iqmma consumes LC-MS data (mzML) and results of post-search analysis (or just peptide identifications) (tsv) and performs multiple tool (Dinosaur, Biosaur and OpenMS FeatureFinderCentroided) feature detection, peptide-intensity matching and quantitation.

Installation

Using pip:

pip install iqmma

It will install, if necessary, the minimum set (Biosaur2 and Diffacto) to iqmma to function. Detailed instruction how to install additional feature detections and some general tooltips are in the IQMMA_installation_guide.pdf.

Usage

Some explanation and requirements

iqmma has two working mods. First of all, it can be quantitation workflow with generating peptide features using multiple tools, matching them on peptides, and two Diffacto quantitation stages (separated and mixed, where the algorithm is choosing the best intensities for each peptide between different feature detections). The second one is stopping after peptide-feature matching to allow user to apply any other quantitation approach on matched intensities.

For iqmma to work properly, each mzML file must have a related PSM file which name starts with the name of mzML.

For basic usage all PSMs and mzML files should be stored in the same directory, otherwise -PSM_folder parameter must be applied. All PSMs files must be *PSM_full.tsv tables obtained from the Scavager output (https://github.com/markmipt/scavager).

Quantitation mode

Basic command for quantitation mode:

iqmma -bio2 path_to_Biosaur2 
      -dino path_to_Dinosaur 
      -openMS path_to_openMS 
      -dif path_to_Diffacto 
      -s1 paths_to_mzml_files_from_sample_1_*.mzML 
      -s2 paths_to_mzml_files_from_sample_2_*.mzML 
      -outdir output_directory

Note 1: -s2 argument is necessary for quantitation mode to activate.

Note 2: at least two feature detections should be given for Mix algorithm to work.

Feature matching mode

Basic command for matching peptide intensities:

iqmma -bio2 path_to_Biosaur2 
      -dino path_to_Dinosaur
      -openMS path_to_openMS
      -dif path_to_Diffacto
      -s1 paths_to_all_mzml_files_*.mzML
      -outdir output_directory

Note: all mzml files go into -s1 - the first sample option - without any differences between them, no quantitation applied.

Advanced example

Full quantitation mode (Linux-based example):

iqmma -bio2 /usr/bin/biosaur2
      -dino /home/user/downloads/Dinosaur-1.2.0.free.jar
      -dif /usr/bin/diffacto
      -s1 /home/user/downloads/sample1_rep1.mzml /home/user/downloads/sample1_rep2.mzml
      -s2 /home/user/downloads/sample2_rep1.mzml /home/user/downloads/sample2_rep2.mzml
      -logs INFO
      -log_path /home/user/iqmma_logs/logs_N.log
      -mbr 1
      -overwrite_matching 1
      -mixed 1
      -fc_threshold 2.5
      -pval_threshold 0.01

Here, two samples with two replicas per sample compared against each other in quantitation mode. At least two replicas per sample are needed for statistical tests in Diffacto to work, without it Diffato output would be empty and quantitation wouldn't work. -psm_folder and -psm_format are not specified so iqmma will search peptide identifications in folder /home/user/downloads (near .mzml default for -psm_folder) by searching files which names start for example with sample1_rep1 (and other names of .mzml files) and end with PSMs_full.tsv (default value of -psm_format, for Scavager results). More than one feature detection is available so by default mixed algorithm would also be turned on (-mixed 1 is default). -mbr 1 turns on match between runs, so matching needs to be overwritten not to use old files without matching between runs. -logs and -log_path specify level of logging messages and where to store them. -fc_threshold 2.5 and -pval_threshold 0.01 specifies thresholds on Fold Change and p-value to apply to differentially expressed proteins in final filtering.

Matching mode (Anaconda, Windows paths):

iqmma -dino c:\user\downloads\dinosaur-1.2.0.free.jar
      -bio2 c:\user\anaconda3\scripts\biosaur2.exe
      -dif c:\user\Anaconda3\Scripts\diffacto.exe
      -s1 c:\user\downloads\sample1_rep1.mzml c:\user\downloads\sample2_rep1.mzml
      -outdir c:\user\iqmma_analysis\out_1
      -logs info
      -log_path c:\user\iqmma_analysis\out_1\logs.log
      -psm_folder c:\user\downloads\mzid_peptides
      -psm_format .mzid 

Here there are two samples in one replica each to match on peptides identifications that are stored in files -psm_folder + \ + (.mzml filename) + -psm_format which results in c:\user\downloads\mzid_peptides\ + sample1_rep1 + .mzid. Two feature detections are given (paths to executable files are given), so there would be two rows of matched files in the -outdir in the end: Dinosaur-generated features matched (ends with _dino.tsv) on peptides and Biosaur2-generated (ends with _bio2.tsv) features matched on peptides.

