Species-specific Typing of DNA Based on Palindrome Frequency Patterns

Lamprea-Burgunder, Estelle; Ludin, Philipp; Mäser, Pascal

doi:10.1093/dnares/dsr004

Cited by 13 publications

(8 citation statements)

References 37 publications

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“…Importantly, some authors have in addition used higher-dimensional vectors of base composition patterns than simple nt counts. A major locus of activity has been in the use of multidimensional dinucleotide, trinucleotide and, most commonly, tetranucleotide composition vectors (4NCV; Teeling et al, 2004 ; Slater and Birney, 2005 ; Chatterji et al, 2007 ; Emmersen et al, 2007 ; Dick et al, 2009 ; Willner et al, 2009 ; Ghosh et al, 2011 ; Lamprea-Burgunder et al, 2011 ; Brisson et al, 2012 ; Saeed et al, 2012 ; Strous et al, 2012 ). Hexanucleotide counting has also been used to separate simple mixtures of a few species ( Hraber and Weller, 2001 ).…”

Section: Discussionmentioning

confidence: 99%

Blobology: exploring raw genome data for contaminants, symbionts and parasites using taxon-annotated GC-coverage plots

et al. 2013

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Generating the raw data for a de novo genome assembly project for a target eukaryotic species is relatively easy. This democratization of access to large-scale data has allowed many research teams to plan to assemble the genomes of non-model organisms. These new genome targets are very different from the traditional, inbred, laboratory-reared model organisms. They are often small, and cannot be isolated free of their environment – whether ingested food, the surrounding host organism of parasites, or commensal and symbiotic organisms attached to or within the individuals sampled. Preparation of pure DNA originating from a single species can be technically impossible, but assembly of mixed-organism DNA can be difficult, as most genome assemblers perform poorly when faced with multiple genomes in different stoichiometries. This class of problem is common in metagenomic datasets that deliberately try to capture all the genomes present in an environment, but replicon assembly is not often the goal of such programs. Here we present an approach to extracting, from mixed DNA sequence data, subsets that correspond to single species’ genomes and thus improving genome assembly. We use both numerical (proportion of GC bases and read coverage) and biological (best-matching sequence in annotated databases) indicators to aid partitioning of draft assembly contigs, and the reads that contribute to those contigs, into distinct bins that can then be subjected to rigorous, optimized assembly, through the use of taxon-annotated GC-coverage plots (TAGC plots). We also present Blobsplorer, a tool that aids exploration and selection of subsets from TAGC-annotated data. Partitioning the data in this way can rescue poorly assembled genomes, and reveal unexpected symbionts and commensals in eukaryotic genome projects. The TAGC plot pipeline script is available from https://github.com/blaxterlab/blobology, and the Blobsplorer tool from https://github.com/mojones/Blobsplorer.

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Section: Discussionmentioning

confidence: 99%

Blobology: exploring raw genome data for contaminants, symbionts and parasites using taxon-annotated GC-coverage plots

et al. 2013

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“…Interestingly, although palindromic motifs have been known to successfully differentiate chromosomes, as shown in [14]. However in Figure 19, we note that palindromes do not successfully recreate the taxonomy tree from Figure 14.…”

Section: Resultsmentioning

confidence: 92%

“…Since the spectrum set motifs were originally inspired from palindromic restriction sites, we also studied the proportions of an exhaustive list of length-6 palindromic motifs (64 in total) across the sequence data. Interestingly, although palindromic motifs have been known to successfully differentiate chromosomes, as shown in [ 14 ]. However in Figure 19 , we note that palindromes do not successfully recreate the taxonomy tree from Figure 14 .…”

Section: Resultsmentioning

confidence: 99%

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A base composition analysis of natural patterns for the preprocessing of metagenome sequences

