Inference of Markovian properties of molecular sequences from NGS data and applications to comparative genomics

Ren, Jie; Song, Kai; Deng, Minghua; Reinert, Gesine; Cannon, Charles H.; Sun, Fengzhu

doi:10.1093/bioinformatics/btv395

Cited by 27 publications

(47 citation statements)

References 40 publications

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“…\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$d_2^*$\end{document} has shown excellent performance in other applications for assessing relatedness of whole genomes or metagenomic samples (22,26–28), so perhaps it is not surprising that it was the top performing measure in virus-host prediction. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$d_2^*$\end{document} along with other measures like \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$d_2^S$\end{document}, Hao and Teeling , distinct from simpler measures like Eu and Ma , in that they take into consideration the background oligonucleotide patterns of the two sequences being compared.…”

Section: Discussionmentioning

confidence: 99%

“…These alignment-free dissimilarity measures compare two sequences based on the normalized ONFs where the expected ONFs based on a Markov model are removed from the observed ONF (see Materials and Methods section). These measures have shown excellent performance in related applications of sequence analysis—phylogenetic relatedness of genomes and congruence in sample clustering based on analysis of metagenomes and environmental conditions of those samples (22,26–28). These sophisticated dissimilarity measures have a potential advantage over simpler ONF measures like Euclidean and Manhattan distances that only use observed ONF patterns.…”

Section: Introductionmentioning

confidence: 99%

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Alignment-free $d_2^*$ oligonucleotide frequency dissimilarity measure improves prediction of hosts from metagenomically-derived viral sequences

Ahlgren

Ren

et al. 2016

Nucleic Acids Research

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Viruses and their host genomes often share similar oligonucleotide frequency (ONF) patterns, which can be used to predict the host of a given virus by finding the host with the greatest ONF similarity. We comprehensively compared 11 ONF metrics using several k-mer lengths for predicting host taxonomy from among ∼32 000 prokaryotic genomes for 1427 virus isolate genomes whose true hosts are known. The background-subtracting measure \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$d_2^*$\end{document} at k = 6 gave the highest host prediction accuracy (33%, genus level) with reasonable computational times. Requiring a maximum dissimilarity score for making predictions (thresholding) and taking the consensus of the 30 most similar hosts further improved accuracy. Using a previous dataset of 820 bacteriophage and 2699 bacterial genomes, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$d_2^*$\end{document} host prediction accuracies with thresholding and consensus methods (genus-level: 64%) exceeded previous Euclidian distance ONF (32%) or homology-based (22-62%) methods. When applied to metagenomically-assembled marine SUP05 viruses and the human gut virus crAssphage, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$d_2^*$\end{document}-based predictions overlapped (i.e. some same, some different) with the previously inferred hosts of these viruses. The extent of overlap improved when only using host genomes or metagenomic contigs from the same habitat or samples as the query viruses. The \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$d_2^*$\end{document} ONF method will greatly improve the characterization of novel, metagenomic viruses.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Alignment-free $d_2^*$ oligonucleotide frequency dissimilarity measure improves prediction of hosts from metagenomically-derived viral sequences

Ahlgren

Ren

et al. 2016

Nucleic Acids Research

Self Cite

259

295

View full text Add to dashboard Cite

show abstract

“…These data have provided a new tool for the genotyping and assessment of genetic resources in non-model species (Davey et al, 2011 ; Ekblom and Galindo, 2011 ; Lin et al, 2011 ). Moreover, the process and analytic power required to handle the huge sequencing data have improved (Aflitos et al, 2015 ; Ren et al, 2015 ). Therefore, the development of a reliable and effective molecular marker system from sequencing data has become feasible in many non-model plants (Durand et al, 2010 ; Yadav et al, 2011 ).…”

