IsoBayes is a Bayesian method to perform inference on single protein isoforms.
Our approach infers the presence/absence of protein isoforms, and also estimates their abundance;
additionally, it provides a measure of the uncertainty of these estimates, via:
i) the posterior probability that a protein isoform is present in the sample;
ii) a posterior credible interval of its abundance.
IsoBayes inputs liquid cromatography Mass Spectrometry (MS) data,
and can work with both PSM counts, and intensities.
When available, trascript isoform abundances (i.e., TPMs) are also incorporated:
TPMs are used to formulate an informative prior for the respective protein isoform relative abundance.
We further identify isoforms where the relative abundance of proteins and transcripts significantly differ.
We use a two-layer latent variable approach to model two sources of uncertainty typical of MS data:
i) peptides may be erroneously detected (even when absent);
ii) many peptides are compatible with multiple protein isoforms.
In the first layer, we sample the presence/absence of each peptide based on its estimated probability
of being mistakenly detected, also known as PEP (i.e., posterior error probability).
In the second layer, for peptides that were estimated as being present,
we allocate their abundance across the protein isoforms they map to.
These two steps allow us to recover the presence and abundance of each protein isoform.
IsoBayes is available on Bioconductor and can be installed with the command:
if (!requireNamespace("BiocManager", quietly=TRUE))
install.packages("BiocManager")
BiocManager::install("IsoBayes")The vignette illustrating how to use the package can be accessed on Bioconductor or from R via:
vignette("IsoBayes")or
browseVignettes("IsoBayes")IsoBayes works directly with the output of MetaMorpheus (MM), or Percolator (via the OpenMS toolkit). Additionally, users can also provide MS data obtained from any bioinformatics tool.
To this aim, the data must be organized in a .tsv file, a data.frame or in the rowData object of a SummarizedExperiment.
In all cases, each row corresponds to a peptide, and columns refer to:
- 'Y': a numeric variable indicating the peptide abundance (PSM counts or intensities, as defined by the user);
- 'EC': Equivalent Classes, a character string indicating the isoform(s) name the peptide maps to. If the peptide maps to multiple protein isoforms, the names must be separated with "|" , i.e. "name_isoform_1|name_isoform_2";
- 'FDR': (optional) a numeric variable indicating the FDR of each peptide;
- 'PEP': (optional) a numeric variable indicating the probability that a peptide is erroneously detected;
- 'sequence': (required when using PEP) a character string indicating the peptide name/id/amino acids sequence.
For more details and examples on how to load user-provided data, see the "Input user-provided data" Section of the vignettes.
In our benchmarks, we tested our model using both MetaMorpheus and Percolator data, and obtained slightly better results, and a shorter runtime with MetaMorpheus.
To generate the MM output files required to run IsoBayes, we need to execute the following commands:
- Install MetaMorpheus via Conda:
conda install -c conda-forge metamorpheus- Inside the folder with the configuration (.toml), spectra (.mzML or .raw) and database (.xml) files run:
metamorpheus -t Task1-SearchTaskconfig.toml Task2-CalibrateTaskconfig.toml Task3-SearchTaskconfig.toml Task4-GPTMDTaskconfig.toml Task5-SearchTaskconfig.toml -s 04-30-13_CAST_Frac4_6uL.raw 04-30-13_CAST_Frac5_4uL.raw -d uniprot-mouse-reviewed-1-24-2018.xml.gz uniprot-cRAP-1-24-2018.xml.gzor
metamorpheus -t Task1-SearchTaskconfig.toml Task2-CalibrateTaskconfig.toml Task3-SearchTaskconfig.toml Task4-GPTMDTaskconfig.toml Task5-SearchTaskconfig.toml -s mzML/04-30-13_CAST_Frac4_6uL.mzML mzML/04-30-13_CAST_Frac5_4uL.mzML -d uniprot-mouse-reviewed-1-24-2018.xml.gz uniprot-cRAP-1-24-2018.xml.gzThere are several ways to install and run MM. For more details see the MM tutorial, where you can also find the example files used here.
