Determining the quality and complexity of next-generation sequencing data without a reference genome

Anvar, Seyed Yahya; Khachatryan, Lusine; Vermaat, Martijn; Galen, M.A. van; Pulyakhina, Irina; Ariyürek, Yavuz; Kraaijeveld, Ken; Dunnen, Johan T. den; Knijff, Peter de; Hoen, Peter A C ‘t; Laros, Jeroen F. J.

doi:10.1186/s13059-014-0555-3

Cited by 27 publications

(30 citation statements)

References 43 publications

(52 reference statements)

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“…Additional information can be extracted through pairwise comparisons of WGS datasets (Anvar et al , 2014), which can identify problematic samples by highlighting differences between spectra.…”

Section: Introductionmentioning

confidence: 99%

KAT: a K-mer analysis toolkit to quality control NGS datasets and genome assemblies

et al. 2016

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Motivation De novo assembly of whole genome shotgun (WGS) next-generation sequencing (NGS) data benefits from high-quality input with high coverage. However, in practice, determining the quality and quantity of useful reads quickly and in a reference-free manner is not trivial. Gaining a better understanding of the WGS data, and how that data is utilized by assemblers, provides useful insights that can inform the assembly process and result in better assemblies.ResultsWe present the K-mer Analysis Toolkit (KAT): a multi-purpose software toolkit for reference-free quality control (QC) of WGS reads and de novo genome assemblies, primarily via their k-mer frequencies and GC composition. KAT enables users to assess levels of errors, bias and contamination at various stages of the assembly process. In this paper we highlight KAT’s ability to provide valuable insights into assembly composition and quality of genome assemblies through pairwise comparison of k-mers present in both input reads and the assemblies.Availability and ImplementationKAT is available under the GPLv3 license at: https://github.com/TGAC/KAT.Supplementary information Supplementary data are available at Bioinformatics online.

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Section: Introductionmentioning

confidence: 99%

KAT: a K-mer analysis toolkit to quality control NGS datasets and genome assemblies

et al. 2016

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“…Indexing HULK utilises the LSH Forest self-tuning indexing scheme as employed in our previous work (Rowe and Winn, 2018) . Briefly, this scheme will take a query and return a subset of nearest-neighbour candidates, based on the number of hash collisions (Bawa et al , 2005) . The two parameters to tune this index are (i) the number of hash functions to encode an item (K) and (ii) the number of hash tables to split an item in to (L).…”

Section: Distance Estimationmentioning

confidence: 99%

“…For example, the pairwise comparison of k-mer spectra is a de novo analysis method that has been routinely used in recent years for clustering microbiomes using dissimilarity measures (Dubinkina et al , 2016;Benoit et al , 2016) . These measures are used to identify microbiome composition changes in studies that involve longitudinal sampling or multiple isolation sites (Anvar et al , 2014) . However, k-mer spectra can still take considerable time to compute, are relatively large in file size and new sample comparisons require additional computation.…”

Section: Introductionmentioning

confidence: 99%

Streaming histogram sketching for rapid microbiome analytics

Rowe

Carrieri²,

Alcon-Giner³

et al. 2018

Preprint

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MotivationThe growth in publically available microbiome data in recent years has yielded an invaluable resource for genomic research; allowing for the design of new studies, augmentation of novel datasets and reanalysis of published works. This vast amount of microbiome data, as well as the widespread proliferation of microbiome research and the looming era of clinical metagenomics, means there is an urgent need to develop analytics that can process huge amounts of data in a short amount of time. To address this need, we propose a new method for the compact representation of microbiome sequencing data using similarity-preserving sketches of streaming k-mer spectra. These sketches allow for dissimilarity estimation, rapid microbiome catalogue searching, and classification of microbiome samples in near real-time.

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“…This could be attributed to a variety of reasons, including the use of a poor reference genome with missing genetic information (Anvar et al 2014); diseasecausing variants are non-coding SNPs or splice-site mutations (Koboldt et al 2013). To identify candidate disease genes within FAME loci, we propose in silico gene prioritization.…”

Section: Introductionmentioning

confidence: 99%

Identity by descent fine mapping of familial adult myoclonus epilepsy (FAME) to 2p11.2–2q11.2

et al. 2016

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Familial adult myoclonus epilepsy (FAME) is a rare autosomal dominant disorder characterized by adult onset, involuntary muscle jerks, cortical myoclonus and occasional seizures. FAME is genetically heterogeneous with more than 70 families reported worldwide and five potential disease loci. The efforts to identify potential causal variants have been unsuccessful in all but three families. To date, linkage analysis has been the main approach to find and narrow FAME critical regions. We propose an alternative method, pedigree free identity-by-descent (IBD) mapping, that infers regions of the genome between individuals that have been inherited from a common ancestor. IBD mapping provides an alternative to linkage analysis in the presence of allelic and locus heterogeneity by detecting clusters of individuals who share a common allele. Succeeding IBD mapping, gene prioritization based on gene co-expression analysis can be used to identify the most promising candidate genes. We performed an IBD analysis using high-density single nucleotide polymorphism (SNP) array data followed by gene prioritization on a FAME cohort of ten European families and one Australian/New Zealander family; eight of which had known disease loci. By identifying IBD regions common to multiple families, we were able to narrow the FAME2 locus to a 9.78 megabase interval within 2p11.2-q11.2. We provide additional evidence of a founder effect in four Italian families and allelic heterogeneity with at least four distinct founders responsible for FAME at the FAME2 locus. In addition, we suggest candidate disease genes using gene prioritization based on gene co-expression analysis.

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Determining the quality and complexity of next-generation sequencing data without a reference genome

Cited by 27 publications

References 43 publications

KAT: a K-mer analysis toolkit to quality control NGS datasets and genome assemblies

KAT: a K-mer analysis toolkit to quality control NGS datasets and genome assemblies

Streaming histogram sketching for rapid microbiome analytics

Identity by descent fine mapping of familial adult myoclonus epilepsy (FAME) to 2p11.2–2q11.2

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