SVIM: Structural Variant Identification using Mapped Long Reads

Motivation: Structural variants are defined as genomic variants larger than 50bp. They have been shown to affect more bases in any given genome than SNPs or small indels. 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 PacBio 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

Single-cell strand sequencing for structural variant analysis and genome assembly

Rapid advances of DNA sequencing technologies and development of computational tools to analyze sequencing data has started a revolution in the field of genetics. DNA sequencing has applications in medical research, disease diagnosis and treatment, and population genetic studies. Different sequencing techniques have their own advantages and limitations, and they can be used together to solve genome assembly and genetic variant detection. The focus of this thesis is on a specific single-cell sequencing technology, called strand sequencing. With its chromosome and haplotype-specific strand information, this technique has very powerful signals for discovery of genomic structural variations, haplotype phasing, and chromosome clustering. We developed statistical and compuptational tools to exploit this information from strand sequencing technology. I first present a computational framework for detecting structural variations in single cells using strand sequencing data. The presented tool is able to detect different types of structural variations in single cells including copy number variations, inversions, and inverted duplications, and also more complex biological events such as translocations and breakage-fusion-bridge (BFB) cycles. These variations and genomic rearrangements have been observed in cancer, therefore the discovery of such events within cell populations can lead to a more accurate picture of cancer genomes and help in diagnosis. In the remainder of this thesis, I elaborate on two computational pipelines for clustering long DNA sequences by their original chromosome and haplotype in the absence of a reference genome. These pipelines are developed to facilitate genome assembly and de novo haplotype phasing in a fast and accurate manner. The resulting haplotype assemblies can be useful in studying genomic variations with no reference bias, gaining insights in population genetics, and detection of compound heterozygosity.Die rasanten Fortschritte im Bereich der DNA-Sequenzierung und die Entwicklung von Computerwerkzeugen für die Analyse von Sequenzierdaten haben eine Revolution auf dem Gebiet der Genetik ausgelöst. Die DNA-Sequenzierung findet Anwendung in der medizinischen Forschung, bei der Diagnose und Behandlung von Krankheiten und bei populationsgenetischen Studien. Verschiedene Sequenzierungstechniken haben jeweils ihre Vorteile und Grenzen, können aber kombiniert werden, um Genome zu assemblieren oder um genetische Varianten zu finden. Der Schwerpunkt dieser Arbeit liegt auf einer speziellen Einzelzell Sequenzierungstechnologie, genannt Strand-Seq. Mit ihren chromosomen- und haplotypspezifischen Stranginformationen liefert diese Technik sehr starke Signale für die Entdeckung genomischer Strukturvariationen, die Rekonstruktion von Haplotypen und das Chromosomenclustering. Wir haben statistische und computergestützte Werkzeuge entwickelt, um diese Informationen der Strand-Seq Technologie zu nutzen. Zunächst präsentiere ich einen mathematisches Modell für die Erkennung struktureller Variationen in einzelnen Zellen unter Verwendung von Strand-Seq Daten. Das vorgestellte Tool ist in der Lage, verschiedene Arten von Strukturvariationen in Einzelzellen zu erkennen, darunter Kopienzahlvariationen, Inversionen und invertierte Duplikationen sowie komplexere biologische Ereignisse wie Translokationen und Break-Fusion- Bridge-Zyklen (BFB). Diese Variationen und genomischen Umlagerungen wurden bei Krebs beobachtet, sodass der Nachweis solcher Ereignisse in Zellpopulationen zu einem genaueren Bild des Krebsgenoms führen und bei der Diagnose helfen kann. Im Folgenden stelle ich zwei Computerpipelines vor, mit denen lange DNA-Sequenzen nach ihrem ursprünglichen Chromosom und Haplotyp geclustert werden können, wenn kein Referenzgenom verfügbar ist. Diese Pipelines wurden entwickelt, um die Genomassemblierung und die de novo Rekonstruktion von Haplotypen auf schnelle und genaue Weise zu erleichtern. Die daraus resultierenden Haplotypen können bei der Untersuchung genomischer Variationen ohne Referenzverzerrung, bei der Gewinnung von Einblicken in die Populationsgenetik und beim Nachweis von zusammengesetzter Heterozygotie nützlich sein

The complete and fully assembled genome sequence of Aeromonas salmonicida subsp. pectinolytica and its comparative analysis with other Aeromonas species: investigation of the mobilome in environmental and pathogenic strains.

Due to the predominant usage of short-read sequencing to date, most bacterial genome sequences reported in the last years remain at the draft level. This precludes certain types of analyses, such as the in-depth analysis of genome plasticity. Here we report the finalized genome sequence of the environmental strain Aeromonas salmonicida subsp. pectinolytica 34mel, for which only a draft genome with 253 contigs is currently available. Successful completion of the transposon-rich genome critically depended on the PacBio long read sequencing technology. Using finalized genome sequences of A. salmonicida subsp. pectinolytica and other Aeromonads, we report the detailed analysis of the transposon composition of these bacterial species. Mobilome evolution is exemplified by a complex transposon, which has shifted from pathogenicity-related to environmental-related gene content in A. salmonicida subsp. pectinolytica 34mel. Obtaining the complete, circular genome of A. salmonicida subsp. pectinolytica allowed us to perform an in-depth analysis of its mobilome. We demonstrate the mobilome-dependent evolution of this strain's genetic profile from pathogenic to environmental

npInv: accurate detection and genotyping of inversions using long read sub-alignment