Section: Introductionmentioning

confidence: 99%

Moderate Genetic Diversity and Genetic Differentiation in the Relict Tree Liquidambar formosana Hance Revealed by Genic Simple Sequence Repeat Markers

et al. 2016

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Chinese sweetgum (Liquidambar formosana) is a relatively fast-growing ecological pioneer species. It is widely used for multiple purposes. To assess the genetic diversity and genetic differentiation of the species, genic SSR markers were mined from transcriptome data for subsequent analysis of the genetic diversity and population structure of natural populations. A total of 10645 potential genic SSR loci were identified in 80482 unigenes. The average frequency was one SSR per 5.12 kb, and the dinucleotide unit was the most abundant motif. A total of 67 alleles were found, with a mean of 6.091 alleles per locus and a mean polymorphism information content of 0.390. Moreover, the species exhibited a relatively moderate level of genetic diversity (He = 0.399), with the highest was found in population XY (He = 0.469). At the regional level, the southwestern region displayed the highest genetic diversity (He = 0.435) and the largest number of private alleles (n = 5), which indicated that the Southwestern region may be the diversity hot spot of L. formosana. The AMOVA results showed that variation within populations (94.02%) was significantly higher than among populations (5.98%), which was in agreement with the coefficient of genetic differentiation (Fst = 0.076). According to the UPGMA analysis and principal coordinate analysis and confirmed by the assignment test, 25 populations could be divided into three groups, and there were different degrees of introgression among populations. No correlation was found between genetic distance and geographic distance (P > 0.05). These results provided further evidence that geographic isolation was not the primary factor leading to the moderate genetic differentiation of L. formosana. As most of the genetic diversity of L. formosana exists among individuals within a population, individual plant selection would be an effective way to use natural variation in genetic improvement programs. This would be helpful to not only protect the genetic resources but also attain effective management and exploit genetic resources.

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“…To address this gap, Fixed Order Markov Chains (FOMC) were used to model the background genome sequences, as reported in previous studies2223. There are several limitations during the applications of FOMC: (1) The order of Markov Chain (MC) needs to be set manually.…”

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confidence: 99%

Alignment-free Transcriptomic and Metatranscriptomic Comparison Using Sequencing Signatures with Variable Length Markov Chains

Liao

Ren

Wang

et al. 2016

Sci Rep

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The comparison between microbial sequencing data is critical to understand the dynamics of microbial communities. The alignment-based tools analyzing metagenomic datasets require reference sequences and read alignments. The available alignment-free dissimilarity approaches model the background sequences with Fixed Order Markov Chain (FOMC) yielding promising results for the comparison of microbial communities. However, in FOMC, the number of parameters grows exponentially with the increase of the order of Markov Chain (MC). Under a fixed high order of MC, the parameters might not be accurately estimated owing to the limitation of sequencing depth. In our study, we investigate an alternative to FOMC to model background sequences with the data-driven Variable Length Markov Chain (VLMC) in metatranscriptomic data. The VLMC originally designed for long sequences was extended to apply to high-throughput sequencing reads and the strategies to estimate the corresponding parameters were developed. The flexible number of parameters in VLMC avoids estimating the vast number of parameters of high-order MC under limited sequencing depth. Different from the manual selection in FOMC, VLMC determines the MC order adaptively. Several beta diversity measures based on VLMC were applied to compare the bacterial RNA-Seq and metatranscriptomic datasets. Experiments show that VLMC outperforms FOMC to model the background sequences in transcriptomic and metatranscriptomic samples. A software pipeline is available at https://d2vlmc.codeplex.com.

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Inference of Markovian properties of molecular sequences from NGS data and applications to comparative genomics

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 27 publications

References 40 publications

Alignment-free $d_2^*$ oligonucleotide frequency dissimilarity measure improves prediction of hosts from metagenomically-derived viral sequences

Alignment-free $d_2^*$ oligonucleotide frequency dissimilarity measure improves prediction of hosts from metagenomically-derived viral sequences

Moderate Genetic Diversity and Genetic Differentiation in the Relict Tree Liquidambar formosana Hance Revealed by Genic Simple Sequence Repeat Markers

Alignment-free Transcriptomic and Metatranscriptomic Comparison Using Sequencing Signatures with Variable Length Markov Chains

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