SUMMARYSpruces (Picea spp.) are coniferous trees widespread in boreal and mountainous forests of the northern hemisphere, with large economic significance and enormous contributions to global carbon sequestration. Spruces harbor very large genomes with high repetitiveness, hampering their comparative analysis. Here, we present and compare the genomes of four different North American spruces: the genome assemblies for Engelmann spruce (Picea engelmannii) and Sitka spruce (Picea sitchensis) together with improved and more contiguous genome assemblies for white spruce (Picea glauca) and for a naturally occurring introgress of these three species known as interior spruce (P. engelmannii × glauca × sitchensis). The genomes were structurally similar, and a large part of scaffolds could be anchored to a genetic map. The composition of the interior spruce genome indicated asymmetric contributions from the three ancestral genomes. Phylogenetic analysis of the nuclear and organelle genomes revealed a topology indicative of ancient reticulation. Different patterns of expansion of gene families among genomes were observed and related with presumed diversifying ecological adaptations. We identified rapidly evolving genes that harbored high rates of non‐synonymous polymorphisms relative to synonymous ones, indicative of positive selection and its hitchhiking effects. These gene sets were mostly distinct between the genomes of ecologically contrasted species, and signatures of convergent balancing selection were detected. Stress and stimulus response was identified as the most frequent function assigned to expanding gene families and rapidly evolving genes. These two aspects of genomic evolution were complementary in their contribution to divergent evolution of presumed adaptive nature. These more contiguous spruce giga‐genome sequences should strengthen our understanding of conifer genome structure and evolution, as their comparison offers clues into the genetic basis of adaptation and ecology of conifers at the genomic level. They will also provide tools to better monitor natural genetic diversity and improve the management of conifer forests. The genomes of four closely related North American spruces indicate that their high similarity at the morphological level is paralleled by the high conservation of their physical genome structure. Yet, the evidence of divergent evolution is apparent in their rapidly evolving genomes, supported by differential expansion of key gene families and large sets of genes under positive selection, largely in relation to stimulus and environmental stress response.

AbstractMotivationSpaced seeds are robust alternatives to k-mers in analyzing nucleotide sequences with high base mismatch rates. Hashing is also crucial for efficiently storing abundant sequence data. Here, we introduce ntHash2, a fast algorithm for spaced seed hashing that can be integrated into various bioinformatics tools for efficient sequence analysis with applications in genome research.ResultsntHash2 is up to 2.1× faster at hashing various spaced seeds than the previous version and 3.8× faster than conventional hashing algorithms with naïve adaptation. Additionally, we reduced the collision rate of ntHash for longer k-mer lengths and improved the uniformity of the hash distribution by modifying the canonical hashing mechanism.Availability and implementationntHash2 is freely available online at github.com/bcgsc/ntHash under an MIT license.Supplementary informationSupplementary data are available at Bioinformatics online.

Alignment-free classification tools have enabled high-throughput processing of sequencing data in many bioinformatics analysis pipelines primarily due to their computational efficiency. Originally k-mer based, such tools often lack sensitivity when faced with sequencing errors and polymorphisms. In response, some tools have been augmented with spaced seeds, which are capable of tolerating mismatches. However, spaced seeds have seen little practical use in classification because they bring increased computational and memory costs compared to methods that use k-mers. These limitations have also caused the design and length of practical spaced seeds to be constrained, since storing spaced seeds can be costly. To address these challenges, we have designed a probabilistic data structure called a multiindex Bloom Filter (miBF), which can store multiple spaced seed sequences with a low memory cost that remains static regardless of seed length or seed design. We formalize how to minimize the false-positive rate of miBFs when classifying sequences from multiple targets or references. Available within BioBloom Tools, we illustrate the utility of miBF in two use cases: read-binning for targeted assembly, and taxonomic read assignment. In our benchmarks, an analysis pipeline based on miBF shows higher sensitivity and specificity for read-binning than sequence alignment-based methods, also executing in less time. Similarly, for taxonomic classification, miBF enables higher sensitivity than a conventional spaced seed-based approach, while using half the memory and an order of magnitude less computational time.

