Investigating genomic structure using changept: A Bayesian segmentation model

Computational and Structural Biotechnology Journal

Volumn 10, Issue 17, 2014, Pages 107-115

  Algama, Manjula ^a   Keith, Jonathan M ^a  

^a MONASH UNIVERSITY   (Australia)

Author keywords

Bayesian modelling; Conservation levels; GC content; Generalised Gibbs sampler; Non coding RNA; Sequence segmentation

Indexed keywords

EID: 84928891982     PISSN: None     EISSN: 20010370     Source Type: Journal    
DOI: 10.1016/j.csbj.2014.08.003     Document Type: Review

References (77)

1. Waterston RH, Lindblad-Toh K, Birney E, Rogers J, Abril JF, Agarwal P, et al. Initial sequencing and comparative analysis of themouse genome. Nature 2002;420: 520-62.
2. Bernstein BE, Birney E, Dunham I, Green ED, Gunter C, Snyder M. An integrated encyclopedia of DNA elements in the human genome. Nature 2012;489: 57-74.
3. Tajima F. Determination of window size for analyzing DNA sequences. J Mol Evol 1991;33: 470-3.
4. Zhang CT, Wang J, Zhang R. A novel method to calculate the G + C content of genomic DNA sequences. J Biomol Struct Dyn 2001;19: 333-41.
5. Bernardi G. Misunderstandings about isochores. Part 1. Gene 2001;276: 3-13.
6. Clay O, Carels N, Douady C, Macaya G, Bernardi G. Compositional heterogeneity within and among isochores inmammalian genomes. I. CsCl and sequence analyses. Gene 2001;276: 15-24.
7. Lander ES, Linton LM, Birren B, Nusbaum C, Zody MC, et al. Initial sequencing and analysis of the human genome. Nature 2001;409: 860-921.
8. Costantini M, Auletta F, Bernardi G. Isochore patterns and gene distributions in fish genomes. Genomics 2007;90: 364-71.
9. Costantini M, Clay O, Auletta F, Bernardi G. An isochore map of human chromosomes. Genome Res 2006;16: 536-41.
10. Lenhard B, Sandelin A, Mendoza L, Engstrom P, Jareborg N, WassermanWW. Identification of conserved regulatory elements by comparative genome analysis. J Biol 2003;2: 13.
11. Turner TL, Hahn MW, Nuzhdin SV. Genomic islands of speciation in Anopheles gambiae. PLoS Biol 2005;3: E285.
12. Spellman PT, Rubin GM. Evidence for large domains of similarly expressed genes in the Drosophila genome. J Biol 2002;1: 5.
13. Takami H, Nakasone K, Takaki Y, Maeno G, Sasaki R, Masui N. Complete genome sequence of the alkaliphilic bacterium Bacillus halodurans and genomic sequence comparison with Bacillus subtilis. Nucleic Acids Research 2000;28: 4317-31.
14. Karlin S. Detecting anomalous gene clusters and pathogenicity islands in diverse bacterial genomes. Trends Microbiol 2001;9: 335-43.
15. Fares MA, Elena SF, Ortiz J, Moya A, Barrio E. A sliding window-based method to detect selective constraints in protein-coding genes and its application to RNA viruses. J Mol Evol 2002;55: 509-21.
16. Carlson CS, Thomas DJ, Eberle MA, Swanson JE, Livingston RJ, Rieder MJ, et al. Genomic regions exhibiting positive selection identified from dense genotype data. Genome Res 2005;15: 1553-65.
17. Stratonovich R. Conditional Markov processes. Theory Probab Appl 1960;5: 156-78.
18. Churchill GA. Stochastic models for heterogeneous DNA sequences. Bull Math Biol 1989;51: 79-94.
19. Churchill GA. Hidden Markov chains and the analysis of genome structure. Comput Chem 1992;16: 107-15.
20. Dempster AP, Laird NM, Rubin DB. Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc Ser B Methodol 1977;39: 1-38.
21. Lukashin AV, Borodovsky M. GeneMark.hmm: New solutions for gene finding. Nucleic Acids Res 1998;26: 1107-15.
22. Peshkin L, Gelfand MS. Segmentation of yeast DNA using hidden Markov models. Bioinformatics 1999;15: 980-6.
23. Nicolas P, Bize L, Muri F, Hoebeke M, Rodolphe F, Ehrlich SD, et al. Mining Bacillus subtilis chromosome heterogeneities using hidden Markov models. Nucleic Acids Res 2002;30: 1418-26.
