Stephen McCauley

University of Oxford, Oxford, ENG, United Kingdom

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Publications (2)10.79 Total impact

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    ABSTRACT: MOTIVATION: Viral genomes tend to code in overlapping reading frames to maximize informational content. This may result in atypical codon bias and particular evolutionary constraints. Due to the fast mutation rate of viruses, there is additional strong evidence for varying selection between intra- and intergenomic regions. The presence of multiple coding regions complicates the concept of K(a)/K(s) ratio, and thus begs for an alternative approach when investigating selection strengths. Building on the paper by McCauley and Hein, we develop a method for annotating a viral genome coding in overlapping reading frames. We introduce an evolutionary model capable of accounting for varying levels of selection along the genome, and incorporate it into our prior single sequence HMM methodology, extending it now to a phylogenetic HMM. Given an alignment of several homologous viruses to a reference sequence, we may thus achieve an annotation both of coding regions as well as selection strengths, allowing us to investigate different selection patterns and hypotheses. RESULTS: We illustrate our method by applying it to a multiple alignment of four HIV2 sequences, as well as of three Hepatitis B sequences. We obtain an annotation of the coding regions, as well as a posterior probability for each site of the strength of selection acting on it. From this we may deduce the average posterior selection acting on the different genes. Whilst we are encouraged to see in HIV2, that the known to be conserved genes gag and pol are indeed annotated as such, we also discover several sites of less stringent negative selection within the env gene. To the best of our knowledge, we are the first to subsequently provide a full selection annotation of the Hepatitis B genome by explicitly modelling the evolution within overlapping reading frames, and not relying on simple K(a)/K(s) ratios.
    Bioinformatics 12/2007; 23(22):2978-86. · 5.47 Impact Factor
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    Stephen McCauley, Jotun Hein
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    ABSTRACT: MOTIVATION: ssRNA (single stranded) viral genomes are generally constrained in length and utilize 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 maximize coding flexibility in all reading frames, overlapping regions are often compositionally biased towards amino acids which are 6-fold 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 parameterized, 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.
    Bioinformatics 07/2006; 22(11):1308-16. · 5.32 Impact Factor

Publication Stats

26 Citations
10.79 Total Impact Points

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Institutions

  • 2006–2007
    • University of Oxford
      • Department of Statistics
      Oxford, ENG, United Kingdom