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

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.

[1]  J. Landolin,et al.  Assembling Large Genomes with Single-Molecule Sequencing and Locality Sensitive Hashing , 2014 .

[2]  Derrick E. Wood,et al.  Kraken: ultrafast metagenomic sequence classification using exact alignments , 2014, Genome Biology.

[3]  Steven J. M. Jones,et al.  Abyss: a Parallel Assembler for Short Read Sequence Data Material Supplemental Open Access , 2022 .

[4]  Justin Chu,et al.  Tigmint: correcting assembly errors using linked reads from large molecules , 2018, BMC Bioinformatics.

[5]  S. Lonardi,et al.  CLARK: fast and accurate classification of metagenomic and genomic sequences using discriminative k-mers , 2015, BMC Genomics.

[6]  David Haussler,et al.  Long-read sequence assembly of the gorilla genome , 2016, Science.

[7]  Anders Krogh,et al.  Fast and sensitive taxonomic classification for metagenomics with Kaiju , 2016, Nature Communications.

[8]  Serafim Batzoglou,et al.  Genome assembly from synthetic long read clouds , 2016, Bioinform..

[9]  Steven J. M. Jones,et al.  Circos: an information aesthetic for comparative genomics. , 2009, Genome research.

[10]  Charlotte A. Darby,et al.  LRSim: A Linked-Reads Simulator Generating Insights for Better Genome Partitioning , 2017, bioRxiv.

[11]  Rob Patro,et al.  Salmon provides fast and bias-aware quantification of transcript expression , 2017, Nature Methods.

[12]  M. Schatz,et al.  Phased diploid genome assembly with single-molecule real-time sequencing , 2016, Nature Methods.

[13]  Lior Pachter,et al.  Near-optimal probabilistic RNA-seq quantification , 2016, Nature Biotechnology.

[14]  N. Weisenfeld,et al.  Direct determination of diploid genome sequences , 2016, bioRxiv.

[15]  Heng Li,et al.  Minimap and miniasm: fast mapping and de novo assembly for noisy long sequences , 2015, Bioinform..

[16]  Andrew C. Adey,et al.  In vitro, long-range sequence information for de novo genome assembly via transposase contiguity , 2014, Genome research.

[17]  Heng Li.  Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM , 2013, 1303.3997.

[18]  Justin Chu,et al.  ARCS: scaffolding genome drafts with linked reads , 2017, Bioinform..

[19]  Wei Wang,et al.  RNA-Skim: a rapid method for RNA-Seq quantification at transcript level , 2014, Bioinform..

[20]  Steven J. M. Jones,et al.  LINKS: Scalable, alignment-free scaffolding of draft genomes with long reads , 2015, GigaScience.

[21]  Andrew C. Adey,et al.  Chromosome-scale scaffolding of de novo genome assemblies based on chromatin interactions , 2013, Nature Biotechnology.

[22]  Justin Chu,et al.  Tigmint: Correcting Assembly Errors Using Linked Reads From Large Molecules , 2018 .

[23]  Karen Y. Oróstica,et al.  chromPlot: visualization of genomic data in chromosomal context , 2015 .

[24]  Alexey A. Gurevich,et al.  QUAST: quality assessment tool for genome assemblies , 2013, Bioinform..

[25]  Hanlee P. Ji,et al.  Haplotyping germline and cancer genomes using high-throughput linked-read sequencing , 2015, Nature Biotechnology.