ARKS: chromosome-scale scaffolding of human genome drafts with linked read kmers

Abstract

BackgroundThe long-range sequencing information captured by linked reads, such as those available from 10× Genomics (10xG), helps resolve genome sequence repeats, and yields accurate and contiguous draft genome assemblies. We introduce ARKS, an alignment-free linked read genome scaffolding methodology that uses linked reads to organize genome assemblies further into contiguous drafts. Our approach departs from other read alignment-dependent linked read scaffolders, including our own (ARCS), and uses a kmer-based mapping approach. The kmer mapping strategy has several advantages over read alignment methods, including better usability and faster processing, as it precludes the need for input sequence formatting and draft sequence assembly indexing. The reliance on kmers instead of read alignments for pairing sequences relaxes the workflow requirements, and drastically reduces the run time.ResultsHere, we show how linked reads, when used in conjunction with Hi-C data for scaffolding, improve a draft human genome assembly of PacBio long-read data five-fold (baseline vs. ARKS NG50 = 4.6 vs. 23.1 Mbp, respectively). We also demonstrate how the method provides further improvements of a megabase-scale Supernova human genome assembly (NG50 = 14.74 Mbp vs. 25.94 Mbp before and after ARKS), which itself exclusively uses linked read data for assembly, with an execution speed six to nine times faster than competitive linked read scaffolders (~ 10.5 h compared to 75.7 h, on average). Following ARKS scaffolding of a human genome 10xG Supernova assembly (of cell line NA12878), fewer than 9 scaffolds cover each chromosome, except the largest (chromosome 1, n = 13).ConclusionsARKS uses a kmer mapping strategy instead of linked read alignments to record and associate the barcode information needed to order and orient draft assembly sequences. The simplified workflow, when compared to that of our initial implementation, ARCS, markedly improves run time performances on experimental human genome datasets. Furthermore, the novel distance estimator in ARKS utilizes barcoding information from linked reads to estimate gap sizes. It accomplishes this by modeling the relationship between known distances of a region within contigs and calculating associated Jaccard indices. ARKS has the potential to provide correct, chromosome-scale genome assemblies, promptly. We expect ARKS to have broad utility in helping refine draft genomes.Electronic supplementary materialThe online version of this article (10.1186/s12859-018-2243-x) contains supplementary material, which is available to authorized users.

Intra-species differences in population size shape life history and genome evolution

“…The total scaffold length is 943,793,727 bp distributed in 7830 scaffolds with an N50 of 421 kb (with gaps). The resulting assembly was further scaffolded by ARCS v1.0 ( Coombe et al, 2018 ) + LINKS v1.8.5 ( Warren et al, 2015 ) with the following parameters: arcs -e 50000 c 3 r 0.05 s 98 and LINKS -m -d 4000 k 20 -e 0.1 l 3 -a 0.3 t 2 -o 0 -z 500 r -p 0.001 -x 0. This increased the scaffold N50 to 1.527 Mb.…”

Intra-Species Differences in Population Size shape Life History and Genome Evolution

“…The total scaffold length is 943,793,727bp distributed in 7830 scaffolds with an N50 of 421kb (with gaps). The resulting assembly was further scaffolded by ARCS v1.0 48 + LINKS v1.8.5 49 with the following parameters: arcs -e 50000 -c 3 -r 0.05 -s 98 and LINKS -m -d 4000 -k 20 -e 0.1 -l 3 -a 0.3 -t 2 –o 0 -z 500 -r -p 0.001 -x 0. This increased the scaffold N50 to 1.527 Mb.…”

A high-quality genome sequence of alkaligrass provides insights into halophyte stress tolerance

“…Analysis of 17-mer sequences revealed a high level of heterozygosity of the alkaligrass genome (1.64%) ( Figure S3, Table S2 in Supporting Information); therefore, we also used the genome sequence of Aegilops tauschii (2n=14; a closely related species of alkaligrass) to remove potential heterozygous contig sequences using ALLMAPS (Tang et al, 2015). Afterwards, the remaining contigs were assembled into scaffolds by ARKS (Coombe et al, 2018) and LINKS (Warren et al, 2015) using 10× Genomics data, and finally a total of 1,715 scaffolds were generated with a total size of 1.50 and an N50 size of 1.46 Mb (Table S3 in Supporting Information).…”

“…The redundancy in the polished contig sequences were removed based on gene synteny compared with the genome of Aegilops tauschii (a closely related species of alkaligrass) using ALLMAPS (v0.8.12;Tang et al, 2015). 10× Genomics data was used to scaffold the assembled contigs using the ARKS-LINKS pipeline with the parameters of ARKS (Coombe et al, 2018) set to '-c 5 -j 0.55 -m 50-10000 -k 30 -r 0.05 -e 3000 -z 500 -d 0' and the parameters of LINKS (Warren et al, 2015) set to '-l 5 -a 0.9 -z 500'. Based on high-throughput chromosome conformation capture (Hi-C) data, scaffolds were anchored to seven pseudo-molecules using LACHESIS (Burton et al, 2013)…”