Detecting overlapping coding sequences in virus genomes

Detecting new coding sequences (CDSs) in viral genomes can be difficult for several reasons. The typically compact genomes often contain a number of overlapping coding and non-coding functional elements, which can result in unusual patterns of codon usage; conservation between related sequences can...

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Bibliographic Details
Published in:BMC bioinformatics 2006-02, Vol.7 (1), p.75-75, Article 75
Main Authors: Firth, Andrew E, Brown, Chris M
Format: Article
Language:English
Subjects:
Algorithms
Binding Sites
Codon
Codon, Terminator
Computational Biology - methods
Conserved Sequence
Databases as Topic
Frameshift Mutation
Genes, Overlapping
Genes, Viral
Genome
Genome, Viral
Likelihood Functions
Models, Statistical
Mutation
Open Reading Frames
Protein Biosynthesis
Sequence Alignment
Sequence Analysis, DNA
Software
Viruses
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Summary:Detecting new coding sequences (CDSs) in viral genomes can be difficult for several reasons. The typically compact genomes often contain a number of overlapping coding and non-coding functional elements, which can result in unusual patterns of codon usage; conservation between related sequences can be difficult to interpret--especially within overlapping genes; and viruses often employ non-canonical translational mechanisms--e.g. frameshifting, stop codon read-through, leaky-scanning and internal ribosome entry sites--which can conceal potentially coding open reading frames (ORFs). In a previous paper we introduced a new statistic--MLOGD (Maximum Likelihood Overlapping Gene Detector)--for detecting and analysing overlapping CDSs. Here we present (a) an improved MLOGD statistic, (b) a greatly extended suite of software using MLOGD, (c) a database of results for 640 virus sequence alignments, and (d) a web-interface to the software and database. Tests show that, from an alignment with just 20 mutations, MLOGD can discriminate non-overlapping CDSs from non-coding ORFs with a typical accuracy of up to 98%, and can detect CDSs overlapping known CDSs with a typical accuracy of 90%. In addition, the software produces a variety of statistics and graphics, useful for analysing an input multiple sequence alignment. MLOGD is an easy-to-use tool for virus genome annotation, detecting new CDSs--in particular overlapping or short CDSs--and for analysing overlapping CDSs following frameshift sites. The software, web-server, database and supplementary material are available at http://guinevere.otago.ac.nz/mlogd.html.
ISSN:1471-2105
1471-2105
DOI:10.1186/1471-2105-7-75