Fast MCMC sampling for hidden markov models to determine copy number variations

BMC Bioinformatics

Volumn 12, Issue 1, 2011, Pages

  Mahmud, Md Pavel ^a   Schliep, Alexander ^a,b  

^a RUTGERS UNIVERSITY   (United States)

^b RUTGERS UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN APPROACHES; CHROMOSOMAL ABERRATION; COMPARATIVE GENOMIC HYBRIDIZATION DATUM; COPY NUMBER VARIATIONS; DISCRETE SEQUENCES; HIGH-DENSITY ARRAYS; MARKOV CHAIN MONTE CARLO; NEXT-GENERATION SEQUENCING;

BAYESIAN NETWORKS; HIDDEN MARKOV MODELS; STATISTICAL TESTS;

TREES (MATHEMATICS);

ALGORITHM; ARTICLE; BAYES THEOREM; BIOLOGICAL MODEL; COMPARATIVE GENOMIC HYBRIDIZATION; COPY NUMBER VARIATION; GENETICS; HUMAN; MANTLE CELL LYMPHOMA; MONTE CARLO METHOD; NUCLEOTIDE SEQUENCE; PROBABILITY;

ALGORITHMS; BASE SEQUENCE; BAYES THEOREM; COMPARATIVE GENOMIC HYBRIDIZATION; DNA COPY NUMBER VARIATIONS; HUMANS; LYMPHOMA, MANTLE-CELL; MARKOV CHAINS; MODELS, GENETIC; MONTE CARLO METHOD; PROBABILITY;

EID: 80055114112     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-12-428     Document Type: Article

References (38)

