Stochastic models of sequence evolution including insertion-deletion events

Statistical Methods in Medical Research

Volumn 18, Issue 5, 2009, Pages 453-485

  Miklós, István ^a,b   Novák, Ádám ^b   Satija, Rahul ^b   Lyngsø, Rune ^b   Hein, Jotun ^b  

^a INSTITUTE OF EXPERIMENTAL MEDICINE   (Hungary)

^b UNIVERSITY OF OXFORD   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

AMINO ACID SEQUENCE; ARTICLE; BASE PAIRING; BINDING SITE; BIOINFORMATICS; BIOSTATISTICS; COMPUTER MODEL; DNA SEQUENCE; HUMAN; INDEL MUTATION; MOLECULAR EVOLUTION; MOLECULAR PHYLOGENY; MONTE CARLO METHOD; NONHUMAN; NUCLEOTIDE SEQUENCE; PHYLOGENETIC TREE; PROBABILITY; PROTEIN STRUCTURE; RNA SEQUENCE; SEQUENCE ALIGNMENT; SEQUENCE ANALYSIS; SEQUENCE HOMOLOGY; STOCHASTIC MODEL;

ALGORITHMS; EVOLUTION, MOLECULAR; GENOME; GENOMICS; HUMANS; MODELS, GENETIC; MODELS, STATISTICAL; PHYLOGENY; SEQUENCE ALIGNMENT; SEQUENCE ANALYSIS, DNA; STOCHASTIC PROCESSES;

EID: 70349738209     PISSN: 09622802     EISSN: None     Source Type: Journal    
DOI: 10.1177/0962280208099500     Document Type: Article

References (95)

