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

Genomic diversity associated with polymorphic inversions in humans and their close relatives

Individuals of one species share the bulk of their genetic material, yet no two genomes are the same. Aside from displaying classical variation such as deletions, insertions, or substitutions of base pairs, two DNA segments can also differ in their orientation relative to the rest of their chromosomes. Such inversions are known for a range of biological implications and contribute critically to genome evolution and disease. However, inversions are notoriously challenging to detect, a fact which still impedes comprehensive analysis of their specific properties. This thesis describes several highly inter-connected projects aimed at identifying and functionally characterizing inversions present in the human population and related great ape species. First, inversions between human and four great ape species were assessed for their potential to disrupt topologically associating domains (TADs), potentially prompting gene misregulation. TAD boundaries co-located with breakpoints of long inversions, and while disrupted TADs displayed elevated rates of differen- tially expressed genes, this effect could be attributed the vicinity to inversion breakpoints, suggesting overall robustness of gene expression in response to TAD disruption. The second part of this thesis describes contributions to a collaborative project aimed at characterizing the full spectrum of inversions in 43 humans. In this study, I co-developed a novel inversion genotyping algorithm based on Strand- specific DNA sequencing and contributed to the description of 398 inversion polymorphisms. Inversions exhibited various underlying formation mechanisms, promotion of gene dysregulation, widespread recurrence, and association with genomic disease. These results suggest that long inversions are much more prominent in humans than previously thought, with at least 0.6% of the genome subject to inversion recurrence and, sometimes, the associated risk of subsequent deleterious mutation. With a focus on the link between inversions and disease-causing copy num- ber variations, the last project describes a novel algorithm to identify loci hit sequentially by several overlapping mutation events. This algorithm enabled the description of detailed mutation sequences in 20 highly dynamic regions in the human genome, and additional complex variants on chromosome Y. Six complex loci associate directly with a genomic disease, thereby highlighting in detail the intrinsic link between inversions and CNVs. In summary, these projects provide novel insights into the landscape of in- versions in humans and primates, which are much more frequent, and often more complex than previously thought. These findings provide a basis for future inversion studies and highlight the crucial contribution of this class of mutation to genome variation

Kevlar: A Mapping-Free Framework for Accurate Discovery of De Novo Variants.

De novo genetic variants are an important source of causative variation in complex genetic disorders. Many methods for variant discovery rely on mapping reads to a reference genome, detecting numerous inherited variants irrelevant to the phenotype of interest. To distinguish between inherited and de novo variation, sequencing of families (parents and siblings) is commonly pursued. However, standard mapping-based approaches tend to have a high false-discovery rate for de novo variant prediction. Kevlar is a mapping-free method for de novo variant discovery, based on direct comparison of sequences between related individuals. Kevlar identifies high-abundance k-mers unique to the individual of interest. Reads containing these k-mers are partitioned into disjoint sets by shared k-mer content for variant calling, and preliminary variant predictions are sorted using a probabilistic score. We evaluated Kevlar on simulated and real datasets, demonstrating its ability to detect both de novo single-nucleotide variants and indels with high accuracy

Detection of Genomic Inversion from Single End Read

Structural Variations (SVs) are genomic rearrangements that include both copy-number variants,such as insertion,deletions, duplications and balanced variants like inversion and translocations. These SVs are getting more attentions for research and investigation because of their role on human phenotype, genetic diseases and genomic rearrangements. Evolution of Next-generation Sequencing has provided golden opportunities to investigate these variants and make their wider and clear spectrum in human genome. This investigation includes identification of type of SVs and their breakpoints at base pair level. For their effective identification and breakpoint resolution, many techniques are devised mainly based on paired end read. With relatively low cost and high efficiency different platforms including ION TORRENT, Illumina can generate high throughput Single End reads. In this thesis we provide a novel approach based on Single End reads to detect genomic inversions in human genome. We also compare our approach with existing methods based on paired end reads and show that our approach is competitive in terms of sensitivity and precision at relatively low coverage for detection of breakpoints of genomic inversion

Detection of Genomic Structural Variants from Next-Generation Sequencing Data

Structural variants are genomic rearrangements larger than 50?bp accounting for around 1% of the variation among human genomes. They impact on phenotypic diversity and play a role in various diseases including neurological/neurocognitive disorders and cancer development and progression. Dissecting structural variants from next-generation sequencing data presents several challenges and a number of approaches have been proposed in the literature. In this mini review, we describe and summarize the latest tools ? and their underlying algorithms ? designed for the analysis of whole-genome sequencing, whole-exome sequencing, custom captures, and amplicon sequencing data, pointing out the major advantages/drawbacks. We also report a summary of the most recent applications of third-generation sequencing platforms. This assessment provides a guided indication ? with particular emphasis on human genetics and copy number variants ? for researchers involved in the investigation of these genomic events

Sparse integrative clustering of multiple omics data sets

High resolution microarrays and second-generation sequencing platforms are powerful tools to investigate genome-wide alterations in DNA copy number, methylation and gene expression associated with a disease. An integrated genomic profiling approach measures multiple omics data types simultaneously in the same set of biological samples. Such approach renders an integrated data resolution that would not be available with any single data type. In this study, we use penalized latent variable regression methods for joint modeling of multiple omics data types to identify common latent variables that can be used to cluster patient samples into biologically and clinically relevant disease subtypes. We consider lasso [J. Roy. Statist. Soc. Ser. B 58 (1996) 267-288], elastic net [J. R. Stat. Soc. Ser. B Stat. Methodol. 67 (2005) 301-320] and fused lasso [J. R. Stat. Soc. Ser. B Stat. Methodol. 67 (2005) 91-108] methods to induce sparsity in the coefficient vectors, revealing important genomic features that have significant contributions to the latent variables. An iterative ridge regression is used to compute the sparse coefficient vectors. In model selection, a uniform design [Monographs on Statistics and Applied Probability (1994) Chapman & Hall] is used to seek "experimental" points that scattered uniformly across the search domain for efficient sampling of tuning parameter combinations. We compared our method to sparse singular value decomposition (SVD) and penalized Gaussian mixture model (GMM) using both real and simulated data sets. The proposed method is applied to integrate genomic, epigenomic and transcriptomic data for subtype analysis in breast and lung cancer data sets.Comment: Published in at http://dx.doi.org/10.1214/12-AOAS578 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

A somatic-mutational process recurrently duplicates germline susceptibility loci and tissue-specific super-enhancers in breast cancers

Somatic rearrangements contribute to the mutagenized landscape of cancer genomes. Here, we systematically interrogated rearrangements in 560 breast cancers by using a piecewise constant fitting approach. We identified 33 hotspots of large (>100 kb) tandem duplications, a mutational signature associated with homologous-recombination-repair deficiency. Notably, these tandem-duplication hotspots were enriched in breast cancer germline susceptibility loci (odds ratio (OR) = 4.28) and breast-specific 'super-enhancer' regulatory elements (OR = 3.54). These hotspots may be sites of selective susceptibility to double-strand-break damage due to high transcriptional activity or, through incrementally increasing copy number, may be sites of secondary selective pressure. The transcriptomic consequences ranged from strong individual oncogene effects to weak but quantifiable multigene expression effects. We thus present a somatic-rearrangement mutational process affecting coding sequences and noncoding regulatory elements and contributing a continuum of driver consequences, from modest to strong effects, thereby supporting a polygenic model of cancer development.DG is supported by the EU-FP7-SUPPRESSTEM project. SN-Z is funded by a Wellcome Trust Intermediate Fellowship (WT100183MA) and is a Wellcome Beit Fellow. For more information, please visit the publisher's website