BACKGROUND: De novo genome assembly is essential to modern genomics studies. As it is not biased by a reference, it is also a useful method for studying genomes with high variation, such as cancer genomes. De novo short-read assemblers commonly use de Bruijn graphs, where nodes are sequences of equal length k, also known as k-mers. Edges in this graph are established between nodes that overlap by [Formula: see text] bases, and nodes along unambiguous walks in the graph are subsequently merged. The selection of k is influenced by multiple factors, and optimizing this value results in a trade-off between graph connectivity and sequence contiguity. Ideally, multiple k sizes should be used, so lower values can provide good connectivity in lesser covered regions and higher values can increase contiguity in well-covered regions. However, current approaches that use multiple k values do not address the scalability issues inherent to the assembly of large genomes. RESULTS: Here we present RResolver, a scalable algorithm that takes a short-read de Bruijn graph assembly with a starting k as input and uses a k value closer to that of the read length to resolve repeats. RResolver builds a Bloom filter of sequencing reads which is used to evaluate the assembly graph path support at branching points and removes paths with insufficient support. RResolver runs efficiently, taking only 26 min on average for an ABySS human assembly with 48 threads and 60 GiB memory. Across all experiments, compared to a baseline assembly, RResolver improves scaffold contiguity (NGA50) by up to 15% and reduces misassemblies by up to 12%. CONCLUSIONS: RResolver adds a missing component to scalable de Bruijn graph genome assembly. By improving the initial and fundamental graph traversal outcome, all downstream ABySS algorithms greatly benefit by working with a more accurate and less complex representation of the genome. The RResolver code is integrated into ABySS and is available at https://github.com/bcgsc/abyss/tree/master/RResolver .

Summary Concurrent programs are difficult to debug because concurrency faults usually occur under specific inputs and thread interleavings. Fault localization techniques for sequential programs are often ineffective because the root causes of concurrency faults involve memory accesses across multiple threads rather than single statements. Previous research has proposed techniques to analyse passing and failing executions obtained from running a set of test cases for identifying faulty memory access patterns. However, stand‐alone access patterns do not provide enough contextual information, such as the path leading to the failure, for developers to understand the bug. We present an approach, Coadec, to automatically generate interthread control flow paths that can link memory access patterns that occurred most frequently in the failing executions to better diagnose concurrency bugs. Coadec consists of two phases. In the first phase, we use feature selection techniques from machine learning to localize suspicious memory access patterns based on failing and passing executions. The patterns with maximum feature diversity information can point to the most suspicious pattern. We then apply a data mining technique and identify the memory access patterns that occurred most frequently in the failing executions. Finally, Coadec identifies faulty program paths by connecting both the frequent patterns and the suspicious pattern. We also evaluate the effectiveness of fault localization using test suites generated from different test adequacy criteria. We introduce and have evaluated Coadec on 10 real‐world multithreaded Java applications. Results indicate that Coadec outperforms state‐of‐the‐art approaches for localizing concurrency faults and that Coadec's context debugging can help developers understand concurrency fault by inspecting a small percentage of code.

bases per run, typically composed of reads 150 bases long. Despite this high throughput, de novo assembly algorithms have difficulty reconstructing contiguous genome sequences using short reads due to both repetitive and difficult-to-sequence regions in these genomes. Some of the short read assembly challenges are mitigated by scaffolding assembled sequences using paired-end reads. However, unresolved sequences in these scaffolds appear as "gaps". Here, we introduce GapPredict - An implementation of a proof of concept that uses a character-level language model to predict unresolved nucleotides in scaffold gaps. We benchmarked GapPredict against the state-of-the-art gap-filling tool Sealer, and observed that the former can fill 65.6% of the sampled gaps that were left unfilled by the latter with high similarity to the reference genome, demonstrating the practical utility of deep learning approaches to the gap-filling problem in genome assembly.

