ArticleMicrobiome2021
Using high-abundance proteins as guides for fast and effective peptide/protein identification from human gut metaproteomic data.
Article in Microbiome, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed.
- Comparative performance of Scribe and database search engines in metaproteomic profiling of a ground-truth microbiome dataset.Journal of proteomics · 2026Article
- Lipid alterations in acute myocardial infarction are associated with gut microbiota.Microbiology spectrum · 2025Article
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- [Microbial metaproteomics--From sample processing to data acquisition and analysis].Se pu = Chinese journal of chromatography · 2024Review
- Contigs directed gene annotation (ConDiGA) for accurate protein sequence database construction in metaproteomics.Microbiome · 2024Article
- NovoLign: metaproteomics by sequence alignment.ISME communications · 2024Article
- GroEL-Proteotyping of Bacterial Communities Using Tandem Mass Spectrometry.International journal of molecular sciences · 2023Article
- MetaProD: A Highly-Configurable Mass Spectrometry Analyzer for Multiplexed Proteomic and Metaproteomic Data.Journal of proteome research · 2023Article
- MetaLab-MAG: A Metaproteomic Data Analysis Platform for Genome-Level Characterization of Microbiomes from the Metagenome-Assembled Genomes Database.Journal of proteome research · 2023Article
- Data-independent acquisition boosts quantitative metaproteomics for deep characterization of gut microbiota.NPJ biofilms and microbiomes · 2023Article
- Gut Microbiome Proteomics in Food Allergies.International journal of molecular sciences · 2023Review
- Metaproteomics as a tool for studying the protein landscape of human-gut bacterial species.PLoS computational biology · 2022Article
- In-Depth Metaproteomics Analysis of Oral Microbiome for Lung Cancer.Research (Washington, D.C.) · 2022Article
- Increasing the power of interpretation for soil metaproteomics data.Microbiome · 2021Article
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Abstract
backgroundA few recent large efforts significantly expanded the collection of human-associated bacterial genomes, which now contains thousands of entities including reference complete/draft genomes and metagenome assembled genomes (MAGs). These genomes provide useful resource for studying the functionality of the human-associated microbiome and their relationship with human health and diseases. One application of these genomes is to provide a universal reference for database search in metaproteomic studies, when matched metagenomic/metatranscriptomic data are unavailable. However, a greater collection of reference genomes may not necessarily result in better peptide/protein identification because the increase of search space often leads to fewer spectrum-peptide matches, not to mention the drastic increase of computation time. Video Abstract
methodsHere, we present a new approach that uses two steps to optimize the use of the reference genomes and MAGs as the universal reference for human gut metaproteomic MS/MS data analysis. The first step is to use only the high-abundance proteins (HAPs) (i.e., ribosomal proteins and elongation factors) for metaproteomic MS/MS database search and, based on the identification results, to derive the taxonomic composition of the underlying microbial community. The second step is to expand the search database by including all proteins from identified abundant species. We call our approach HAPiID (HAPs guided metaproteomics IDentification).
resultsWe tested our approach using human gut metaproteomic datasets from a previous study and compared it to the state-of-the-art reference database search method MetaPro-IQ for metaproteomic identification in studying human gut microbiota. Our results show that our two-steps method not only performed significantly faster but also was able to identify more peptides. We further demonstrated the application of HAPiID to revealing protein profiles of individual human-associated bacterial species, one or a few species at a time, using metaproteomic data.
conclusionsThe HAP guided profiling approach presents a novel effective way for constructing target database for metaproteomic data analysis. The HAPiID pipeline built upon this approach provides a universal tool for analyzing human gut-associated metaproteomic data.
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