Search

Magnetic Resonance Imaging (MRI) is a pivotal clinical diagnostic tool, yet its extended scanning times often compromise patient comfort and image quality, especially in volumetric, temporal and quantitative scans. This review elucidates recent advances in MRI acceleration via data and physics-driven models, leveraging techniques from algorithm unrolling models, enhancement-based methods, and plug-and-play models to the emerging full spectrum of generative model-based methods. We also explore the synergistic integration of data models with physics-based insights, encompassing the advancements in multi-coil hardware accelerations like parallel imaging and simultaneous multi-slice imaging, and the optimization of sampling patterns. We then focus on domain-specific challenges and opportunities, including image redundancy exploitation, image integrity, evaluation metrics, data heterogeneity, and model generalization. This work also discusses potential solutions and future research directions, with an emphasis on the role of data harmonization and federated learning for further improving the general applicability and performance of these methods in MRI reconstruction.

AirLift is the first read remapping tool that enables users to quickly and comprehensively map a read set, that had been previously mapped to one reference genome, to another similar reference. Users can then quickly run a downstream analysis of read sets for each latest reference release. Compared to the state-of-the-art method for remapping reads (i.e., full mapping), AirLift reduces the overall execution time to remap read sets between two reference genome versions by up to 27.4×. We validate our remapping results with GATK and find that AirLift provides high accuracy in identifying ground truth SNP/INDEL variants.

Accelerated MRI protocols routinely involve a predefined sampling pattern that undersamples the k-space. Finding an optimal pattern can enhance the reconstruction quality, however this optimization is a challenging task. To address this challenge, we introduce a novel deep learning framework, AutoSamp, based on variational information maximization that enables joint optimization of sampling pattern and reconstruction of MRI scans. We represent the encoder as a non-uniform Fast Fourier Transform that allows continuous optimization of k-space sample locations on a non-Cartesian plane, and the decoder as a deep reconstruction network. Experiments on public 3D acquired MRI datasets show improved reconstruction quality of the proposed AutoSamp method over the prevailing variable density and variable density Poisson disc sampling for both compressed sensing and deep learning reconstructions. We demonstrate that our data-driven sampling optimization method achieves 4.4dB, 2.0dB, 0.75dB, 0.7dB PSNR improvements over reconstruction with Poisson Disc masks for acceleration factors of R = 5, 10, 15, 25, respectively. Prospectively accelerated acquisitions with 3D FSE sequences using our optimized sampling patterns exhibit improved image quality and sharpness. Furthermore, we analyze the characteristics of the learned sampling patterns with respect to changes in acceleration factor, measurement noise, underlying anatomy, and coil sensitivities. We show that all these factors contribute to the optimization result by affecting the sampling density, k-space coverage and point spread functions of the learned sampling patterns.

Rift Valley fever (RVF) is a mosquito-borne zoonotic viral disease endemic to Africa and the Middle East. Live-attenuated RVF vaccines have been studied for both veterinary and human use due to their strong immunogenicity and cost-effective manufacturing. The live-attenuated MP-12 vaccine has been conditionally approved for veterinary use in the U.S.A., and next-generation live-attenuated RVF vaccine candidates are being actively researched. Assessing the virulence phenotype of vaccine seeds or lots is crucial for managing vaccine safety. Previously, preweaning 19-day-old outbred CD1 mice have been used to evaluate the MP-12 strain. This study aimed to characterize the relative virulence of three live-attenuated RVF vaccine strains in 19-day-old inbred C57BL/6 mice: the recombinant MP-12 (rMP-12), the RVax-1, and the ∆NSs-∆NSm-rZH501 strains. Although this mouse model did not show dose-dependent pathogenesis, mice that succumbed to the infection exhibited distinct brain pathology. Mice infected with ∆NSs-∆NSm-rZH501 showed an infiltration of inflammatory cells associated with infected neurons, and focal lesions formed around virus-infected cells. In contrast, mice infected with rMP-12 or RVax-1 showed a minimal association of inflammatory cells in the brain, yet the virus spread diffusely. The preweaning model is likely useful for evaluating host responses to attenuated RVFV strains, although further refinement may be necessary to quantitate the virulence among different RVFV strains or vaccine lots.

Antagonistic relationships such as host-virus interactions potentially lead to rapid evolution and specificity in interactions. The Orsay virus is so far the only horizontal virus naturally infecting the nematode C. elegans. In contrast, several related RNA viruses infect its congener C. briggsae, including Santeuil (SANTV) and Le Blanc (LEBV) viruses. Here we focus on the host's intraspecific variation in sensitivity to these two intestinal viruses. Many temperate-origin C. briggsae strains, including JU1264 and JU1498, are sensitive to both, while many tropical strains, such as AF16, are resistant to both. Interestingly, some C. briggsae strains exhibit a specific resistance, such as the HK104 strain, specifically resistant to LEBV. The viral sensitivity pattern matches the strains' geographic and genomic relationships. The heavily infected strains mount a seemingly normal small RNA response that is insufficient to suppress viral infection, while the resistant strains show no small RNA response, suggesting an early block in viral entry or replication. We use a genetic approach from the host side to map genomic regions participating in viral resistance polymorphisms. Using Advanced Intercrossed Recombinant Inbred Lines (RILs) between virus-resistant AF16 and SANTV-sensitive HK104, we detect Quantitative Trait Loci (QTLs) on chromosomes IV and III. Building RILs between virus-sensitive JU1498 and LEBV-resistant HK104 followed by bulk segregant analysis, we identify a chromosome II QTL. In both cases, further introgressions of the regions confirmed the QTLs. This diversity provides an avenue for studying virus entry, replication, and exit mechanisms, as well as host-virus specificity and the host response to a specific virus infection., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Alkan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Heartland virus (HRTV) is an emerging tick-borne bandavirus that causes a febrile illness of varying severity in humans, with cases reported in eastern and midwestern regions of the United States. No vaccines or approved therapies are available to prevent or treat HRTV disease. Here, we describe the genetic changes, natural history of disease, and pathogenesis of a mouse-adapted HRTV (MA-HRTV) that is uniformly lethal in 7- to 8-week-old AG129 mice at low challenge doses. We used this model to assess the efficacy of the ribonucleoside analog, 4'-fluorouridine (EIDD-2749), and showed that once-daily oral treatment with 3 mg/kg of drug, initiated after the onset of disease, protects mice against lethal MA-HRTV challenge and reduces viral loads in blood and tissues. Our findings provide insights into HRTV virulence and pathogenesis and support further development of EIDD-2749 as a therapeutic intervention for HRTV disease., Importance: More than 60 cases of HRTV disease spanning 14 states have been reported to the United States Centers for Disease Control and Prevention. The expanding range of the Lone Star tick that transmits HRTV, the growing population of at-risk persons living in geographic areas where the tick is abundant, and the lack of antiviral treatments or vaccines raise significant public health concerns. Here, we report the development of a new small-animal model of lethal HRTV disease to gain insight into HRTV pathogenesis and the application of this model for the preclinical development of a promising new antiviral drug candidate, EIDD-2749. Our findings shed light on how the virus causes disease and support the continued development of EIDD-2749 as a therapeutic for severe cases of HRTV infection., Competing Interests: G.R.B. and G.R.P. are coinventors on patent WO 2019/1736002 covering the composition of matter and use of EIDD-2749 and its analogs as an antiviral treatment. All other authors declare that they have no competing interests.

