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---
title: 'Appendix for "A Bioconductor workflow for processing, evaluating, and interpreting
expression proteomics data"'
subtitle: Cambridge Centre for Proteomics, University of Cambridge
author: Charlotte Hutchings, Charlotte S. Dawson, Thomas Krueger, Kathryn S. Lilley, Lisa M. Breckels
output: pdf_document
bibliography: refs.bib
---
# Appendix
This Appendix accompanies the paper ["A Bioconductor workflow for processing,
evaluating, and interpreting expression proteomics
data"](https://github.com/CambridgeCentreForProteomics/f1000_expression_proteomics/blob/main/workflow_expressions.pdf)
by Hutchings et al, submitted to F1000Research in August 2023. Associated data can be found
on Zenodo at [http://doi.org/10.5281/zenodo.13753944](http://doi.org/10.5281/zenodo.13753944)
and also in the Github repository https://github.com/CambridgeCentreForProteomics/f1000_expression_proteomics/.
# Identification search with Proteome Discoverer
The use-case data analyzed in this workflow was initially processed using
[Proteome Discoverer](https://www.thermofisher.com/uk/en/home/industrial/mass-spectrometry/liquid-chromatography-mass-spectrometry-lc-ms/lc-ms-software/multi-omics-data-analysis/proteome-discoverer-software.html)
version 2.5. Whilst much of the identification and quantification takes place
out of sight of the user, Proteome Discoverer incorporates several user-defined
search parameters which must be specified according to the sample preparation
methods and MS instrumentation used. There is also the option to apply both
basic and advanced data filtering parameters during the search. Users must be
aware of these parameters as they will directly influence the data output and
downstream processing.
Whilst an in-depth discussion of identification searches is outside of the scope
of this workflow, a few key parameters are discussed to put the data into
context. During sample preparation, TMT-labelled cell pellets were combined and
separated into 8 fractions using a Pierce High pH Reversed-Phase Peptide
Fractionation Kit (Thermo Fisher Scientific). After being analyzed by MS, the 8
resulting raw files were uploaded to Proteome Discoverer 2.5 and processed using
a single processing and consensus workflow. LFQ supernatant fractions were each
analyzed on a separate mass spectrometry run resulting in 6 raw files. These
files were imported into Proteome Discoverer with each sample having its own
independent processing step followed by a single multi-consensus step. All
processing and consensus workflow templates are provided in the supplementary
materials.
For both TMT and LFQ workflows, SequestHT was selected as the search engine and
trypsin specified as the enzyme used for proteolytic digestion. Since the
digestion was carried out overnight with a 1:20 w/w ratio of trypsin:protein,
digestion was expected to be complete and a low threshold of 2 missed cleavages
was allowed. For MS analysis, a Fourier Transform orbitrap with a resolving
power of 120,000 m/z was used as the mass analyzer for precursor ion mass, and a
linear ion trap was used to measure fragment ion mass. This information
determined the thresholds for precursor and fragment mass tolerances, two key
parameters for the identification search. The precursor mass tolerance
determines which mass range of peptide sequences are considered for each
observed spectrum, whilst the fragment mass tolerance specifies how similar the
observed and theoretical peptide fragment spectra should be for a match. If
these tolerances are too narrow then the correct peptide sequence may be omitted
and true positives are lost. However, if thresholds are set too wide then
incorrect peptide sequences are considered and false positives arise. Based on
the instrumentation used in this experiment, standard mass tolerances of 10 ppm
and 0.5 Da were allowed for precursors and fragments, respectively. Given the
intrinsic variability of LFQ between MS runs, RT alignment was used for the
label-free samples with a 10-minute retention time window.
