Immunolyser: A web-based computational pipeline for analysing and mining immunopeptidomic data

Abstract

Immunopeptidomics has made tremendous contributions to our understanding of antigen processing and presentation, by identifying and quantifying antigenic peptides presented on the cell surface by Major Histocompatibility Complex (MHC) molecules. Large and complex immunopeptidomics datasets can now be routinely generated using Liquid Chromatography-Mass Spectrometry techniques. The analysis of this data - often consisting of multiple replicates/conditions - rarely follows a standard data processing pipeline, hindering the reproducibility and depth of analysis of immunopeptidomic data. Here, we present Immunolyser, an automated pipeline designed to facilitate computational analysis of immunopeptidomic data with a minimal initial setup. Immunolyser brings together routine analyses, including peptide length distribution, peptide motif analysis, sequence clustering, peptide-MHC binding affinity prediction, and source protein analysis. Immunolyser provides a user-friendly and interactive interface via its webserver and is freely available for academic purposes at https://immunolyser.erc.monash.edu/. The open-access source code can be downloaded at our GitHub repository: https://github.com/prmunday/Immunolyser. We anticipate that Immunolyser will serve as a prominent computational pipeline to facilitate effortless and reproducible analysis of immunopeptidomic data.

Keywords: Antigen processing and presentation; Immunopeptidomics; Peptide analysis.

Fig. 1

A schematic illustration of the Immunolyser framework. (A) Dataset upload and quality check; (B) the initialiser module handles data pre-processing and parameter configuration; (C) the analytics module assesses submitted peptides for length distribution, peptide clustering, peptide-HLA binding affinity prediction and peptide-to-protein alignment (via Pepscanner); (D) Immunolyser is accessible via a web application with a user-friendly interface.

Fig. 2

Immunolyser screenshots from analysis results for the HLPC and MWCO filtration datasets extracted from the study of Pandey et al. . Peptide length distribution includes (A) the relative frequency distribution, and the frequency distribution of peptides in both datasets. Error bars represent the standard deviation for every n-mer from 3 replicates. The HLPC data is in blue and the MWCO data is in red. (B) Toggle button to switch between relative frequency or peptide number charts (here, both are shown to showcase the different charts made). (C) Peptide overlap analysis using an UpSet plot across conditions. (D) Peptide motif analysis includes the sequence logos generated for all three replicates of peptides from the RP-HPLC and the MWCO Filtration methods, respectively. (E) Peptide clustering results and the corresponding sequence logos generated by GibbsCluster 2.0 for the RP-HPLC dataset. The results for all three HPLC replicates are shown. Each replicate is represented with a bar graph of KLD scores for clustering attempts using from 1 to 5 subgroups and the sequence logos for the sub-groups of the cluster with the highest KLD score. On the top right is a selection menu to select samples to be displayed. Users can drag motifs or use the dots and arrows to navigate to the remaining logos. (F) A drop-down menu to re-generate clustering results using a different number of sub-groups from 1 to 5 or to reset back to the number yielding the maximum KLD score. (G) A pannel for users to select to remove/add clustering results of different samples.

Fig. 3

The UpSet plot generated using HLA-allotype binding prediction results for the HLA-B*15:11 allele. (A) The selection menu to choose the allele to be viewed. (B) The interactive UpSet plot showing the numbers of predicted binders. (C) The dropdown menu to select between majority-voted binders or any of the three binding prediction tools. (D) The pop-up dialogue box showing the sequence logo generated using the binders belonging to the selected bar (can be any subset or set). (E) A link to download the list of binders belonging to the selected bar. (F) The name of the subset and the number of total 9-mer binders used to generate the sequence logo out of the total binders belonging to the selected bar. (G) Downloadable peptide-MHC binding results generated by prediction tools against each allele selected.

Fig. 4

Using Pepscanner to analyse contextual information of submitted peptides and their location within protein sequences. (A) The ‘Pepscanner’ tab. (B) Metadata table of the uploaded input file that includes information on the top 10 occurring proteins in the file. (C) The generated heatmap demonstrating the distributions of peptides in different proteins. (D) Upload of data in the CSV format for the alignment to source proteins. (E) Selecting specific proteins of interest to investigate (for human proteins only). A demo has been provided at the bottom of the Pepscanner page.