We provide a brief pipeline where several OpenMS applications are chained together to generate an idXML file required to run IsoBayes with Percolator output. The pipeline starts from peptide identification results stored in mzID files.
First, install OpenMS toolkit and Percolator tool. For instructions on how to install them on your operating system see OpenMS Installation and Percolator Installation.
Next, declare some useful global variable:
path_to_data=/path/to/mzIDfiles
path_out=/path/to/output
NTHREADS=4
ENZYME_indexer="Chymotrypsin"
ENZYME_percolator="chymotrypsin"
DECOY_STRING="mz|DECOY_"
fdr=1Below, we show an example with chymotrypsin enzyme.
If the data was generated with another enzyme, please search for the corresponding enzyme in the following documentation below, and reset the global variables ENZYME_indexer and ENZYME_percolator with the correct enzyme.
PeptideIndexer --help
PercolatorAdapter --helpThis pipeline also assumes that in the /path/to/mzIDfiles folder there is a fasta file listing target and decoy protein isoforms.
The DECOY_STRING allows you to change the string needed to identify a decoy in the fasta file.
cd $path_out
# convert mzID files into idXML files
for mz in $path_to_data/*.mzID
do
IDFileConverter -in $mz -threads $NTHREADS -out $mz.idXML
done
# merge the files
IDMerger -in $path_to_data/*.idXML -threads $NTHREADS -merge_proteins_add_PSMs -out $path_out/merge.idXML
rm $path_to_data/*.idXML
# index the peptide file with the fasta file
PeptideIndexer -in $path_out/merge.idXML -enzyme:name $ENZYME_indexer -threads $NTHREADS -decoy_string_position prefix -decoy_string $DECOY_STRING -fasta $path_to_data/genecodeAndDecoy.fasta -out $path_out/merge_index.idXML
rm $path_out/merge.idXML
# run percolator
PercolatorAdapter -in $path_out/merge_index.idXML -enzyme $ENZYME_percolator -threads $NTHREADS -generic_feature_set -score_type pep -out $path_out/merge_index_percolator_pep.idXML
rm $path_out/merge_index.idXML
# Estimate the false discovery rate on peptide level using decoy searches and keep the ones with FDR < $fdr
FalseDiscoveryRate -in $path_out/merge_index_percolator_pep.idXML -out $path_out/merge_index_percolator_pep_$fdr.idXML -protein false -threads $NTHREADS -FDR:PSM $fdr -algorithm:add_decoy_peptides -algorithm:add_decoy_proteins
rm $path_out/merge_index_percolator_pep.idXML
# Associate each peptite with Posterior Error Probability score
IDScoreSwitcher -in $path_out/merge_index_percolator_pep_$fdr.idXML -out $path_out/merge_index_percolator_pep_switched_$fdr.idXML -new_score 'Posterior Error Probability_score' -new_score_orientation lower_better -new_score_type pep -threads $NTHREADS
rm $path_out/merge_index_percolator_pep_$fdr.idXMLFor more details on OpenMS tools see its Documentation.
Röst, H. L., Sachsenberg, T., Aiche, S., Bielow, C., Weisser, H., Aicheler, F., ... & Kohlbacher, O. (2016). OpenMS: a flexible open-source software platform for mass spectrometry data analysis. Nature methods, 13(9), 741-748.
The, M., MacCoss, M. J., Noble, W. S., & Käll, L. (2016). Fast and accurate protein false discovery rates on large-scale proteomics data sets with percolator 3.0. Journal of the American Society for Mass Spectrometry, 27, 1719-1727.
Solntsev, S. K., Shortreed, M. R., Frey, B. L., & Smith, L. M. (2018). Enhanced global post-translational modification discovery with MetaMorpheus. Journal of proteome research, 17(5), 1844-1851.