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

Abstract

Background. The long-range sequencing information captured by linked reads, such as those available from 10x 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.Results. Here, 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, which itself exclusively uses linked read data for assembly, with an execution speed six to nine times faster than competitive linked read scaffolders. 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). Conclusions. ARKS 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, ARKS utilizes barcoding information from linked reads to estimate gap size. 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.

Fonio millet genome unlocks African orphan crop diversity for agriculture in a changing climate

“…Integration of 10x and Hi-C reads was done differently. For the 10x scaffolding, we used tigmint v1.1.2 50 to detect and cut sequences at positions with few spanning molecules, arks v1.0.3 51 to generate graphs of scaffolds with connection evidence, and LINKS 52 for a second step of scaffolding. For the Hi-C data, we used BWA 53 to map the reads against the previous scaffolds and juicer tools v1.5 54 for the super-scaffolding.…”

Chromosome-level genome assembly, annotation and evolutionary analysis of the ornamental plant Asparagus setaceus

“…The scaffolds were extended with 10× Genomics data by using ARKS 43 with the following parameters: "-c 5 -j 0.55 -m 50-10000 -k 30 -r 0.05 -e 3000 -z 500 -d 0", and LINKS 44 with the following parameters: "-l 5 -a 0.9 -z 500". Thereafter, redundancy in the assembly was removed using redundans 0-13c 45 with the following parameters: "-identity 0.7 -overlap 0.7".…”

Divergence and Remarkable Diversity of the Y Chromosome in Guppies

“…70 to increase the contiguity of each of our non-redundant assemblies. ARKS uses a k-mer approach to infer graph edges by determining the Chromium barcodes associated with the best-matching contig end for each read and selecting the contig end with the largest fraction of k-mer overlap.…”