SUMMARY: The ability to generate high-quality genome sequences is cornerstone to modern biological research. Even with recent advancements in sequencing technologies, many genome assemblies are still not achieving reference-grade. Here, we introduce ntJoin, a tool that leverages structural synteny between a draft assembly and reference sequence(s) to contiguate and correct the former with respect to the latter. Instead of alignments, ntJoin uses a lightweight mapping approach based on a graph data structure generated from ordered minimizer sketches. The tool can be used in a variety of different applications, including improving a draft assembly with a reference-grade genome, a short-read assembly with a draft long-read assembly and a draft assembly with an assembly from a closely related species. When scaffolding a human short-read assembly using the reference human genome or a long-read assembly, ntJoin improves the NGA50 length 23- and 13-fold, respectively, in under 13 m, using <11 GB of RAM. Compared to existing reference-guided scaffolders, ntJoin generates highly contiguous assemblies faster and using less memory. AVAILABILITY AND IMPLEMENTATION: ntJoin is written in C++ and Python and is freely available at https://github.com/bcgsc/ntjoin. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Establishing a fundamental understanding of the specific molecular biology of a species benefits substantially from reconstructing its genome (DNA) and transcriptome (RNA). These efforts are enabled by modern high throughput sequencing technologies. For over a decade, assembling the generated data into coherent information has been a primary focus of the bioinformatics field. However, the expanding data volume in the field and growing read lengths from evolving sequencing platforms require adapting bioinformatics tools to properly leverage the potential of new genomics technologies. This study is about efficient and scalable algorithms to perform a set of unit operations in genomics studies to guide sequence assembly. Here, we report on a software package, ntPack, with two components: ntHash, for nucleotide hashing, and ntCard for cardinality estimation. We characterize the statistical properties of these algorithms, and demonstrate their application on whole genome shotgun sequencing datasets describing the roundworm, human, and Canadian white spruce genomes. The software that implements these algorithms can be downloaded from our github repository at https://github.com/bcgsc.

Motivation: Sequencing studies on non-model organisms often interrogate both genomes and transcriptomes with massive amounts of short sequences. Such studies require de novo analysis tools and techniques, when the species and closely related species lack high quality reference resources. For certain applications such as de novo annotation, information on putative exons and alternative splicing may be desirable. Results: Here we present ChopStitch, a new method for finding putative exons de novo and constructing splice graphs using an assembled transcriptome and whole genome shotgun sequencing (WGSS) data. ChopStitch identifies exon-exon boundaries in de novo assembled RNA-Seq data with the help of a Bloom filter that represents the k-mer spectrum of WGSS reads. The algorithm also accounts for base substitutions in transcript sequences that may be derived from sequencing or assembly errors, haplotype variations, or putative RNA editing events. The primary output of our tool is a FASTA file containing putative exons. Further, exon edges are interrogated for alternative exon-exon boundaries to detect transcript isoforms, which are represented as splice graphs in DOT output format. Availability and implementation: ChopStitch is written in Python and C++ and is released under the GPL license. It is freely available at https://github.com/bcgsc/ChopStitch. Contact: [email protected] or [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

Motivation: Sequencing of human genomes is now routine, and assembly of shotgun reads is increasingly feasible. However, assemblies often fail to inform about chromosome-scale structure due to a lack of linkage information over long stretches of DNA-a shortcoming that is being addressed by new sequencing protocols, such as the GemCode and Chromium linked reads from 10 × Genomics. Results: Here, we present ARCS, an application that utilizes the barcoding information contained in linked reads to further organize draft genomes into highly contiguous assemblies. We show how the contiguity of an ABySS H.sapiens genome assembly can be increased over six-fold, using moderate coverage (25-fold) Chromium data. We expect ARCS to have broad utility in harnessing the barcoding information contained in linked read data for connecting high-quality sequences in genome assembly drafts. Availability and implementation: https://github.com/bcgsc/ARCS/. Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

BACKGROUND: The 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. 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 (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). 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, 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.