1. Needleman S., Wunsch C. A general method applicable to the search for similarities in the amino acid sequence of two proteins. J Mol Biol 1970; 48(3): 443-53.
2. Krogh A., Brown M., Mian I., Sjolander K., Haussler D. Hidden Markov models in computational biology: Applications to protein modeling. J Mol Biol 1994; 235: 1501-31.
3. Smith T., Waterman M. Identification of common molecular subsequences. J Mol Biol 1981; 147(1): 195-97.
4. Altschul S., Gish W., Miller W., Myers E., Lipman D. Basic local alignment search tool. J Mol Biol 1990; 215(3): 403-10.
5. Fitch W. Toward defining the course of evolution: Minimum change for a specific tree topology. Syst Zool 1971 ; 20: 406 - 16.
6. Hartigan J. Minimum evolution fits to a given tree. Biometrics 1973; 29: 53-65.
7. Felsenstein J. Evolutionary trees from DNA sequences: A maximum likelihood approach. J Mol Evol 1981; 17(6): 368-76.
8. Felsenstein J. The troubled growth of statistical phylogenetics. Syst Biol 2001; 50: 465-67.
9. Thorne J., Kishino H., Felsenstein J. An evolutionary model for maximum likelihood alignment of DNA sequences. J Mol Evol 1991; 33(2): 114-24.
10. Miklós I., Lunter GA, Holmes I. A 'long indel' model for evolutionary sequence alignment. Mol Biol Evol 2004; 21(3): 529-40.
11. Steel M., Hein J. Applying the Thorne-Kishino-Felsenstein model to sequence evolution on a star-shaped tree. Appl Math Let 2001; 14: 679-84.
12. Hein J. An Algorithm for Statistical Alignment of Sequences Related by a Binary Tree. In: Pacific Symposium on Biocomputing, vol. 6; 2001, pp. 179-90.
13. Hein J., Jensen J., Pedersen C. Recursions for statistical multiple alignment. PNAS 2003; 100(25): 14960-65.
14. Lunter G., Miklós I., Drummond A., Jensen J., Hein J. Bayesian phylogenetic inference under a statistical indel model. Lect Notes Bioinf 2003; 2812: 228-44.
15. Lunter G., Miklós I., Drummond A., Jensen J., Hein J. Bayesian Coestimation of Phylogeny and Sequence Alignment. BMC Bioinformatics 2005; 6: 83.
16. Churchill G. Monte Carlo Sequence Alignment. In: Proceedings of RECOMB 97 ; 1997, pp. 93 - 97.
17. Holmes I., Bruno W. Evolutionary HMMs: A Bayesian approach to multiple alignment. Bioinformatics 2001; 17(9): 803-20.
18. Thorne J., Kishino H., Felsenstein J. Inching toward reality: An improved likelihood model of sequence evolution. J Mol Evol 1992; 34(1): 3-16.
19. Gotoh O. An improved algorithm for matching biological sequences. J Mol Biol 1982; 162: 705-08.
20. Felsenstein J. Evolutionary trees from DNA sequences: A maximum likelihood approach. J Mol Evol 1981; 17: 68-376.
21. Metzler D. Statistical alignment based on fragment insertion and deletion models. Bioinformatics 2003; 19(4): 490-99.
22. Goldman N. Effects of sequence alignment procedures on estimates of phylogeny. BioEssays 1998; 20: 287-90.
23. Wong K., Suchard M., Huelsenbeck J. Alignment uncertainty and genomic analysis. Science 2008; 319(5862): 473-6.
24. Rannala B., Yang Z. Probability distribution of molecular evolutionary trees: A new method of phylogenetic inference. J Mol Evol 1996; 43: 304-11.
25. Drummond A., Nicholls G., Rodrigo A., Solomon W. Estimating mutation parameters, population history and genealogy simultaneously from temporally spaced sequence data. Genetics 2002; 161(3): 1307-20.
26. Durbin R, Eddy S, Krogh A, Mitchison G. Biological sequence analysis. Probabilistic models of proteins and nucleic acids, Cambridge University Press, Cambridge; 1998.
27. Huson D., Bryant D. Application of phylogenetic networks in evolutionary studies. Mol Biol Evol 2006; 23(2): 254-67.
28. Novák Á., Miklós I., Lyngsø R., Hein J. Stat Align: An extendable software package for joint bayesian estimation of alignments and evolutionary trees. Bioinforamtics 2008; 24(20): 2403-4.
29. Mizuguchi K., Deane C., Blundell T., JP O. HOMSTRAD: A database of protein structure alignments for homologous families. Protein Sci 1998; 7: 2469-71.
30. Miklós I., Novák Á., Dombai B., Hein J. How reliably can we predict the reliability of protein structure predictions? BMC Bioinformatics 2008; 9: 137.
31. Holland B., Moulton V. Consensus Networks: A Method for Visualising Incompatibilities in Collections of Trees. Lecture Notes in Computer Science, In: Proceedings of WABI2003, 2003; 2812: 165-76.
32. Waterman M., Smith T., Beyer W. Some biological sequence metrics. Advan Math 1976; 20: 367-87.
33. Waterman M. Parametric and ensemble sequence alignment algorithms. Bull Math Bio 1994; 5(4): 743-67.