24. Azad RK, Borodovsky M. Probabilistic methods of identifying genes in prokaryotic genomes: Connections to the HMM theory. Brief Bioinform 2004;5: 118-30.
25. Krogh A, Brown M, Mian IS, Sjolander K, Haussler D. Hidden Markov models in computational biology: Applications to protein modeling. J Mol Biol 1994;235: 1501-31.
26. Stjernqvist S, Ryden T, Skold M, Staaf J. Continuous-index hidden Markov modelling of array CGH copy number data. Bioinformatics 2007;23: 1006-14.
27. Marioni JC, Thorne NP, Tavare S. BioHMM: A heterogeneous hidden Markov model for segmenting array CGH data. Bioinformatics 2006;22: 1144-6.
28. Willenbrock H, Fridlyand J. A comparison study: Applying segmentation to array CGH data for downstream analyses. Bioinformatics 2005;21: 4084-91.
29. Fridlyand J, Snijders AM, Pinkel D, Albertson DG, Jain AN. Hidden Markov models approach to the analysis of array CGH data. J Multivar Anal 2004;90: 132-53.
30. Gueguen L. Sarment: Python modules for HMM analysis and partitioning of sequences. Bioinformatics 2005;21: 3427-8.
31. Boys RJ, Henderson DA, Wilkinson DJ. Detecting homogeneous segments in DNA sequences by using hidden Markov models. J R Stat Soc: Ser C: Appl Stat 2000;49: 269-85.
32. Boys RJ, Henderson DA. A Bayesian approach to DNA sequence segmentation. Biometrics 2004;60: 573-88.
33. Kedzierska A, Husmeier D. A heuristic Bayesian method for segmenting DNA sequence alignments and detecting evidence for recombination and gene conversion. Stat Appl Genet Mol Biol 2006;5 [Article27].
34. Nur D, AllinghamD, Rousseau J, Mengersen KL, McVinish R. Bayesian hidden Markov model for DNA sequence segmentation: A prior sensitivity analysis. Comput Stat Data Anal 2009;53: 1873-82.
35. Hawkins DM. Testing a sequence of observations for a shift in location. J Am Stat Assoc 1977;72: 180-6.
36. Worsley KJ. On the likelihood ratio test for a shift in location of normal populations. J Am Stat Assoc 1979;74: 365-7.
37. Liu JS, Lawrence CE. Bayesian inference on biopolymermodels. Bioinformatics 1999; 15: 38-52.
38. Ramensky VE, Makeev V, Roytberg MA, Tumanyan VG. DNA segmentation through the Bayesian approach. J Comput Biol 2000;7: 215-31.
39. Finkelstein AV, Roytberg MA. Computation of biopolymers: A general approach to different problems. Biosystems 1993;30: 1-19.
40. Salmenkivi M, Kere J, Mannila H. Genome segmentation using piecewise constant intensity models and reversible jump MCMC. Bioinformatics 2002;18(Suppl. 2): S211-8.
41. Green PJ. Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika 1995;82: 711-32.
42. Husmeier D, Wright F. A Bayesian approach to discriminate between alternative DNA sequence segmentations. Bioinformatics 2002;18: 226-34.
43. Keith JM. Segmenting eukaryotic genomes with the Generalized Gibbs Sampler. J Comput Biol 2006;13: 1369-83.
44. Keith JM, Adams P, Stephen S, Mattick JS. Delineating slowly and rapidly evolving fractions of the Drosophila genome. J Comput Biol 2008;15: 407-30.
45. Oldmeadow C, Mengersen K, Mattick JS, Keith JM. Multiple evolutionary rate classes in animal genome evolution. Mol Biol Evol 2010;27: 942-53.
46. Algama M, Oldmeadow C, Tasker E, Mengersen K, Keith JM. Drosophila 3' UTRS are more complex than protein-coding sequences. PLoS ONE 2014;9: E97336.
47. Keith J, Kroese D, Bryant D. A Generalized Markov Sampler. Methodol Comput Appl Probab 2004;6: 29-53.
48. Bernaola-Galvan P, Roman-Roldan R, Oliver JL. Compositional segmentation and long-range fractal correlations in DNA sequences. Phys Rev 1996;53: 5181-9.
49. Oliver JL, Carpena P, Hackenberg M, Bernaola-Galvan P. IsoFinder: Computational prediction of isochores in genome sequences. Nucleic Acids Res 2004;32: W287-92.
50. Oliver JL, Roman-Roldan R, Perez J, Bernaola-Galvan P. SEGMENT: Identifying compositional domains in DNA sequences. Bioinformatics 1999;15: 974-9.