BACKGROUND: Genome sequencing yields the sequence of many short snippets of DNA (reads) from a genome. Genome assembly attempts to reconstruct the original genome from which these reads were derived. This task is difficult due to gaps and errors in the sequencing data, repetitive sequence in the underlying genome, and heterozygosity. As a result, assembly errors are common. In the absence of a reference genome, these misassemblies may be identified by comparing the sequencing data to the assembly and looking for discrepancies between the two. Once identified, these misassemblies may be corrected, improving the quality of the assembled sequence. Although tools exist to identify and correct misassemblies using Illumina paired-end and mate-pair sequencing, no such tool yet exists that makes use of the long distance information of the large molecules provided by linked reads, such as those offered by the 10x Genomics Chromium platform. We have developed the tool Tigmint to address this gap. RESULTS: To demonstrate the effectiveness of Tigmint, we applied it to assemblies of a human genome using short reads assembled with ABySS 2.0 and other assemblers. Tigmint reduced the number of misassemblies identified by QUAST in the ABySS assembly by 216 (27%). While scaffolding with ARCS alone more than doubled the scaffold NGA50 of the assembly from 3 to 8 Mbp, the combination of Tigmint and ARCS improved the scaffold NGA50 of the assembly over five-fold to 16.4 Mbp. This notable improvement in contiguity highlights the utility of assembly correction in refining assemblies. We demonstrate the utility of Tigmint in correcting the assemblies of multiple tools, as well as in using Chromium reads to correct and scaffold assemblies of long single-molecule sequencing. CONCLUSIONS: Scaffolding an assembly that has been corrected with Tigmint yields a final assembly that is both more correct and substantially more contiguous than an assembly that has not been corrected. Using single-molecule sequencing in combination with linked reads enables a genome sequence assembly that achieves both a high sequence contiguity as well as high scaffold contiguity, a feat not currently achievable with either technology alone.

Identifying overlaps between error-prone long reads, specifically those from Oxford Nanopore Technologies (ONT) and Pacific Biosciences (PB), is essential for certain downstream applications, including error correction and de novo assembly. Though akin to the read-to-reference alignment problem, read-to-read overlap detection is a distinct problem that can benefit from specialized algorithms that perform efficiently and robustly on high error rate long reads. Here, we review the current state-of-the-art read-to-read overlap tools for error-prone long reads, including BLASR, DALIGNER, MHAP, GraphMap and Minimap. These specialized bioinformatics tools differ not just in their algorithmic designs and methodology, but also in their robustness of performance on a variety of datasets, time and memory efficiency and scalability. We highlight the algorithmic features of these tools, as well as their potential issues and biases when utilizing any particular method. To supplement our review of the algorithms, we benchmarked these tools, tracking their resource needs and computational performance, and assessed the specificity and precision of each. In the versions of the tools tested, we observed that Minimap is the most computationally efficient, specific and sensitive method on the ONT datasets tested; whereas GraphMap and DALIGNER are the most specific and sensitive methods on the tested PB datasets. The concepts surveyed may apply to future sequencing technologies, as scalability is becoming more relevant with increased sequencing throughput. CONTACT: [email protected] , [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Despite considerable advancements in sequencing and computing technologies, de novo assembly of whole eukaryotic genomes is still a time-consuming task that requires a significant amount of computational resources and expertise. A targeted assembly approach to perform local assembly of sequences of interest remains a valuable option for some applications. This is especially true for gene-centric assemblies, whose resulting sequence can be readily utilized for more focused biological research. Here we describe Kollector, an alignment-free targeted assembly pipeline that uses thousands of transcript sequences concurrently to inform the localized assembly of corresponding gene loci. Kollector robustly reconstructs introns and novel sequences within these loci, and scales well to large genomes-properties that makes it especially useful for researchers working on non-model eukaryotic organisms. RESULTS: We demonstrate the performance of Kollector for assembling complete or near-complete Caenorhabditis elegans and Homo sapiens gene loci from their respective, input transcripts. In a time- and memory-efficient manner, the Kollector pipeline successfully reconstructs respectively 99% and 80% (compared to 86% and 73% with standard de novo assembly techniques) of C.elegans and H.sapiens transcript targets in their corresponding genomic space using whole genome shotgun sequencing reads. We also show that Kollector outperforms both established and recently released targeted assembly tools. Finally, we demonstrate three use cases for Kollector, including comparative and cancer genomics applications. AVAILABILITY AND IMPLEMENTATION: Kollector is implemented as a bash script, and is available at https://github.com/bcgsc/kollector. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Bioinformatics (2017) 33(12), 1782–1788 The authors of the above article wish to inform readers that the following was omitted after Table 1: “The white spruce whole-genome shotgun sequence read data reported in Table 1 dataset no. 3 is available at the SRA under accession SRP041401 and Genome Annotation from the high-confidence genes are available at ftp://ftp.bcgsc.ca/supplementary/PG29_20140822/high_confidence_genes.fasta”. The paper has now been corrected online.