Non-silent single nucleotide genetic variants, like nonsense changes and insertion-deletion variants, that affect protein function and length substantially are prevalent and are frequently misclassified. The low sensitivity and specificity of existing variant effect predictors for nonsense and indel variations restrict their use in clinical applications. We propose the Pathogenic Mutation Prediction (PMPred) method to predict the pathogenicity of single nucleotide variations, which impair protein function by prematurely terminating a protein's elongation during its synthesis. The prediction starts by monitoring functional effects (Gene Ontology annotation changes) of the change in sequence, using an existing ensemble machine learning model (UniGOPred). This, in turn, reveals the mutations that significantly deviate functionally from the wild-type sequence. We have identified novel harmful mutations in patient data and present them as motivating case studies. We also show that our method has increased sensitivity and specificity compared to state-of-the-art, especially in single nucleotide variations that produce large functional changes in the final protein. As further validation, we have done a comparative docking study on such a variation that is misclassified by existing methods and, using the altered binding affinities, show how PMPred can correctly predict the pathogenicity when other tools miss it. PMPred is freely accessible as a web service at https://pmpred.kansil.org/, and the related code is available at https://github.com/kansil/PMPred., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Rift Valley fever is a zoonotic viral disease transmitted by mosquitoes, impacting both humans and livestock. Currently, there are no approved vaccines or antiviral treatments for humans. This study aimed to evaluate the in vitro efficacy of chemical compounds targeting the Gc fusion mechanism. These compounds were identified through virtual screening of millions of commercially available small molecules using a structure-based artificial intelligence bioactivity predictor. In our experiments, a pretreatment with small molecule compounds revealed that 3 out of 94 selected compounds effectively inhibited the replication of the Rift Valley fever virus MP-12 strain in Vero cells. As anticipated, these compounds did not impede viral RNA replication when administered three hours after infection. However, significant inhibition of viral RNA replication occurred upon viral entry when cells were pretreated with these small molecules. Furthermore, these compounds exhibited significant inhibition against Arumowot virus, another phlebovirus, while showing no antiviral effects on tick-borne bandaviruses. Our study validates AI-based virtual high throughput screening as a rational approach for identifying effective antiviral candidates for Rift Valley fever virus and other bunyaviruses.

Copy number variants (CNV) are shown to contribute to the etiology of several genetic disorders. Accurate detection of CNVs on whole exome sequencing (WES) data has been a long sought-after goal for use in clinics. This was not possible despite recent improvements in performance because algorithms mostly suffer from low precision and even lower recall on expert-curated gold standard call sets. Here, we present a deep learning-based somatic and germline CNV caller for WES data, named ECOLE. Based on a variant of the transformer architecture, the model learns to call CNVs per exon, using high-confidence calls made on matched WGS samples. We further train and fine-tune the model with a small set of expert calls via transfer learning. We show that ECOLE achieves high performance on human expert labelled data for the first time with 68.7% precision and 49.6% recall. This corresponds to precision and recall improvements of 18.7% and 30.8% over the next best-performing methods, respectively. We also show that the same fine-tuning strategy using tumor samples enables ECOLE to detect RT-qPCR-validated variations in bladder cancer samples without the need for a control sample. ECOLE is available at https://github.com/ciceklab/ECOLE ., (© 2024. The Author(s).)

The NSs protein is a major virulence factor in bunyaviruses, crucial for viral pathogenesis. However, assessing NSs protein function can be challenging due to its inhibition of cellular RNA polymerase II, impacting NSs protein expression from plasmid DNA. The recombinant Rift Valley fever virus (RVFV) MP-12 strain (rMP-12), a highly attenuated vaccine strain, can be safely manipulated under biosafety level 2 conditions. Leveraging a reverse genetics system, we can engineer rMP-12 variants expressing heterologous NSs genes, enabling functional testing in cultured cells. Human macrophages hold a central role in viral pathogenesis, making them an ideal model for assessing NSs protein functions. Consequently, we can comprehensively compare and analyze the functional significance of various NSs proteins in human macrophages using rMP-12 NSs variants. In this chapter, we provide a detailed overview of the preparation process for rMP-12 NSs variants and introduce two distinct human macrophage models: THP-1 cells and primary macrophages. This research framework promises valuable insights into the virulence mechanisms of RVFV and other bunyaviruses and the potential for vaccine development., (© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Rift Valley fever (RVF) is a zoonotic viral disease transmitted by mosquitoes and causes abortion storms, fetal malformations, and newborn animal deaths in livestock ruminants. In humans, RVF can manifest as hemorrhagic fever, encephalitis, or retinitis. Outbreaks of RVF have been occurring in Africa since the early 20th century and continue to pose a threat to both humans and animals in various regions such as Africa, Madagascar, the Comoros, Saudi Arabia, and Yemen. The development of RVF vaccines is crucial in preventing mortality and morbidity and reducing the spread of the virus. While several veterinary vaccines have been licensed in endemic countries, there are currently no licensed RVF vaccines for human use. This review provides an overview of the existing RVF vaccines, as well as potential candidates for future studies on RVF vaccine development, including next-generation vaccines that show promise in combating the disease in both humans and animals., (© 2023. The Author(s).)

Motivation: Identifying and prioritizing disease-related proteins is an important scientific problem to develop proper treatments. Network science has become an important discipline to prioritize such proteins. Multiple sclerosis, an autoimmune disease for which there is still no cure, is characterized by a damaging process called demyelination. Demyelination is the destruction of myelin, a structure facilitating fast transmission of neuron impulses, and oligodendrocytes, the cells producing myelin, by immune cells. Identifying the proteins that have special features on the network formed by the proteins of oligodendrocyte and immune cells can reveal useful information about the disease., Results: We investigated the most significant protein pairs that we define as bridges among the proteins providing the interaction between the two cells in demyelination, in the networks formed by the oligodendrocyte and each type of two immune cells (i.e. macrophage and T-cell) using network analysis techniques and integer programming. The reason, we investigated these specialized hubs was that a problem related to these proteins might impose a bigger damage in the system. We showed that 61%-100% of the proteins our model detected, depending on parameterization, have already been associated with multiple sclerosis. We further observed the mRNA expression levels of several proteins we prioritized significantly decreased in human peripheral blood mononuclear cells of multiple sclerosis patients. We therefore present a model, BriFin, which can be used for analyzing processes where interactions of two cell types play an important role., Availability and Implementation: BriFin is available at https://github.com/BilkentCompGen/brifin., (© The Author(s) 2023. Published by Oxford University Press.)

Cardiac magnetic resonance (CMR) is an essential clinical tool for the assessment of cardiovascular disease. Deep learning (DL) has recently revolutionized the field through image reconstruction techniques that allow unprecedented data undersampling rates. These fast acquisitions have the potential to considerably impact the diagnosis and treatment of cardiovascular disease. Herein, we provide a comprehensive review of DL-based reconstruction methods for CMR. We place special emphasis on state-of-the-art unrolled networks, which are heavily based on a conventional image reconstruction framework. We review the main DL-based methods and connect them to the relevant conventional reconstruction theory. Next, we review several methods developed to tackle specific challenges that arise from the characteristics of CMR data. Then, we focus on DL-based methods developed for specific CMR applications, including flow imaging, late gadolinium enhancement, and quantitative tissue characterization. Finally, we discuss the pitfalls and future outlook of DL-based reconstructions in CMR, focusing on the robustness, interpretability, clinical deployment, and potential for new methods.