2013

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BackgroundOn the pretext that sequence reads and contigs often exhibit the same kinds of base usage that is also observed in the sequences from which they are derived, we offer a base composition analysis tool. Our tool uses these natural patterns to determine relatedness across sequence data. We introduce spectrum sets (sets of motifs) which are permutations of bacterial restriction sites and the base composition analysis framework to measure their proportional content in sequence data. We suggest that this framework will increase the efficiency during the pre-processing stages of metagenome sequencing and assembly projects.ResultsOur method is able to differentiate organisms and their reads or contigs. The framework shows how to successfully determine the relatedness between these reads or contigs by comparison of base composition. In particular, we show that two types of organismal-sequence data are fundamentally different by analyzing their spectrum set motif proportions (coverage). By the application of one of the four possible spectrum sets, encompassing all known restriction sites, we provide the evidence to claim that each set has a different ability to differentiate sequence data. Furthermore, we show that the spectrum set selection having relevance to one organism, but not to the others of the data set, will greatly improve performance of sequence differentiation even if the fragment size of the read, contig or sequence is not lengthy.ConclusionsWe show the proof of concept of our method by its application to ten trials of two or three freshly selected sequence fragments (reads and contigs) for each experiment across the six organisms of our set. Here we describe a novel and computationally effective pre-processing step for metagenome sequencing and assembly tasks. Furthermore, our base composition method has applications in phylogeny where it can be used to infer evolutionary distances between organisms based on the notion that related organisms often have much conserved code.

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“…, a 16 ) with a i = f i . The introduction of this signature is motivated by a study where the frequency distribution of palindromic tetranucleotides was shown to exhibit highest inter-species but low intra-species variance on 10,000 bp sequences [40]. Combination Signature (ρ max , ρ min , ρ P ): As a combination of ρ max , ρ min and ρ P , it maps a sequence s to a vector (a 1 , .…”

Section: Genomic Signatures and Their Rationalementioning

confidence: 99%

The importance of Chargaff’s second parity rule for genomic signatures in metagenomics

Gori

Mavroeidis

Jetten

et al. 2017

Preprint

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An important problem in metagenomic data analysis is to identify the source organism, or at least taxon, of each sequence. Most methods tackle this problem in two steps by using an alignment-free approach: first the DNA sequences are represented as points of a real n-dimensional space via a mapping function then either clustering or classification algorithms are applied. Those mapping functions require to be genomic signatures: the dissimilarity between the mapped points must reflect the degree of phylogenetic similarity of the source species. Designing good signatures for metagenomics can be challenging due to the special characteristics of metagenomic sequences; most of the existing signatures were not designed accordingly and they were tested only on error-free sequences sampled from a few dozens of species.In this work we analyze comparatively the goodness of existing and novel signatures based on tetranucleotide frequencies via statistical models and computational experiments; we also study how they are affected by the generalized Chargaff's second parity rule (GCSPR), which states that in a given sequence longer than 50kbp, inverse oligonucleotides are approximately equally frequent. We analyze 38 million sequences of 150 bp-1,000 bp with 1% base-calling error, sampled from 1,284 microbes. Our models indicate that GCSPR reduces strand-dependence of signatures, that is, their values are less affected by the source strand; GCSPR is further exploited by some signatures to reduce the intra-species dispersion. Two novel signatures stand out both in the models and in the experiments: the combination signature and the operation signature. The former achieves strand-independence without grouping oligonucleotides; this could be valuable for alignment-free sequence comparison methods when distinguishing inverse oligonucleotides matters. Operation signature sums the frequencies of reverse, complement, and inverse tetranucleotides; having 72 features it reduces the computational intensity of the analysis.

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Species-specific Typing of DNA Based on Palindrome Frequency Patterns

Cited by 13 publications

References 37 publications

Blobology: exploring raw genome data for contaminants, symbionts and parasites using taxon-annotated GC-coverage plots

Blobology: exploring raw genome data for contaminants, symbionts and parasites using taxon-annotated GC-coverage plots

A base composition analysis of natural patterns for the preprocessing of metagenome sequences

The importance of Chargaff’s second parity rule for genomic signatures in metagenomics

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