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Peptide Identification Using Tandem Mass Spectrometry

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A Practical Guide to Phylogenetics for Nonexperts
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A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

Profile-Based LC-MS data alignment--a Bayesian approach.

Tsung-Heng Tsai1, Mahlet G Tadesse, Yue Wang

  • 1Department of Electrical and Computer Engineering,Virginia Tech, Washington, DC 20057, USA

IEEE/ACM Transactions on Computational Biology and Bioinformatics
|August 10, 2013
PubMed
Summary
This summary is machine-generated.

A new Bayesian alignment model (BAM) accurately aligns liquid chromatography-mass spectrometry (LC-MS) data using efficient MCMC methods and adaptive knot selection. This profile-based approach improves upon existing techniques for complex biological datasets.

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Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry (UPLC-MS)
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Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry (UPLC-MS)

Published on: March 14, 2013

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Last Updated: May 9, 2026

A Practical Guide to Phylogenetics for Nonexperts
12:00

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Published on: February 5, 2014

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry (UPLC-MS)
07:34

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry (UPLC-MS)

Published on: March 14, 2013

Area of Science:

  • Computational Biology
  • Analytical Chemistry
  • Statistical Modeling

Background:

  • Accurate alignment of liquid chromatography-mass spectrometry (LC-MS) data is critical for reliable analysis of complex biological samples.
  • Existing alignment methods often struggle with the non-linear retention time shifts common in LC-MS data.
  • Profile-based alignment approaches, utilizing prototype and mapping functions, offer a promising avenue for improved accuracy.

Purpose of the Study:

  • To introduce a novel Bayesian alignment model (BAM) for enhanced LC-MS data alignment.
  • To improve upon existing Markov chain Monte Carlo (MCMC) based alignment methods through algorithmic enhancements.
  • To demonstrate the efficacy of BAM in accurately correcting retention time variations in proteomic and metabolomic data.

Main Methods:

  • Development of a Bayesian alignment model (BAM) incorporating a prototype function and mapping functions.
  • Implementation of an efficient MCMC sampler with a block Metropolis-Hastings algorithm to avoid local modes.
  • Application of stochastic search variable selection (SSVS) for adaptive knot determination in mapping functions.
  • Comparison of BAM against Bayesian hierarchical curve registration (BHCR), dynamic time-warping (DTW), and continuous profile model (CPM).

Main Results:

  • BAM demonstrated robust performance on simulated, proteomic, and metabolomic LC-MS datasets.
  • The efficient MCMC sampler and adaptive knot selection in BAM led to improved alignment accuracy.
  • The study highlighted the benefits of profile-based retention time correction before feature-based analysis, particularly for metabolomic data.

Conclusions:

  • The proposed Bayesian alignment model (BAM) offers a significant advancement in LC-MS data alignment.
  • BAM's innovative MCMC sampler and knot selection methodology provide more accurate and reliable data processing.
  • Profile-based retention time correction using BAM is crucial for enhancing downstream feature-based analyses in omics studies.