A Non-parametric Bayesian Approach for Predicting RNA Secondary Structures

Since many functional RNAs form stable secondary structures which are related to their functions, RNA secondary structure prediction is a crucial problem in bioinformatics. We propose a novel model for generating RNA secondary structures based on a non-parametric Bayesian approach, called hierarchical Dirichlet processes for stochastic context-free grammars (HDP-SCFGs). Here non-parametric means that some meta-parameters, such as the number of non-terminal symbols and production rules, do not have to be fixed. Instead their distributions are inferred in order to be adapted (in the Bayesian sense) to the training sequences provided. The results of our RNA secondary structure predictions show that HDP-SCFGs are more accurate than the MFE-based and other generative models.

[1]  Michael I. Jordan,et al.  Variational inference for Dirichlet process mixtures , 2006 .

[2]  Andrew McCallum,et al.  An Introduction to Conditional Random Fields for Relational Learning , 2007 .

[3]  Kiyoshi Asai,et al.  Prediction of RNA secondary structure using generalized centroid estimators , 2009, Bioinform..

[4]  Bjarne Knudsen,et al.  RNA secondary structure prediction using stochastic context-free grammars and evolutionary history , 1999, Bioinform..

[5]  Yasubumi Sakakibara,et al.  RNA secondary structural alignment with conditional random fields , 2005, ECCB/JBI.

[6]  Ivo L. Hofacker,et al.  Vienna RNA secondary structure server , 2003, Nucleic Acids Res..

[7]  R. Durbin,et al.  RNA sequence analysis using covariance models. , 1994, Nucleic acids research.

[8]  Ian Holmes,et al.  Stem Stem Stem Stem Loop Loop Loop LoopLoop Loop Loop Loop Loop Loop Loop , 2005 .

[9]  Sean R. Eddy,et al.  Efficient pairwise RNA structure prediction and alignment using sequence alignment constraints , 2006, BMC Bioinformatics.

[10]  Sean R. Eddy,et al.  Evaluation of several lightweight stochastic context-free grammars for RNA secondary structure prediction , 2004, BMC Bioinformatics.

[11]  Durbin,et al.  Biological Sequence Analysis , 1998 .

[12]  Sean R. Eddy,et al.  Rfam: annotating non-coding RNAs in complete genomes , 2004, Nucleic Acids Res..

[13]  Elena Rivas,et al.  Secondary structure alone is generally not statistically significant for the detection of noncoding RNAs , 2000, Bioinform..

[14]  Sean R. Eddy,et al.  A memory-efficient dynamic programming algorithm for optimal alignment of a sequence to an RNA secondary structure , 2002, BMC Bioinformatics.

[15]  Sean R. Eddy,et al.  RSEARCH: Finding homologs of single structured RNA sequences , 2003, BMC Bioinformatics.

[16]  C. Lawrence,et al.  RNA secondary structure prediction by centroids in a Boltzmann weighted ensemble. , 2005, RNA.

[17]  Michael I. Jordan,et al.  Hierarchical Dirichlet Processes , 2006 .

[18]  J. McCaskill.  The equilibrium partition function and base pair binding probabilities for RNA secondary structure , 1990, Biopolymers.

[19]  Chuan-Sheng Foo,et al.  A max-margin model for efficient simultaneous alignment and folding of RNA sequences , 2008, ISMB.

[20]  Yasubumi Sakakibara,et al.  Pair hidden Markov models on tree structures , 2003, ISMB.

[21]  Michael Zuker,et al.  Optimal computer folding of large RNA sequences using thermodynamics and auxiliary information , 1981, Nucleic Acids Res..

[22]  D. Searls,et al.  Robots in invertebrate neuroscience , 2002, Nature.

[23]  R. C. Underwood,et al.  Stochastic context-free grammars for tRNA modeling. , 1994, Nucleic acids research.

[24]  Sean R. Eddy,et al.  Biological sequence analysis: Contents , 1998 .

[25]  Koby Crammer,et al.  Online Passive-Aggressive Algorithms , 2003, J. Mach. Learn. Res..

[26]  Y. Singer,et al.  Ultraconservative online algorithms for multiclass problems , 2003 .

[27]  Dan Klein,et al.  The Infinite PCFG Using Hierarchical Dirichlet Processes , 2007, EMNLP.

[28]  David Haussler,et al.  Identification and Classification of Conserved RNA Secondary Structures in the Human Genome , 2006, PLoS Comput. Biol..

[29]  Serafim Batzoglou,et al.  CONTRAfold: RNA secondary structure prediction without physics-based models , 2006, ISMB.

[30]  Elena Rivas,et al.  Noncoding RNA gene detection using comparative sequence analysis , 2001, BMC Bioinformatics.

[31]  Andrew McCallum,et al.  Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data , 2001, ICML.

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

[33]  C. Lawrence,et al.  Centroid estimation in discrete high-dimensional spaces with applications in biology , 2008, Proceedings of the National Academy of Sciences.