Motivation Repeat elements such as satellites, transposons, high number of gene copies, and segmental duplications are abundant in eukaryotic genomes. They often induce many local alignments, complicating sequence assembly and comparisons between genomes and analysis of large-scale duplications and rearrangements. Hence, identification and classification of repeats is a fundamental step in many genomics applications and their downstream analysis tools. Results In this work, we present an efficient streaming algorithm and software tool, ntHits, for de novo repeat identification based on the statistical analysis of the k-mer content profile of large-scale DNA sequencing data. In the proposed algorithm, we first obtain the k-mer coverage histograms of input datasets using the ntCard algorithm, an efficient streaming algorithm for estimating the k-mer coverage histograms. From the obtained k-mer coverage histogram, the repetitive k-mers would present a long tail to the distribution of k-mer coverage profile. Experimental results show that ntHits can efficiently and accurately identify the repeat content in large-scale DNA sequencing data. For example, ntHits accurately identifies the repeat k-mers in the white spruce sequencing data set with 96× sequencing coverage in about 12 hours and using less than 150GB of memory, while using the exact methods for reporting the repeated k-mers takes several days and terabytes of memory and disk space. Availability ntHits is written in C++ and is released under the MIT License. It is freely available at https://github.com/bcgsc/ntHits. Contact hmohamadi@bcgsc.ca

Despite the rapid advance in single-cell RNA sequencing (scRNA-seq) technologies within the last decade, single-cell transcriptome analysis workflows have primarily used gene expression data while isoform sequence analysis at the single-cell level still remains fairly limited. Detection and discovery of isoforms in single cells is difficult because of the inherent technical shortcomings of scRNA-seq data, and existing transcriptome assembly methods are mainly designed for bulk RNA samples. To address this challenge, we developed RNA-Bloom, an assembly algorithm that leverages the rich information content aggregated from multiple single-cell transcriptomes to reconstruct cell-specific isoforms. Assembly with RNA-Bloom can be either reference-guided or reference-free, thus enabling unbiased discovery of novel isoforms or foreign transcripts. We compared both assembly strategies of RNA-Bloom against five state-of-the-art reference-free and reference-based transcriptome assembly methods. In our benchmarks on a simulated 384-cell data set, reference-free RNA-Bloom reconstructed 37.9%–38.3% more isoforms than the best reference-free assembler, whereas reference-guided RNA-Bloom reconstructed 4.1%–11.6% more isoforms than reference-based assemblers. When applied to a real 3840-cell data set consisting of more than 4 billion reads, RNA-Bloom reconstructed 9.7%–25.0% more isoforms than the best competing reference-based and reference-free approaches evaluated. We expect RNA-Bloom to boost the utility of scRNA-seq data beyond gene expression analysis, expanding what is informatically accessible now.

AbstractIn the modern genomics era, genome sequence assemblies are routine practice. However, depending on the methodology, resulting drafts may contain considerable base errors. Although utilities exist for genome base polishing, they work best with high read coverage and do not scale well. We developed ntEdit, a Bloom filter-based genome sequence editing utility that scales to large mammalian and conifer genomes.We first tested ntEdit and the state-of-the-art assembly improvement tools GATK, Pilon and Racon on controlled E. coli and C. elegans sequence data. Generally, ntEdit performs well at low sequence depths (<20X), fixing the majority (>97%) of base substitutions and indels, and its performance is largely constant with increased coverage. In all experiments conducted using a single CPU, the ntEdit pipeline executed in <14s and <3m, on average, on E. coli and C. elegans, respectively. We performed similar benchmarks on a sub-20X coverage human genome sequence dataset, inspecting accuracy and resource usage in editing chromosomes 1 and 21, and whole genome. ntEdit scaled linearly, executing in 30-40m on those sequences. We show how ntEdit ran in <2h20m to improve upon long and linked read human genome assemblies of NA12878, using high coverage (54X) Illumina sequence data from the same individual, fixing frame shifts in coding sequences. We also generated 17-fold coverage spruce sequence data from haploid sequence sources (seed megagametophyte), and used it to edit our pseudo haploid assemblies of the 20 Gbp interior and white spruce genomes in <4 and <5h, respectively, making roughly 50M edits at a (substitution+indel) rate of 0.0024.Availabilityhttps://github.com/bcgsc/nteditSupplemental materialavailable online.

