Long-read single-molecule sequencing has revolutionized de novo genome assembly and enabled the automated reconstruction of reference-quality genomes. However, given the relatively high error rates of such technologies, efficient and accurate assembly of large repeats and closely related haplotypes remains challenging. We address these issues with Canu, a successor of Celera Assembler that is specifically designed for noisy single-molecule sequences. Canu introduces support for nanopore sequencing, halves depth-of-coverage requirements, and improves assembly continuity while simultaneously reducing runtime by an order of magnitude on large genomes versus Celera Assembler 8.2. These advances result from new overlapping and assembly algorithms, including an adaptive overlapping strategy based on tf-idf weighted MinHash and a sparse assembly graph construction that avoids collapsing diverged repeats and haplotypes. We demonstrate that Canu can reliably assemble complete microbial genomes and near-complete eukaryotic chromosomes using either Pacific Biosciences (PacBio) or Oxford Nanopore technologies and achieves a contig NG50 of >21 Mbp on both human and Drosophila melanogaster PacBio data sets. For assembly structures that cannot be linearly represented, Canu provides graph-based assembly outputs in graphical fragment assembly (GFA) format for analysis or integration with complementary phasing and scaffolding techniques. The combination of such highly resolved assembly graphs with long-range scaffolding information promises the complete and automated assembly of complex genomes.

/pdf/canu-scalable-and-accurate-long-read-assembly-via-adaptive-k-39dddv6mbp.pdf

Canu: scalable and accurate long-read assembly via adaptive k-mer weighting and repeat separation.

The Illumina DNA sequencing platform generates accurate but short reads, which can be used to produce accurate but fragmented genome assemblies. Pacific Biosciences and Oxford Nanopore Technologies DNA sequencing platforms generate long reads that can produce complete genome assemblies, but the sequencing is more expensive and error-prone. There is significant interest in combining data from these complementary sequencing technologies to generate more accurate "hybrid" assemblies. However, few tools exist that truly leverage the benefits of both types of data, namely the accuracy of short reads and the structural resolving power of long reads. Here we present Unicycler, a new tool for assembling bacterial genomes from a combination of short and long reads, which produces assemblies that are accurate, complete and cost-effective. Unicycler builds an initial assembly graph from short reads using the de novo assembler SPAdes and then simplifies the graph using information from short and long reads. Unicycler uses a novel semi-global aligner to align long reads to the assembly graph. Tests on both synthetic and real reads show Unicycler can assemble larger contigs with fewer misassemblies than other hybrid assemblers, even when long-read depth and accuracy are low. Unicycler is open source (GPLv3) and available at github.com/rrwick/Unicycler.

/pdf/unicycler-resolving-bacterial-genome-assemblies-from-short-2rdma73s0x.pdf

Unicycler: Resolving bacterial genome assemblies from short and long sequencing reads.

Basecalling, the computational process of translating raw electrical signal to nucleotide sequence, is of critical importance to the sequencing platforms produced by Oxford Nanopore Technologies (ONT). Here, we examine the performance of different basecalling tools, looking at accuracy at the level of bases within individual reads and at majority-rule consensus basecalls in an assembly. We also investigate some additional aspects of basecalling: training using a taxon-specific dataset, using a larger neural network model and improving consensus basecalls in an assembly by additional signal-level analysis with Nanopolish. Training basecallers on taxon-specific data results in a significant boost in consensus accuracy, mostly due to the reduction of errors in methylation motifs. A larger neural network is able to improve both read and consensus accuracy, but at a cost to speed. Improving consensus sequences (‘polishing’) with Nanopolish somewhat negates the accuracy differences in basecallers, but pre-polish accuracy does have an effect on post-polish accuracy. Basecalling accuracy has seen significant improvements over the last 2 years. The current version of ONT’s Guppy basecaller performs well overall, with good accuracy and fast performance. If higher accuracy is required, users should consider producing a custom model using a larger neural network and/or training data from the same species.

/pdf/performance-of-neural-network-basecalling-tools-for-oxford-4uysow34lo.pdf

Performance of neural network basecalling tools for Oxford Nanopore sequencing.