34. Kececioglu J., Kim E. Simple and fast inverse alignment. Lect Notes Comp Sci 2006; 3909: 441-55.
35. Knudsen B., Miyamoto M. Sequence alignments and pair hidden Markov models using evolutionary history. J Mol Biol 2003; 333: 453-60.
36. Löytynoja A., Milinkovitch M. A hidden Markov model for progressive multiple alignment. Bioinformatics 2003; 19(12): 1505-13.
37. Wang L., Jiang T. On the complexity of multiple sequence alignment. J Comp Biol 1994; 1(4): 337-48.
38. Karplus K., Barrett C., Hughey R. Hidden markov models for detecting remote protein homologies. Bioinformatics 1998; 14(10): 846-56.
39. Eddy S. Profile Hidden Markov Models. Bioinformatics 1998; 14: 755-63.
40. Hogeweg P., Hesper B. The alignment of sets of sequences and the construction of phyletic trees: An integrated method. J Mol Evol 1984; 20(2): 175-86.
41. Feng D., Doolittle R. Progressive sequence alignment as a prerequisite to correct phylogenetic trees. J Mol Evol 1987; 25: 351-60.
42. Lóytynoja A., Goldman N. An algorithm for progressive multiple alignment of sequences with insertions. PNAS 2005; 102(30): 10557-62.
43. Holmes I. Using guide trees to construct multiple-sequence evolutionary HMMs. Bioinformatics 2003; 19(90001): 147-57.
44. Bradley R., Holmes I. Transducers: An emerging probabilistic framework for modeling indels on trees. Bioinformatics 2007; Doi:10.1093/ bioinformatics/btm402.
45. Metzler D., Fleissner R., von Haeseler A., Wakolbinger A. Assessing variability by joint sampling of alignments and mutation rates. J Mol Evol 2001; 53: 660-69.
46. Fleissner R., Metzler D., von Haesaler A. Simultaneous Statistical Multiple Alignment and Phylogeny Reconstruction. Syst Bio 2005; 54: 548-61.
47. Redelings B., Suchard M. Joint Bayesian estimation of alignment and phylogeny. Syst Biol 2005; 50: 401-18.
48. Suchard M., Redelings B. BAli-Phy: Simultaneous Bayesian inference of alignment and phylogeny. Bioinformatics 2006 ; 22 (16): 2047 - 8.
49. Metropolis N., Rosenbluth A., Rosenbluth M., Teller A., Teller E. Equations of state calculations by fast computing machines. J Chem Phys 1953; 21(6): 1087-91.
50. Hastings W. Monte Carlo sampling methods using Markov chains and their applications. Biometrica 1970; 57(1): 97-109.
51. Ronquist F., Huelsenbeck J. MrBayes 3: Bayesian phylogenetic inference under mixed models. Bioinformatics 2003; 19(12): 1572-4.
52. Mizuguchi K., Deane C., Johnson M., Blundell T., Overington J. JOY: protein sequencestructure representation and analysis. Bioinformatics 1998; 14: 617-23.
53. Gusfield D. Algorithms on strings, trees and sequences: Computer science and computational biology. Cambridge University Press, Cambridge ; 1997.
54. Hubbard T., Lesk A., Tramontano A. Gathering them into the fold. Nature Structural Biology 1996; 3: 313.
55. Skolnick J., Kolinski A., Kihara D., Betancourt M., Rotkiewicz PMB Ab initio protein structure prediction via a combination of threading, lattice folding, clustering, and structure refinement. Proteins 2002; 44(S5): 149-56.
56. Wu S., Skolnick J., Zhang Y. Ab initio modeling of small proteins by iterative TASSER simulations. BMC Biology 2007; 5: 17.
57. Zhou H., Skolnick J. Ab initio protein structure prediction using chunk-TASSER. Biophysical Journal 2007; 93: 1510-18.
58. Goldman N., Thorne J., Jones D. Using evolutionary trees in protein secondary structure prediction and other comparative sequence analyses. J Mol Biol 1996; 263(2): 196-208.
59. Kneller D., Cohen F., Langridge R. Improvements in protein secondary structure prediction by an enhanced neural network. J Mol Biol 1990; 214: 171-82.
60. Garnier J., Gibrat JF, BR. GOR secondary structure prediction method version IV. Methods in Enzymology 1996; 266: 540-53.
61. Stark A., Lin M., Kheradpour P. et al. Discovery of functional elements in 12 Drosophila genomes using evolutionary signatures. Nature 2007; 450(7167): 219-32.
62. Cliften P., Sudarsanam P., Desikan A. et al. Finding functional features in saccharomyces genomes by phylogenetic footprinting. Science 2003; 301(5629): 71-6.
63. Boffelli D., McAuliffe J., Ovcharenko D. et al. Phylogenetic shadowing of primate sequences to find functional regions of the human genome. Science 2003; 299(5611): 1391-4.
64. Wasserman W., Palumbo M., Thompson W., Fickett J., Lawrence C. Human-mouse genome comparisons to locate regulatory sites. Nature Genetics 2000; 26: 225-8.