BACKGROUND: Detection of genomic inversions remains challenging. Many existing methods primarily target inzversions with a non repetitive breakpoint, leaving inverted repeat (IR) mediated non-allelic homologous recombination (NAHR) inversions largely unexplored. RESULT: We present npInv, a novel tool specifically for detecting and genotyping NAHR inversion using long read sub-alignment of long read sequencing data. We benchmark npInv with other tools in both simulation and real data. We use npInv to generate a whole-genome inversion map for NA12878 consisting of 30 NAHR inversions (of which 15 are novel), including all previously known NAHR mediated inversions in NA12878 with flanking IR less than 7kb. Our genotyping accuracy on this dataset was 94%. We used PCR to confirm the presence of two of these novel inversions. We show that there is a near linear relationship between the length of flanking IR and the minimum inversion size, without inverted repeats. CONCLUSION: The application of npInv shows high accuracy in both simulation and real data. The results give deeper insight into understanding inversion

SVNN:An efficient PacBio-specific pipeline for structural variations calling using neural networks

Abstract Background Once aligned, long-reads can be a useful source of information to identify the type and position of structural variations. However, due to the high sequencing error of long reads, long-read structural variation detection methods are far from precise in low-coverage cases. To be accurate, they need to use high-coverage data, which in turn, results in an extremely time-consuming pipeline, especially in the alignment phase. Therefore, it is of utmost importance to have a structural variation calling pipeline which is both fast and precise for low-coverage data. Results In this paper, we present SVNN, a fast yet accurate, structural variation calling pipeline for PacBio long-reads that takes raw reads as the input and detects structural variants of size larger than 50 bp. Our pipeline utilizes state-of-the-art long-read aligners, namely NGMLR and Minimap2, and structural variation callers, videlicet Sniffle and SVIM. We found that by using a neural network, we can extract features from Minimap2 output to detect a subset of reads that provide useful information for structural variation detection. By only mapping this subset with NGMLR, which is far slower than Minimap2 but better serves downstream structural variation detection, we can increase the sensitivity in an efficient way. As a result of using multiple tools intelligently, SVNN achieves up to 20 percentage points of sensitivity improvement in comparison with state-of-the-art methods and is three times faster than a naive combination of state-of-the-art tools to achieve almost the same accuracy. Conclusion Since prohibitive costs of using high-coverage data have impeded long-read applications, with SVNN, we provide the users with a much faster structural variation detection platform for PacBio reads with high precision and sensitivity in low-coverage scenarios

Complete genomic and transcriptional landscape analysis using third-generation sequencing: a case study of Saccharomyces cerevisiae CEN.PK113-7D

Completion of eukaryal genomes can be difficult task with the highly repetitive sequences along the chromosomes and short read lengths of secondgeneration sequencing. Saccharomyces cerevisiae strain CEN. PK113-7D, widely used as a model organism and a cell factory, was selected for this study to demonstrate the superior capability of very long sequence reads for de novo genome assembly. We generated long reads using two common third-generation sequencing technologies (Oxford Nanopore Technology (ONT) and Pacific Biosciences (PacBio)) and used short reads obtained using Illumina sequencing for error correction. Assembly of the reads derived from all three technologies resulted in complete sequences for all 16 yeast chromosomes, as well as themitochondrial chromosome, in one step. Further, we identified three types of DNA methylation (5mC, 4mC and 6mA). Comparison between the reference strain S288C and strain CEN. PK113-7D identified chromosomal rearrangements against a background of similar gene content between the two strains. We identified full-length transcripts through ONT direct RNA sequencing technology. This allows for the identification of transcriptional landscapes, including untranslated regions (UTRs) (5\u27 UTR and 3\u27 UTR) as well as differential gene expression quantification. About 91% of the predicted transcripts could be consistently detected across biological replicates grown either on glucose or ethanol. Direct RNA sequencing identified many polyadenylated non-coding RNAs, rRNAs, telomere-RNA, long non-coding RNA and antisense RNA. This work demonstrates a strategy to obtain complete genome sequences and transcriptional landscapes that can be applied to other eukaryal organisms

Chromosomal-level assembly of the Asian Seabass genome using long sequence reads and multi-layered scaffolding

We report here the ~670 Mb genome assembly of the Asian seabass (Lates calcarifer), a tropical marine teleost. We used long-read sequencing augmented by transcriptomics, optical and genetic mapping along with shared synteny from closely related fish species to derive a chromosome-level assembly with a contig N50 size over 1 Mb and scaffold N50 size over 25 Mb that span ~90% of the genome. The population structure of L. calcarifer species complex was analyzed by re-sequencing 61 individuals representing various regions across the species' native range. SNP analyses identified high levels of genetic diversity and confirmed earlier indications of a population stratification comprising three clades with signs of admixture apparent in the South-East Asian population. The quality of the Asian seabass genome assembly far exceeds that of any other fish species, and will serve as a new standard for fish genomics