July 19, 2019  |  

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

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 PacBio or Oxford Nanopore technologies, and achieves a contig NG50 of greater than 21 Mbp on both human and Drosophila melanogaster PacBio datasets. 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. Published by Cold Spring Harbor Laboratory Press.


July 19, 2019  |  

Single-molecule sequencing resolves the detailed structure of complex satellite DNA loci in Drosophila melanogaster.

Highly repetitive satellite DNA (satDNA) repeats are found in most eukaryotic genomes. SatDNAs are rapidly evolving and have roles in genome stability and chromosome segregation. Their repetitive nature poses a challenge for genome assembly and makes progress on the detailed study of satDNA structure difficult. Here, we use single-molecule sequencing long reads from Pacific Biosciences (PacBio) to determine the detailed structure of all major autosomal complex satDNA loci in Drosophila melanogaster, with a particular focus on the 260-bp and Responder satellites. We determine the optimal de novo assembly methods and parameter combinations required to produce a high-quality assembly of these previously unassembled satDNA loci and validate this assembly using molecular and computational approaches. We determined that the computationally intensive PBcR-BLASR assembly pipeline yielded better assemblies than the faster and more efficient pipelines based on the MHAP hashing algorithm, and it is essential to validate assemblies of repetitive loci. The assemblies reveal that satDNA repeats are organized into large arrays interrupted by transposable elements. The repeats in the center of the array tend to be homogenized in sequence, suggesting that gene conversion and unequal crossovers lead to repeat homogenization through concerted evolution, although the degree of unequal crossing over may differ among complex satellite loci. We find evidence for higher-order structure within satDNA arrays that suggest recent structural rearrangements. These assemblies provide a platform for the evolutionary and functional genomics of satDNAs in pericentric heterochromatin. © 2017 Khost et al.; Published by Cold Spring Harbor Laboratory Press.


July 7, 2019  |  

LRCstats, a tool for evaluating long reads correction methods.

Third-generation sequencing (TGS) platforms that generate long reads, such as PacBio and Oxford Nanopore technologies, have had a dramatic impact on genomics research. However, despite recent improvements, TGS reads suffer from high-error rates and the development of read correction methods is an active field of research. This motivates the need to develop tools that can evaluate the accuracy of noisy long reads correction tools.We introduce LRCstats, a tool that measures the accuracy of long reads correction tools. LRCstats takes advantage of long reads simulators that provide each simulated read with an alignment to the reference genome segment they originate from, and does not rely on a step of mapping corrected reads onto the reference genome. This allows for the measurement of the accuracy of the correction while being consistent with the actual errors introduced in the simulation process used to generate noisy reads. We illustrate the usefulness of LRCstats by analyzing the accuracy of four hybrid correction methods for PacBio long reads over three datasets.https://github.com/cchauve/lrcstats.laseanl@sfu.ca or cedric.chauve@sfu.ca.Supplementary data are available at Bioinformatics online.© The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com


July 7, 2019  |  

Convergent evolution of Y chromosome gene content in flies.

Sex-chromosomes have formed repeatedly across Diptera from ordinary autosomes, and X-chromosomes mostly conserve their ancestral genes. Y-chromosomes are characterized by abundant gene-loss and an accumulation of repetitive DNA, yet the nature of the gene repertoire of fly Y-chromosomes is largely unknown. Here we trace gene-content evolution of Y-chromosomes across 22 Diptera species, using a subtraction pipeline that infers Y genes from male and female genome, and transcriptome data. Few genes remain on old Y-chromosomes, but the number of inferred Y-genes varies substantially between species. Young Y-chromosomes still show clear evidence of their autosomal origins, but most genes on old Y-chromosomes are not simply remnants of genes originally present on the proto-sex-chromosome that escaped degeneration, but instead were recruited secondarily from autosomes. Despite almost no overlap in Y-linked gene content in different species with independently formed sex-chromosomes, we find that Y-linked genes have evolved convergent gene functions associated with testis expression. Thus, male-specific selection appears as a dominant force shaping gene-content evolution of Y-chromosomes across fly species.While X-chromosome gene content tends to be conserved, Y-chromosome evolution is dynamic and difficult to reconstruct. Here, Mahajan and Bachtrog use a subtraction pipeline to identify Y-linked genes in 22 Diptera species, revealing patterns of Y-chromosome gene-content evolution.


July 7, 2019  |  

HISEA: HIerarchical SEed Aligner for PacBio data.

