Detecting the Dependent Evolution of Biosequences

A probabilistic graphical model is developed in order to detect the dependent evolution between different sites in biological sequences. Given a multiple sequence alignment for each molecule of interest and a phylogenetic tree, the model can predict potential interactions within or between nucleic acids and proteins. Initial validation of the model is carried out using tRNA sequence data. The model is able to accurately identify the secondary structure of tRNA as well as several known tertiary interactions.

[1]  Dr. Susumu Ohno.  Evolution by Gene Duplication , 1970, Springer Berlin Heidelberg.

[2]  H. Noller,et al.  Secondary structure of 16S ribosomal RNA. , 1981, Science.

[3]  M. Pagel.  Detecting correlated evolution on phylogenies: a general method for the comparative analysis of discrete characters , 1994, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[4]  Z. Yang,et al.  A space-time process model for the evolution of DNA sequences. , 1995, Genetics.

[5]  J. Felsenstein,et al.  A Hidden Markov Model approach to variation among sites in rate of evolution. , 1996, Molecular biology and evolution.

[6]  M. Huynen,et al.  Automatic detection of conserved RNA structure elements in complete RNA virus genomes. , 1998, Nucleic acids research.

[7]  W R Taylor,et al.  Coevolving protein residues: maximum likelihood identification and relationship to structure. , 1999, Journal of molecular biology.

[8]  F. Cohen,et al.  Co-evolution of proteins with their interaction partners. , 2000, Journal of molecular biology.

[9]  M. Lynch,et al.  The evolutionary fate and consequences of duplicate genes. , 2000, Science.

[10]  S. Eddy,et al.  Computational identification of noncoding RNAs in E. coli by comparative genomics , 2001, Current Biology.

[11]  S. Eddy.  Non–coding RNA genes and the modern RNA world , 2001, Nature Reviews Genetics.

[12]  J. Huelsenbeck,et al.  MRBAYES : Bayesian inference of phylogeny , 2001 .

[13]  A. E. Hirsh,et al.  Evolutionary Rate in the Protein Interaction Network , 2002, Science.

[14]  Diego di Bernardo,et al.  ddbRNA: detection of conserved secondary structures in multiple alignments , 2003, Bioinform..

[15]  Arun K. Ramani,et al.  Exploiting the co-evolution of interacting proteins to discover interaction specificity. , 2003, Journal of molecular biology.

[16]  E. Koonin,et al.  Conservation and coevolution in the scale-free human gene coexpression network. , 2004, Molecular biology and evolution.

[17]  B. Berger,et al.  MSARI: multiple sequence alignments for statistical detection of RNA secondary structure. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[18]  David Haussler,et al.  Combining Phylogenetic and Hidden Markov Models in Biosequence Analysis , 2004, J. Comput. Biol..

[19]  Irmtraud M. Meyer,et al.  A comparative method for finding and folding RNA secondary structures within protein-coding regions. , 2004, Nucleic acids research.

[20]  Lior Pachter,et al.  Bioinformatics for Whole-Genome Shotgun Sequencing of Microbial Communities , 2005, PLoS Comput. Biol..

[21]  A. E. Hirsh,et al.  Functional genomic analysis of the rates of protein evolution. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[22]  H. Kishino,et al.  Dating of the human-ape splitting by a molecular clock of mitochondrial DNA , 2005, Journal of Molecular Evolution.

[23]  Peter F Stadler,et al.  Fast and reliable prediction of noncoding RNAs , 2005, Proc. Natl. Acad. Sci. USA.

[24]  Mark Pagel,et al.  Predicting Functional Gene Links from Phylogenetic-Statistical Analyses of Whole Genomes , 2005, 2005 IEEE Computational Systems Bioinformatics Conference - Workshops (CSBW'05).

[25]  P. Stadler,et al.  Mapping of conserved RNA secondary structures predicts thousands of functional noncoding RNAs in the human genome , 2005, Nature Biotechnology.

[26]  J. Felsenstein.  Evolutionary trees from DNA sequences: A maximum likelihood approach , 2005, Journal of Molecular Evolution.