title: Bayesian peak picking for NMR spectra.
abstract: Protein structure determination is a very important topic in structural genomics, which helps people to understand varieties of biological functions such as protein-protein interactions, protein-DNA interactions and so on. Nowadays, nuclear magnetic resonance (NMR) has often been used to determine the three-dimensional structures of protein in vivo. This study aims to automate the peak picking step, the most important and tricky step in NMR structure determination. We propose to model the NMR spectrum by a mixture of bivariate Gaussian densities and use the stochastic approximation Monte Carlo algorithm as the computational tool to solve the problem. Under the Bayesian framework, the peak picking problem is casted as a variable selection problem. The proposed method can automatically distinguish true peaks from false ones without preprocessing the data. To the best of our knowledge, this is the first effort in the literature that tackles the peak picking problem for NMR spectrum data using Bayesian method.
authors.affiliation: Department of Statistics, Texas A&M University, College Station, TX 77843, USA.
authors.forename: Yichen
authors.initials: Y
authors.lastname: Cheng
authors.affiliation: Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia.
authors.forename: Xin
authors.initials: X
authors.lastname: Gao
authors.affiliation: Department of Statistics, Texas A&M University, College Station, TX 77843, USA. Electronic address: fliang@stat.tamu.edu.
authors.forename: Faming
authors.initials: F
authors.lastname: Liang
mesh_terms: D000465:Algorithms; D001331:Automation; D001499:Bayes Theorem; D009682:Magnetic Resonance Spectroscopy / Q000379:methods; D009010:Monte Carlo Method; D011506:Proteins / Q000737:chemistry; D012984:Software
chemical_list: D011506:Proteins
keywords: Markov chain Monte Carlo; Nuclear magnetic resonance; Peak picking