Whole genome molecular phylogeny of large dsDNA viruses using composition vector method

Gao, Lei; Qi, Ji

doi:10.1186/1471-2148-7-41

Cited by 96 publications

(83 citation statements)

References 55 publications

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“…Viral classification and evolution could be based either on genome features (size/type of genome) or on their host range (1,2). Though viruses are known to infect almost all spectra of living organisms, the fact that they require a host to survive and replicate, makes it difficult to study the evolutionary aspect of viruses in the traditional way.…”

Section: Introductionmentioning

confidence: 99%

Microsatellite Diversity and Complexity in Eighteen Staphylococcus Phage Genomes

Alam

Iqbal²,

Tripathi

et al. 2017

Gene Cell Tissue

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The present study focused on Staphylococcus phage genomes, which have been classified to 3 categories on the basis of their size. Overall, 18 classes of II Staphylococcus phage genomes with genome size around 40 kbp were investigated to elucidate the presence, distribution, and complexity of SSRs therein. The full length genome sequences from NCBI were analyzed using the IMEx software. A total of 3656 simple sequence repeats (SSRs) and 213 compound SSRs (cSSR) were present in the studied genomes. The incident frequency of SSR and cSSR per genome ranged from 183 to 308 and 8 to 19, respectively. The SSRs distribution across genomes was non-linear and so was its conversion to cSSR (the range of cSSR percentage was from 4.15 to 9.13) implicating a non-uniform incidence and clustering in genomes. The AT rich content of genomes was reflected in the prevalence of repeats with A, AT/TA, and AAG/GAA being the highest represented mono-, di-, and tri-nucleotide repeat motifs, respectively. An increase in dMAX was accompanied by greater cSSRs in the genomes yet the increase was neither uniform across genomes nor linear. The SSRs and cSSRs are predominantly localized on the coding region. The non-coding region accounts for~19% in SSR and~30% in cSSR while a hypothetical protein accounted for~30% in SSRs as well as cSSR. The relative frequencies and distribution of different classes of simple and compound microsatellites within and across genomes are suggestive of these sequences being involved in genome evolution and adaptation.

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Section: Introductionmentioning

confidence: 99%

Microsatellite Diversity and Complexity in Eighteen Staphylococcus Phage Genomes

Alam

Iqbal²,

Tripathi

et al. 2017

Gene Cell Tissue

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“…The classification and evolution of viruses have been based either on the genome features (size/type) or on their host range [1,2]. A single viral genome encodes from 2 to about a thousand proteins [3,4].…”

Section: Introductionmentioning

confidence: 99%

Imex Based Analysis of Repeat Sequences in Flavivirus Genomes, Including Dengue Virus

Alam¹

2016

J Data Mining Genomics Proteomics

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Simple sequence repeats (SSRs), also known as microsatellites, are 1-6 nucleotides repeat motif, present in varying number of iterations, across coding and non-coding regions of prokaryotes, eukaryotes and viruses. Present study focuses on simple sequence repeats (SSRs) in 27 Flavivirus genomes, which includes dengue virus. The comparative viral genomics in the light of SSRs would help us understand the diversity and adaptability to new hosts. A total of 1164 SSRs and 53 cSSRs were uncovered from the 27studied genomes. Mononucleotide A was the most prevalent repeat motif with an average distribution of around 6. This was followed by G (average distribution of 2). Amongst the dinucleotides AG/GA repeat motif was the most prevalent with an average distribution of 14 across studied genomes. The Flavivirus genomes lacked two essential features responsible for genome evolution, dinucleotide repeat motif AT/TA (least represented with average distribution of ~0.5) and cSSR in non-coding regions, suggesting a stable genome or evolution by hitherto unexplained mechanisms. The unveiling of conserved sequences in the isolates of Dengue virus suggests a basis for biomarker development for viral diagnostics.

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“…By using this alignment-free approach, Hao's group obtained valuable results for both protein and genome sequences (Gao and Qi, 2007;Qi et al, 2004). However, for this method, when K becomes larger, the dimension of composition vector increases exponentially.…”

Section: Comparison With Another Alignment-free Methodsmentioning

confidence: 97%

“…The recent reviews (Davies et al, 2008;Vinga and Almeida, 2003) on published methods of alignment-free sequence comparison report several concepts of distance measures, such as Markov chain models and Kullback-Leibler discrepancy (Wu et al, 2001), chaos theory (Almeida et al, 2001), Kolmogorov complexity , decision tree induction algorithm (Huang et al, 2004), graphical representation (Liao and Wang, 2004;Randic et al, 2003;Yau et al, 2003), probabilistic measure (Pham and Zuegg, 2004;Yu et al, 2011). Furthermore, sequence vector representation approaches without alignment are also prevalent, such as composition vector (Chan et al, 2010;Chu et al, 2004;Gao and Qi, 2007;Qi et al, 2004), feature vector (Carr et al, 2010;Liu et al, 2006), and moment vector Yu et al, 2010). However, all of these methods treat the DNA or protein sequences as simple character strings, and then consider occurrence frequencies and distribution of nucleotides or amino acids, or complexity of sequences and so on.…”

Section: Introductionmentioning

confidence: 98%

Protein map: An alignment-free sequence comparison method based on various properties of amino acids

Cheng

et al. 2011

Gene

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Whole genome molecular phylogeny of large dsDNA viruses using composition vector method

Cited by 96 publications

References 55 publications

Microsatellite Diversity and Complexity in Eighteen Staphylococcus Phage Genomes

Microsatellite Diversity and Complexity in Eighteen Staphylococcus Phage Genomes

Imex Based Analysis of Repeat Sequences in Flavivirus Genomes, Including Dengue Virus

Protein map: An alignment-free sequence comparison method based on various properties of amino acids

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