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Related Concept Videos

Proteomics01:33

Proteomics

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 proteomics...
Peptide Identification Using Tandem Mass Spectrometry01:33

Peptide Identification Using Tandem Mass Spectrometry

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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Related Experiment Video

Updated: Jul 3, 2026

Comprehensive Workflow of Mass Spectrometry-based Shotgun Proteomics of Tissue Samples
14:51

Comprehensive Workflow of Mass Spectrometry-based Shotgun Proteomics of Tissue Samples

Published on: November 13, 2021

PatternLab for proteomics: a tool for differential shotgun proteomics.

Paulo C Carvalho1, Juliana S G Fischer, Emily I Chen

  • 1Systems Engineering and Computer Science Program, COPPE, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil. carvalhopc@cos.ufrj.br

BMC Bioinformatics
|July 23, 2008
PubMed
Summary
This summary is machine-generated.

PatternLab software aids proteomics by offering new methods for normalizing spectral count data and identifying protein expression differences, even with limited or varied experimental protocols. It provides tools for analyzing complex biological system states.

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Last Updated: Jul 3, 2026

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Area of Science:

  • Proteomics
  • Computational Biology
  • Biostatistics

Background:

  • Distinguishing biological system states relies on identifying protein expression differences.
  • Existing spectral counting methods in proteomics face challenges in data normalization and pinpointing profile differences, especially with varied experimental protocols.

Purpose of the Study:

  • To introduce PatternLab, a software program designed to address open issues in proteomics data analysis.
  • To provide novel methods for normalizing spectral counting data and identifying differentially expressed proteins.

Main Methods:

  • PatternLab implements existing strategies and introduces two new methods: ACFold for experiments with limited replicates or diverse protocols, and nSVM for designs with multiple replicates.
  • ACFold utilizes expression fold changes, the AC test, and false-discovery rate for a broad overview of differentially expressed proteins.
  • nSVM, rooted in evolutionary computing and statistical learning theory, is suited for selecting minimal protein sets for classification.

Main Results:

  • Both ACFold and nSVM methods were demonstrated effectively on experimental data.
  • ACFold provides a "bird's-eye view" of differentially expressed proteins in challenging experimental setups.
  • nSVM shows promise for projects requiring minimal protein sets for classification, such as early disease detection.

Conclusions:

  • PatternLab provides unified access to diverse feature selection and normalization strategies for proteomics.
  • The software includes graphing tools to facilitate the analysis of high-throughput experimental data.
  • PatternLab is accessible at http://pcarvalho.com/patternlab.