The next generation sequencing (NGS) techniques have been around for over a decade. Many of their fundamental applications rely on the ability to compute good genome assemblies. As the technology evolves, the assembly algorithms and tools have to continuously adjust and improve. The currently dominant technology of Illumina produces reads that are too short to bridge many repeats, setting limits on what can be successfully assembled. The emerging SMRT (Single Molecule, Real-Time) sequencing technique from Pacific Biosciences produces uniform coverage and long reads of length up to sixty thousand base pairs, enabling significantly better genome assemblies. However, SMRT reads are much more expensive and have a much higher error rate than Illumina’s – around 10-15% – mostly due to indels. New algorithms are very much needed to take advantage of the long reads while mitigating the effect of high error rate and lowering the required coverage.An essential step in assembling SMRT data is the detection of alignments, or overlaps, between reads. High error rate and very long reads make this a much more challenging problem than for Illumina data. We present a new pairwise read aligner, or overlapper, HISEA (Hierarchical SEed Aligner) for SMRT sequencing data. HISEA uses a novel two-step k-mer search, employing consistent clustering, k-mer filtering, and read alignment extension.We compare HISEA against several state-of-the-art programs – BLASR, DALIGNER, GraphMap, MHAP, and Minimap – on real datasets from five organisms. We compare their sensitivity, precision, specificity, F1-score, as well as time and memory usage. We also introduce a new, more precise, evaluation method. Finally, we compare the two leading programs, MHAP and HISEA, for their genome assembly performance in the Canu pipeline.Our algorithm has the best alignment detection sensitivity among all programs for SMRT data, significantly higher than the current best. The currently best assembler for SMRT data is the Canu program which uses the MHAP aligner in its pipeline. We have incorporated our new HISEA aligner in the Canu pipeline and benchmarked it against the best pipeline for multiple datasets at two relevant coverage levels: 30x and 50x. Our assemblies are better than those using MHAP for both coverage levels. Moreover, Canu+HISEA assemblies for 30x coverage are comparable with Canu+MHAP assemblies for 50x coverage, while being faster and cheaper.The HISEA algorithm produces alignments with highest sensitivity compared with the current state-of-the-art algorithms. Integrated in the Canu pipeline, currently the best for assembling PacBio data, it produces better assemblies than Canu+MHAP.


July 7, 2019  |  

Whole-genome sequencing recommendations

Recent technological developments have revolutionized the way we perform genetic analyses. In particular whole-genome sequencing provides access to the entire genetic makeup of an individual, and it is now an affordable approach for many research groups. As a consequence genome sequencing is pervading many fields of biological research. Sequencing technologies are evolving rapidly and so do their applications. Here we provide a first primer on whole-genome sequencing, focusing on two of the most popular applications: (1) de novo genome sequencing, in which the objective is obtaining a high-quality genome assembly that can serve as a reference for a species or variety, and (2) genome resequencing, when there is an available reference genome and the objective is to map sequence variation of an individual or a set of individuals. It is not our intention to provide a comprehensive overview of current methodologies that will likely soon become obsolete, but rather focus on general principles that will have a more general applicability.


July 7, 2019  |  

Jabba: hybrid error correction for long sequencing reads.

Third generation sequencing platforms produce longer reads with higher error rates than second generation technologies. While the improved read length can provide useful information for downstream analysis, underlying algorithms are challenged by the high error rate. Error correction methods in which accurate short reads are used to correct noisy long reads appear to be attractive to generate high-quality long reads. Methods that align short reads to long reads do not optimally use the information contained in the second generation data, and suffer from large runtimes. Recently, a new hybrid error correcting method has been proposed, where the second generation data is first assembled into a de Bruijn graph, on which the long reads are then aligned.In this context we present Jabba, a hybrid method to correct long third generation reads by mapping them on a corrected de Bruijn graph that was constructed from second generation data. Unique to our method is the use of a pseudo alignment approach with a seed-and-extend methodology, using maximal exact matches (MEMs) as seeds. In addition to benchmark results, certain theoretical results concerning the possibilities and limitations of the use of MEMs in the context of third generation reads are presented.Jabba produces highly reliable corrected reads: almost all corrected reads align to the reference, and these alignments have a very high identity. Many of the aligned reads are error-free. Additionally, Jabba corrects reads using a very low amount of CPU time. From this we conclude that pseudo alignment with MEMs is a fast and reliable method to map long highly erroneous sequences on a de Bruijn graph.


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