1. Bishop CM. Pattern Recognition and Machine Learning (Information Science and Statistics) 2006, Secaucus, NJ, USA: Springer-Verlag New York, Inc.
2. Durbin R, Eddy SR, Krogh A, Mitchison GJ. Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids 1998, Cambridge University Press.
3. Krogh A. Hidden Markov models for labeled sequences. Pattern Recognition, 1994. Vol. 2 - Conference B: Computer Vision & Image Processing., Proceedings of the 12th IAPR International. Conference on 1994, 2:140-144. vol 2.
4. Eddy SR. Multiple Alignment Using Hidden Markov Models. ISMB 1995, 114-120.
5. Schliep A, Costa IG, Steinhoff C, Schönhuth A. Analyzing gene expression time-courses. IEEE/ACM transactions on computational biology and bioinformatics/IEEE, ACM 2005, 2(3):179-93. 10.1109/TCBB.2005.31, 17044182.
6. Olshen AB, Venkatraman ES, Lucito R, Wigler M. Circular binary segmentation for the analysis of array-based DNA copy number data. Biostatistics (Oxford, England) 2004, 5(4):557-72.
7. Picard F, Robin S, Lebarbier E, Daudin JJ. A segmentation/clustering model for the analysis of array CGH data. Biometrics 2007, 63(3):758-66. 10.1111/j.1541-0420.2006.00729.x, 17825008.
8. Eilers PHC, de Menezes RX. Quantile smoothing of array CGH data. Bioinformatics 2005, 21(7):1146-53. 10.1093/bioinformatics/bti148, 15572474.
9. Tibshirani R, Wang P. Spatial smoothing and hot spot detection for CGH data using the fused lasso. Biostatistics 2008, 9:18-29.
10. Wang P, Kim Y, Pollack J, Narasimhan B, Tibshirani R. A method for calling gains and losses in array CGH data. Biostatistics (Oxford, England) 2005, 6:45-58.
11. Andersson R, Bruder CEG, Piotrowski A, Menzel U, Nord H, Sandgren J, Hvidsten TR, Diaz de Ståhl T, Dumanski JP, Komorowski J. A segmental maximum a posteriori approach to genome-wide copy number profiling. Bioinformatics 2008, 24(6):751-758. 10.1093/bioinformatics/btn003, 18204059.
12. Fridlyand J, Snijders A, Pinkel D, Albertson D, Jain A. Hidden Markov models approach to the analysis of array CGH data. J Multivariate Anal 2004, 90:132-153.
13. Wang K, Li M, Hadley D, Liu R, Glessner J, Grant SFA, Hakonarson H, Bucan M. PennCNV: an integrated hidden Markov model designed for high-resolution copy number variation detection in whole-genome SNP genotyping data. Genome Research 2007, 17(11):1665-74. 10.1101/gr.6861907, 2045149, 17921354.
14. Viterbi A. Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. Information Theory, IEEE Transactions on 1967, 13(2):260-269.
15. Gilks W, Gilks W, Richardson S, Spiegelhalter D. Markov chain Monte Carlo in practice 1996, Interdisciplinary statistics, Chapman & Hall.
16. Geman S, Geman D. Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images. Pattern Analysis and Machine Intelligence, IEEE Transactions on 1984, PAMI-6(6):721-741.
17. Scott S. Bayesian Methods for Hidden Markov Models: Recursive Computing in the 21st Century. Journal of the American Statistical Association 2002, 337-351.
18. Rabiner L. A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE 1989, 77(2):257-286.
19. Mozes S, Weimann O, Ziv-Ukelson M. Speeding Up HMM Decoding and Training by Exploiting Sequence Repetitions. Lecture Notes in Computer Science 2007,
20. Pelleg D, Moore A. Accelerating exact k-means algorithms with geometric reasoning. KDD '99: Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining 1999, 277-281. New York, NY, USA: ACM.
21. Fritsch J, Rogina I. The Bucket Box Intersection (BBI) Algorithm For Fast Approximative Evaluation Of Diagonal Mixture Gaussians. In Proc ICASSP 1996, 837-840.
22. Srivastava S. Fast gaussian evaluations in large vocabulary continuous speech recognition. M.S. Thesis, Department of Electrical and Computer Engineering, Mississippi State University 2002,
23. Baum LE, Petrie T, Soules G, Weiss N. A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains. The Annals of Mathematical Statistics 1970, 41:164-171.
24. Guha S, Li Y, Neuberg D. Bayesian Hidden Markov Modeling of Array CGH Data. Journal of the American Statistical Association 2008, 103:485-497.
25. Shah SP, Xuan X, DeLeeuw RJ, Khojasteh M, Lam WL, Ng R, Murphy KP. Integrating copy number polymorphisms into array CGH analysis using a robust HMM. Bioinformatics 2006, 22(14):e431-e439. 10.1093/bioinformatics/btl238, 16873504.
26. Chib S. Calculating posterior distributions and modal estimates in Markov mixture models. Journal of Econometrics 1996, 75:79-97.
27. Salvador S, Chan P. Determining the Number of Clusters/Segments in Hierarchical Clustering/Segmentation Algorithms. Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence, ICTAI '04 2004, 576-584. Washington, DC, USA: IEEE Computer Society.
28. Willenbrock H, Fridlyand J. A comparison study: applying segmentation to array CGH data for downstream analyses. Bioinformatics 2005, 21(22):4084-4091. 10.1093/bioinformatics/bti677, 16159913.
29. Leeuw RJD, Davies JJ, Rosenwald A, Bebb G, Gascoyne YD, Dyer MJS, Staudt LM, Martinez-climent JA, Lam WL. Comprehensive whole genome array CGH profiling of mantle cell lymphoma model genomes. Hum Mol Genet 2004, 13:1827-1837. 10.1093/hmg/ddh195, 15229187.
30. Snijders AM, Nowak N, Segraves R, Blackwood S, Brown N, Conroy J, Hamilton G, Hindle AK, Huey B, Kimura K, Law S, Myambo K, Palmer J, Ylstra B, Yue JP, Gray JW, Jain AN, Pinkel D, Albertson DG. Assembly of microarrays for genome-wide measurement of DNA copy number. Nat Genet 2001, 29(3):263-264. 10.1038/ng754, 11687795.
31. Bredel M, Bredel C, Juric D, Harsh GR, Vogel H, Recht LD, Sikic BI. High-Resolution Genome-Wide Mapping of Genetic Alterations in Human Glial Brain Tumors. Cancer Research 2005, 65(10):4088-4096. 10.1158/0008-5472.CAN-04-4229, 15899798.
32. Harada T, Chelala C, Bhakta V, Chaplin T, Caulee K, Baril P, Young BD, Lemoine NR. Genome-wide DNA copy number analysis in pancreatic cancer using high-density single nucleotide polymorphism arrays. Oncogene 2007, 27(13):1951-1960. 2492386, 17952125.
33. Lai WR, Johnson MD, Kucherlapati R, Park PJ. Comparative analysis of algorithms for identifying amplifications and deletions in array CGH data. Bioinformatics 2005, 21(19):3763-70. 10.1093/bioinformatics/bti611, 2819184, 16081473.
34. Nannya Y, Sanada M, Nakazaki K, Hosoya N, Wang L, Hangaishi A, Kurokawa M, Chiba S, Bailey DK, Kennedy GC, Ogawa S. A robust algorithm for copy number detection using high-density oligonucleotide single nucleotide polymorphism genotyping arrays. Cancer Res 2005, 65(14):6071-6079. 10.1158/0008-5472.CAN-05-0465, 16024607.
35. McNemar Q. Note on the sampling error of the difference between correlated proportions or percentages. Psychometrika 1947, 12:153-157. 10.1007/BF02295996, 20254758.
36. Morganella S, Cerulo L, Viglietto G, Ceccarelli M. VEGA: variational segmentation for copy number detection. Bioinformatics 2010, 26(24):3020-3027. 10.1093/bioinformatics/btq586, 20959380.
37. Pinto D, Pagnamenta AT, Klei L, Anney R, Merico D, Regan R, Conroy J, Magalhaes TR, Correia C, Abrahams BS, Almeida J, Bacchelli E, Bader GD, Bailey AJ, Baird G, Battaglia A, Berney T, Bolshakova N, Bölte S, Bolton PF, Bourgeron T, Brennan S, Brian J, Bryson SE, Carson AR, Casallo G, Casey J, Chung BHY, Cochrane L, Corsello C. Functional impact of global rare copy number variation in autism spectrum disorders. Nature 2010,
38. Shah SP, Lam WL, Ng RT, Murphy KP. Modeling recurrent DNA copy number alterations in array CGH data. Bioinformatics 2007, 23(13):i450-i458. 10.1093/bioinformatics/btm221, 17646330.