Sequencing costs have dropped much faster than Moore's law in the past decade, and sensitive sequence searching has become the main bottleneck in the analysis of large (meta)genomic datasets. While previous methods sacrificed sensitivity for speed gains, the parallelized, open-source software MMseqs2 overcomes this trade-off: In three-iteration profile searches it reaches 50% higher sensitivity than BLAST at 83-fold speed and the same sensitivity as PSI-BLAST at 270 times its speed. MMseqs2 therefore offers great potential to increase the fraction of annotatable (meta)genomic sequences.

/pdf/mmseqs2-enables-sensitive-protein-sequence-searching-for-the-4rq9tvkofu.pdf

MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets

Long-read technologies are overcoming early limitations in accuracy and throughput, broadening their application domains in genomics. Dedicated analysis tools that take into account the characteristics of long-read data are thus required, but the fast pace of development of such tools can be overwhelming. To assist in the design and analysis of long-read sequencing projects, we review the current landscape of available tools and present an online interactive database, long-read-tools.org, to facilitate their browsing. We further focus on the principles of error correction, base modification detection, and long-read transcriptomics analysis and highlight the challenges that remain.

/pdf/opportunities-and-challenges-in-long-read-sequencing-data-1x76imf5e7.pdf

Opportunities and challenges in long-read sequencing data analysis.

The assembly of long reads from Pacific Biosciences and Oxford Nanopore Technologies typically requires resource-intensive error-correction and consensus-generation steps to obtain high-quality assemblies. We show that the error-correction step can be omitted and that high-quality consensus sequences can be generated efficiently with a SIMD-accelerated, partial-order alignment-based, stand-alone consensus module called Racon. Based on tests with PacBio and Oxford Nanopore data sets, we show that Racon coupled with miniasm enables consensus genomes with similar or better quality than state-of-the-art methods while being an order of magnitude faster.

/pdf/fast-and-accurate-de-novo-genome-assembly-from-long-3yhc44t4h6.pdf

Fast and accurate de novo genome assembly from long uncorrected reads

The SIFT (sorting intolerant from tolerant) algorithm helps bridge the gap between mutations and phenotypic variations by predicting whether an amino acid substitution is deleterious. SIFT has been used in disease, mutation and genetic studies, and a protocol for its use has been previously published with Nature Protocols. This updated protocol describes SIFT 4G (SIFT for genomes), which is a faster version of SIFT that enables practical computations on reference genomes. Users can get predictions for single-nucleotide variants from their organism of interest using the SIFT 4G annotator with SIFT 4G's precomputed databases. The scope of genomic predictions is expanded, with predictions available for more than 200 organisms. Users can also run the SIFT 4G algorithm themselves. SIFT predictions can be retrieved for 6.7 million variants in 4 min once the database has been downloaded. If precomputed predictions are not available, the SIFT 4G algorithm can compute predictions at a rate of 2.6 s per protein sequence. SIFT 4G is available from http://sift-dna.org/sift4g.

SIFT missense predictions for genomes

Whole genome sequencing technologies are unable to invariably read DNA molecules intact, a shortcoming that assemblers try to resolve by stitching the obtained fragments back together. Here, we present methods for the improvement of de novo genome assembly from erroneous long reads incorporated into a tool called Raven. Raven maintains similar performance for various genomes and has accuracy on par with other assemblers that support third-generation sequencing data. It is one of the fastest options while having the lowest memory consumption on the majority of benchmarked datasets. Raven is designed to democratize genome assembly, being a simple and efficient tool while keeping high accuracy. Using a method for detection of false overlaps based on graph drawing, it can be employed for various genome sizes.

Time- and memory-efficient genome assembly with Raven

Raven source code used in the manuscript "Time and memory efficient genome assembly with Raven"

We present new methods for the improvement of long-read de novo genome assembly incorporated into a straightforward tool called Raven (https://github.com/lbcb-sci/raven). Compared with other assemblers, Raven is one of two fastest, it reconstructs the sequenced genome in the least amount of fragments, has better or comparable accuracy, and maintains similar performance for various genomes. Raven takes 500 CPU hours to assemble a 44x human genome dataset in only 259 fragments.

/pdf/raven-a-de-novo-genome-assembler-for-long-reads-2bmluwtv8a.pdf

Robert Vaser

Papers

Fast and accurate de novo genome assembly from long uncorrected reads

SIFT missense predictions for genomes

Time- and memory-efficient genome assembly with Raven

Raven source code used in the manuscript "Time and memory efficient genome assembly with Raven"

Raven: a de novo genome assembler for long reads