Regression trees for analysis of mutational spectra in nucleotide sequences.

Berikov, Vladimir; Rogozin, Igor B.

doi:10.1093/bioinformatics/15.7.553

Cited by 12 publications

(14 citation statements)

References 20 publications

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“…Regression trees have also been used to analyze the effect of nucleotide sequence context on mutation frequency [90]. Regression trees are mathematically tenable, do not restrain the number of variables (as do heuristic methods) and are recommended for study of simulated and real mutation spectra [90].…”

Section: Regression Analysis Of Nucleotide Sequence Context Effectsmentioning

confidence: 99%

“…Regression trees are mathematically tenable, do not restrain the number of variables (as do heuristic methods) and are recommended for study of simulated and real mutation spectra [90]. However, these approaches are based on complex assumptions and need large datasets (http://www.stat.…”

Section: Regression Analysis Of Nucleotide Sequence Context Effectsmentioning

confidence: 99%

“…Hotspot context analysis of these transversions using regression trees (Section 2.5) revealed AA mutable sequence [90]. Comparison of the mutT − spectrum and A·T → C·T transversions in a spectrum of spontaneous mutations in the lacI gene (lacI −d test system) [126] did not reveal significant differences between them (Table 3).…”

Section: Analysis Of Mutations In the Mutt Deficient Strain Of Eschermentioning

confidence: 99%

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Theoretical analysis of mutation hotspots and their DNA sequence context specificity

Rogozin

Pavlov

2003

Mutation Research/Reviews in Mutation Research

165

132

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Mutation frequencies vary significantly along nucleotide sequences such that mutations often concentrate at certain positions called hotspots. Mutation hotspots in DNA reflect intrinsic properties of the mutation process, such as sequence specificity, that manifests itself at the level of interaction between mutagens, DNA, and the action of the repair and replication machineries. The hotspots might also reflect structural and functional features of the respective DNA sequences. When mutations in a gene are identified using a particular experimental system, resulting hotspots could reflect the properties of the gene product and the mutant selection scheme. Analysis of the nucleotide sequence context of hotspots can provide information on the molecular mechanisms of mutagenesis. However, the determinants of mutation frequency and specificity are complex, and there are many analytical methods for their study. Here we review computational approaches for analyzing mutation spectra (distribution of mutations along the target genes) that include many mutable (detectable) positions. The following methods are reviewed: derivation of a consensus sequence, application of regression approaches to correlate nucleotide sequence features with mutation frequency, mutation hotspot prediction, analysis of oligonucleotide composition of regions containing mutations, pairwise comparison of mutation spectra, analysis of multiple spectra, and analysis of "context-free" characteristics. The advantages and pitfalls of these methods are discussed and illustrated by examples from the literature. The most reliable analyses were obtained when several methods were combined and information from theoretical analysis and experimental observations was considered simultaneously. Simple, robust approaches should be used with small samples of mutations, whereas combinations of simple and complex approaches may be required for large samples. We discuss several well-documented studies where analysis of mutation spectra has substantially contributed to the current understanding of molecular mechanisms of mutagenesis. The nucleotide sequence context of mutational hotspots is a fingerprint of interactions between DNA and DNA repair, replication, and modification enzymes, and the analysis of hotspot context provides evidence of such interactions. Published by Elsevier B.V.

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Section: Regression Analysis Of Nucleotide Sequence Context Effectsmentioning

confidence: 99%

Section: Regression Analysis Of Nucleotide Sequence Context Effectsmentioning

confidence: 99%

Section: Analysis Of Mutations In the Mutt Deficient Strain Of Eschermentioning

confidence: 99%

See 1 more Smart Citation

Theoretical analysis of mutation hotspots and their DNA sequence context specificity

Rogozin

Pavlov

2003

Mutation Research/Reviews in Mutation Research

165

132

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“…The context of hotspots being revealed in this way can be addressed by various methodologies (Rogozin and Pavlov 2003). We used two implementations of regression analysis of mutational spectra (Rogozin and Kolchanov 1992;Berikov and Rogozin 1999) to identify the mutational hotspot.…”

Section: Mutational Spectrum and Hotspot Analysesmentioning

confidence: 99%

Comparative mutational analyses of influenza A viruses

Cheung

Rogozin

Choy

et al. 2014

RNA

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The error-prone RNA-dependent RNA polymerase (RdRP) and external selective pressures are the driving forces for RNA viral diversity. When confounded by selective pressures, it is difficult to assess if influenza A viruses (IAV) that have a wide host range possess comparable or distinct spontaneous mutational frequency in their RdRPs. We used in-depth bioinformatics analyses to assess the spontaneous mutational frequencies of two RdRPs derived from human seasonal (A/Wuhan/359/95; Wuhan) and H5N1 (A/Vietnam/1203/04; VN1203) viruses using the mini-genome system with a common firefly luciferase reporter serving as the template. High-fidelity reverse transcriptase was applied to generate high-quality mutational spectra which allowed us to assess and compare the mutational frequencies and mutable motifs along a target sequence of the two RdRPs of two different subtypes. We observed correlated mutational spectra (τ correlation P < 0.0001), comparable mutational frequencies (H3N2:5.8 ± 0.9; H5N1:6.0 ± 0.5), and discovered a highly mutable motif "(A)AAG" for both Wuhan and VN1203 RdRPs. Results were then confirmed with two recombinant A/Puerto Rico/8/34 (PR8) viruses that possess RdRP derived from Wuhan or VN1203 (RG-PR8×Wuhan PB2, PB1, PA, NP and RG-PR8×VN1203 PB2, PB1, PA, NP ). Applying novel bioinformatics analysis on influenza mutational spectra, we provide a platform for a comprehensive analysis of the spontaneous mutation spectra for an RNA virus.

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“…It has been shown that these consensus sequences are characteristic of somatic mutations in various organisms [Rogozin and Kolchanov, 1992;Rogozin et al, 1996]. Regression trees have been applied for analysis of context influence on mutations [Berikov and Rogozin, 1999]. Models such as these are well substantiated mathematically and do not place restraints on the variables being analyzed as the heuristic approaches do.…”

Section: Context Of Mutational Hotspotsmentioning

confidence: 99%