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Related Concept Videos

Proteomics01:33

Proteomics

7.2K
A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...
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Peptide Identification Using Tandem Mass Spectrometry01:33

Peptide Identification Using Tandem Mass Spectrometry

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Tandem mass spectrometry, also known as MS/MS or MS2, is an analytical technique that employs two mass analyzers. Essentially it is a series of mass spectrometers that helps isolate a particular biomolecule and then helps study its chemical properties.
This technique helps gather information regarding the protein from which the peptide was obtained and to study the peptides’ amino acid sequence. Identifying peptides from a complex mixture is an important component of the growing field of...
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Updated: Jun 10, 2025

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
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Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

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LineageFilter: Improved Proteotyping of Complex Samples Using Metaproteomics and Machine Learning.

Hamid Hachemi1,2, Jean Armengaud1, Lucia Grenga1

  • 1Département Médicaments et Technologies pour la Santé (DMTS), Université Paris-Saclay, CEA, INRAE, SPI, 30200 Bagnols-sur-Cèze, France.

Journal of Proteome Research
|October 19, 2024
PubMed
Summary
This summary is machine-generated.

LineageFilter is new AI software that refines metaproteomics analysis for complex microbial samples. It improves taxonomic classification by using machine learning to assess taxon presence, aiding in accurate sample-specific database construction.

Keywords:
machine learningmetaproteomicsmicrobiomesproteotypingtaxonomy

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Area of Science:

  • Microbiology
  • Bioinformatics
  • Proteomics

Background:

  • Metaproteomics analyzes microbial protein content to understand microbiota function.
  • Accurate taxonomic profiling of complex microbial communities from metaproteomic data is challenging.
  • Current methods struggle with precise identification based solely on peptide sequences.

Purpose of the Study:

  • To introduce LineageFilter, a novel Python-based AI software.
  • To enable refined proteotyping of complex samples using metaproteomics data and machine learning.
  • To improve the accuracy of taxonomic composition assessment in microbial communities.

Main Methods:

  • LineageFilter utilizes machine learning algorithms.
  • It processes taxon lists, abundances, and peptide scores from metaproteomic data.
  • The software computes taxon-specific features across all taxonomic ranks.

Main Results:

  • LineageFilter assesses the likelihood of taxon presence based on computed features.
  • It provides refined proteotyping for complex microbial samples.
  • The software facilitates the construction of sample-specific databases.

Conclusions:

  • LineageFilter enhances the interpretation of metaproteomic data for microbial community analysis.
  • The AI-driven approach improves taxonomic resolution and accuracy.
  • This tool aids in a deeper understanding of microbial ecosystem functions.