Motivation: In the modern genomics era, genome sequence assemblies are routine practice. However, depending on the methodology, resulting drafts may contain considerable base errors. Although utilities exist for genome base polishing, they work best with high read coverage and do not scale well. We developed ntEdit, a Bloom filter-based genome sequence editing utility that scales to large mammalian and conifer genomes. Results: We first tested ntEdit and the state-of-the-art assembly improvement tools GATK, Pilon and Racon on controlled Escherichia coli and Caenorhabditis elegans sequence data. Generally, ntEdit performs well at low sequence depths (<20x), fixing the majority (>97%) of base substitutions and indels, and its performance is largely constant with increased coverage. In all experiments conducted using a single CPU, the ntEdit pipeline executed in <14s and <3m, on average, on E.coli and C.elegans, respectively. We performed similar benchmarks on a sub-20x coverage human genome sequence dataset, inspecting accuracy and resource usage in editing chromosomes 1 and 21, and whole genome. ntEdit scaled linearly, executing in 30-40m on those sequences. We show how ntEdit ran in <2 h 20 m to improve upon long and linked read human genome assemblies of NA12878, using high-coverage (54x) Illumina sequence data from the same individual, fixing frame shifts in coding sequences. We also generated 17-fold coverage spruce sequence data from haploid sequence sources (seed megagametophyte), and used it to edit our pseudo haploid assemblies of the 20 Gb interior and white spruce genomes in <4 and <5h, respectively, making roughly 50M edits at a (substitution+indel) rate of 0.0024.

We present RNA-Bloom, a de novo RNA-seq assembly algorithm that leverages the rich information content in single-cell transcriptome sequencing (scRNA-seq) data to reconstruct cell-specific isoforms. We benchmark RNA-Bloom’s performance against leading bulk RNA-seq assembly approaches, and illustrate its utility in detecting cell-specific gene fusion events using sequencing data from HiSeq-4000 and BGISEQ-500 platforms. We expect RNA-Bloom to boost the utility of scRNA-seq data, expanding what is informatically accessible now.