MOTIVATION: Hashing has been widely used for indexing, querying and rapid similarity search in many bioinformatics applications, including sequence alignment, genome and transcriptome assembly, k-mer counting and error correction. Hence, expediting hashing operations would have a substantial impact in the field, making bioinformatics applications faster and more efficient. RESULTS: We present ntHash, a hashing algorithm tuned for processing DNA/RNA sequences. It performs the best when calculating hash values for adjacent k-mers in an input sequence, operating an order of magnitude faster than the best performing alternatives in typical use cases. AVAILABILITY AND IMPLEMENTATION: ntHash is available online at http://www.bcgsc.ca/platform/bioinfo/software/nthash and is free for academic use. CONTACTS: [email protected] or [email protected] information: Supplementary data are available at Bioinformatics online.

This past decade, genome sciences have benefitted from rapid advances in DNA sequencing technologies, and development of efficient algorithms for processing short nucleotide sequences played a key role in enabling their uptake in the field. In particular, reassembly of human genomes (de novo or reference-guided) from short DNA sequence reads had a substantial impact on health research. De novo assembly of a genome is essential in the absence of a reference genome sequence of a species. It is also gaining traction even when one is available, due to the utility of the method to resolve ambiguous or rearranged genomic regions with high specificity. With commercial high-throughput sequencing technologies increasing their throughput and their read lengths, the de Bruijn graph (DBG) paradigm used by many assembly algorithms needs to be revisited. DBG uses a table of k-mers, sequences of length k base pairs derived from the reads, and their k-1 base pair overlaps to assemble sequences. Despite longer k-mers unlocking longer genomic features for assembly, associated increases in memory usage and other compute resources are tradeoffs that limit the practicability of DBG over other assembly archetypes already designed for longer reads. Here, we introduce three data structure designs for paired k-mers, or spaced seeds, each addressing memory and run time constraints imposed by longer reads. In spaced seeds, a fixed distance separates k-mer pairs, providing increased sequence specificity with increased distance, while keeping memory usage low. Further, we describe a data structure based on Bloom filters that would be suitable to implicitly store spaced seeds, and would be tolerant to sequencing errors. Building on the spaced seeds Bloom filter, we describe a data structure for tracking the frequencies of observed spaced seeds. We expect the data structure designs we introduce in this study to have broad applications in genomics research, with niche applications in genome, transcriptome and metagenome assemblies, and in read error correction.

Paired-end sequencing yields a read from each end of a DNA fragment, typically leaving a gap of unsequenced nucleotides in the middle. Closing this gap using information from other reads in the same sequencing experiment offers the potential to generate longer “pseudo-reads” using short read sequencing platforms. Such long reads may benefit downstream applications such as de novo sequence assembly, gap filling, and variant detection. With these possible applications in mind, we have developed Konnector, a software tool to fill in the nucleotides of the sequence gap between read pairs by navigating a de Bruijn graph. Konnector represents the de Bruijn graph using a Bloom filter, a probabilistic and memory-efficient data structure. Our implementation is able to store the de Bruijn graph using a mean 1.5 bytes of memory per k-mer, which represents a marked improvement over the typical hash table data structure. The memory usage per k-mer is independent of the k-mer length, enabling application of the tool to large genomes. We report the performance of the tool on simulated and experimental datasets, and discuss its utility for downstream analysis. Availability-Konnector is open-source software, free for academic use, released under the British Columbia Cancer Agency's academic license. The tool is included with ABySS version 1.5.2 and later, and is available for download from http://www.bcgsc.ca/platform/bioinfo/software/abyss.

Abstract Large datasets can be screened for sequences from a specific organism, quickly and with low memory requirements, by a data structure that supports time- and memory-efficient set membership queries. Bloom filters offer such queries but require that false positives be controlled. We present BioBloom Tools, a Bloom filter-based sequence-screening tool that is faster than BWA, Bowtie 2 (popular alignment algorithms) and FACS (a membership query algorithm). It delivers accuracies comparable with these tools, controls false positives and has low memory requirements. Availability and implementaion: www.bcgsc.ca/platform/bioinfo/software/biobloomtools Contact: [email protected] or [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.