Exploring and solving functional analysis gaps in metaproteomics

[tivdnbos]

Title

Exploring and solving functional analysis gaps in metaproteomics.

Abstract

Metaproteomics, the study of the full protein complement of microbial communities, is becoming an increasingly popular approach to study microbiomes and is mostly combined with metagenomics or -transcriptomics. Although the latter approaches are more commonly used, they only reveal the potential functions of the microbiome. Metaproteomics, in contrast, reveals the actual functions of the microbiome because it studies the proteins, the worker molecules of the cell [doi: 10.1080/14789450.2020.1738931]. Specifically, these functions are described as functional annotations such as InterPro and GO terms, and these terms can often be mapped to functional pathways. Although this shows the added value of metaproteomics, there are still some major hurdles to take in optimizing the functional analyses of microbiomes. The input from the community will prove particularly useful here, as every lab or research group might have their own tools and therefore own problems in performing a functional analysis. This project will explore the challenges in the functional analyses of metaproteomics data, prioritize them, and undertake the first steps in solving the most urgent and technical feasible one.

Project Plan

The first step will be to create an overview with the biggest challenges in the functional analysis in metaproteomics. We will create this overview based on the input from the participants from the hackathon, the literature, and from information received from multiple wet-lab research groups in the Metaproteomics Initiative [doi: 10.1186/s40168-021-01176-w] who will be consulted before the hackathon starts. These challenges could be summarized in a review. In the second step, we will prioritize these challenges based on urgency. In the third step, we will start solving one of these challenges based on urgency and technical feasibility. After the hackathon, we will continue the work on this project, make the open-source software freely available, and report the results in a technical note.

Technical Details

This must be discussed with the hackathon group, but we aim to work in Python because it is a very accessible language, and often used in the community.

Contact Information

Bart Mesuere, Ghent University, Bart.Mesuere@UGent.be Tim Van Den Bossche, VIB - Ghent University, Tim.VanDenBossche@UGent.be Tibo Vande Moortele, Ghent University, Tibo.VandeMoortele@UGent.be Pieter Verschaffelt, Ghent University, Pieter.Verschaffelt@UGent.be

[tobiasko]

Hi @tivdnbos and co-applicants,

thanks for submitting your hackathon proposal. Could you please add some more details (at least an abstract) so it becomes more clear the hack would be about?

THX, Tobi

[tivdnbos]

Hi Tobi,

Of course, we just created a placeholder. We're going to submit our proposal latest on the submission deadline of September 30.

Best, Tim

[tobiasko]

Dear @tivdnbos ,

I am happy to inform you that your proposal has been selected for the DevMeeting2023! Participants will decide which hackathon to join after the pitch on Monday. Co-submitters: Please leave a short comment so you become part of this issue!

Best, Tobi

[tobiasko]

Hello everyone,

I just created a slack workspace for the DevMeeting and a channel named gaps-in-metaproteomics for this hack. You should receive an invite to join by email.

Best, Tobi

[tivdnbos]

During our hackathon, titled “Exploring and solving functional analysis gaps in metaproteomics”, we kicked off with a brainstorm session to identify the most pressing challenges in metaproteomics. One of the gaps we identified was the lack of a peptide-centric pathway visualization tool that allows users to filter on specific taxa and conditions. Indeed, the common procedure for functional analysis assigns identified proteins to their functions through functional identifiers, such as those present in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database[1,2]. Nevertheless, peptide-centric downstream analysis tools such as Unipept[3] require a list of peptides, by-passing the error-prone protein grouping step. During the hackathon, we therefore developed such a web application that allows users to visualize metabolic KEGG pathways where certain taxa or conditions can be specifically highlighted, given a list of PSMs, peptides or proteins. In the future, we’ll also foresee the input of genes to facilitate metagenomics users.

(GitHub: TBD after having an appropriate name, will be discussed later this week)

(1) Lohmann, P.; Schäpe, S. S.; Haange, S.-B.; Oliphant, K.; Allen-Vercoe, E.; Jehmlich, N.; Von Bergen, M. Function Is What Counts: How Microbial Community Complexity Affects Species, Proteome and Pathway Coverage in Metaproteomics. Expert Rev. Proteomics 2020, 17 (2), 163–173. (2) Kanehisa, M.; Goto, S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 2000, 28 (1), 27–30. (3) Gurdeep Singh, R.; Tanca, A.; Palomba, A.; Van der Jeugt, F.; Verschaffelt, P.; Uzzau, S.; Martens, L.; Dawyndt, P.; Mesuere, B. Unipept 4.0: Functional Analysis of Metaproteome Data. J. Proteome Res. 2019, 18 (2), 606–615.