Background Sequencing large molecules of DNA has drastically improved the contiguity of genome sequence assemblies. Long read sequencing has reduced sequence fidelity compared to short read sequencing, and it is currently more expensive. With linked read sequencing from 10x Genomics Chromium, reads originating from the same large molecule all have the same 16 nucleotide barcode identifier. Linked read sequencing combines the benefits of large DNA molecules with the sequence fidelity and cost of short read sequencing. Two key challenges present themselves when assembling linked read sequencing. First, each of the roughly one million barcodes contain approximately ten large molecules of DNA. Second, each individual molecule is sequenced to less than one fold depth of coverage. For this reason, the assembly of linked reads typically follows a bottom-up approach, starting with an assembly of all the reads, at first ignoring the linked-read barcodes. These initial contigs are then scaffolded using the long-range information of the linked reads. Scaffolding may be difficult if, for any reason, the contiguity of this initial assembly is low. A top-down approach could be modeled after the overlap-layout-consensus (OLC) assembly paradigm, first determining the order of the large DNA molecules, before determining the fine-scale sequence. Our tool, Physlr, implements this top-down approach to construct a physical map of large molecules sequenced using linked reads. Methods Each barcode is a set of reads, and Physlr treats each barcode as a bag of k -mers. To reduce memory requirements and improve computational efficiency, only those k -mers that are minimizers of each read are retained. Minimizers that occur in only a single barcode are discarded, as likely resulting from a sequencing error. The most frequent minimizers are also removed, as likely derived from repetitive sequence. All pairs of barcodes that share a substantial number of minimizers are then identified. A weighted, undirected barcode-overlap graph is constructed, wherein each vertex represents a barcode, each edge represents the intersection of the minimizers of two barcodes, and the weight of the edge is the number of common minimizers. If each barcode contained a single molecule of DNA, we would have the desired molecule-overlap graph, and we could move on to the layout step of OLC. Because each barcode is composed of multiple molecules, each vertex must first be separated into its component molecules. For each vertex we identify a vertex-induced subgraph of its adjacent vertices and the edges between those vertices. This subgraph is composed of communities of vertices, where each community is born from a DNA molecule. These communities are identified using k -clique community detection. This barcode vertex is then removed and replaced by one molecule vertex for each of its detected communities, and each of its incident edges is assigned to one of these newly-created molecule vertices. This method reconstructs the underlying molecule-overlap graph from the barcode-overlap graph. With the molecule-overlap graph constructed, we proceed to the layout stage of OLC assembly. A maximum spanning tree (MST) is used to compute a fast and approximate solution to the traveling salesman problem. The MST is computed for each connected component of the graph, identifying the longest path through the MST. This path naturally does not visit every vertex, but it is not necessary at this point to include every molecule in the physical map. All vertices in a path are removed from the graph, and this process is repeated to complete the construction of the physical map. The physical map is a set of contigs, where each contig is an ordered list of barcodes. With the physical map constructed, two applications readily present themselves. First, a subset of reads taken from a set of barcodes from one physical map contig (or a portion of one) may be assembled to yield a local assembly of a particular genomic region. This process may be repeated to assemble the entire genome. Second, an existing assembly may be mapped to the contigs of the physical map to order and orient the sequences of that existing assembly. Results We constructed a physical map of the 137 Mbp fruit fly ( Drosophila melanogaster ) genome from linked reads using Physlr. The physical map is composed of one large contig for each chromosome, with an NG50 of 21.5 Mbp. Comparing the physical map to the reference genome indicates no interchromosomal misassemblies. An ABySS 2.0 assembly of the linked reads was scaffolded by mapping it to this physical map. The resulting assembly has an NG50 of 16.9 Mbp, approaching the 20.8 Mbp NG50 of Supernova. When the Supernova contigs (derived by splitting its scaffolds at Ns) are scaffolded using the physical map, the resulting assembly has an NG50 of 20.5 Mbp. We constructed a physical map of the 1.34 Gbp zebrafish ( Danio rerio ) genome from linked reads using Physlr. The physical map has an NG50 of 12.7 Mbp. Comparing the physical map to the reference genome indicates one interchromosomal misassembly. The scaffolds of the Supernova assembly of the linked reads was further scaffolded by mapping it to this physical map. The NG50 of the Supernova assembly improved from 4.8 Mbp to 13.0 Mbp by scaffolding with Physlr. We constructed a physical map of the 3.09 Gbp human ( Homo sapiens ) genome from linked reads using Physlr. The physical map has an NG50 of 40.9 Mbp, and twelve chromosome arms are each assembled into a single contig per arm. Comparing the physical map to the reference genome indicates one interchromosomal misassembly. We use this physical map to scaffold a Supernova assembly, which has a scaffold NG50 of 38.5 Mbp and contig NG50 of 150 kbp, derived by splitting its scaffolds at Ns. Mapping the Supernova contigs to the physical map produces scaffolds with an NG50 of 33.0 Mbp. Mapping the Supernova scaffolds to the physical map to produce super-scaffolds yields an assembly with an NG50 of 59.2 Mbp. Conclusions Physlr constructs a physical map of large DNA molecules sequenced using linked reads from 10x Genomics Chromium without first assembling those reads. We plan to scale Physlr to assemble conifer genomes, which can exceed 20 Gbp.

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.

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.

Sequence classification has applications in contamination screening, pathogen detection, metagenomics, targeted assembly and lightweight sequence mapping. The fastest methods for sequence classification are hash table-based, and they require a large amount of memory, promoting use of Bloom filter-based or other probabilistic data structures. Previously, spaced seeds were shown to achieve greater accuracy of classification, but at the cost of memory and time, and to our knowledge they have not yet been effectively applied to any probabilistic data structures. We have devised a novel probabilistic data structure called a multi-index Bloom filter (miBF), which is capable of storing key-value associations using mismatch tolerant multiple spaced seeds in a compact fashion.

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.

Alignment-free classification of sequences against collections of sequences has enabled high-throughput processing of sequencing data in many bioinformatics analysis pipelines. Originally hash-table based, much work has been done to improve and reduce the memory requirement of indexing of k -mer sequences with probabilistic indexing strategies. These efforts have led to lower memory highly efficient indexes, but often lack sensitivity in the face of sequencing errors or polymorphism because they are k -mer based. To address this, we designed a new memory efficient data structure that can tolerate mismatches using multiple spaced seeds, called a multi-index Bloom Filter. Implemented as part of BioBloom Tools, we demonstrate our algorithm in two applications, read binning for targeted assembly and taxonomic read assignment. Our tool shows a higher sensitivity and specificity for read-binning than BWA MEM at an order of magnitude less time. For taxonomic classification, we show higher sensitivity than CLARK-S at an order of magnitude less time while using half the memory.

