Dataset Information

Soil metaproteomics data analysis based on de novo sequencing with the deep learning-based Kaiko model

ABSTRACT: We generated a protein database directly from soil metaproteomic data by identifying the microbial composition using the Kaiko model's de novo sequencing methods. We first analyzed the mass spectra de novo (without a database), identifying species from the observed peptides. We next gathered full proteomic databases for the identified species and searched the mass spec data using MS-GF+ and this custom-assembled protein sequence database.

INSTRUMENT(S): Q Exactive HF

ORGANISM(S): Environmental Samples <bacteria> (ncbitaxon:48479)

SUBMITTER: Janet Jansson Kristin Burnum-Johnson

PROVIDER: MSV000086336 | MassIVE | Tue Oct 20 12:32:00 BST 2020

REPOSITORIES: MassIVE

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Publications

Uncovering Hidden Members and Functions of the Soil Microbiome Using <i>De Novo</i> Metaproteomics.

Lee Joon-Yong JY Mitchell Hugh D HD Burnet Meagan C MC Wu Ruonan R Jenson Sarah C SC Merkley Eric D ED Nakayasu Ernesto S ES Nicora Carrie D CD Jansson Janet K JK Burnum-Johnson Kristin E KE Payne Samuel H SH

Journal of proteome research 20220706 8

Metaproteomics has been increasingly utilized for high-throughput characterization of proteins in complex environments and has been demonstrated to provide insights into microbial composition and functional roles. However, significant challenges remain in metaproteomic data analysis, including creation of a sample-specific protein sequence database. A well-matched database is a requirement for successful metaproteomics analysis, and the accuracy and sensitivity of PSM identification algorithms s ...[more]

PMID: 35793793

Dataset Information

Soil metaproteomics data analysis based on de novo sequencing with the deep learning-based Kaiko model

Dataset's files

Publications

Uncovering Hidden Members and Functions of the Soil Microbiome Using <i>De Novo</i> Metaproteomics.

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