Assembly of long, error-prone reads using repeat graphs

Abstract

The problem of genome assembly is ultimately linked to the problem of the characterization of all repeat families in a genome as a repeat graph. The key reason the de Bruijn graph emerged as a popular short read assembly approach is because it offered an elegant representation of all repeats in a genome that reveals their mosaic structure. However, most algorithms for assembling long error-prone reads use an alternative overlap-layout-consensus (OLC) approach that does not provide a repeat characterization. We present the Flye algorithm for constructing the A-Bruijn (assembly) graph from long error-prone reads, that, in contrast to the k-mer-based de Bruijn graph, assembles genomes using an alignment-based A-Bruijn graph. In difference from existing assemblers, Flye does not attempt to construct accurate contigs (at least at the initial assembly stage) but instead simply generates arbitrary paths in the (unknown) assembly graph and further constructs an assembly graph from these paths. Counter-intuitively, this fast but seemingly reckless approach results in the same graph as the assembly graph constructed from accurate contigs. Flye constructs (overlapping) contigs with possible assembly errors at the initial stage, combines them into an accurate assembly graph, resolves repeats in the assembly graph using small variations between various repeat instances that were left unresolved during the initial assembly stage, constructs a new, less tangled assembly graph based on resolved repeats, and finally outputs accurate contigs as paths in this graph. We benchmark Flye against several state-of-the-art Single Molecule Sequencing assemblers and demonstrate that it generates better or comparable assemblies for all analyzed datasets..

Standing genetic variation and chromosomal rearrangements facilitate local adaptation in a marine fish

“…A total of 47 Gbp (~99x coverage) of sequencing data was generated on the Pacific Biosystems Sequel sequencing machine. Flye version 2.3.3 (Kolmogorov et al 2019) was run on the PacBio sequencing data specifying a genome size of 700 Mbp (which is between 0.5x and 2.0x of expected genome size of related marine fishes; http://www.genomesize.com/) and otherwise default options. We ran BUSCO version 3.0.1 to assess genome assembly completeness (Simão et al 2015).…”

Yersinia canariaesp. nov., isolated from a human yersiniosis case

“…canariae NCTC 14382 T was previously sequenced by an Illumina HiSeq 2500 at Public Health England using the Nextera XP library preparation kit following a retrospective study on yersiniosis isolates cultured from patients between April 2004 and March 2018 (8 For ONT MinION data, the run metrics were inspected using NanoPlot (version 1.0) (14) before raw FAST5 files were base-called using Guppy (version 3.2.2) with the high accuracy model to FASTQ files. Adapters were trimmed from the raw reads by Porechop (version 0.2.4) using default parameters for SQK-RAD004 before the genome was de novo assembled with Flye (version 2.5) (15,16). The best assembly parameters were empirically determined to include the option flags "meta" and "plasmid" with coverage reduced to 30X for initial contig assembly based on a predicted genome size of~4.73 Mbp as informed by de novo assembly of short read Illumina data (17).…”

WENGAN: Efficient and high quality hybrid de novo assembly of human genomes

“…minimizers [18], 15 homopolymers compressed k-mers [14], minhash [17] etc.). The reduced long-read representation 16is appropriate for detecting overlaps >2kb in a fast way [14,16,17]. The newest long-read assem-17 blers are therefore starting to be good also at goal 3 [14,16,17].…”

“…The reduced long-read representation 16is appropriate for detecting overlaps >2kb in a fast way [14,16,17]. The newest long-read assem-17 blers are therefore starting to be good also at goal 3 [14,16,17]. However, assembling uncorrected 18 long-reads has the undesirable effect of giving more work to the consensus polisher [15,17,[19][20][21].…”

“…Usually, 21 long-read assemblers perform a single round of long-read polishing [14,16,17], that is followed by 22 several rounds of polishing with long [15,17,19,21] and short [15,20,22] reads using third-party 23 tools [15,17,[19][20][21][22]. 24 Currently, polishing large genomes, such as the human genome, can take much more com-25 putational time than the long-read assembly itself [14,16,17]. Moreover, there is no standard 26 practice for polishing large genomes, and usually several rounds of polishing are employed with 27 user-defined criteria in order to remove consensus errors, notably the short-indels occurring in 28 homopolymer regions, which are the characteristic error signature of current long-read technolo-29 3 gies [14,16,17].…”