, with summarized median isobaric quant ratios, highest MS1 intensity PSM as the peptide MS1 quant intensity, and an additional linear-modeled q-value column:

msstitch peptides -i set1_target_psms.txt -o set1_target_peptides.txt \
  --scorecolpattern svm --modelqvals --ms1quant \
  --isobquantcolpattern tmt10plex --denompatterns _126 _127C 

Create a protein table, with isobaric quantification as for peptides, the average of the top-3 highest intensity peptides for MS1 quantification:

msstitch proteins -i set1_target_peptides.txt --decoyfn set1_decoy_peptides \
  -o set1_proteins.txt \
  --scorecolpattern '^q-value' --logscore \
  --ms1quant \
  --isobquantcolpattern tmt10plex --denompatterns _126 _127C 

Or the analogous process for genes

msstitch genes -i set1_target_peptides.txt --decoyfn set1_decoy_peptides \
  -o set1_genes.txt \
  --scorecolpattern '^q-value' --logscore \
  --ms1quant \
  --isobquantcolpattern tmt10plex --denompatterns _126 _127C 

Or when there are ENSEMBL entries in the fasta search database, even for ENSG:

msstitch ensg -i set1_target_peptides.txt --decoyfn set1_decoy_peptides \
  -o set1_ensg.txt \
  --scorecolpattern '^q-value' --logscore \
  --ms1quant \
  --isobquantcolpattern tmt10plex --denompatterns _126 _127C 

Finally, merge multiple sets of proteins (or genes/ENSG) into a single output. Here we set an cutoff so that features with FDR > 0.01 are set to NA for the respective sample set.

msstitch merge -i set1_proteins.txt set2_proteins.txt \
  --setnames sampleset1 sampleset2 \
  --dbfile db.sqlite \
  --fdrcolpattern 'q-value' --mergecutoff 0.01 \
   --ms1quantcolpattern area --isobquantcolpattern plex --psmnrcolpattern quanted

Some other useful commands are:

Trypsinize a fasta file (minimum retained peptide length, do cut K/RP, allow 1 missed cleavage)

msstitch trypsinize -i uniprot.fasta -o tryp_up.fasta --minlen 7 --cutproline --miscleav 1

Create an SQLite file with tryptic sequences for filtering out e.g. known-sequence data. Options as for trypsinization, --insourcefrag builds lookup with support for in-source fragmented peptides that have lost some N-terminal residues:

msstitch storeseq -i canonical.fa --cutproline --minlen 7 --miscleav 1 --insourcefrag

Filter a percolator output file using the created SQLite, removing sequences that match those stored in the SQLite. The below also removes sequences in the sample which are deamidated (i.e. D -> N), and sequences that have lost at most 2 N-terminal amino acids due to in-source fragmentation (DB must have been built with support for that).

msstitch filterperco -i perco.xml --dbfile tryptic.sqlite --insourcefrag 2 --deamidate -o filtered.xml

Create an SQLite file with full-protein sequences for filtering any peptide of a minimum length specified that matches to those. Slower than filtering tryptic sequences but more comprehensive:

msstitch storeseq -i canonical.fa --fullprotein --minlen 7

Filter a percolator output file on protein sequences using the SQLite, removing sequences in sample which match to anywhere in the protein. Sequences may be deamidated, and minimum length parameter must match the one the database is built with.

msstitch filterperco -i perco.xml --dbfile proteins.sqlite --fullprotein --deamidate --minlen 7 -o filtered.xml

Create an isobaric ratio table median-summarizing the PSMs by any column number you want in a PSM table. E.g. you have added a column with exons. The following uses average of two channels as denominator, outputs a new table with first column the features found in column nr.20 of the PSM table:

msstitch isosummarize -i psm_table.txt --featcol 20 --isobquantcolpattern tmt10plex --denompatterns 126 127C