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A hexanucleotide repeat expansion (HRE) consisting of GGGGCC24+ in the C9ORF72 gene is the most common genetic cause of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). Both are fatal neurodegenerative diseases with no current approved treatments that significantly slow disease progression or extend life expectancy. Several hypotheses have emerged to explain how this HRE causes neuronal death, including C9ORF72 haploinsufficiency, sequestration of RNA-binding proteins in the nucleus, and production of dipeptide repeat proteins. In the present study we used a CRISPR/Cas9 gene-editing approach to remove the HRE from the C9ORF72 genomic locus, designing guide RNAs (gRNAs) flanking the HRE, and delivered Cas9 and gRNAs using adeno-associated virus serotype 9 (AAV9) vectors. Here, we demonstrate successful excision of the HRE in C9ORF72 in primary cortical neurons and brains of three mouse models containing the C9ORF72 expanded HRE (ranging from 500-600 repeats) as well as in patient-derived iPSC motor neurons and brain organoids (450 repeats). This resulted in a reduction of RNA foci, poly-dipeptides and haploinsufficiency, the major hallmarks of C9-ALS/FTD, making this an extremely attractive therapeutic approach to these diseases.

Long read sequencing technologies have the potential to accurately detect and phase variation in genomic regions that are difficult to fully characterize with conventional short read methods. These difficult to sequence regions include several clinically relevant genes with highly homologous pseudogenes, many of which are prone to gene conversions or other types of complex structural rearrangements. We present PB-Motif, a new method for identifying rearrangements between two highly homologous genomic regions using PacBio long reads. PB-Motif leverages clustering and filtering techniques to efficiently report rearrangements in the presence of sequencing errors and other systematic artifacts. Supporting reads for each high-confidence rearrangement can then be used for copy number estimation and phased variant calling. First, we demonstrate PB-Motif's accuracy with simulated sequence rearrangements of PMS2 and its pseudogene PMS2CL using simulated reads sweeping over a range of sequencing error rates. We then apply PB-Motif to 26 clinical samples, characterizing CYP21A2 and its pseudogene CYP21A1P as part of a diagnostic assay for congenital adrenal hyperplasia. We successfully identify damaging variation and patient carrier status concordant with clinical diagnosis obtained from multiplex ligation-dependent amplification (MLPA) and Sanger sequencing. The source code is available at: github.com/zstephens/pb-motif.

The integration of viruses into the human genome is known to be associated with tumorigenesis in many cancers, but the accurate detection of integration breakpoints from short read sequencing data is made difficult by human-viral homologies, viral genome heterogeneity, coverage limitations, and other factors. To address this, we present Exogene, a sensitive and efficient workflow for detecting viral integrations from paired-end next generation sequencing data. Exogene’s read filtering and breakpoint detection strategies yield integration coordinates that are highly concordant with those found in long read validation sets. We demonstrate this concordance across 6 TCGA Hepatocellular carcinoma (HCC) tumor samples, identifying integrations of hepatitis B virus that are validated by long reads. Additionally, we applied Exogene to targeted capture data from 426 previously studied HCC samples, achieving 98.9% concordance with existing methods and identifying 238 high-confidence integrations that were not previously reported. Exogene is applicable to multiple types of paired-end sequence data, including genome, exome, RNA-Seq or targeted capture.

Background With applications in cancer, drug metabolism, and disease etiology, understanding structural variation in the human genome is critical in advancing the thrusts of individualized medicine. However, structural variants (SVs) remain challenging to detect with high sensitivity using short read sequencing technologies. This problem is exacerbated when considering complex SVs comprised of multiple overlapping or nested rearrangements. Longer reads, such as those from Pacific Biosciences platforms, often span multiple breakpoints of such events, and thus provide a way to unravel small-scale complexities in SVs with higher confidence. Results We present CORGi (COmplex Rearrangement detection with Graph-search), a method for the detection and visualization of complex local genomic rearrangements. This method leverages the ability of long reads to span multiple breakpoints to untangle SVs that appear very complicated with respect to a reference genome. We validated our approach against both simulated long reads, and real data from two long read sequencing technologies. We demonstrate the ability of our method to identify breakpoints inserted in synthetic data with high accuracy, and the ability to detect and plot SVs from NA12878 germline, achieving 88.4% concordance between the two sets of sequence data. The patterns of complexity we find in many NA12878 SVs match known mechanisms associated with DNA replication and structural variant formation, and highlight the ability of our method to automatically label complex SVs with an intuitive combination of adjacent or overlapping reference transformations. Conclusions CORGi is a method for interrogating genomic regions suspected to contain local rearrangements using long reads. Using pairwise alignments and graph search CORGi produces labels and visualizations for local SVs of arbitrary complexity.

A composite material consisting of TiO2 nanotubes (NT) with WO3 electrodeposited on its surface has been fabricated, detached from its Ti substrate, and attached to a fluorine-doped tin oxide (FTO) film on glass for application to electrochromic (EC) reactions. Several adhesion layers were tested, finding that a paste of TiO2 made from commercially available TiO2 nanoparticles creates an interface for the TiO2 NT film to attach to the FTO glass, which is conductive and does not cause solution-phase ions in an electrolyte to bind irreversibly with the material. The effect of NT length and WO3 concentration on the EC performance were studied. The composite WO3/TiO2 nanostructures showed higher ion storage capacity, better stability, enhanced EC contrast, and longer memory time compared with the pure WO3 and TiO2 materials.

