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A Bayesian Approach to Protein Inference Problem in Shotgun Proteomics

Yong Fuga Li, Randy J. Arnold, Yixue Li, Predrag Radivojac, Quanhu Sheng, Haixu Tang
2009 Journal of Computational Biology  
The protein inference problem represents a major challenge in shotgun proteomics.  ...  We propose a rigorious probabilistic model for protein inference and provide practical algoritmic solutions to this problem.  ...  DISCUSSION In this study, we proposed and evaluated a new methodology for protein inference in shotgun proteomics.  ... 
doi:10.1089/cmb.2009.0018 pmid:19645593 pmcid:PMC2799497 fatcat:skxoag5cuzaznbrqr2ruxrrlde

A Bayesian Approach to Protein Inference Problem in Shotgun Proteomics [chapter]

Yong Fuga Li, Randy J. Arnold, Yixue Li, Predrag Radivojac, Quanhu Sheng, Haixu Tang
Lecture Notes in Computer Science  
The protein inference problem represents a major challenge in shotgun proteomics.  ...  We propose a rigorious probabilistic model for protein inference and provide practical algoritmic solutions to this problem.  ...  DISCUSSION In this study, we proposed and evaluated a new methodology for protein inference in shotgun proteomics.  ... 
doi:10.1007/978-3-540-78839-3_15 dblp:conf/recomb/LiALRST08 fatcat:nrxoonbmnffytckvv6hcwydv74

Computational approaches to protein inference in shotgun proteomics

Yong Fuga Li, Predrag Radivojac
2012 BMC Bioinformatics  
Shotgun proteomics has recently emerged as a powerful approach to characterizing proteomes in biological samples.  ...  Protein identification is fundamentally a statistical inference problem with a number of methods proposed to address its challenges.  ...  This article has been published as part of BMC Bioinformatics Volume 13 Supplement 16, 2012: Statistical mass spectrometry-based proteomics.  ... 
doi:10.1186/1471-2105-13-s16-s4 pmid:23176300 pmcid:PMC3489551 fatcat:rob47kvrwrfo3dzbuhkcrazqzy

How to talk about protein-level false discovery rates in shotgun proteomics

Matthew The, Ayesha Tasnim, Lukas Käll
2016 Proteomics  
A frequently sought output from a shotgun proteomics experiment is a list of proteins that we believe to have been present in the analyzed sample before proteolytic digestion.  ...  A common approach to arrive at a set of discovered proteins is to infer the proteins from FDR-thresholded lists of peptides or PSMs, and rest at reporting the peptide, or PSM-level FDR.  ...  The authors would like to thank Professor William Stafford Noble, University of Washington, for valuable comments on an early version of the manuscript.  ... 
doi:10.1002/pmic.201500431 pmid:27503675 pmcid:PMC5096025 fatcat:nvyogahhlvb4liz5ghjj4wiphq

Current algorithmic solutions for peptide-based proteomics data generation and identification

Michael R Hoopmann, Robert L Moritz
2013 Current Opinion in Biotechnology  
Protein inference is a tricky problem when analyzing large data sets of degenerate peptide identifications.  ...  Peptide-based proteomic data sets are ever increasing in size and complexity.  ...  The authors would like to thank David Shteynberg for review of this manuscript.  ... 
doi:10.1016/j.copbio.2012.10.013 pmid:23142544 pmcid:PMC3857305 fatcat:k2m7gvkvuzdhpeafujwm6usohm

Modeling Signaling Networks Using High-throughput Phospho-proteomics [chapter]

Camille Terfve, Julio Saez-Rodriguez
2011 Advances in Experimental Medicine and Biology  
We consider a variety of formalisms from clustering and data mining approaches to differential equation-based mechanistic models, rule-based, and logic based models, and on through Bayesian network inference  ...  In the case of signaling networks, we build mainly upon data at the proteome level, in particular about the phosphorylation of proteins.  ...  Limitations of the Shotgun MS/MS Approach Although shotgun MS/MS approaches offer a coverage of the proteome that no other technology can currently approach (i.e., about 7,000 proteins can be quantified  ... 
doi:10.1007/978-1-4419-7210-1_2 pmid:22161321 fatcat:5dxuky3jlfgmdb5x22rgeajtzq

Session Introduction

Bobbie-Jo M. Webb-Robertson, William R. Cannon, Joshua N. Adkins, Deborah K. Gracio
2006 Pacific Symposium on Biocomputing  
Acknowledgments The session organizers would like to thank the authors of the 30 submissions to this session and express our regret that only a handful of the excellent papers can be presented.  ...  We would also like to express deep gratitude to the anonymous referees who together volunteered uncountable hours to provide the key input to make this session successful.  ...  A problem that has been encountered with shotgun proteomics experiments in applying this method, however, is that the alignment of the spectra in both elution time and mass-to-charge values is difficult  ... 
dblp:conf/psb/Webb-RobertsonCAG06 fatcat:37mfkjcx5vahfhlcauzdyyo6he

A multi-model statistical approach for proteomic spectral count quantitation

Owen E. Branson, Michael A. Freitas
2016 Journal of Proteomics  
Label-free quantitation has proven to be a valid approach for discovery shotgun proteomics, especially when sample is limited.  ...  Here we show that statistical approaches developed to evaluate differential expression in RNA sequencing experiments can be applied to detect differential protein expression in labelfree discovery proteomics  ...  In shotgun proteomics mere protein identification is usually not sufficient to understand the complexity of biological phenomena.  ... 
doi:10.1016/j.jprot.2016.05.032 pmid:27260494 pmcid:PMC4967010 fatcat:t2cx6tlkzfdtrjlfzuntgign5y