Generating the hash values of short subsequences, called seeds, enables quickly identifying similarities between genomic sequences by matching seeds with a single lookup of their hash values. However, these hash values can be used only for finding exact-matching seeds as the conventional hashing methods assign distinct hash values for different seeds, including highly similar seeds. Finding only exact-matching seeds causes either (i) increasing the use of the costly sequence alignment or (ii) limited sensitivity. We introduce BLEND , the first efficient and accurate mechanism that can identify both exact-matching and highly similar seeds with a single lookup of their hash values, called fuzzy seed matches. BLEND (i) utilizes a technique called SimHash, that can generate the same hash value for similar sets, and (ii) provides the proper mechanisms for using seeds as sets with the SimHash technique to find fuzzy seed matches efficiently. We show the benefits of BLEND when used in read overlapping and read mapping. For read overlapping, BLEND is faster by 2.4×-83.9× (on average 19.3×), has a lower memory footprint by 0.9×-14.1× (on average 3.8×), and finds higher quality overlaps leading to accurate de novo assemblies than the state-of-the-art tool, minimap2. For read mapping, BLEND is faster by 0.8×-4.1× (on average 1.7×) than minimap2. Source code is available at https://github.com/CMU-SAFARI/BLEND., (© The Author(s) 2023. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics.)

To date, ancient genome analyses have been largely confined to the study of single nucleotide polymorphisms (SNPs). Copy number variants (CNVs) are a major contributor of disease and of evolutionary adaptation, but identifying CNVs in ancient shotgun-sequenced genomes is hampered by typical low genome coverage (<1×) and short fragments (<80 bps), precluding standard CNV detection software to be effectively applied to ancient genomes. Here we present CONGA, tailored for genotyping CNVs at low coverage. Simulations and down-sampling experiments suggest that CONGA can genotype deletions >1 kbps with F-scores >0.75 at ≥1×, and distinguish between heterozygous and homozygous states. We used CONGA to genotype 10,002 outgroup-ascertained deletions across a heterogenous set of 71 ancient human genomes spanning the last 50,000 years, produced using variable experimental protocols. A fraction of these (21/71) display divergent deletion profiles unrelated to their population origin, but attributable to technical factors such as coverage and read length. The majority of the sample (50/71), despite originating from nine different laboratories and having coverages ranging from 0.44×-26× (median 4×) and average read lengths 52-121 bps (median 69), exhibit coherent deletion frequencies. Across these 50 genomes, inter-individual genetic diversity measured using SNPs and CONGA-genotyped deletions are highly correlated. CONGA-genotyped deletions also display purifying selection signatures, as expected. CONGA thus paves the way for systematic CNV analyses in ancient genomes, despite the technical challenges posed by low and variable genome coverage., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2022 Söylev et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Motivation: A genome read dataset can be quickly and efficiently remapped from one reference to another similar reference (e.g., between two reference versions or two similar species) using a variety of tools, e.g., the commonly used CrossMap tool. With the explosion of available genomic datasets and references, high-performance remapping tools will be even more important for keeping up with the computational demands of genome assembly and analysis., Results: We provide FastRemap, a fast and efficient tool for remapping reads between genome assemblies. FastRemap provides up to a 7.82× speedup (6.47×, on average) and uses as low as 61.7% (80.7%, on average) of the peak memory consumption compared to the state-of-the-art remapping tool, CrossMap., Availability and Implementation: FastRemap is written in C++. Source code and user manual are freely available at: github.com/CMU-SAFARI/FastRemap. Docker image available at: https://hub.docker.com/r/alkanlab/fastremap. Also available in Bioconda at: https://anaconda.org/bioconda/fastremap-bio., (© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Rift Valley fever (RVF) is a mosquito-borne zoonosis endemic to Africa and the Arabian Peninsula, which causes large outbreaks among humans and ruminants. Single dose vaccinations using live-attenuated RVF virus (RVFV) support effective prevention of viral spread in endemic countries. Due to the segmented nature of RVFV genomic RNA, segments of vaccine strain-derived genomic RNA could be incorporated into wild-type RVFV within co-infected mosquitoes or animals. Rationally designed vaccine candidate RVax-1 displays protective epitopes fully identical to the previously characterized MP-12 vaccine. Additionally, all genome segments of RVax-1 contribute to the attenuation phenotype, which prevents the formation of pathogenic reassortant strains. This study demonstrated that RVax-1 cannot replicate efficiently in orally fed Aedes aegypti mosquitoes, while retaining strong immunogenicity and protective efficacy in an inbred mouse model, which were indistinguishable from the MP-12 vaccine. These findings support further development of RVax-1 as the next generation MP-12-based vaccine for prevention of Rift Valley fever in humans and animals., (© 2022. The Author(s).)

Purpose: Deep learning (DL) based reconstruction using unrolled neural networks has shown great potential in accelerating MRI. However, one of the major drawbacks is the loss of high-frequency details and textures in the output. The purpose of the study is to propose a novel refinement method that uses null-space kernel to refine k-space and improve blurred image details and textures., Methods: The proposed method constrains the output of the DL to comply to the linear neighborhood relationship calibrated in the auto-calibration lines. To demonstrate efficacy, we tested our refinement method on the DL reconstruction under a variety of conditions (i.e., dataset, unrolled neural networks, and under-sampling scheme). Specifically, the method was tested on three large-scale public datasets (knee and brain) from fastMRI's multi-coil track., Results: The proposed scheme visually reduces the structural error in the k-space domain, enhance the homogeneity of the k-space intensity. Consequently, reconstructed image shows sharper images with enhanced details and textures. The proposed method is also successful in improving high-frequency image details (SSIM, GMSD) without sacrificing overall image error (PSNR)., Conclusion: Our findings imply that refining DL output using the proposed method may generally improve DL reconstruction as tested with various large-scale dataset and networks., (© 2022 International Society for Magnetic Resonance in Medicine.)

Accurate and efficient detection of copy number variants (CNVs) is of critical importance owing to their significant association with complex genetic diseases. Although algorithms that use whole-genome sequencing (WGS) data provide stable results with mostly valid statistical assumptions, copy number detection on whole-exome sequencing (WES) data shows comparatively lower accuracy. This is unfortunate as WES data are cost-efficient, compact, and relatively ubiquitous. The bottleneck is primarily due to the noncontiguous nature of the targeted capture: biases in targeted genomic hybridization, GC content, targeting probes, and sample batching during sequencing. Here, we present a novel deep learning model, DECoNT , which uses the matched WES and WGS data, and learns to correct the copy number variations reported by any off-the-shelf WES-based germline CNV caller. We train DECoNT on the 1000 Genomes Project data, and we show that we can efficiently triple the duplication call precision and double the deletion call precision of the state-of-the-art algorithms. We also show that our model consistently improves the performance independent of (1) sequencing technology, (2) exome capture kit, and (3) CNV caller. Using DECoNT as a universal exome CNV call polisher has the potential to improve the reliability of germline CNV detection on WES data sets., (© 2022 Özden et al.; Published by Cold Spring Harbor Laboratory Press.)

