Using hidden Markov models and observed evolution to annotate viral genomes.

@article{citeulike:588876, abstract = {MOTIVATION: ssRNA (single stranded) viral genomes are generally constrained in length and utilise overlapping reading frames to maximally exploit the coding potential within the genome length restrictions. This overlapping coding phenomenon leads to complex evolutionary constraints operating on the genome. In regions which code for more than one protein, silent mutations in one reading frame generally have a protein coding effect in another. To maximise coding flexibility in all reading frames, overlapping regions are often compositionally biased towards amino acids which are sixfold degenerate with respect to the 64 codon alphabet. Previous methodologies have used this fact in an ad-hoc manner to look for overlapping genes by motif matching. In this paper differentiated nucleotide compositional patterns in overlapping regions are incorporated into a probabilistic Hidden Markov Model (HMM) framework which is used to annotate ssRNA viral genomes. This work focuses on single sequence annotation and applies an HMM framework to ssRNA viral annotation. A description of how the HMM is parameterised, whilst annotating within a missing data framework is given. A Phylogenetic HMM (Phylo-HMM) extension, as applied to 14 aligned HIV2 sequences is also presented. This evolutionary extension serves as an illustration of the potential of the Phylo-HMM framework for ssRNA viral genomic annotation. RESULTS: The Single Sequence Annotation procedure (SSA) is applied to 14 different strains of the HIV2 virus. Further results on alternative ssRNA viral genomes are presented to illustrate more generally the performance of the method. The results of the SSA method are encouraging however there is still room for improvement, and since there is overwhelming evidence to indicate that comparative methods can improve coding sequence (CDS) annotation, the SSA method is extended to a Phylo-HMM to incorporate evolutionary information. The Phylo-HMM extension is applied to the same set of 14 HIV2 sequences which are pre-aligned. The performance improvement that results from including the evolutionary information in the analysis is illustrated. AVAILABILITY: We implement the SSA method in the MATLAB programming language and provide the source code at http://www.stats.ox.ac.uk/~mccauley. Additional supplementary material referred to in the text is available onthe same webpage.}, author = {McCauley, Stephen and Hein, Jotun }, citeulike-article-id = {588876}, issn = {1367-4803}, journal = {Bioinformatics}, keywords = {annotation, genome, markov, virus}, month = {April}, posted-at = {2006-04-17 18:26:55}, priority = {3}, title = {Using hidden Markov models and observed evolution to annotate viral genomes.}, url = {http://view.ncbi.nlm.nih.gov/pubmed/16613911}, year = {2006} }

TY - JOUR ID - citeulike:588876 L3 - citeulike-article-id:588876 TI - Using hidden Markov models and observed evolution to annotate viral genomes. JF - Bioinformatics SN - 1367-4803 N2 - MOTIVATION: ssRNA (single stranded) viral genomes are generally constrained in length and utilise overlapping reading frames to maximally exploit the coding potential within the genome length restrictions. This overlapping coding phenomenon leads to complex evolutionary constraints operating on the genome. In regions which code for more than one protein, silent mutations in one reading frame generally have a protein coding effect in another. To maximise coding flexibility in all reading frames, overlapping regions are often compositionally biased towards amino acids which are sixfold degenerate with respect to the 64 codon alphabet. Previous methodologies have used this fact in an ad-hoc manner to look for overlapping genes by motif matching. In this paper differentiated nucleotide compositional patterns in overlapping regions are incorporated into a probabilistic Hidden Markov Model (HMM) framework which is used to annotate ssRNA viral genomes. This work focuses on single sequence annotation and applies an HMM framework to ssRNA viral annotation. A description of how the HMM is parameterised, whilst annotating within a missing data framework is given. A Phylogenetic HMM (Phylo-HMM) extension, as applied to 14 aligned HIV2 sequences is also presented. This evolutionary extension serves as an illustration of the potential of the Phylo-HMM framework for ssRNA viral genomic annotation. RESULTS: The Single Sequence Annotation procedure (SSA) is applied to 14 different strains of the HIV2 virus. Further results on alternative ssRNA viral genomes are presented to illustrate more generally the performance of the method. The results of the SSA method are encouraging however there is still room for improvement, and since there is overwhelming evidence to indicate that comparative methods can improve coding sequence (CDS) annotation, the SSA method is extended to a Phylo-HMM to incorporate evolutionary information. The Phylo-HMM extension is applied to the same set of 14 HIV2 sequences which are pre-aligned. The performance improvement that results from including the evolutionary information in the analysis is illustrated. AVAILABILITY: We implement the SSA method in the MATLAB programming language and provide the source code at http://www.stats.ox.ac.uk/~mccauley. Additional supplementary material referred to in the text is available onthe same webpage. KW - annotation KW - genome KW - markov KW - virus AU - McCauley, Stephen AU - Hein, Jotun PY - 2006/04/13/ UR - http://view.ncbi.nlm.nih.gov/pubmed/16613911 ER -