65. Siepel A., Bejerano G., Pedersen J. et al. Evolutionarily conserved elements in vertebrate, insect, worm, and yeast genomes. Genome Research 2005; 15(8): 1034-50.
66. Tagle D., Koop B., Goodman M., Slightom J., Hess D., Jones R. Embryonic epsilon and gamma globin genes of a prosimian primate (Galago crassicaudatus). Nucleotide and amino acid sequences, developmental regulation and phylogenetic footprints. J Mol Biol 1988; 203(2): 439-55.
67. Guha Thakurta D. Computational identification of transcriptional regulatory elements in DNA sequence. Nucleic Acids Research 2006; 34(12): 3585-98.
68. Pollard D., Moses A., Iyer V., Eisen M. Detecting the limits of regulatory element conservation and divergence estimation using pairwise and multiple alignments. BMC Bioinformatics 2006; 7: 376.
69. Lunter G., Rocco A., Mimouni N., Heger A., Caldeira A., Hein J. Uncertainty in homology inferences: Assessing and improving genomic sequence alignment. Genome Res 2007; 8: 298-309.
70. Fan X., Zhu J., Schadt E., Liu J. Statistical power of phylo-HMM for evolutionarily conserved element detection. BMC Bioinformatics 2007; 8: 374.
71. Zhu J. Bayesian adaptive sequence alignment algorithms. Bioinformatics 1998; 14(1): 25-39.
72. Sinha S., He X. MORPH: Probabilistic alignment combined with hidden Markov models of cis-regulatory modules. PLoS Comput Biol 2007; 3(11): e216.
73. Satija R., Pachter L., Hein J. Combining statistical alignment and phylogenetic footprinting to detect regulatory elements. Bioinformatics 2008; 24(10): 1236-42.
74. Eddy S. A model of the statistical power of comparative genome sequence analysis. PLoS Biology 2005; 3(1): E10.
75. Lunter G., Drummond A., Miklós I., Hein J. Statistical alignment: recent progress, new applications, and challenges. In: Nielsen, R. (ed): Statistical methods in molecular evolution. Springer-Verlag, New-York ; 2004, pp. 381-412.
76. Lunter G., Miklós I., Song Y., Hein J. An efficient algorithm for statistical multiple alignment on arbitrary phylogenetic trees. J Comp Biol 2003; 10(6): 869-89.
77. Chao KM, Pearson W., Miller W. Aligning two sequences within a specified diagonal band. Comp Appli Biosci (CABIOS) 1992; 8(5): 481-7.
78. Lunter G. HMMoC-a compiler for hidden markov models. Bioinformatics 2007; 23(18): 2485-7.
79. de Groot S., Mailund T., Lunter G., Hein J. Investigating selection on viruses: A statistical alignment approach. BMC Bioinformatics 2008; 9: 304.
80. Hein J., Wiuf C., Knudsen B., Moller M., Wibling G. Statistical alignment: Computational properties, homology testing and goodness-of-fit. J Mol Biol 2000; 302: 265-79.
81. Holmes I. Accelerated probabilistic inference of RNA structure evolution. BMC Bioinformatics 2005; 6: 73.
82. Do C., Mahabhashyam M., Brudno M., Batzoglou S. ProbCons: Probabilistic consistency based multiple sequence alignment. Genome Research 2005; 15: 330-40.
83. Hein J. Unified approach to alignment and phylogenies. Methods in Enzymology 1990; 183: 626-45.
84. Robins G., Zelikovsky A. Improved steiner tree approximation in graphs. In Proceedings of the 11th Annual Symposium on Discrete Algorithms (SODA). 2000; 770-779.
85. Qian B., Goldstein R. Distribution of indel lengths. Proteins: Struc Func Gen 2001; 45: 102-4.
86. Lunter G., Hein J. A nucleotide substitution model with nearest-neighbour interactions. Bioinformatics 2004; 20: I216-i223.
87. Arndt P., CB B., Hwa T. DNA sequence evolution with neighbor-dependent mutation. J Comp Biol 2003; 10: 313-22.
88. Pedersen AM, Jensen J. A dependent rates model and MCMC based methodology for the maximum likelihood analysis of sequences with overlapping reading frames. Mol Biol Evol 2001; 18: 763-76.
89. Robinson D., Jones D., Kishino H., Goldman N., Thorne J. Protein evolution with dependence among codons due to tertiary structure. Mol Biol Evol 2003; 20: 1692-704.
90. Holmes I. A probabilistic model for the evolution of RNA structure. BMC Bioinformatics 2004; 5: 166.
91. Holmes I., Rubin G. Pairwise RNA structure comparison using stochastic context-free grammars. In: Pacific Symposium on Biocomputing; 2002, pp. 163-74.
92. Nye T. Modelling the evolution of multi-gene families. Statistical Methods in Medical Research. 2008.
93. Bishop M., Thompson E. Maximum likelihood alignment of DNA sequences. J Mol Biol 1986 ; 190 (2): 159 - 65.
94. Liu J. Monte Carlo strategies in scientific computing. Springer Verlag, New York, 2001.
95. Green P. Reversible jump Markov Chain Monte Carlo computation and Bayesian model determination. Biometrika 1995; 82: 711-32.