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: cjustin@bcgsc.ca, ibirol@bcgsc.ca Supplementary information: Supplementary data are available at Bioinformatics online.

Motivation: Many bioinformatics algorithms are designed for the analysis of sequences of some uniform length, conventionally referred to as k‐mers. These include de Bruijn graph assembly methods and sequence alignment tools. An efficient algorithm to enumerate the number of unique k‐mers, or even better, to build a histogram of k‐mer frequencies would be desirable for these tools and their downstream analysis pipelines. Among other applications, estimated frequencies can be used to predict genome sizes, measure sequencing error rates, and tune runtime parameters for analysis tools. However, calculating a k‐mer histogram from large volumes of sequencing data is a challenging task. Results: Here, we present ntCard, a streaming algorithm for estimating the frequencies of k‐mers in genomics datasets. At its core, ntCard uses the ntHash algorithm to efficiently compute hash values for streamed sequences. It then samples the calculated hash values to build a reduced representation multiplicity table describing the sample distribution. Finally, it uses a statistical model to reconstruct the population distribution from the sample distribution. We have compared the performance of ntCard and other cardinality estimation algorithms. We used three datasets of 480 GB, 500 GB and 2.4 TB in size, where the first two representing whole genome shotgun sequencing experiments on the human genome and the last one on the white spruce genome. Results show ntCard estimates k‐mer coverage frequencies >15× faster than the state‐of‐the‐art algorithms, using similar amount of memory, and with higher accuracy rates. Thus, our benchmarks demonstrate ntCard as a potentially enabling technology for large‐scale genomics applications. Availability and Implementation: ntCard is written in C ++ and is released under the GPL license. It is freely available at https://github.com/bcgsc/ntCard. Contact: hmohamadi@bcgsc.ca or ibirol@bcgsc.ca Supplementary information: Supplementary data are available at Bioinformatics online.

The assembly of DNA sequences de novo is fundamental to genomics research. It is the first of many steps toward elucidating and characterizing whole genomes. Downstream applications, including analysis of genomic variation between species, between or within individuals critically depend on robustly assembled sequences. In the span of a single decade, the sequence throughput of leading DNA sequencing instruments has increased drastically, and coupled with established and planned large-scale, personalized medicine initiatives to sequence genomes in the thousands and even millions, the development of efficient, scalable and accurate bioinformatics tools for producing high-quality reference draft genomes is timely. With ABySS 1.0, we originally showed that assembling the human genome using short 50-bp sequencing reads was possible by aggregating the half terabyte of compute memory needed over several computers using a standardized message-passing system (MPI). We present here its redesign, which departs from MPI and instead implements algorithms that employ a Bloom filter, a probabilistic data structure, to represent a de Bruijn graph and reduce memory requirements. We benchmarked ABySS 2.0 human genome assembly using a Genome in a Bottle data set of 250-bp Illumina paired-end and 6-kbp mate-pair libraries from a single individual. Our assembly yielded a NG50 (NGA50) scaffold contiguity of 3.5 (3.0) Mbp using <35 GB of RAM. This is a modest memory requirement by today's standards and is often available on a single computer. We also investigate the use of BioNano Genomics and 10x Genomics' Chromium data to further improve the scaffold NG50 (NGA50) of this assembly to 42 (15) Mbp.