Motivation: The increasing availability of high-quality genome assemblies raised interest in the characterization of genomic architecture. Major architectural elements, such as common repeats and segmental duplications (SDs), increase genome plasticity that stimulates further evolution by changing the genomic structure and inventing new genes. Optimal computation of SDs within a genome requires quadratic-time local alignment algorithms that are impractical due to the size of most genomes. Additionally, to perform evolutionary analysis, one needs to characterize SDs in multiple genomes and find relations between those SDs and unique (non-duplicated) segments in other genomes. A naïve approach consisting of multiple sequence alignment would make the optimal solution to this problem even more impractical. Thus there is a need for fast and accurate algorithms to characterize SD structure in multiple genome assemblies to better understand the evolutionary forces that shaped the genomes of today., Results: Here we introduce a new approach, BISER, to quickly detect SDs in multiple genomes and identify elementary SDs and core duplicons that drive the formation of such SDs. BISER improves earlier tools by (i) scaling the detection of SDs with low homology to multiple genomes while introducing further 7-33[Formula: see text] speed-ups over the existing tools, and by (ii) characterizing elementary SDs and detecting core duplicons to help trace the evolutionary history of duplications to as far as 300 million years., Availability and Implementation: BISER is implemented in Seq programming language and is publicly available at https://github.com/0xTCG/biser ., (© 2022. The Author(s).)

Machine learning (ML) and Artificial intelligence (AI) has the potential to dramatically improve radiology practice at multiple stages of the imaging pipeline. Most of the attention has been garnered by applications focused on improving the end of the pipeline: image interpretation. However, this article reviews how AI/ML can be applied to improve upstream components of the imaging pipeline, including exam modality selection, hardware design, exam protocol selection, data acquisition, image reconstruction, and image processing. A breadth of applications and their potential for impact is shown across multiple imaging modalities, including ultrasound, computed tomography, and MRI., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Aligning sequencing reads onto a reference is an essential step of the majority of genomic analysis pipelines. Computational algorithms for read alignment have evolved in accordance with technological advances, leading to today's diverse array of alignment methods. We provide a systematic survey of algorithmic foundations and methodologies across 107 alignment methods, for both short and long reads. We provide a rigorous experimental evaluation of 11 read aligners to demonstrate the effect of these underlying algorithms on speed and efficiency of read alignment. We discuss how general alignment algorithms have been tailored to the specific needs of various domains in biology., (© 2021. The Author(s).)

Motivation: We introduce SneakySnake, a highly parallel and highly accurate pre-alignment filter that remarkably reduces the need for computationally costly sequence alignment. The key idea of SneakySnake is to reduce the approximate string matching (ASM) problem to the single net routing (SNR) problem in VLSI chip layout. In the SNR problem, we are interested in finding the optimal path that connects two terminals with the least routing cost on a special grid layout that contains obstacles. The SneakySnake algorithm quickly solves the SNR problem and uses the found optimal path to decide whether or not performing sequence alignment is necessary. Reducing the ASM problem into SNR also makes SneakySnake efficient to implement on CPUs, GPUs and FPGAs., Results: SneakySnake significantly improves the accuracy of pre-alignment filtering by up to four orders of magnitude compared to the state-of-the-art pre-alignment filters, Shouji, GateKeeper and SHD. For short sequences, SneakySnake accelerates Edlib (state-of-the-art implementation of Myers's bit-vector algorithm) and Parasail (state-of-the-art sequence aligner with a configurable scoring function), by up to 37.7× and 43.9× (>12× on average), respectively, with its CPU implementation, and by up to 413× and 689× (>400× on average), respectively, with FPGA and GPU acceleration. For long sequences, the CPU implementation of SneakySnake accelerates Parasail and KSW2 (sequence aligner of minimap2) by up to 979× (276.9× on average) and 91.7× (31.7× on average), respectively. As SneakySnake does not replace sequence alignment, users can still obtain all capabilities (e.g. configurable scoring functions) of the aligner of their choice, unlike existing acceleration efforts that sacrifice some aligner capabilities., Availabilityand Implementation: https://github.com/CMU-SAFARI/SneakySnake., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

New technologies and analysis methods are enabling genomic structural variants (SVs) to be detected with ever-increasing accuracy, resolution and comprehensiveness. To help translate these methods to routine research and clinical practice, we developed a sequence-resolved benchmark set for identification of both false-negative and false-positive germline large insertions and deletions. To create this benchmark for a broadly consented son in a Personal Genome Project trio with broadly available cells and DNA, the Genome in a Bottle Consortium integrated 19 sequence-resolved variant calling methods from diverse technologies. The final benchmark set contains 12,745 isolated, sequence-resolved insertion (7,281) and deletion (5,464) calls ≥50 base pairs (bp). The Tier 1 benchmark regions, for which any extra calls are putative false positives, cover 2.51 Gbp and 5,262 insertions and 4,095 deletions supported by ≥1 diploid assembly. We demonstrate that the benchmark set reliably identifies false negatives and false positives in high-quality SV callsets from short-, linked- and long-read sequencing and optical mapping.

Background and Objective: Periodontitis is the chronic destructive disease of the periodontium, which causes severe inflammation in the tissues. Cinnamic acid as an unsaturated carboxylic acid might prevent inflammation and periodontal destruction. The present study aimed to evaluate the effects of cinnamic acid in two different forms as free cinnamic acid and cinnamic acid liposome on experimental periodontitis in Wistar rats., Methods: Thirty-two female rats were used in the present study. Four main groups were created as follows: C: control group; P: periodontitis group; C-P: free cinnamic acid-administered periodontitis group; and CL-P: cinnamic acid liposome applied group. Periodontitis was induced via ligating 4-0 silk sutures around lower first molar teeth on both right and left mandibles. The study duration was 30 days, and the ligatures were removed from half of the rats in the periodontitis-induced groups. The other half carried the ligatures throughout 30 days, and all rats were euthanized at 30th day. Mandibles were removed and evaluated via stereomicroscope and underwent histological procedures. Inflammatory cell counts, osteoblast, and osteoclast cell counts were determined in hematoxylin-eosin-stained slides, and peroxisome proliferator-activated receptor (PPAR)-γ, cyclooxygenase (COX)-2, receptor activator of nuclear factor κ-B (RANKL), and osteoprotegerin (OPG) expressions were evaluated by immunohistochemistry., Results: Control group had the lowest bone loss, and periodontitis group which kept ligatures had the highest bone loss compared to the other groups. Ligature removal provided significant improvement in bone measurements. Cinnamic acid groups also showed lower bone loss compared to the periodontitis group. The inflammatory cell and osteoclast counts were also higher in the periodontitis group, and both applications of cinnamic acid decreased these values. Osteoblast cells were the lowest in the periodontitis group, and cinnamic acid increased these counts. PPAR-γ and COX-2 levels were higher in the periodontitis group, and cinnamic acid decreased these levels but not to a significant level except for the cinnamic acid liposome ligature removal group, which had significantly lower values in the PPAR-γ and COX-2. OPG levels were lower in the periodontitis group compared to the other groups. Cinnamic acid significantly decreased RANKL and increased OPG levels., Conclusion: Periodontitis caused increased inflammation and bone destruction accompanied by increased PPAR-γ, COX-2, and RANKL levels and osteoclast counts. Cinnamic acid decreased osteoclast counts and inflammation and increased osteoblast counts and OPG expression in the present animal model of periodontitis., (© 2020 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

