Motivation The emergence of high-throughput sequencing technologies revolutionized genomics in early 2000s. The next revolution came with the era of long-read sequencing. These technological advances along with novel computational approaches became the next step towards the automatic pipelines capable to assemble nearly complete mammalian-size genomes. Results In this manuscript, we demonstrate performance of the state-of-the-art genome assembly software on six eukaryotic datasets sequenced using different technologies. To evaluate the results, we developed QUAST-LG-a tool that compares large genomic de novo assemblies against reference sequences and computes relevant quality metrics. Since genomes generally cannot be reconstructed completely due to complex repeat patterns and low coverage regions, we introduce a concept of upper bound assembly for a given genome and set of reads, and compute theoretical limits on assembly correctness and completeness. Using QUAST-LG, we show how close the assemblies are to the theoretical optimum, and how far this optimum is from the finished reference. Availability and implementation http://cab.spbu.ru/software/quast-lg. Supplementary information Supplementary data are available at Bioinformatics online.

/pdf/versatile-genome-assembly-evaluation-with-quast-lg-2ri5j8mpc5.pdf

Versatile genome assembly evaluation with QUAST-LG.

An important assessment prior to genome assembly and related analyses is genome profiling, where the k-mer frequencies within raw sequencing reads are analyzed to estimate major genome characteristics such as size, heterozygosity, and repetitiveness. Here we introduce GenomeScope 2.0 (https://github.com/tbenavi1/genomescope2.0), which applies combinatorial theory to establish a detailed mathematical model of how k-mer frequencies are distributed in heterozygous and polyploid genomes. We describe and evaluate a practical implementation of the polyploid-aware mixture model that quickly and accurately infers genome properties across thousands of simulated and several real datasets spanning a broad range of complexity. We also present a method called Smudgeplot (https://github.com/KamilSJaron/smudgeplot) to visualize and estimate the ploidy and genome structure of a genome by analyzing heterozygous k-mer pairs. We successfully apply the approach to systems of known variable ploidy levels in the Meloidogyne genus and the extreme case of octoploid Fragaria × ananassa. Prior to genome assembly, the raw sequencing reads must be analyzed for assessment of major genome characteristics such as genome size, heterozygosity, and repetitiveness. For this purpose, the authors introduce GenomeScope 2.0, an extension of GenomeScope for polyploid genomes, and Smudgeplot, which can estimate a genome’s ploidy.

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GenomeScope 2.0 and Smudgeplot for reference-free profiling of polyploid genomes

Rapeseed (Brassica napus) is the second most important oilseed crop in the world but the genetic diversity underlying its massive phenotypic variations remains largely unexplored. Here, we report the sequencing, de novo assembly and annotation of eight B. napus accessions. Using pan-genome comparative analysis, millions of small variations and 77.2–149.6 megabase presence and absence variations (PAVs) were identified. More than 9.4% of the genes contained large-effect mutations or structural variations. PAV-based genome-wide association study (PAV-GWAS) directly identified causal structural variations for silique length, seed weight and flowering time in a nested association mapping population with ZS11 (reference line) as the donor, which were not detected by single-nucleotide polymorphisms-based GWAS (SNP-GWAS), demonstrating that PAV-GWAS was complementary to SNP-GWAS in identifying associations to traits. Further analysis showed that PAVs in three FLOWERING LOCUS C genes were closely related to flowering time and ecotype differentiation. This study provides resources to support a better understanding of the genome architecture and acceleration of the genetic improvement of B. napus. The assembly of eight high-quality rapeseed genomes allows identification of presence and absence variations (PAVs) and small variations. PAV-based genome-wide association analysis uncovered causal variations for agronomic traits and ecotype differentiation.

/pdf/eight-high-quality-genomes-reveal-pan-genome-architecture-3ffs0b1w1s.pdf

Eight high-quality genomes reveal pan-genome architecture and ecotype differentiation of Brassica napus.