MODERN COMPUTATIONAL STRATEGIES FOR PROTEIN INFERENCE IN SHOTGUN PROTEOMIC
СОВРЕМЕННЫЕ ВЫЧИСЛИТЕЛЬНЫЕ СТРАТЕГИИ ДЛЯ ВЫВОДА БЕЛКОВ В ПРОТЕОМИКЕ ДРОБОВИКА

Y. S. Golenko, A. A. Ismailova
2021 Izvestiâ Nacionalʹnoj akademii nauk Respubliki Kazahstan. Seriâ fiziko-matematičeskaâ  
Today, shotgun proteomics is a powerful approach to characterize proteomes in biological samples.  ...  In this article, we propose to consider protein identification only as a problem of statistical inference, and also describe a number of methods that can be used to solve it.  ...  Today, shotgun proteomics is a powerful approach to characterize proteomes in biological samples.  ... 
doi:10.32014/2021.2518-1726.21 fatcat:is52cmbl5ff6bognbbufrlcf2y

Protein identification problem from a Bayesian point of view

Randy J. Arnold, Yong Fuga Li, Predrag Radivojac, Haixu Tang
2012 Statistics and its Interface  
We present a generic Bayesian framework for the peptide and protein identification in proteomics, and provide a unified interpretation for the database searching and the de novo peptide sequencing approaches  ...  Protein identification problem from a Bayesian point of view 23 Because m k and t k are independently observed from s k given y k , P D and T , we have, P mtDME (y k |m k , t k , P D, T ) ∝ P DME (y k  ...  ACKNOWLEDGEMENTS The authors would like to thank Dr. Karen Kafadar for comments on the manuscript, Dr.  ... 
doi:10.4310/sii.2012.v5.n1.a3 pmid:24761189 pmcid:PMC3992622 fatcat:paqmzyx3gjculivsjteua2wqdy

Bayesian methods for proteomic biomarker development

Belinda Hernández, Stephen R Pennington, Andrew C Parnell
2015 EuPA Open Proteomics  
[36] proposed a Bayesian framework for protein inference with degenerate peptides to calculate the posterior probability of a given peptide belonging to a protein, which was found to outperform the  ...  For a Bayesian CART model, the data in each terminal node of the tree is assumed to follow a multinomial distribution for classification problems.  ... 
doi:10.1016/j.euprot.2015.08.001 fatcat:mlwn2bwlsjdxbbmkvn6iiinl4e

Hardware accelerated protein inference framework [article]

S.M. Vidanagamachchi, S.D. Dewasurendra, R.G. Ragel
2014 arXiv   pre-print
Protein inference plays a vital role in the proteomics study. Two major approaches could be used to handle the problem of protein inference; top-down and bottom-up.  ...  This paper presents a framework for protein inference, which uses hardware accelerated protein inference framework for handling the most important step in a bottom-up approach, viz. peptide identification  ...  Bottom-up protein inference problem was addressed by Alexey et al. and they have analyzed shotgun proteomics considering degenerate peptides [7] .  ... 
arXiv:1403.1319v1 fatcat:rb6mcmljm5e4nnj2zl5htlatca

Spectrum Identification using a Dynamic Bayesian Network Model of Tandem Mass Spectra [article]

Ajit P. Singh, John Halloran, Jeff A. Bilmes, Katrin Kirchoff, William S. Noble
2012 arXiv   pre-print
Shotgun proteomics is a high-throughput technology used to identify unknown proteins in a complex mixture.  ...  At the heart of this process is a prediction task, the spectrum identification problem, in which each fragmentation spectrum produced by a shotgun proteomics experiment must be mapped to the peptide (protein  ...  At the heart of shotgun proteomics is a machine learning problem. Proteins are broken down into small fragments, called peptides.  ... 
arXiv:1210.4904v1 fatcat:67mktmwabndtxaxs52v64nrbcu

Fast and Accurate Protein False Discovery Rates on Large-Scale Proteomics Data Sets with Percolator 3.0

Matthew The, Michael J. MacCoss, William S. Noble, Lukas Käll
2016 Journal of the American Society for Mass Spectrometry  
With our new scalable approach, we can now also analyze millions of PSMs in a matter of minutes on a commodity computer.  ...  Percolator is a widely used software tool that increases yield in shotgun proteomics experiments and assigns reliable statistical confidence measures, such as q values and posterior error probabilities  ...  any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.  ... 
doi:10.1007/s13361-016-1460-7 pmid:27572102 pmcid:PMC5059416 fatcat:nbbf2tc5ejcurmnpyxjyjkojwu

Spectrum Identification using a Dynamic Bayesian Network Model of Tandem Mass Spectra

Ajit P Singh, John Halloran, Jeff A Bilmes, Katrin Kirchoff, William S Noble
2012 Uncertainty in artificial intelligence : proceedings of the ... conference. Conference on Uncertainty in Artificial Intelligence  
Shotgun proteomics is a high-throughput technology used to identify unknown proteins in a complex mixture.  ...  At the heart of this process is a prediction task, the spectrum identification problem, in which each fragmentation spectrum produced by a shotgun proteomics experiment must be mapped to the peptide (protein  ...  At the heart of shotgun proteomics is a machine learning problem. Proteins are broken down into small fragments, called peptides.  ... 
pmid:25383048 pmcid:PMC4221238 fatcat:y4xyt3e22vhfhk3ybfccoyz4je
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