Motivation: Third-generation sequencing technologies can sequence long reads that contain as many as 2 million base pairs. These long reads are used to construct an assembly (i.e. the subject's genome), which is further used in downstream genome analysis. Unfortunately, third-generation sequencing technologies have high sequencing error rates and a large proportion of base pairs in these long reads is incorrectly identified. These errors propagate to the assembly and affect the accuracy of genome analysis. Assembly polishing algorithms minimize such error propagation by polishing or fixing errors in the assembly by using information from alignments between reads and the assembly (i.e. read-to-assembly alignment information). However, current assembly polishing algorithms can only polish an assembly using reads from either a certain sequencing technology or a small assembly. Such technology-dependency and assembly-size dependency require researchers to (i) run multiple polishing algorithms and (ii) use small chunks of a large genome to use all available readsets and polish large genomes, respectively., Results: We introduce Apollo, a universal assembly polishing algorithm that scales well to polish an assembly of any size (i.e. both large and small genomes) using reads from all sequencing technologies (i.e. second- and third-generation). Our goal is to provide a single algorithm that uses read sets from all available sequencing technologies to improve the accuracy of assembly polishing and that can polish large genomes. Apollo (i) models an assembly as a profile hidden Markov model (pHMM), (ii) uses read-to-assembly alignment to train the pHMM with the Forward-Backward algorithm and (iii) decodes the trained model with the Viterbi algorithm to produce a polished assembly. Our experiments with real readsets demonstrate that Apollo is the only algorithm that (i) uses reads from any sequencing technology within a single run and (ii) scales well to polish large assemblies without splitting the assembly into multiple parts., Availability and Implementation: Source code is available at https://github.com/CMU-SAFARI/Apollo., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Most existing methods for structural variant detection focus on discovery and genotyping of deletions, insertions, and mobile elements. Detection of balanced structural variants with no gain or loss of genomic segments, for example, inversions and translocations, is a particularly challenging task. Furthermore, there are very few algorithms to predict the insertion locus of large interspersed segmental duplications and characterize translocations. Here, we propose novel algorithms to characterize large interspersed segmental duplications, inversions, deletions, and translocations using linked-read sequencing data. We redesign our earlier algorithm, VALOR, and implement our new algorithms in a new software package, called VALOR2.

Motivation: The ability to generate massive amounts of sequencing data continues to overwhelm the processing capability of existing algorithms and compute infrastructures. In this work, we explore the use of hardware/software co-design and hardware acceleration to significantly reduce the execution time of short sequence alignment, a crucial step in analyzing sequenced genomes. We introduce Shouji, a highly parallel and accurate pre-alignment filter that remarkably reduces the need for computationally-costly dynamic programming algorithms. The first key idea of our proposed pre-alignment filter is to provide high filtering accuracy by correctly detecting all common subsequences shared between two given sequences. The second key idea is to design a hardware accelerator that adopts modern field-programmable gate array (FPGA) architectures to further boost the performance of our algorithm., Results: Shouji significantly improves the accuracy of pre-alignment filtering by up to two orders of magnitude compared to the state-of-the-art pre-alignment filters, GateKeeper and SHD. Our FPGA-based accelerator is up to three orders of magnitude faster than the equivalent CPU implementation of Shouji. Using a single FPGA chip, we benchmark the benefits of integrating Shouji with five state-of-the-art sequence aligners, designed for different computing platforms. The addition of Shouji as a pre-alignment step reduces the execution time of the five state-of-the-art sequence aligners by up to 18.8×. Shouji can be adapted for any bioinformatics pipeline that performs sequence alignment for verification. Unlike most existing methods that aim to accelerate sequence alignment, Shouji does not sacrifice any of the aligner capabilities, as it does not modify or replace the alignment step., Availability and Implementation: https://github.com/CMU-SAFARI/Shouji., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Motivation: Several algorithms have been developed that use high-throughput sequencing technology to characterize structural variations (SVs). Most of the existing approaches focus on detecting relatively simple types of SVs such as insertions, deletions and short inversions. In fact, complex SVs are of crucial importance and several have been associated with genomic disorders. To better understand the contribution of complex SVs to human disease, we need new algorithms to accurately discover and genotype such variants. Additionally, due to similar sequencing signatures, inverted duplications or gene conversion events that include inverted segmental duplications are often characterized as simple inversions, likewise, duplications and gene conversions in direct orientation may be called as simple deletions. Therefore, there is still a need for accurate algorithms to fully characterize complex SVs and thus improve calling accuracy of more simple variants., Results: We developed novel algorithms to accurately characterize tandem, direct and inverted interspersed segmental duplications using short read whole genome sequencing datasets. We integrated these methods to our TARDIS tool, which is now capable of detecting various types of SVs using multiple sequence signatures such as read pair, read depth and split read. We evaluated the prediction performance of our algorithms through several experiments using both simulated and real datasets. In the simulation experiments, using a 30× coverage TARDIS achieved 96% sensitivity with only 4% false discovery rate. For experiments that involve real data, we used two haploid genomes (CHM1 and CHM13) and one human genome (NA12878) from the Illumina Platinum Genomes set. Comparison of our results with orthogonal PacBio call sets from the same genomes revealed higher accuracy for TARDIS than state-of-the-art methods. Furthermore, we showed a surprisingly low false discovery rate of our approach for discovery of tandem, direct and inverted interspersed segmental duplications prediction on CHM1 (<5% for the top 50 predictions)., Availability and Implementation: TARDIS source code is available at https://github.com/BilkentCompGen/tardis, and a corresponding Docker image is available at https://hub.docker.com/r/alkanlab/tardis/., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Microsatellite polymorphism has always been a challenge for genome assembly and sequence alignment due to sequencing errors, short read lengths, and high incidence of polymerase slippage in microsatellite regions. Despite the information they carry being very valuable, microsatellite variations have not gained enough attention to be a routine step in genome sequence analysis pipelines. After the completion of the 1000 Genomes Project, which aimed to establish the most detailed genetic variation catalog for humans, the consortium released only two microsatellite prediction sets generated by two tools. Many other large research efforts have failed to shed light on microsatellite variations. We evaluated the performance of three different local assembly methods on three different experimental settings, focusing on genotype-based performance, coverage impact, and preprocessing including flanking regions. All these experiments supported our initial expectations on assembly. We also demonstrate that overlap-layout-consensus (OLC)-basedassembly methods show higher sensitivity to microsatellite variant calling when compared to a de Bruijn graph-based approach. We conclude that assembly with OLC is the better method for genotyping microsatellites. Our pipeline is available at https://github.com/gulfemd/STRAssembly., Competing Interests: CONFLICT OF INTEREST: none declared

