ABySS 2.0: resource-efficient assembly of large genomes using a Bloom filter

Jackman, Shaun D.; Vandervalk, Benjamin P.; Mohamadi, Hamid; Chu, Justin; Yeo, Sarah; Hammond, S. Austin; Jahesh, Golnaz; Khan, Hira; Coombe, Lauren; Warren, René L.; Birol, İnanç

doi:10.1101/gr.214346.116

Cited by 561 publications

(386 citation statements)

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“…We trimmed shotgun libraries and the raw UCE reads with trimgalore (Krueger ) in order to remove adapters and low‐quality bases. We assembled the two shotgun libraries using abyss 2.0 (Jackman et al, ), selecting an optimal khmer size with KMERGENIE (Chikhi & Medvedev, ). We assembled the UCE raw reads using “ phyluce_assembly_assemblo_trinity ” with trinity v2013–02–25 (Grabherr et al, ).…”

Section: Methodsmentioning

confidence: 99%

Advancing mite phylogenomics: Designing ultraconserved elements for Acari phylogeny

Dam

Trautwein

Spicer

et al. 2019

Molecular Ecology Resources

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Mites (Acari) are one of the most diverse groups of life on Earth; yet, their evolutionary relationships are poorly understood. Also, the resolution of broader arachnid phylogeny has been hindered by an underrepresentation of mite diversity in phylogenomic analyses. To further our understanding of Acari evolution, we design targeted ultraconserved genomic elements (UCEs) probes, intended for resolving the complex relationships between mite lineages and closely related arachnids. We then test our Acari UCE baits in‐silico by constructing a phylogeny using 13 existing Acari genomes, as well as 6 additional taxa from a variety of genomic sources. Our Acari‐specific probe kit improves the recovery of loci within mites over an existing general arachnid UCE probe set. Our initial phylogeny recovers the major mite lineages, yet finds mites to be non‐monophyletic overall, with Opiliones (harvestmen) and Ricinuleidae (hooded tickspiders) rendering Parasitiformes paraphyletic.

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Section: Methodsmentioning

confidence: 99%

Advancing mite phylogenomics: Designing ultraconserved elements for Acari phylogeny

Dam

Trautwein

Spicer

et al. 2019

Molecular Ecology Resources

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“…For this new data type, Weisenfeld et al (2017) introduces a new assembler, Supernova, for the de novo assembly of diploid human genomes from linked reads. Additionally, Jackman et al (2017) describes a new version of the ABySS assembler and explores linked reads and optical mapping for improved scaffolding.…”

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confidence: 99%

New advances in sequence assembly

Phillippy

2017

Genome Res.

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“…ntCard has been used in the new version of our genome assembly software package, ABySS 2.0 (Jackman et al , 2016), to determine the values for total memory size and number of hash functions. It has been also utilized to set the Bloom filter sizes in BioBloom tools (Chu et al , 2014), which is a general use fast sequence categorization tool utilizing Bloom filters.…”

Section: Discussionmentioning

confidence: 99%

ntCard: a streaming algorithm for cardinality estimation in genomics data

2017

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MotivationMany bioinformatics algorithms are designed for the analysis of sequences of some uniform length, conventionally referred to as k-mers. These include de Bruijn graph assembly methods and sequence alignment tools. An efficient algorithm to enumerate the number of unique k-mers, or even better, to build a histogram of k-mer frequencies would be desirable for these tools and their downstream analysis pipelines. Among other applications, estimated frequencies can be used to predict genome sizes, measure sequencing error rates, and tune runtime parameters for analysis tools. However, calculating a k-mer histogram from large volumes of sequencing data is a challenging task.ResultsHere, we present ntCard, a streaming algorithm for estimating the frequencies of k-mers in genomics datasets. At its core, ntCard uses the ntHash algorithm to efficiently compute hash values for streamed sequences. It then samples the calculated hash values to build a reduced representation multiplicity table describing the sample distribution. Finally, it uses a statistical model to reconstruct the population distribution from the sample distribution. We have compared the performance of ntCard and other cardinality estimation algorithms. We used three datasets of 480 GB, 500 GB and 2.4 TB in size, where the first two representing whole genome shotgun sequencing experiments on the human genome and the last one on the white spruce genome. Results show ntCard estimates k-mer coverage frequencies >15× faster than the state-of-the-art algorithms, using similar amount of memory, and with higher accuracy rates. Thus, our benchmarks demonstrate ntCard as a potentially enabling technology for large-scale genomics applications.Availability and ImplementationntCard is written in C ++ and is released under the GPL license. It is freely available at https://github.com/bcgsc/ntCard.Supplementary information Supplementary data are available at Bioinformatics online.

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ABySS 2.0: resource-efficient assembly of large genomes using a Bloom filter

Cited by 561 publications

References 43 publications

Advancing mite phylogenomics: Designing ultraconserved elements for Acari phylogeny

Advancing mite phylogenomics: Designing ultraconserved elements for Acari phylogeny

New advances in sequence assembly

ntCard: a streaming algorithm for cardinality estimation in genomics data

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