Long-read sequencing technologies have greatly improved assembly contiguity, but at a cost roughly ten times that of short-read sequencing technology. For population studies and when sequencing large genomes, such as conifer genomes and other economically important crop species, this cost may be prohibitive. The 10x Genomics Chromium technology generates linked reads from large DNA molecules at a cost comparable to standard short-read sequencing technologies. Whereas paired-end sequencing gives two reads from a small DNA fragment, linked reads yield roughly a hundred reads from molecules with a typical size of 10 to 100 kilobases. Linked reads indicate which reads were derived from the same DNA molecule, and so should be in close proximity in the underlying genome. Linked reads have been used previously to phase diploid genomes using a reference, de novo assemble complex genomes in the gigabase scale, and further scaffold draft assemblies. In de novo sequencing projects, it is challenging yet important to measure the correctness of the resulting assemblies. Linked reads from technologies such as Chromium offer an opportunity to algorithmically address this problem. Here we introduce a software tool, Tigmint, to identify misassemblies using linked reads. The reads are first aligned to the assembly, and the extents of the large DNA molecules are inferred from the alignments of the reads. The physical coverage of the large molecules is more consistent and less prone to coverage dropouts than that of the short read sequencing data. Atypical drops in physical molecule coverage, less than the median minus 1.5 times the inter-quartile range, reveal possible misassemblies. Clipped alignments of the first and last reads of a molecule are used to refine the coordinates of the misassembly with base-pair accuracy. No software tool currently exists for the specific purpose of identifying misassemblies using linked reads. The tool Long Ranger by 10x Genomics detects structural variants, which is a similar task. It requires however a reference genome assembled in fewer than 500 contigs, whereas a de novo assembly is often more fragmented. Tigmint addresses specifically the unaddressed problem of identifying misassemblies using linked reads. Assemblies of short read sequencing data are easily confounded by repetitive sequence larger than the fragment size of the sequencing library. When the size of a repeat exceeds the library fragment size, the contig comes to an end in the best case, or results in misassembled sequence in the worst case. Tigmint is particularly useful in correcting these misassemblies when the initial assembly of the Illumina paired-end reads did not employ the barcodes of the linked reads, and this rich source of evidence is yet untapped. Misassemblies not only complicate downstream analyses, but also limit the contiguity of the assembly, when incorrectly assembled sequences prevent joining their adjacent and correctly assembled sequences. To demonstrate the utility of Tigmint, we assemble the six megabase mitochondrial genome of Sitka spruce ( Picea sitchensis ) from 10x Genomics Chromium data using ABySS 2.0, identify and correct misassemblies using Tigmint, and scaffold using ARCS. Tigmint identifies 16 structural misassemblies in this case. After scaffolding with ARCS, the mitochondrial genome is assembled in 12 scaffolds larger than 100 kbp, with an N50 of 493 kbp. We plan to apply this method to assemble the twenty gigabase nuclear genome of Sitka spruce. Chromium reads permit cost-effective assembly of large genomes with high-throughput, short-read sequencing technology, while also providing large-molecule scaffolding data.

&NA; 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: cjustin@bcgsc.ca, ibirol@bcgsc.ca Supplementary information: Supplementary data are available at Bioinformatics online.

Long-read sequencing technologies have greatly improved assembly contiguity, but at a cost roughly ten times that of short-read sequencing technology. For population studies and when sequencing large genomes, such as conifer genomes and other economically important crop species, this cost may be prohibitive. The 10x Genomics Chromium technology generates linked reads from large molecules at a cost comparable to standard short-read sequencing technologies. Whereas paired-end sequencing gives two reads from a small DNA fragment, Chromium yields roughly a hundred reads from molecules with a typical size of 10 to 100 kilobases. This technology has been applied to assemble genomes as large as the human genome. Here we demonstrate its utility to localize assemblies of genomic loci, in particular isolating the organellar genomes from the 20 gigabase Sitka spruce ( Picea sitchensis ) nuclear genome. We have reported earlier the assembly of the plastid genome of Sitka spruce using the Gemcode technology, the precursor of Chromium. Using the Chromium technology, we have now assembled the mitochondrial genome of Sitka spruce. Our preliminary draft assembles 75% of the estimated six megabase mitochondrial genome in 15 scaffolds larger than 100 kbp. Animal mitochondrial genomes are typically quite small, tens of kilobases, and plant mitochondria are often much larger, hundreds of kilobases. Conifer mitochondria are, in contrast, surprisingly large, nearly six megabases for white spruce ( Picea glauca ). With a mere two gymnosperm mitochondria in NCBI Genbank and a single conifer mitochondrion, few examples of these mysteriously large organellar genomes are available. Our results will thus be of interest to evolutionary biology researchers beyond the field of forestry genomics. Chromium reads permit cost-effective assembly of large genomes with high-throughput, short-read sequencing technology, while also providing large-molecule scaffolding data. Here we assemble Chromium reads using ABySS 2.0, which uses one-tenth the memory of previous versions, and only a few hundred gigabytes of RAM for large conifer genomes.