Nanopore sequencing technology has the potential to render other sequencing technologies obsolete with its ability to generate long reads and provide portability. However, high error rates of the technology pose a challenge while generating accurate genome assemblies. The tools used for nanopore sequence analysis are of critical importance, as they should overcome the high error rates of the technology. Our goal in this work is to comprehensively analyze current publicly available tools for nanopore sequence analysis to understand their advantages, disadvantages and performance bottlenecks. It is important to understand where the current tools do not perform well to develop better tools. To this end, we (1) analyze the multiple steps and the associated tools in the genome assembly pipeline using nanopore sequence data, and (2) provide guidelines for determining the appropriate tools for each step. Based on our analyses, we make four key observations: (1) the choice of the tool for basecalling plays a critical role in overcoming the high error rates of nanopore sequencing technology. (2) Read-to-read overlap finding tools, GraphMap and Minimap, perform similarly in terms of accuracy. However, Minimap has a lower memory usage, and it is faster than GraphMap. (3) There is a trade-off between accuracy and performance when deciding on the appropriate tool for the assembly step. The fast but less accurate assembler Miniasm can be used for quick initial assembly, and further polishing can be applied on top of it to increase the accuracy, which leads to faster overall assembly. (4) The state-of-the-art polishing tool, Racon, generates high-quality consensus sequences while providing a significant speedup over another polishing tool, Nanopolish. We analyze various combinations of different tools and expose the trade-offs between accuracy, performance, memory usage and scalability. We conclude that our observations can guide researchers and practitioners in making conscious and effective choices for each step of the genome assembly pipeline using nanopore sequence data. Also, with the help of bottlenecks we have found, developers can improve the current tools or build new ones that are both accurate and fast, to overcome the high error rates of the nanopore sequencing technology., (© The Author(s) 2018. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Objective: The aim of the present study was to determine the demographic, medical, and treatment characteristics of patients followed up with the diagnosis of carbon monoxide (CO) poisoning in emergency care and also to determine the relationship of these patients' clinical process and outcome between carboxyhemoglobin (COHb), lactate, and troponin levels., Methods: The present study was conducted retrospectively between 01/01/2013 and 01/01/2016 by examining 450 patients who were referred to the emergency service for CO poisoning. The ages; sexualities; manners of application; clinical findings; levels of blood COHb, lactate, and troponin; applied oxygen treatment method; and outcome of patients were evaluated. Data analysis was done by Shapiro-Wilk, Student's t, Mann-Whitney U, and chi-square tests., Results: A total of 450 patients were included in the study. The median age of the patients was 35 (interquartile range (IQR) 26.75-45.00) years. In the study where data are not homogeneously distributed, the median levels of COHb, lactate, and troponin were 11.80% (IQR 3-23), 1.60 (IQR 1.10-2.5) mmol/l, and 0.00 (IQR 0.000-0.003) ng/ml, respectively. The levels of lactate were detected to be statistically high in patients who had syncope and who received hyperbaric oxygen treatment (p<0.05). In addition, the levels of lactate and troponin were significantly higher in patients who were hospitalized (p<0.05)., Conclusion: The levels of COHb, lactate, and troponin can provide an insight to the clinician about hospitalization and the type of treatment., Competing Interests: Conflict of Interest: No conflict of interest was declared by the authors.

DNA sequencing technologies hold great promise in generating information that will guide scientists to understand how the genome effects human health and organismal evolution. The process of generating raw genome sequence data becomes cheaper and faster, but more error-prone. Assembly of such data into high-quality finished genome sequences remains challenging. Many genome assembly tools are available, but they differ in terms of their performance and their final output. More importantly, it remains largely unclear how to best assess the quality of assembled genome sequences. Here we evaluate the accuracies of several genome scaffolding algorithms using two different types of data generated from the genome of the same human individual: whole genome shotgun sequencing (WGS) and pooled clone sequencing (PCS). We observe that it is possible to obtain better assemblies if PCS data are used, compared to using only WGS data. However, the current scaffolding algorithms are developed only for WGS, and PCS-aware scaffolding algorithms remain an open problem., Competing Interests: CONFLICT OF INTEREST: none declared

Choosing whether to use second or third generation sequencing platforms can lead to trade-offs between accuracy and read length. Several types of studies require long and accurate reads. In such cases researchers often combine both technologies and the erroneous long reads are corrected using the short reads. Current approaches rely on various graph or alignment based techniques and do not take the error profile of the underlying technology into account. Efficient machine learning algorithms that address these shortcomings have the potential to achieve more accurate integration of these two technologies. We propose Hercules, the first machine learning-based long read error correction algorithm. Hercules models every long read as a profile Hidden Markov Model with respect to the underlying platform's error profile. The algorithm learns a posterior transition/emission probability distribution for each long read to correct errors in these reads. We show on two DNA-seq BAC clones (CH17-157L1 and CH17-227A2) that Hercules-corrected reads have the highest mapping rate among all competing algorithms and have the highest accuracy when the breadth of coverage is high. On a large human CHM1 cell line WGS data set, Hercules is one of the few scalable algorithms; and among those, it achieves the highest accuracy.

Motivation: Segmental duplications (SDs) or low-copy repeats, are segments of DNA > 1 Kbp with high sequence identity that are copied to other regions of the genome. SDs are among the most important sources of evolution, a common cause of genomic structural variation and several are associated with diseases of genomic origin including schizophrenia and autism. Despite their functional importance, SDs present one of the major hurdles for de novo genome assembly due to the ambiguity they cause in building and traversing both state-of-the-art overlap-layout-consensus and de Bruijn graphs. This causes SD regions to be misassembled, collapsed into a unique representation, or completely missing from assembled reference genomes for various organisms. In turn, this missing or incorrect information limits our ability to fully understand the evolution and the architecture of the genomes. Despite the essential need to accurately characterize SDs in assemblies, there has been only one tool that was developed for this purpose, called Whole-Genome Assembly Comparison (WGAC); its primary goal is SD detection. WGAC is comprised of several steps that employ different tools and custom scripts, which makes this strategy difficult and time consuming to use. Thus there is still a need for algorithms to characterize within-assembly SDs quickly, accurately, and in a user friendly manner., Results: Here we introduce SEgmental Duplication Evaluation Framework (SEDEF) to rapidly detect SDs through sophisticated filtering strategies based on Jaccard similarity and local chaining. We show that SEDEF accurately detects SDs while maintaining substantial speed up over WGAC that translates into practical run times of minutes instead of weeks. Notably, our algorithm captures up to 25% 'pairwise error' between segments, whereas previous studies focused on only 10%, allowing us to more deeply track the evolutionary history of the genome., Availability and Implementation: SEDEF is available at https://github.com/vpc-ccg/sedef.

Genomics data introduce a substantial computational burden as well as data privacy and ownership issues. Data sets generated by high-throughput sequencing platforms require immense amounts of computational resources to align to reference genomes and to call and annotate genomic variants. This problem is even more pronounced if reanalysis is needed for new versions of reference genomes, which may impose high loads to existing computational infrastructures. Additionally, after the compute-intensive analyses are completed, the results are either kept in centralized repositories with access control, or distributed among stakeholders using standard file transfer protocols. This imposes two main problems: (1) Centralized servers become gatekeepers of the data, essentially acting as an unnecessary mediator between the actual data owners and data users; and (2) servers may create single points of failure both in terms of service availability and data privacy. Therefore, there is a need for secure and decentralized platforms for data distribution with user-level data governance. A new technology, blockchain, may help ameliorate some of these problems. In broad terms, the blockchain technology enables decentralized, immutable, incorruptible public ledgers. In this Perspective, we aim to introduce current developments toward using blockchain to address several problems in omics, and to provide an outlook of possible future implications of the blockchain technology to life sciences., (© 2018 Ozercan et al.; Published by Cold Spring Harbor Laboratory Press.)

