SVenX: A highly parallelized pipeline for structural variation detection using linked read whole genome sequencing data

Genomic rearrangements larger than 50 bp are called structural variants. As a group, they affect the phenotypic diversity among humans and have been associated with many human disorders including neurodevelopmental disorder and cancer. Recent advances in whole genome sequencing (WGS) technologies have made it possible to identify many more disease-causing genetic variants relevant in clinical diagnostics and sometimes affecting treatment. Numerous approaches have been proposed to detect structural variants, but to acquire and filter out the most significant information from the multitude of called variants in the sequencing data has shown to be a challenge. Another obstacle is the high computational cost of data analyses and difficulties in configuring and operating the softwares and databases. Here, we present SVenX, a highly automated and parallelized pipeline that analyzes and call structural variants using linked read WGS data. It performs variant calling using three different approaches, as well as annotation of variants and variant filtering. We also introduce a new tool, SVGenT, that reanalyzes the called structural variants by performing de novo assembly using the aligned reads at the identified breakpoint junctions. By comparing assembled contigs and analyzing the read coverage between the breakpoint junctions, SVGenT improves both variant and genotype classification and the breakpoint localization.Tool for detection of genomic rearrangements in humans Genomic rearrangements larger than 50 base pairs are referred to as structural variants (SVs), and impact phenotypic differences between humans. Some of these variants have been associated with human diseases such as cancer and neurodevelopmental disorders. Recent advances in whole genome sequencing (WGS) technologies have made it possible to analyze and identify many structural variants. Yet, the existing tools used for analyzing these data are not perfect, and require a fair amount of knowledge in bioinformatics to operate. SVenX is a highly parallelized and automated pipeline, executing all steps from whole genome sequencing data to filtered SVs. This includes 1) verifying that all required data exist, 2) making sure no data duplications exist, 3) finding variants using different methods, and 4) annotating and filtering the detected SVs. SVenX performs 10 separate steps including 3 different variant detection tools (also known as variant callers). Normally, these steps are performed one by one, waiting for the output before running the next. Not only does it take longer for the programs to run with this approach, it also requires an employee to execute the steps. Except from the installation, SVenX takes at the most a few minutes to setup and launch and can analyze multiple samples of WGS data at the same time. The whole pipeline takes about 4 to 5 days to complete, requiring minimal work effort and bioinformatic knowledge. Another challenge in SV research is not only detecting the variants, but also to be confident that the detected SVs are true calls. The performance of existing variant callers differ significantly between each other. One tool can perform really good using one dataset and fail totally in detecting SVs in another dataset, while a second tool might be good in detecting only a single type of SV. Using multiple bioinformatics methods to detect SVs have shown to result in a higher detection rate. We have created a novel tool, SVGenT, that re-analyzes already detected SVs by doing de novo assembly. SVGenT classifies the SV type (deletion, duplication, inversion or break-end), genotype (homozygous or heterozygous), and update the genomic position of the SV breakpoints. SVGenT has been tested using two datasets: one public large-scale WGS dataset and one simulated dataset with 4000 SVs. Three different variant callers were used to detect the variants before SVGenT was run on the output files. The detection rate was calculated before and after SVGenT was applied. In most cases, SVGenT improved the classification of both SV-type and SV-genotype. Master’s Degree Project in Biology/Molecular Biology/Bioinformatics 60 credits 2017 Department of Biology, Lund University Advisor: Anna Lindstrand M.D., Ph.D. Karolinska Institutet

The genetics of glioma : and the use of dogs as model for human glioma

Glioma is the most common type of primary brain tumor in humans, and the second most common in canines. This tumor type originates from glial cells in the brain and is a genetic disorder caused by mutations in genes regulating important cellular functions. The current diagnosis of glioma is based on histopathological evaluations and gradings. The complexity of the disease requests advanced gene technologies and bioinformatics tools which can aid in the development of new and better diagnosis criteria and therapies. Using Genome wide association studies (GWAS) several genes have been found to be associated with glioma. And with next generation sequencing (NGS) methods, large amounts of genetic information can be produced, stored and analyzed for a low cost. Glioma develops spontaneously in dogs in a similar fashion as in humans and is proposed as a model in glioma research. The findings of new genes associated with glioma can be used for gene, small molecular and immune therapies. Receptor tyrosine kinases VEGFR-1, VEGFR-2, EGFR-1, PDGFR, EGFR and c-MET have been found to be overexpressed in both canine and human gliomas, and growth-factor-targeted therapies have been proposed as treatment for gliomas in canine and humans. Gene therapies including methods as; conditionally cytotoxic therapies, suppression of angiogenesis, immune stimulation, tumor suppressors etc. are progressing in research and clinical trials. No therapy has yet been developed that alone can cure or slow the growth of glioma effectively, but several are in use for complementary treatment in humans. The use of dogs in glioma research and clinical trials can hopefully provide novel findings on how to proceed with more effective therapies and earlier diagnosis. This is a review of the genetics behind glioma and how this information can be used in research for better treatment

ADP-ribosylating adjuvant reveals plasticity in cDC1 cells that drive mucosal Th17 cell development and protection against influenza virus infection

Migratory dendritic cells expressing CD103 are the targets for mucosal vaccines. These belong to either of two lineage-restricted subsets, cDC1 or cDC2 cells, which have been linked to priming of functionally distinct CD4 T cells. However, recent studies have identified plasticity in cDC2 cells with overlapping functions with cDC1 cells, while the converse has not been reported. We genetically engineered a vaccine adjuvant platform that targeted the cholera toxin A1 (CTA1) ADP-ribosylating enzyme to CD103+ cDC1 and cDC2 cells using a single-chain antibody (scFv) to CD103. Unexpectedly, intranasal immunization with the CTA1-svFcCD103 adjuvant modified cDC1 cells to effectively prime Th17 cells, a function previously limited to cDC2 cells. In fact, cDC2 cells were dispensible, while cDC1 cells, lacking in Batf3−/− mice, were critical. Following intranasal immunizations isolated cDC1 cells from mLN exclusively promoted Rorgt+ T cells and IL-17, IL-21, and IL-22 production. Strong CD8 T cell responses through antigen cross presentation by cDC1 cells were also observed. Single-cell RNAseq analysis revealed upregulation of Th17-promoting gene signatures in sorted cDC1 cells. Gene expression in isolated cDC2 cells was largely unaffected. Our finding represents a major shift of paradigm as we have documented functional plasticity in cDC1 cells