Alignment is the cornerstone of many long-read pipelines and plays an essential role in resolving structural variants (SVs). However, forced alignments of SVs embedded in long reads, inflexibility of integrating novel SVs models and computational inefficiency remain problems. Here, we investigate the feasibility of resolving long-read SVs with alignment-free algorithms. We ask: (1) Is it possible to resolve long-read SVs with alignment-free approaches? and (2) Does it provide an advantage over existing approaches? To this end, we implemented the framework named Linear, which can flexibly integrate alignment-free algorithms such as the generative model for long-read SV detection. Furthermore, Linear addresses the problem of compatibility of alignment-free approaches with existing software. It takes as input long reads and outputs standardized results existing software can directly process. We conducted large-scale assessments in this work and the results show that the sensitivity, and flexibility of Linear outperform alignment-based pipelines. Moreover, the computational efficiency is orders of magnitude faster.

MOTIVATION: With the availability of new sequencing technologies, the generation of haplotype-resolved genome assemblies up to chromosome scale has become feasible. These assemblies capture the complete genetic information of both parental haplotypes, increase structural variant (SV) calling sensitivity and enable direct genotyping and phasing of SVs. Yet, existing SV callers are designed for haploid genome assemblies only, do not support genotyping or detect only a limited set of SV classes. RESULTS: We introduce our method SVIM-asm for the detection and genotyping of six common classes of SVs from haploid and diploid genome assemblies. Compared against the only other existing SV caller for diploid assemblies, DipCall, SVIM-asm detects more SV classes and reached higher F1 scores for the detection of insertions and deletions on two recently published assemblies of the HG002 individual. AVAILABILITY AND IMPLEMENTATION: SVIM-asm has been implemented in Python and can be easily installed via bioconda. Its source code is available at github.com/eldariont/svim-asm. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Reconstructing haplotypes from sequencing data is one of the major challenges in genetics. Haplotypes play a crucial role in many analyses, including genome-wide association studies and population genetics. Haplotype reconstruction becomes more difficult for higher numbers of homologous chromosomes, as it is often the case for polyploid plants. This complexity is compounded further by higher heterozygosity, which denotes the frequent presence of variants between haplotypes. We have designed Ranbow, a new tool for haplotype reconstruction of polyploid genome from short read sequencing data. Ranbow integrates all types of small variants in bi- and multi-allelic sites to reconstruct haplotypes. To evaluate Ranbow and currently available competing methods on real data, we have created and released a real gold standard dataset from sweet potato sequencing data. Our evaluations on real and simulated data clearly show Ranbow's superior performance in terms of accuracy, haplotype length, memory usage, and running time. Specifically, Ranbow is one order of magnitude faster than the next best method. The efficiency and accuracy of Ranbow makes whole genome haplotype reconstruction of complex genome with higher ploidy feasible.

MOTIVATION: Structural variants are defined as genomic variants larger than 50 bp. They have been shown to affect more bases in any given genome than single-nucleotide polymorphisms or small insertions and deletions. Additionally, they have great impact on human phenotype and diversity and have been linked to numerous diseases. Due to their size and association with repeats, they are difficult to detect by shotgun sequencing, especially when based on short reads. Long read, single-molecule sequencing technologies like those offered by Pacific Biosciences or Oxford Nanopore Technologies produce reads with a length of several thousand base pairs. Despite the higher error rate and sequencing cost, long-read sequencing offers many advantages for the detection of structural variants. Yet, available software tools still do not fully exploit the possibilities. RESULTS: We present SVIM, a tool for the sensitive detection and precise characterization of structural variants from long-read data. SVIM consists of three components for the collection, clustering and combination of structural variant signatures from read alignments. It discriminates five different variant classes including similar types, such as tandem and interspersed duplications and novel element insertions. SVIM is unique in its capability of extracting both the genomic origin and destination of duplications. It compares favorably with existing tools in evaluations on simulated data and real datasets from Pacific Biosciences and Nanopore sequencing machines. AVAILABILITY AND IMPLEMENTATION: The source code and executables of SVIM are available on Github: github.com/eldariont/svim. SVIM has been implemented in Python 3 and published on bioconda and the Python Package Index. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.