Background: Seed location filtering is critical in DNA read mapping, a process where billions of DNA fragments (reads) sampled from a donor are mapped onto a reference genome to identify genomic variants of the donor. State-of-the-art read mappers 1) quickly generate possible mapping locations for seeds (i.e., smaller segments) within each read, 2) extract reference sequences at each of the mapping locations, and 3) check similarity between each read and its associated reference sequences with a computationally-expensive algorithm (i.e., sequence alignment) to determine the origin of the read. A seed location filter comes into play before alignment, discarding seed locations that alignment would deem a poor match. The ideal seed location filter would discard all poor match locations prior to alignment such that there is no wasted computation on unnecessary alignments., Results: We propose a novel seed location filtering algorithm, GRIM-Filter, optimized to exploit 3D-stacked memory systems that integrate computation within a logic layer stacked under memory layers, to perform processing-in-memory (PIM). GRIM-Filter quickly filters seed locations by 1) introducing a new representation of coarse-grained segments of the reference genome, and 2) using massively-parallel in-memory operations to identify read presence within each coarse-grained segment. Our evaluations show that for a sequence alignment error tolerance of 0.05, GRIM-Filter 1) reduces the false negative rate of filtering by 5.59x-6.41x, and 2) provides an end-to-end read mapper speedup of 1.81x-3.65x, compared to a state-of-the-art read mapper employing the best previous seed location filtering algorithm., Conclusion: GRIM-Filter exploits 3D-stacked memory, which enables the efficient use of processing-in-memory, to overcome the memory bandwidth bottleneck in seed location filtering. We show that GRIM-Filter significantly improves the performance of a state-of-the-art read mapper. GRIM-Filter is a universal seed location filter that can be applied to any read mapper. We hope that our results provide inspiration for new works to design other bioinformatics algorithms that take advantage of emerging technologies and new processing paradigms, such as processing-in-memory using 3D-stacked memory devices.

Trastuzumab-refractory, HER2 (human epidermal growth factor receptor 2)-positive breast cancer is commonly treated with trastuzumab emtansine (T-DM1), an antibody-drug conjugate of trastuzumab and the microtubule-targeting agent, DM1. However, drug response reduces greatly over time due to acquisition of resistance whose molecular mechanisms are mostly unknown. Here, we uncovered a novel mechanism of resistance against T-DM1 by combining whole transcriptome sequencing (RNA-Seq), proteomics and a targeted small interfering RNA (siRNA) sensitization screen for molecular level analysis of acquired and de novo T-DM1-resistant models of HER2-overexpressing breast cancer. We identified Polo-like kinase 1 (PLK1), a mitotic kinase, as a resistance mediator whose genomic as well as pharmacological inhibition restored drug sensitivity. Both acquired and de novo resistant models exhibited synergistic growth inhibition upon combination of T-DM1 with a selective PLK1 inhibitor, volasertib, at a wide concentration range of the two drugs. Mechanistically, T-DM1 sensitization upon PLK1 inhibition with volasertib was initiated by a spindle assembly checkpoint (SAC)-dependent mitotic arrest, leading to caspase activation, followed by DNA damage through CDK1-dependent phosphorylation and inactivation of Bcl-2/xL. Furthermore, we showed that Ser70 phosphorylation of Bcl-2 directly regulates apoptosis by disrupting the binding to and sequestration of the pro-apoptotic protein Bim. Importantly, T-DM1 resistance signature or PLK1 expression correlated with cell cycle progression and DNA repair, and predicted a lower sensitivity to taxane/trastuzumab combination in HER2-positive breast cancer patients. Finally, volasertib in combination with T-DM1 greatly synergized in models of T-DM1 resistance in terms of growth inhibition both in three dimensional (3D) cell culture and in vivo. Altogether, our results provide promising pre-clinical evidence for potential testing of T-DM1/volasertib combination in T-DM1 refractory HER2-positive breast cancer patients for whom there is currently no treatment available.

With the developments in high-throughput sequencing (HTS) technologies, researchers have gained a powerful tool to identify structural variants (SVs) in genomes with substantially less cost than before. SVs can be broadly classified into two main categories: balanced rearrangements and copy number variations (CNVs). Many algorithms have been developed to characterize CNVs using HTS data, with focus on different types and size range of variants using different read signatures. Read depth (RD) based tools are more common in characterizing large (>10 kb) CNVs since RD strategy does not rely on the fragment size and read length, which are limiting factors in read pair and split read analysis. Here we provide a guideline for a user friendly tool for detecting large segmental duplications and deletions that can also predict integer copy numbers for duplicated genes.

Phlebotomine sandflies are vectors of phleboviruses that cause sandfly fever or meningitis with significant implications for public health. Although several strains of these viruses had been isolated in Iran in the late 1970's, there was no recent data about the present situation at the outset of this study. Entomological investigations performed in 2009 and 2011 in Iran collected 4,770 sandflies from 10 different regions. Based on morphological identification, they were sorted into 315 pools according to species, sex, trapping station and date of capture. A phlebovirus, provisionally named Dashli virus (DASHV), was isolated from one pool of Sergentomyia spp, and subsequently DASHV RNA was detected in a second pool of Phlebotomus papatasi. Genetic and phylogenetic analyses based on complete coding genomic sequences indicated that (i) DASHV is most closely related to the Iranian isolates of Sandfly fever Sicilian virus [SFSV], (ii) there is a common ancestor to DASHV, Sandfly fever Sicilian- (SFS) and SFS-like viruses isolated in Italy, India, Turkey, and Cyprus (lineage I), (iii) DASHV is more distantly related with Corfou and Toros viruses (lineage II) although common ancestry is supported with 100% bootstrap, (iii) lineage I can be subdivided into sublineage Ia including all SFSV, SFCV and SFTV except those isolated in Iran which forms sublineage Ib (DASHV). Accordingly, we suggest to approve Sandfly fever Sicilian virus species consisting of the all aforementioned viruses. Owing that most of these viruses have been identified in human patients with febrile illness, DASHV should be considered as a potential human pathogen in Iran.

Motivation: High throughput DNA sequencing (HTS) technologies generate an excessive number of small DNA segments -called short reads- that cause significant computational burden. To analyze the entire genome, each of the billions of short reads must be mapped to a reference genome based on the similarity between a read and 'candidate' locations in that reference genome. The similarity measurement, called alignment, formulated as an approximate string matching problem, is the computational bottleneck because: (i) it is implemented using quadratic-time dynamic programming algorithms and (ii) the majority of candidate locations in the reference genome do not align with a given read due to high dissimilarity. Calculating the alignment of such incorrect candidate locations consumes an overwhelming majority of a modern read mapper's execution time. Therefore, it is crucial to develop a fast and effective filter that can detect incorrect candidate locations and eliminate them before invoking computationally costly alignment algorithms., Results: We propose GateKeeper, a new hardware accelerator that functions as a pre-alignment step that quickly filters out most incorrect candidate locations. GateKeeper is the first design to accelerate pre-alignment using Field-Programmable Gate Arrays (FPGAs), which can perform pre-alignment much faster than software. When implemented on a single FPGA chip, GateKeeper maintains high accuracy (on average >96%) while providing, on average, 90-fold and 130-fold speedup over the state-of-the-art software pre-alignment techniques, Adjacency Filter and Shifted Hamming Distance (SHD), respectively. The addition of GateKeeper as a pre-alignment step can reduce the verification time of the mrFAST mapper by a factor of 10., Availability and Implementation: https://github.com/BilkentCompGen/GateKeeper., Contact: mohammedalser@bilkent.edu.tr or onur.mutlu@inf.ethz.ch or calkan@cs.bilkent.edu.tr., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com)

