EurekaMag.com logo
+ Site Statistics
References:
53,517,315
Abstracts:
29,339,501
+ Search Articles
+ Subscribe to Site Feeds
EurekaMag Most Shared ContentMost Shared
EurekaMag PDF Full Text ContentPDF Full Text
+ PDF Full Text
Request PDF Full TextRequest PDF Full Text
+ Follow Us
Follow on FacebookFollow on Facebook
Follow on TwitterFollow on Twitter
Follow on Google+Follow on Google+
Follow on LinkedInFollow on LinkedIn

+ Translate

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



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



Ieee/Acm Transactions on Computational Biology and Bioinformatics 10(2): 494-503



A Bayesian alignment model (BAM) is proposed for alignment of liquid chromatography-mass spectrometry (LC-MS) data. BAM belongs to the category of profile-based approaches, which are composed of two major components: a prototype function and a set of mapping functions. Appropriate estimation of these functions is crucial for good alignment results. BAM uses Markov chain Monte Carlo (MCMC) methods to draw inference on the model parameters and improves on existing MCMC-based alignment methods through 1) the implementation of an efficient MCMC sampler and 2) an adaptive selection of knots. A block Metropolis-Hastings algorithm that mitigates the problem of the MCMC sampler getting stuck at local modes of the posterior distribution is used for the update of the mapping function coefficients. In addition, a stochastic search variable selection (SSVS) methodology is used to determine the number and positions of knots. We applied BAM to a simulated data set, an LC-MS proteomic data set, and two LC-MS metabolomic data sets, and compared its performance with the Bayesian hierarchical curve registration (BHCR) model, the dynamic time-warping (DTW) model, and the continuous profile model (CPM). The advantage of applying appropriate profile-based retention time correction prior to performing a feature-based approach is also demonstrated through the metabolomic data sets.

(PDF same-day service: $19.90)

Accession: 055205919

Download citation: RISBibTeXText

PMID: 23929872

DOI: 10.1109/TCBB.2013.25



Related references

Multi-profile Bayesian alignment model for LC-MS data analysis with integration of internal standards. Bioinformatics 29(21): 2774-2780, 2014

ReformAlign: improved multiple sequence alignments using a profile-based meta-alignment approach. Bmc Bioinformatics 15(): 265-265, 2014

Multiple sequence alignment based on profile alignment of intermediate sequences. Journal of Computational Biology 15(7): 767-777, 2008

Alignment-based approach for durable data storage into living organisms. Biotechnology Progress 23(2): 501-505, 2007

A Bayesian approach to the alignment of mass spectra. Bioinformatics 25(24): 3213-3220, 2010

Multiple peak alignment in sequential data analysis: a scale-space-based approach. Ieee/Acm Transactions on Computational Biology and Bioinformatics 3(3): 208-219, 2006

Evolutionary HMMs: a Bayesian approach to multiple alignment. Bioinformatics 17(9): 803-820, 2001

Impacts of atypical data on Bayesian inference and robust Bayesian approach in fisheries. Canadian Journal of Fisheries & Aquatic Sciences 56(9): 1525-1533, Sept, 1999

Bayesian model-based clustering of temporal gene expression using autoregressive panel data approach. Bioinformatics 28(15): 2004-2007, 2013

Bayesian inference on prevalence using a missing-data approach with simulation-based techniques: applications to HIV screening. Statistics in Medicine 15(20): 2161-2176, 1996

A Bayesian approach to functional-based multilevel modeling of longitudinal data: applications to environmental epidemiology. Biostatistics 9(4): 686-699, 2008

Triangular Alignment (TAME): A Tensor-based Approach for Higher-order Network Alignment. Ieee/Acm Transactions on Computational Biology and Bioinformatics (): -, 2016

Risk-based volcanology; a Bayesian approach to lahar frequency analysis incorporating data and model uncertainties. Open-File Report - U S, 1999

A wavelet-based Bayesian approach to regression models with long memory errors and its application to FMRI data. Biometrics 69(1): 184-196, 2013

A Bayesian approach for estimating the parameters of a forest process model based on long-term growth data. Ecological Modelling 119(2/3): 249-265, 1999