In this paper, we present a graph search approach for identifying arbitrarily complex structural genomic variation. Our method leverages the ability of long reads (e.g. from Pacific Biosciences platforms) to span multiple breakpoints of complicated local rearrangements, allowing us to resolve small-scale complexities that may be overlooked by other tools. We applied our method to a subset of NA12878 germline events using two long read datasets and demonstrate, with a concordance rate of 88.4% between the two sets, an increased ability to denote complex events over baseline calls from short read data. In a majority of the regions analyzed we detected small complexities that flank the breakpoints of larger events, including small insertions, inversions, and duplicated sequences. These patterns of complexity match known mechanisms associated with DNA replication and structural variant formation, and showcase the ability of our approach to efficiently unravel such events. Our method automatically classifies complex structural variant calls as a combination of nested or adjacent reference transformations, allowing users to identify specific structure types of interest. Additionally, an output report is generated for each event with interactive visual representations of the rearrangement.

As the pace of implementing personalized medicine concepts increases, high-throughput variant calling on hundreds of individual genomes per day is a reality that will likely be faced by sequencing facilities across the country in the near future. While the scientific best practices for human variant calling workflows have been well defined, they also pose serious computational challenges at this high scale. Therefore, efforts in both academia and the private sector have focused on developing alternative workflows that may substantially reduce the computational cost per individual genome. iSAAC is an "ultra-fast" variant calling workflow, designed by Illumina, Inc, and is claimed to be six times faster than BWA-GATK, with comparable sensitivity and specificity. This report is an independent review of iSAAC, mainly focused on the accuracy of variant calls. We note that iSAAC is indeed quite fast, and provide some benchmarks on a few hardware architectures. The overall conclusion from our analysis is that the iSAAC workflow has undergone substantial improvement from version 01.14.11.27 to iSAAC_2.0. The call accuracy is especially high on NA12878, however exomes and genomic data outside the Platinum sets tend to have a high fraction of false positive calls. We did not manage to reproduce the 99% sensitivity and specificity reported in the Illumina whitepaper, however that might be improved with further tweaking of the options. This report includes the information about some of the command-line parameters and documentation.

Genomics is a Big Data science and is going to get much bigger, very soon, but it is not known whether the needs of genomics will exceed other Big Data domains. Projecting to the year 2025, we compared genomics with three other major generators of Big Data: astronomy, YouTube, and Twitter. Our estimates show that genomics is a “four-headed beast”—it is either on par with or the most demanding of the domains analyzed here in terms of data acquisition, storage, distribution, and analysis. We discuss aspects of new technologies that will need to be developed to rise up and meet the computational challenges that genomics poses for the near future. Now is the time for concerted, community-wide planning for the “genomical” challenges of the next decade., This perspective considers the growth of genomics over the next ten years and assesses the computational needs that we will face relative to other "Big Data" activities such as astronomy, YouTube, and Twitter.

We explore the performance cost of virtualisation for the fast growing application domain of genomics. Traditionally, scientific applications have been considered too high-performance to pay the performance cost of virtualisation. However, as the demand for computing power for genomics is ever-increasing, the cloud can become an attractive way to meet the scaling challenge presented by Next-Generation Sequencing (NGS). We seek to explore the feasibility of running an NGS pipeline in a cloud, and in doing so consider two prevalent short-read sequence alignment programs, BWA and Novoalign. We executed those applications in three separate open-source system virtualisation solutions: the KVM hypervisor, the Xen para-virtualised hypervisor, and Linux Containers. We compare the runtime in each environment against the runtime of the same system without virtualisation and measure the relative performance of each hypervisor. We investigate and reduce as much as possible any overhead, presenting tuning suggestions for cloud implementers and users. Overall, we find that the overhead introduced by virtualisation can be reduced to low single-digit percentages, a cost we believe to be more than acceptable, especially given that two of the three solutions, Xen and Containers, exhibit near-zero overhead.

The prospect of simpler infrastructure management and affordability has garnered interest in cloud computing from bioinformaticians. However, the performance cost of adopting such an infrastructure model for bioinformatics is not fully known. In an effort to help quantify this performance cost, we ran synthetic benchmarks and measured the runtimes of two short-read alignment applications on cloud-like virtualization environments. The environments were implemented utilizing the KVM hypervisor, the Xen hypervisor, and Linux Containers. We compare the runtime in each environment against a physical server and offer discussion and insights. Though the applications perform similar operations, we observe that their performance characteristics differ, as do their performance in the different virtualized environments. We attribute the differences to the way that these programs utilize system resources. We find that the more CPU-bound Novoalign is much less sensitive to virtualization environments than BWA is, and has near-physical server performance even when virtualized. Additionally, we find that static CPU pinning can improve performance, and we demonstrate that Linux Containers offer performance comparable to that of a physical server.