ArticleBMC bioinformatics2012
Exploration of multivariate analysis in microbial coding sequence modeling.
Article in BMC bioinformatics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Effect of chemotherapy on urinary volatile biomarkers for lung cancer by HS-SPME-GC-MS and chemometrics.Thoracic cancer · 2023Article
- Feasibility of using volatile urine fingerprints for the differentiation of sexually transmitted infections.Applied microbiology and biotechnology · 2023Article
- Comparing K-mer based methods for improved classification of 16S sequences.BMC bioinformatics · 2015Article
- A systematic search for discriminating sites in the 16S ribosomal RNA gene.Microbial informatics and experimentation · 2014Article
- Comparative metabolomics of muscle interstitium fluid in human trapezius myalgia: an in vivo microdialysis study.European journal of applied physiology · 2013Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundGene finding is a complicated procedure that encapsulates algorithms for coding sequence modeling, identification of promoter regions, issues concerning overlapping genes and more. In the present study we focus on coding sequence modeling algorithms; that is, algorithms for identification and prediction of the actual coding sequences from genomic DNA. In this respect, we promote a novel multivariate method known as Canonical Powered Partial Least Squares (CPPLS) as an alternative to the commonly used Interpolated Markov model (IMM). Comparisons between the methods were performed on DNA, codon and protein sequences with highly conserved genes taken from several species with different genomic properties.
resultsThe multivariate CPPLS approach classified coding sequence substantially better than the commonly used IMM on the same set of sequences. We also found that the use of CPPLS with codon representation gave significantly better classification results than both IMM with protein (p < 0.001) and with DNA (p < 0.001). Further, although the mean performance was similar, the variation of CPPLS performance on codon representation was significantly smaller than for IMM (p < 0.001).
conclusionsThe performance of coding sequence modeling can be substantially improved by using an algorithm based on the multivariate CPPLS method applied to codon or DNA frequencies.
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Registered trials
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