51. Li W, Bernaola-Galvan P, Haghighi F, Grosse I. Applications of recursive segmentation to the analysis of DNA sequences. Comput Chem 2002;26: 491-510.
52. Cohen N, Dagan T, Stone L, Graur D. GC composition of the human genome: In search of isochores. Mol Biol Evol 2005;22: 1260-72.
53. Deng S, Shi Y, Yuan L, Li Y, Ding G. Detecting the borders between coding and noncoding DNA regions in prokaryotes based on recursive segmentation and nucleotide doublets statistics. BMC Genomics 2012;13(Suppl. 8): S19.
54. Elhaik E, Graur D, Josic K, Landan G. Identifying compositionally homogeneous and nonhomogeneous domains within the human genome using a novel segmentation algorithm. Nucleic Acids Res 2010;38: E158.
55. Azad RK, Li J. Interpreting genomic data via entropic dissection. Nucleic Acids Res 2013;41: E23.
56. Haiminen N, Mannila H. Discovering isochores by least-squares optimal segmentation. Gene 2007;394: 53-60.
57. Wen SY, Zhang CT. Identification of isochore boundaries in the human genome using the technique of wavelet multiresolution analysis. Biochem Biophys Res Commun 2003;311: 215-22.
58. Sofronov G, Evans G, Keith J, Kroese D. Identifying change-points in biological sequences via sequential importance sampling. Environ Model Assess 2009;14: 577-84.
59. Evans GE, Sofronov GY, Keith JM, Kroese DP. Estimating change-points in biological sequences via the cross-entropy method. Ann Oper Res 2011;189: 155-65.
60. Sofronov G. Change-point modelling in biological sequences via the Bayesian adaptive independent sampler, 5. , International Conference on Telecommunication Technology and Applications; 2011. p. 22-126.
61. Olshen AB, Venkatraman ES, Lucito R, WiglerM. Circular binary segmentation for the analysis of array-based DNA copy number data. Biostatistics 2004;5: 557-72.
62. Venkatraman ES, Olshen AB. A faster circular binary segmentation algorithm for the analysis of array CGH data. Bioinformatics 2007;23: 657-63.
63. Tibshirani R. Regression shrinkage and selection via the lasso. J R Stat Soc Ser B Methodol 1996;58: 267-88.
64. Tibshirani R, Saunders M, Rosset S, Zhu J, Knight K. Sparsity and smoothness via the fused lasso. J R Stat Soc Ser B (Stat Methodol) 2005;67: 91-108.
65. Tibshirani R, Wang P. Spatial smoothing and hot spot detection for CGH data using the fused lasso. Biostatistics 2008;9: 18-29.
66. Zhang NR, Siegmund DO. A modified Bayes information criterion with applications to the analysis of comparative genomic hybridization data. Biometrics 2007;63: 22-32.
67. Oldmeadow C, Keith JM. Model selection in Bayesian segmentation of multiple DNA alignments. Bioinformatics 2011;27: 604-10.
68. Futschik A, Hotz T, Munk A, Sieling H. Multiscale DNA partitioning: Statistical evidence for segments. Bioinformatics 2014. http://dx.doi.org/10.1093/bioinformatics/btu1180.
69. Braun JV, Muller H-G. Statistical methods for DNA sequence segmentation. Stat Sci 1998;13: 142-62.
70. Elhaik E, Graur D, Josic K. Comparative testing of DNA segmentation algorithms using benchmark simulations. Mol Biol Evol 2010;27: 1015-24.
71. Fitch WM, Margoliash E. Construction of phylogenetic trees. Science 1967;155: 279-84.
72. Gelman A, Carlin JB, Stern HS, Rubin DB. Bayesian data analysis. 2nd ed. Taylor & Francis; 2003.
73. Keith JM. Sequence segmentation. Methods Mol Biol 2008;452: 207-29.
74. Boyd SE, Nair B, Ng SW, Keith JM, Orian JM. Computational characterization of 3' splice variants in the GFAP isoform family. PLoS ONE 2012;7: E33565.
75. Kitazawa S, Kitazawa R, Tamada H, Maeda S. Promoter structure of human sonic hedgehog gene. Biochim Biophys Acta 1998;1443: 358-63.
76. Brudno M, Do CB, Cooper GM, Kim MF, Davydov E, Green ED, et al. LAGAN and Multi-LAGAN: Efficient tools for large-scale multiple alignment of genomic DNA. Genome Res 2003;13: 721-31.
77. Mattick JS. Non-coding RNAs: The architects of eukaryotic complexity. EMBO Rep 2001;2: 986-91.