In addition to the parameters based on the experimental protocol, we also
applied some basic non-specific filtering. We only retained high confidence PSMs
from the identification search. Such filtering is necessary because only a
fraction of the PSMs outputted by any given search engine will be genuine
matches, or true discoveries, whilst the remainder are incorrect false
discoveries. To deal with this problem, PSM confidence level (high, medium or
low) is determined via the Proteome Discoverer Percolator node [@Kll2007] which
estimates each PSM's false discovery rate (FDR). The raw spectra are searched
against the database of interest as well as a decoy database containing
randomised peptide sequences, often generated by shuffling or reversing the
original peptide sequences. False discovery rate is then defined as the
proportion of total PSMs that are matched to the decoy database, and, therefore,
are known false discoveries. This is done for all spectra and we considered a
PSM to be of 'high confidence' if it had a false discovery rate <1 %, 'medium
confidence' if <5 %, and 'low confidence' if the false discovery rate exceeded
5 %. Only PSMs annotated as high confidence were kept.
Whilst the basic filtering steps completed during this identification search
could just have easily been carried out in R using the `SummarizedExperiment`
and `QFeatures` infrastructure, applying them here saves time later on and
reduces the burden of storing large data files. These steps are also relatively
standard and non-specific so we do not need to assess the data prior to their
implementation. However, Proteome Discoverer also provides the option to carry
out more in-depth filtering through the use of parameters such as the SPS Mass
Match %, co-isolation interference % and signal-to-noise thresholds. We advise
against implementing such filtering at this stage since decisions regarding
thresholds will likely be influenced by the quality of data output, as
demonstrated later in this workflow. Instead, thresholds for the three
aforementioned parameters were set to 0 during the identification search.
# Using this workflow with MaxQuant data
This workflow was written for proteomics data processed using the Proteome
Discoverer software. Nevertheless, the workflow and basic principles discussed
are also applicable to the output of any similar proteomics raw data processing
software, including MaxQuant. Below we outline the differences to be aware of
when following this workflow using MaxQuant output text files. The code as
written will require some minor modifications to work properly with MaxQuant
formatted data.
1. The rough equivalent of the PSMs.txt file output by Proteome Discoverer is
the evidence.txt file output by MaxQuant.
2. Decoy PSMs (known false discoveries which are used to calculate false discovery
rate) are automatically filtered out by Proteome Discoverer, but this is not the
case with MaxQuant. Hence when working with MaxQuant outputs it is important to
filter out rows with '+' in the `Reverse` column.
3. Equivalent column names and the type of data contained are described here.
Ellipses are put where there no equivalent column exists.
In PD the file `PSMs.txt` is equivalent to the MaxQuant `evidence.txt` file.
Equivalent column names are shown in the table below:
| Proteome Discoverer | MaxQuant |
| ------------------- | ------------------------ |
| `Abundance` (float) | `Reporter.intensity.corrected` (integer) |
| `Sequence` (string) | `Sequence` (string) |
| `Master.Protein.Accessions` (string) | `Leading.proteins` (string) |
| `Master.Protein.Descriptions` (string) | ... |
| `Contaminants` (string, True or False) | `Potential.contaminant` (string, + or blank) |
| ... | `Reverse` (string, + or blank) |
| `Rank` (integer) | ... |
| `Search.Engine.Rank` (integer) | ... |
| `PSM.Ambiguity` (string) | ... |
| `Number.of.Protein.Groups` (integer) | ... (You might calculate this by counting the number of ; in the Leading.proteins column and adding 1) |
| `Average.Reporter.SN` (float) | ... (You might calculate the average reporter ion intensity and threshold based on that instead) |
| `Isolation.Interference.in.Percent` (float) | `PIF` (float, to get the data in exactly the same format you have to calculate (1 - PIF)/100) |
| `SPS.Mass.Matches.in.Percent` (integer) | ... |
In PD the `Proteins.txt` file is equivalent to MaxQuant `proteinGroups.txt` file
and equivalent columns are:
| Proteome Discoverer | MaxQuant |
| ------------------- | --------- |
| `Accession` (string) | `Majority.protein.IDs` (string) |
| `Protein.FDR.Confidence.Combined` (string; High, Medium, or Low) | `Q.value` (float, a Proteome Discoverer protein FDR of 'High' is equivalent to a Q.value < 0.01) |
## References