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MS Annika Combine Results

A script to merge and optionally validate several MS Annika search results. The main use case would be for merging results from different MS Annika runs, e.g. combining results from a cleavable and non-cleavable MS Annika search or combining results from different doublet distances.

Usage

  • Install python 3.7+: https://www.python.org/downloads/
  • Install requirements: pip install -r requirements.txt
  • Export MS Annika CSM results from Proteome Discoverer to Microsoft Excel format.
    • Important: CSMs should not be filtered! Export all (unvalidated) CSMs including decoy hits!
  • Run python msannika_merge.py filename1.xlsx filename2.xlsx -fdr 0.01 (see below for more examples).
  • The script may take a few minutes, depending on the number of CSMs to process.
  • Done!

Examples

msannika_merge.py takes one positional and two optional arguments. The first argument always has to be the filename(s) of the MS Annika CSM result file(s). You may specify any number of result files! For demonstration purposes we will use the files supplied in the /data folder:

  • DSSO_CSMs.xlsx contains unvalidated CSMs from a cleavable MS Annika search using the crosslinker DSSO.
  • ncDSSO_CSMs.xlsx contains unvalidated CSMs from a non-cleavable MS Annika search using the crosslinker DSSO.

The following is a valid msannika_merge.py call:

python msannika_merge.py DSSO_CSMs.xlsx ncDSSO_CSMs.xlsx

This will merge CSMs from all given files, in this case DSSO_CSMs.xlsx and ncDSSO_CSMs.xlsx into a result file called CSMs_merged.xlsx. You can also set a prefix for the generated result file(s) like this:

python msannika_merge.py DSSO_CSMs.xlsx ncDSSO_CSMs.xlsx -o All_CSMs.xlsx

This will merge CSMs from all given files, exactly like the last command, but the generated result file will now be named All_CSMs_merged.xlsx.

If you suppy the optional argument -fdr or --false_discovery_rate and the desired FDR as a floating point number, the CSMs will be merged, then validated, then grouped by sequence and position to crosslinks and those crosslinks will again be validated for the given FDR. To group CSMs and validate CSMs and crosslinks the MS Annika FDR script is downloaded and used. Validation therefore requires an active internet connection!

python msannika_merge.py DSSO_CSMs.xlsx ncDSSO_CSMs.xlsx -fdr 0.01

This will merge CSMs from all given files, then validate the merged CSMs for estimated 1% FDR, then group CSMs to crosslinks and finally validate the crosslinks for estimated 1% FDR. The following files will be generated:

  • CSMs_merged.xlsx: The merged CSMs from all given files.
  • CSMs_merged_validated.xlsx: The merged CSMs that are above the estimated 1% FDR threshold.
  • Crosslinks.xlsx: The crosslinks that result from grouping the merged CSMs.
  • Crosslinks_validated.xlsx: The crosslinks that are above the estimated 1% FDR threshold.

Note that the following command will produce the same output (FDR values >= 1 will automatically be divided by 100):

python msannika_merge.py DSSO_CSMs.xlsx ncDSSO_CSMs.xlsx -fdr 1

It is also possible to only validate CSMs and not validate crosslinks by adding the flag -csms to the command:

python msannika_merge.py DSSO_CSMs.xlsx ncDSSO_CSMs.xlsx -fdr 0.01 -csms

The following files will be generated:

  • CSMs_merged.xlsx: The merged CSMs from all given files.
  • CSMs_merged_validated.xlsx: The merged CSMs that are above the estimated 1% FDR threshold.
  • Crosslinks.xlsx: The crosslinks that result from grouping the merged CSMs.

The same also works for crosslinks by adding the flag -crosslinks which will only validate crosslinks but not CSMs:

python msannika_merge.py DSSO_CSMs.xlsx ncDSSO_CSMs.xlsx -fdr 0.01 -crosslinks

The following files will be generated:

  • CSMs_merged.xlsx: The merged CSMs from all given files.
  • Crosslinks.xlsx: The crosslinks that result from grouping the merged CSMs.
  • Crosslinks_validated.xlsx: The crosslinks that are above the estimated 1% FDR threshold.

Parameters

"""
DESCRIPTION:
A script to combine results from several MS Annika searches.
USAGE:
msannika_merge.py f [f ...]
                    [-fdr FDR][--false_discovery_rate FDR]
                    [-o PREFIX][--output PREFIX]
                    [-csms][--csms]
                    [-crosslinks][--crosslinks]
                    [-h][--help]
                    [--version]
positional arguments:
  f                     MS Annika result files in Microsoft Excel format (.xlsx)
                        to process. MS Annika result files have to be
                        unvalidated CSMs including decoys!
optional arguments:
  -fdr FDR, --false_discovery_rate FDR
                        False discovery rate to validate results for. Supports
                        both percentage input (e.g. 1) or fraction input (e.g.
                        0.01). By default not set and results will only be
                        merged. Validation requires internet connection because
                        the MS Annika FDR module will be downloaded to calculate
                        FDR.
                        Default: None
  -o PREFIX, --output PREFIX
                        Prefix of the output file(s).
                        Default: None
-csms, --csms
                        Only validate CSMs and not crosslinks.
                        Default: False
-crosslinks, --crosslinks
                        Only validate crosslinks and not CSMs.
                        Default: False
  -h, --help            show this help message and exit
  --version             show program's version number and exit
"""

Function Documentation

If you want to integrate the MS Annika Combine Results process into your own scripts, you can import the following function as given:

import pandas as pd

cdsso = pd.read_excel("DSSO_CSMs.xlsx")
ncdsso = pd.read_excel("ncDSSO_CSMs.xlsx")

# Merging CSMs
from msannika_merge import merge
all_csms = merge([cdsso, ncdsso])

# The function signature of merge is:
def merge(files: List[str]) -> pd.DataFrame:
    """code omitted"""
    return

For validation please use the functions provided in MS Annika FDR.

Known Issues

List of known issues

Citing

If you are using the MS Annika Combine Results script please cite:

MS Annika 2.0 Identifies Cross-Linked Peptides in MS2–MS3-Based Workflows at High Sensitivity and Specificity
Micha J. Birklbauer, Manuel Matzinger, Fränze Müller, Karl Mechtler, and Viktoria Dorfer
Journal of Proteome Research 2023 22 (9), 3009-3021
DOI: 10.1021/acs.jproteome.3c00325

If you are using MS Annika please cite:

MS Annika 2.0 Identifies Cross-Linked Peptides in MS2–MS3-Based Workflows at High Sensitivity and Specificity
Micha J. Birklbauer, Manuel Matzinger, Fränze Müller, Karl Mechtler, and Viktoria Dorfer
Journal of Proteome Research 2023 22 (9), 3009-3021
DOI: 10.1021/acs.jproteome.3c00325

or

MS Annika: A New Cross-Linking Search Engine
Georg J. Pirklbauer, Christian E. Stieger, Manuel Matzinger, Stephan Winkler, Karl Mechtler, and Viktoria Dorfer
Journal of Proteome Research 2021 20 (5), 2560-2569
DOI: 10.1021/acs.jproteome.0c01000

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