The, M. & Käll, L. (2019). Integrated identification and quantification error probabilities for shotgun proteomics. Molecular & Cellular Proteomics, 18 (3), 561-570.
Python 2 or 3 installation
Packages needed:
- numpy 1.12+
- scipy 0.17+
pip install triqler
git clone https://github.com/statisticalbiotechnology/triqler.git cd triqler pip install .
usage: python -m triqler [-h] [--out_file OUT] [--fold_change_eval F]
[--decoy_pattern P] [--min_samples N] [--num_threads N]
[--ttest] [--write_spectrum_quants]
[--write_protein_posteriors P_OUT]
[--write_group_posteriors G_OUT]
[--write_fold_change_posteriors F_OUT]
IN_FILE
positional arguments:
IN_FILE List of PSMs with abundances (not log transformed!)
and search engine score. See README for a detailed
description of the columns.
optional arguments:
-h, --help show this help message and exit
--out_file OUT Path to output file (writing in TSV format). N.B. if
more than 2 treatment groups are present, suffixes
will be added before the file extension. (default:
proteins.tsv)
--fold_change_eval F log2 fold change evaluation threshold. (default: 1.0)
--decoy_pattern P Prefix for decoy proteins. (default: decoy_)
--min_samples N Minimum number of samples a peptide needed to be
quantified in. (default: 2)
--num_threads N Number of threads, by default this is equal to the
number of CPU cores available on the device. (default:
8)
--ttest Use t-test for evaluating differential expression
instead of posterior probabilities. (default: False)
--write_spectrum_quants
Write quantifications for consensus spectra. Only
works if consensus spectrum index are given in input.
(default: False)
--write_protein_posteriors P_OUT
Write raw data of protein posteriors to the specified
file in TSV format. (default: )
--write_group_posteriors G_OUT
Write raw data of treatment group posteriors to the
specified file in TSV format. (default: )
--write_fold_change_posteriors F_OUT
Write raw data of fold change posteriors to the
specified file in TSV format. (default: )
A sample file iPRG2016.tsv is provided in the example folder. You can
run Triqler on this file by running the following command:
python -m triqler --fold_change_eval 0.8 example/iPRG2016.tsv
The simplest input format is a tab-separated file consisting of a header line followed by one PSM per line in the following format:
run <tab> condition <tab> charge <tab> searchScore <tab> intensity <tab> peptide <tab> proteins r1 <tab> 1 <tab> 2 <tab> 1.345 <tab> 21359.123 <tab> A.PEPTIDE.A <tab> proteinA <tab> proteinB r2 <tab> 1 <tab> 2 <tab> 1.945 <tab> 24837.398 <tab> A.PEPTIDE.A <tab> proteinA <tab> proteinB r3 <tab> 2 <tab> 2 <tab> 1.684 <tab> 25498.869 <tab> A.PEPTIDE.A <tab> proteinA <tab> proteinB ... r1 <tab> 1 <tab> 3 <tab> 0.452 <tab> 13642.232 <tab> A.NTPEPTIDE.- <tab> decoy_proteinA
Alternatively, if you have match-between-run probabilities, a slightly more complicated input format can be used as input:
run <tab> condition <tab> charge <tab> searchScore <tab> spectrumId <tab> linkPEP <tab> featureClusterId <tab> intensity <tab> peptide <tab> proteins r1 <tab> 1 <tab> 2 <tab> 1.345 <tab> 3 <tab> 0.0 <tab> 1 <tab> 21359.123 <tab> A.PEPTIDE.A <tab> proteinA <tab> proteinB r2 <tab> 1 <tab> 2 <tab> 1.345 <tab> 3 <tab> 0.021 <tab> 1 <tab> 24837.398 <tab> A.PEPTIDE.A <tab> proteinA <tab> proteinB r3 <tab> 2 <tab> 2 <tab> 1.684 <tab> 4 <tab> 0.0 <tab> 1 <tab> 25498.869 <tab> A.PEPTIDE.A <tab> proteinA <tab> proteinB ... r1 <tab> 1 <tab> 3 <tab> 0.452 <tab> 6568 <tab> 0.15 <tab> 9845 <tab> 13642.232 <tab> A.NTPEPTIDE.- <tab> decoy_proteinA
Some remarks:
- For Triqler to work, it also needs decoy PSMs, preferably resulting from a search engine search with a reversed protein sequence database concatenated to the target database.
- The intensities should not be log transformed, Triqler will do this transformation for you.
- The search engine scores should be such that higher scores indicate a higher confidence in the PSM.
- We recommend usage of well calibrated search engine scores, e.g. the SVM scores from Percolator.
- Multiple proteins can be specified at the end of the line, separated by tabs. However, it should be noted that Triqler currently discards shared peptides.
The output format is a tab-separated file consisting of a header line followed by one protein per line in the following format:
q_value <tab> posterior_error_prob <tab> protein <tab> num_peptides <tab> protein_id_PEP <tab> log2_fold_change <tab> diff_exp_prob_<FC> <tab> <condition1>:<run1> <tab> <condition1>:<run2> <tab> ... <tab> <conditionM>:<runN> <tab> peptides
Some remarks:
- The reported protein expressions are the expected value of the protein's expression in the run. They are calculated relative to the protein's mean expression and are not log transformed.
- The reported fold change is log2 transformed and is the expected value based on the posterior distribution of the fold change.
- If more than 2 treatment groups are present, separate files will be written out for each pairwise comparison with suffixes added before the file extension, e.g. proteins.1vs3.tsv.