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Proteogenomics 2: Database Searching

Galaxy Training Network Smörgåsbord

February 18^th, 2021

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‘Omics Technologies In The Era Of Systems Biology

A prominent approach in modern biological research is Systems Biology, wherein a plurality of biomolecules in a system are measured simultaneously to ascertain the response of that system to stimuli. This is accomplished through multi-omics technologies, each of which each measure a different biological molecule. The contents of a genome are determined through sequencing in a process known as genomics; similarly, the degree of genomic methylation and chromatin architecture through epigenomics. The totality of gene transcription can be examined through mRNA sequencing, a processing known as transcriptomics. The phenotype of a given system can be more directly ascertained through ‘omics technologies such as proteomics and metabolomics, which measure the proteins present in a system and the small molecules produced by those proteins, respectively.

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Mass Spectrometry-based Proteomics

Our lab’s particular focus is on proteomics and on the analysis of proteomics data in Galaxy. Modern proteomics approaches utilize mass spectrometry to identify and at times quantify the proteins in a biological sample. In a conventional proteomics experiment, proteins are digested enzymatically into shorter constituents called peptides for ease of downstream analysis, a technique called “bottom up” proteomics. This results in an exceedingly complex mixture; to analyze them all at the same time would be akin to trying to judge the makeup of an entire crowd all at once. Just as this crowd can be separated using queues and turnstiles, the peptide mixture is separated using liquid chromatography, resulting in a few peptides entering the mass spectrometer at a time. Within the mass spectrometer, the mass to charge ratio of the peptides is measured before the peptides are then fragmented into smaller pieces; the mass to charge ratios of these pieces are then measured in a separate event called an MS2 spectrum.

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Once MS2 spectra are collected, the mass spectra can then be searched against a reference database using bioinformatics to determine the peptides (and therefore proteins) present in the sample. When the peptide fragments in the mass spectrometer, the peptide breaks up in a predictable way along the peptide backbone, giving series of ions made up of the peptide fragmented at discrete locations on the backbone. Within the proteomics software, these ion series are treated as a sort of fingerprint and compared against the theoretical peptides within the database, looking for a match that would correspond to these ion series. Once a spectrum is annotated with peptide sequence, it is designated a peptide-spectral match and is assigned a score depending on the quality of the match. These PSMs can then be assembled to identify proteins; in this way, thousands of proteins can be identified and potentially quantified in a single sample.

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Looking Beyond The Known Proteome

Mass spectrum

Reference Protein Database

from genomic annotation

Cancer / Disease related

Databases such as COSMIC,

IARC p53, OMIM…

Deep genome sequencing data from ICGC, TCGA and CPTAC

RNASeq data

(Customized OR Combined)

6-frame DNA sequences.

3-frame cDNA sequences.

Identification of peptides corresponding to novel proteoforms.

Ideally, identifying all the peptides in sample would be as simple as optimizing the collection of data by the mass spectrometer. However, it is important to note that there could be spectra within the dataset that might be originating from unannotated portion of the proteome. By using only canonical reference proteome databases in bottom-up proteomics, it is theoretically possible that an enormous amount of biological information is lost. This can be corrected for by making custom databases that contain the canonical reference protein in addition to experiment specific extra data. For example, any sequence variants that are not present in the reference proteome can be identified using a six-frame translation of genome, three-frame translation of the transcriptome, or protein database derived from RNA-Seq experiments. This leads to identification of single amino acid substitutions, frame-shift mutations, and alternative splice isoforms – now called proteoforms. The use of transcriptomic and/or genomic data to supplement proteomic analysis and identify novel proteoforms is termed “proteogenomics”, and this is the approach my colleagues and I hope to communicate to you.

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Proteogenomics Workflows in Galaxy

Database Searching

Using MS/MS data

Performing proteogenomics analyses, as it has been with most multi-omics analyses, has historically been somewhat taxing, requiring considerable time and computational finesse on the part of the analyst. Fortunately, many tools used in proteogenomic analyses have been uploaded into Galaxy, where even the most novice bioinformaticion can readily use them in a simple graphical user interface. What’s more, the tools can be utilized together in workflows, allowing for analyses to occur automatically when given the starting datasets, ultimately saving the analysts time. This graphic represents a workflow containing all the requisite steps in proteogenomic analysis; for this tutorial, I will be focused on this section highlighted in red where mass spectrometry data is searched against custom databases to identify putative novel peptides.

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Proteogenomics Database Search Workflow

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Database Search Workflow: Input files

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Input files for Database Searching

Created with Biorender.com

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Input files for Database Searching

Created with Biorender.com

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Input files for Database Searching

Created with Biorender.com

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Input files for Database Searching

Created with Biorender.com

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Database Search Workflow: SearchGUI

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SearchGUI matches MS/MS spectra to peptide sequences

Several peptide matching algorithms have been developed over the years
SearchGUI allows for multiple search engines to run simultaneously

Created with Biorender.com

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SearchGUI matches MS/MS spectra to peptide sequences

Specific digestion conditions can be selected
Mass spectrometer parameters can be selected to maximize the efficacy of spectral matches
Post-Translational Modifications (PTMs) can be added to the search parameters

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Database Search Workflow: Peptide Shaker

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Peptide Shaker filters the results of Search GUI

Search GUI results are filtered by FDR to yield most confident peptide spectral matches (PSMs)
Peptide Shaker outputs mzIdentML files for future analysis

Vaudel et al. Nat Biotechnol 33, 22–24 (2015).

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Database Search Workflow: Data Filtration

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Data Filtration

Search GUI + Peptide Shaker will identify PSMs for all peptides in a sample
In proteogenomic analysis we’re interested primarily in novel peptides that are not found in the conventional proteome
These filtration steps remove peptides corresponding to normal peptides, contaminating peptides etc.

All PSMs identified in MS data

Novel PSMs

Novel Peptides

Contaminants

Normal Peptides

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Database Search Workflow: Mz to SQLite

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MZ to SQLite

Peptide Shaker generates mzIdenML files to store peptide/protein identification data
MZ-to-SQLite converts mzIdenML files into the mzsqlite format for analysis in the Multi-omics Viewing Platform

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Database Search Workflow: Tabular to FASTA

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Tabular to FASTA

FASTA files are required for subsequent BLAST-P analyses in the third proteogenomics workflow
Ensure that the Title column is column 1 of the previous output, Sequence Column is column 2

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Now let’s go through how to set up this workflow in Galaxy…

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Other Galaxy-P Tutorials in the GTN Smörgåsbord

Custom Database Creation

James Johnson

Novel Peptide Analysis

Subina Mehta

Metaproteomics

Pratik Jagtap

Introduction to Proteogenomics

Tim Griffin

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Supplementary tutorials for proteogenomics can be found at the Galaxy Training Network ��https://training.galaxyproject.org/training-material/topics/proteomics/tutorials/proteogenomics-dbsearch/tutorial.html