The biological function of Z-DNA and Z-RNA, nucleic acid structures with a left-handed double helix, is poorly understood1–3. Z-DNA-binding protein 1 (ZBP1; also known as DAI or DLM-1) is a nucleic acid sensor that contains two Zα domains that bind Z-DNA4,5 and Z-RNA6–8. ZBP1 mediates host defence against some viruses6,7,9–14 by sensing viral nucleic acids6,7,10. RIPK1 deficiency, or mutation of its RIP homotypic interaction motif (RHIM), triggers ZBP1-dependent necroptosis and inflammation in mice15,16. However, the mechanisms that induce ZBP1 activation in the absence of viral infection remain unknown. Here we show that Zα-dependent sensing of endogenous ligands induces ZBP1-mediated perinatal lethality in mice expressing RIPK1 with mutated RHIM (Ripk1mR/mR), skin inflammation in mice with epidermis-specific RIPK1 deficiency (RIPK1E-KO) and colitis in mice with intestinal epithelial-specific FADD deficiency (FADDIEC-KO). Consistently, functional Zα domains were required for ZBP1-induced necroptosis in fibroblasts that were treated with caspase inhibitors or express RIPK1 with mutated RHIM. Inhibition of nuclear export triggered the Zα-dependent activation of RIPK3 in the nucleus resulting in cell death, which suggests that ZBP1 may recognize nuclear Z-form nucleic acids. We found that ZBP1 constitutively bound cellular double-stranded RNA in a Zα-dependent manner. Complementary reads derived from endogenous retroelements were detected in epidermal RNA, which suggests that double-stranded RNA derived from these retroelements may act as a Zα-domain ligand that triggers the activation of ZBP1. Collectively, our results provide evidence that the sensing of endogenous Z-form nucleic acids by ZBP1 triggers RIPK3-dependent necroptosis and inflammation, which could underlie the development of chronic inflammatory conditions—particularly in individuals with mutations in RIPK1 and CASP817–20. Analyses of mouse models of inflammation suggest some chronic inflammatory conditions may result from Z-DNA-binding protein 1 sensing endogenous Z-form nucleic acids—such as those of endogenous retroelements—through its Zα domains.

Z-nucleic-acid sensing triggers ZBP1-dependent necroptosis and inflammation.

Structural variants (SVs) remain challenging to represent and study relative to point mutations despite their demonstrated importance. We show that variation graphs, as implemented in the vg toolkit, provide an effective means for leveraging SV catalogs for short-read SV genotyping experiments. We benchmark vg against state-of-the-art SV genotypers using three sequence-resolved SV catalogs generated by recent long-read sequencing studies. In addition, we use assemblies from 12 yeast strains to show that graphs constructed directly from aligned de novo assemblies improve genotyping compared to graphs built from intermediate SV catalogs in the VCF format.

/pdf/genotyping-structural-variants-in-pangenome-graphs-using-the-1uwt5era6m.pdf

Genotyping structural variants in pangenome graphs using the vg toolkit.

Counting all k -mers in a given dataset is a standard procedure in many bioinformatics applications. We introduce KMC3, a significant improvement of the former KMC2 algorithm together with KMC tools for manipulating k -mer databases. Usefulness of the tools is shown on a few real problems.Program is freely available at http://sun.aei.polsl.pl/REFRESH/kmc .sebastian.deorowicz@polsl.pl.Supplementary data are available at Bioinformatics online.

KMC 3: counting and manipulating k-mer statistics.

Summary: Counting all k-mers in a given dataset is a standard procedure in many bioinformatics applications. We introduce KMC3, a significant improvement of the former KMC2 algorithm together with KMC tools for manipulating k-mer databases. Usefulness of the tools is shown on a few real problems. Availability: Program is freely available at this http URL. Contact: sebastian.deorowicz@polsl.pl

KMC 3: counting and manipulating k-mer statistics

Summary: Presence of sequencing errors in data produced by next-generation sequencers affects quality of downstream analyzes. Accuracy of them can be improved by performing error correction of sequencing reads. We introduce a new correction algorithm capable of processing eukaryotic close to 500 Mbp-genome-size, high error-rated data using less than 4 GB of RAM in about 35 min on 16-core computer. Availability and Implementation: Program is freely available at http://sun.aei.polsl.pl/REFRESH/reckoner. Contact: sebastian.deorowicz@polsl.pl Supplementary information: Supplementary data are available at Bioinformatics online.

RECKONER: read error corrector based on KMC.

Summary Kmer-db is a new tool for estimating evolutionary relationship on the basis of k-mers extracted from genomes or sequencing reads. Thanks to an efficient data structure and parallel implementation, our software estimates distances between 40 715 pathogens in <7 min (on a modern workstation), 26 times faster than Mash, its main competitor. Availability and implementation https://github.com/refresh-bio/kmer-db and http://sun.aei.polsl.pl/REFRESH/kmer-db. Supplementary information Supplementary data are available at Bioinformatics online.

/pdf/kmer-db-instant-evolutionary-distance-estimation-kztfyi76dy.pdf

Kmer-db: instant evolutionary distance estimation.

In this paper we introduce RADULS2, the fastest parallel sorter based on radix algorithm. It is optimized to process huge amounts of data making use of modern multicore CPUs. The main novelties include: extremely optimized algorithm for handling tiny arrays (up to about a hundred of records) that could appear even billions times as subproblems to handle and improved processing of larger subarrays with better use of non-temporal memory stores.

Maciej Dlugosz

Papers

KMC 3: counting and manipulating k-mer statistics.

KMC 3: counting and manipulating k-mer statistics

RECKONER: read error corrector based on KMC.

Kmer-db: instant evolutionary distance estimation.

Even faster sorting of (not only) integers