Informed and automated <i>k</i>-mer size selection for genome assembly

Chikhi, Rayan; Medvedev, Paul

doi:10.1093/bioinformatics/btt310

Cited by 684 publications

(584 citation statements)

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“…In the k-mer profiling problem [6,7] we are given a string T , an interval [k 1 ..k 2 ] of lengths and an interval [f 1 ..f 2 ] of frequencies, and we are asked to compute the matrix profile[k 1 ..k 2 , f 1 ..f 2 ] defined as follows:…”

Section: Kernels and Complexity Measures On K-mersmentioning

confidence: 99%

“…In practice profile is often computed by running a k-mer extraction algorithm k 2 − k 1 + 1 times, and by scanning the output of all such runs (see e.g. [6] and references therein). The following lemma shows that we can compute profile in just one pass over the BWT of the input string, and in linear time in the size of profile:…”

Section: Kernels and Complexity Measures On K-mersmentioning

confidence: 99%

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A Framework for Space-Efficient String Kernels

Belazzougui

Cunial

2017

Algorithmica

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String kernels are typically used to compare genome-scale sequences whose length makes alignment impractical, yet their computation is based on data structures that are either space-inefficient, or incur large slowdowns. We show that a number of exact string kernels, like the k-mer kernel, the substrings kernels, a number of length-weighted kernels, the minimal absent words kernel, and kernels with Markovian corrections, can all be computed in O(nd) time and in o(n) bits of space in addition to the input, using just a rangeDistinct data structure on the Burrows-Wheeler transform of the input strings, which takes O(d) time per element in its output. The same bounds hold for a number of measures of compositional complexity based on multiple value of k, like the k-mer profile and the k-th order empirical entropy, and for calibrating the value of k using the data.

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Section: Kernels and Complexity Measures On K-mersmentioning

confidence: 99%

Section: Kernels and Complexity Measures On K-mersmentioning

confidence: 99%

A Framework for Space-Efficient String Kernels

Belazzougui

Cunial

2017

Algorithmica

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“…were analysed in Kmergenie (Chikhi and Medvedev, 2014) using a variety of kmers (15,20,25,30,35,40,45) and assuming a diploid model, to estimate the unique haploid average coverage, which was used to estimate genome size by dividing the total length of the reads (16552689327 bp) by the unique average coverage (peak height ranging from 15-20). C) Mapping the combined datasets to 18737 open reading frames derived from a salivary gland transcriptome (manuscript in preparation) using CLC Genomics Workbench.…”

Section: Estimating Genome Sizementioning

confidence: 99%

Next-generation sequencing as means to retrieve tick systematic markers, with the focus on Nuttalliella namaqua (Ixodoidea: Nuttalliellidae)

Mans

Klerk

Pienaar

et al. 2015

Ticks and Tick-borne Diseases

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Nuclear ribosomal RNA (18S and 28S rRNA) and mitochondrial genomes are commonly used in tick systematics. The ability to retrieve these markers using nextgeneration sequencing was investigated using the tick Nuttalliella namaqua. Issues related to nuclear markers may be resolved using this approach, notably, the monotypic status of N. namaqua and its basal relationship to other tick families. Four different Illumina datasets (~55 million, 100 bp reads each) were generated from a single tick specimen and assembled to give 350k-390k contigs. A genome size of ~1 Gbp was estimated with low levels of repetitive elements. Contigs (>1000 bp, >50-fold coverage) present in most assemblies (n=69), included host-derived 18S and 28S rRNA, tick and host-derived transposable elements, full-length tick 18S and 28 rRNA, the mitochondrial genome in single contig assemblies and the histone cassette.Coverage for the nuclear rRNA genes was above 1000-fold confirming previous sequencing errors in the 18S rRNA gene, thereby maintaining the monotypic status of this tick. Nuclear markers for the soft tick Argas africolumbae were also retrieved from next-generation data. Phylogenetic analysis of a concatenated 18S-28S rRNA dataset supported the grouping of N. namaqua at the base of the tick tree and the two main tick families in separate clades. This study confirmed the monotypic status of N. namaqua and its basal relationship to other tick families. Next-generation sequencing of genomic material to retrieve high quality nuclear and mitochondrial systematic markers for ticks is viable and may resolve issues around conventional sequencing errors when comparing closely related tick species.

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“…The quality-fi ltered sequence reads were assembled into a number of contig sequences. The analysis has been performed using the "De novo assembly" option of the CL C Genomics Workbench version 6.0.4.The optimal k-mer size was automatically determined using KmerGenie [3]. The contigs were linked and placed into scaffolds or supercontigs.…”

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confidence: 99%