Full quantitation mode with peptides and proteins filtering (Linux-based example):

iqmma -bio2 /usr/bin/biosaur2
      -dino /home/user/downloads/Dinosaur-1.2.0.free.jar
      -dif /usr/bin/diffacto
      -s1 /home/user/downloads/sample1_rep1.mzml /home/user/downloads/sample1_rep2.mzml
      -s2 /home/user/downloads/sample2_rep1.mzml /home/user/downloads/sample2_rep2.mzml
      -logs INFO
      -log_path /home/user/iqmma_logs/logs_N.log
      -allowed_prots /home/user/downloads/post_search/file_with_users_proteins.tsv
      -allowed_pepts /home/user/downloads/post_search/file_with_users_peptides.tsv

Here, same samples, as in the first example, but user wants to quantify only specified peptides and proteins. To do so, user made a file with peptides allowed for analysis (and one more for proteins). Then -allowed_pepts parameters allows to point such a file for iqmma to use only sequences that present in /home/user/downloads/post_search/file_with_users_peptides.tsv file in quantitation. If -allowed_pepts parameter is not applied iqmma will try to find q (for q-value) column in the PSMs file and filter peptides on 0.01 q-value. If neither -allowed_pepts is specified and q column is not present in PSMs files then all peptides will be allowed for quantitation. -allowed_prots parameter behaves similarly, except when it is not specified then all proteins allowed for quantitation without any filtering. Example structure of the /home/user/downloads/post_search/file_with_users_peptides.tsv :

0    peptide\n
1    AAABBBCCC\n
2    AABBBDDD\n
...

Note 1: Paths to feature detections or Diffacto should be paths to its executable files. In Linux-based systems, executable files are usually stored in /usr/bin/; on Windows with Anaconda - in C:\User\Anaconda3\Scripts or C:\User\Anaconda3\envs\current_environment\Scripts.

Note 2: To use Dinosaur, java should be installed in the environment.

Note 3: Since Windows has a case-insensitive file system, despite iqmma's overall compatibility some options related to other used programs (-diffacto_args, -dino_args to be precise) could not work properly according to Diffacto and Dinosaur case-sensitive option's names. With that fact in mind, it is recommended to use iqmma on Linux-based system.

Config file

Both mods could be used with config file for an advanced settings configuration:

iqmma -cfg path_to_config_file
      -cfg_category name_of_category_in_cfg

Example config file could be downloaded from here (example.ini) or could be generated by the command:

iqmma -example_cfg path_to_file_to_be_created

Full option's description could be obtained with:

iqmma -h

Input files

Multiple formats could be used for input PSMs files. In simple matching mode it could be .tsv Identipy output or .pep.xml (.pepxml) from Identipy or MSFragger output or .mzid from msgf+ output or user's .tsv table with specified columns. However, -PSM_format parameter should be applied when using other formats except standart.

Columns: 'spectrum' - MS/MS spectrum id for peptide (it should be unique), 'peptide' - peptide sequence, 'protein' - protein name, related to this peptide, 'assumed_charge' - charge of the peptide, 'precursor_neutral_mass' - mass of the neutral peptide calculated by the formula 'precursor_neutral_mass' = mz * charge - charge * 1.00727649, 'RT exp' - experimental Retention Time of the peptide.

For full quantitation mode PSMs files assumed to be PSMs_full.tsv tables of Scavager output.

Output files

As an output iqmma generates /feats_matched directory with .tsv tables that contain information about the peptide and feature matched for it, table with differentially expressed proteins and their fold change for each feature detection method and Mix algorithm, and Venn diagram to show distribution of those DE proteins between feature detection related methods. Also Diffacto raw output for users filtering could be accessed in /diffacto directory or in the directory that was passed to -diffacto_folder option.

Reanalyzing

In terms of the amount of time some stages of analysis could consume, iqmma tries to use existing files, that may have been left over from past runs, rather than overwriting them. Because of that, some options were added to avoid repeatable stages or unwanted usage.

-overwrite_features and -feature_folder - The most time-consuming stage often appears to be feature detection. So there are two possibilities to reanalyze data with already existing features. The first is to set -overwrite_features to 0 (default) and let iqmma to find /features directory nearby either PSMs files or mzML files if it was already used on that files. And the second is to specify -feature_folder parameter with directory, where features you need are stored, and also keep -overwrite_features set to 0 not to overwrite them.

-overwrite_matching and -matching_folder - Matching is far less time-consuming than feature detection. If some parameters or even PSM or feature files were changed, and it is needed to reanalyze data, the right way to do so is either setting -overwrite_matching to 1 (default 0) or pass another directory to the -matching_folder.

-overwrite_first_diffacto and -diffacto_folder - The first option overwrites results of the first stage of the quantitation strategy, where diffacto is used only on matched features from one feature detection at a time. Any changes in parameters referred to the previous stages need to turn it on, so it is set to 1 by default. The second changes the directory of storage of unfiltered quantitation results.

Links

• Diffacto repo: https://github.com/statisticalbiotechnology/diffacto

• Dinosaur repo: https://github.com/fickludd/dinosaur

• Biosaur2 repo: https://github.com/markmipt/biosaur2

• OpenMS guide: https://openms.readthedocs.io/en/latest/openms-applications-and-tools/installation.html

• Pypi: https://pypi.org/project/iqmma/

• Github: https://github.com/PostoenkoVI/IQMMA

• Mailing list: v.i.postoenko@gmail.com, garibova.02@gmail.com

Citing iqmma

IQMMA: an efficient MS1 intensity extraction using multiple feature detection algorithms for DDA proteomics

Valeriy I. Postoenko, Leyla A. Garibova, Lev I. Levitsky, Julia A. Bubis, Mikhail V. Gorshkov, Mark V. Ivanov.

doi: https://doi.org/10.1101/2023.02.03.526776, biorxiv: https://www.biorxiv.org/content/10.1101/2023.02.03.526776v1