Structural variations (SV) are broadly defined as genomic alterations that affect >50bp of DNA, which are shown to have significant effect on evolution and disease. The advent of high throughput sequencing (HTS) technologies and the ability to perform whole genome sequencing (WGS), makes it feasible to study these variants in depth. However, discovery of all forms of SV using WGS has proven to be challenging as the short reads produced by the predominant HTS platforms (<200bp for current technologies) and the fact that most genomes include large amounts of repeats make it very difficult to unambiguously map and accurately characterize such variants. Furthermore, existing tools for SV discovery are primarily developed for only a few of the SV types, which may have conflicting sequence signatures (i.e. read pairs, read depth, split reads) with other, untargeted SV classes. Here we are introduce a new framework, Tardis, which combines multiple read signatures into a single package to characterize most SV types simultaneously, while preventing such conflicts. Tardis also has a modular structure that makes it easy to extend for the discovery of additional forms of SV., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Motivation: Despite recent advances in algorithms design to characterize structural variation using high-throughput short read sequencing (HTS) data, characterization of novel sequence insertions longer than the average read length remains a challenging task. This is mainly due to both computational difficulties and the complexities imposed by genomic repeats in generating reliable assemblies to accurately detect both the sequence content and the exact location of such insertions. Additionally, de novo genome assembly algorithms typically require a very high depth of coverage, which may be a limiting factor for most genome studies. Therefore, characterization of novel sequence insertions is not a routine part of most sequencing projects., Result: Here, we present Pamir, a new algorithm to efficiently and accurately discover and genotype novel sequence insertions using either single or multiple genome sequencing datasets. Pamir is able to detect breakpoint locations of the insertions and calculate their zygosity (i.e. heterozygous versus homozygous) by analyzing multiple sequence signatures, matching one-end-anchored sequences to small-scale de novo assemblies of unmapped reads, and conducting strand-aware local assembly. We test the efficacy of Pamir on both simulated and real data, and demonstrate its potential use in accurate and routine identification of novel sequence insertions in genome projects., Availability and Implementation: Pamir is available at https://github.com/vpc-ccg/pamir ., Contact: fhach@{sfu.ca, prostatecentre.com } or calkan@cs.bilkent.edu.tr., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com)

Background: Although many algorithms are now available that aim to characterize different classes of structural variation, discovery of balanced rearrangements such as inversions remains an open problem. This is mainly due to the fact that breakpoints of such events typically lie within segmental duplications or common repeats, which reduces the mappability of short reads. The algorithms developed within the 1000 Genomes Project to identify inversions are limited to relatively short inversions, and there are currently no available algorithms to discover large inversions using high throughput sequencing technologies., Results: Here we propose a novel algorithm, VALOR, to discover large inversions using new sequencing methods that provide long range information such as 10X Genomics linked-read sequencing, pooled clone sequencing, or other similar technologies that we commonly refer to as long range sequencing. We demonstrate the utility of VALOR using both pooled clone sequencing and 10X Genomics linked-read sequencing generated from the genome of an individual from the HapMap project (NA12878). We also provide a comprehensive comparison of VALOR against several state-of-the-art structural variation discovery algorithms that use whole genome shotgun sequencing data., Conclusions: In this paper, we show that VALOR is able to accurately discover all previously identified and experimentally validated large inversions in the same genome with a low false discovery rate. Using VALOR, we also predicted a novel inversion, which we validated using fluorescent in situ hybridization. VALOR is available at https://github.com/BilkentCompGen/VALOR.

Grapevine (Vitis vinifera L.) is one of the world's most important crop plants, which is of large economic value for fruit and wine production. There is much interest in identifying genomic variations and their functional effects on inter-varietal, phenotypic differences. Using an approach developed for the analysis of human and mammalian genomes, which combines high-throughput sequencing, array comparative genomic hybridization, fluorescent in situ hybridization and quantitative PCR, we created an inter-varietal atlas of structural variations and single nucleotide variants (SNVs) for the grapevine genome analyzing four economically and genetically relevant table grapevine varieties. We found 4.8 million SNVs and detected 8% of the grapevine genome to be affected by genomic variations. We identified more than 700 copy number variation (CNV) regions and more than 2000 genes subjected to CNV as potential candidates for phenotypic differences between varieties., (© 2016 The Authors The Plant Journal © 2016 John Wiley & Sons Ltd.)

Results: Here, we present a comprehensive analysis on the reproducibility of computational characterization of genomic variants using high throughput sequencing data. We reanalyzed the same datasets twice, using the same tools with the same parameters, where we only altered the order of reads in the input (i.e. FASTQ file). Reshuffling caused the reads from repetitive regions being mapped to different locations in the second alignment, and we observed similar results when we only applied a scatter/gather approach for read mapping-without prior shuffling. Our results show that, some of the most common variation discovery algorithms do not handle the ambiguous read mappings accurately when random locations are selected. In addition, we also observed that even when the exact same alignment is used, the GATK HaplotypeCaller generates slightly different call sets, which we pinpoint to the variant filtration step. We conclude that, algorithms at each step of genomic variation discovery and characterization need to treat ambiguous mappings in a deterministic fashion to ensure full replication of results., Availability and Implementation: Code, scripts and the generated VCF files are available at DOI:10.5281/zenodo.32611., Contact: calkan@cs.bilkent.edu.tr, Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

Motivation: Optimizing seed selection is an important problem in read mapping. The number of non-overlapping seeds a mapper selects determines the sensitivity of the mapper while the total frequency of all selected seeds determines the speed of the mapper. Modern seed-and-extend mappers usually select seeds with either an equal and fixed-length scheme or with an inflexible placement scheme, both of which limit the ability of the mapper in selecting less frequent seeds to speed up the mapping process. Therefore, it is crucial to develop a new algorithm that can adjust both the individual seed length and the seed placement, as well as derive less frequent seeds., Results: We present the Optimal Seed Solver (OSS), a dynamic programming algorithm that discovers the least frequently-occurring set of x seeds in an L-base-pair read in [Formula: see text] operations on average and in [Formula: see text] operations in the worst case, while generating a maximum of [Formula: see text] seed frequency database lookups. We compare OSS against four state-of-the-art seed selection schemes and observe that OSS provides a 3-fold reduction in average seed frequency over the best previous seed selection optimizations., Availability and Implementation: We provide an implementation of the Optimal Seed Solver in C++ at: https://github.com/CMU-SAFARI/Optimal-Seed-Solver, Contact: hxin@cmu.edu, calkan@cs.bilkent.edu.tr or onur@cmu.edu, Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

In this study, PMMA/heneicosane (C21) and PMMA/octacosane (C28) micro-nano capsules were fabricated via emulsion polymerisation method. The chemical structures of the fabricated capsules were verified with the FT-IR spectroscopy analysis. The results of POM, SEM and PSD analysis indicated that most of the capsules were consisted of micro/nano-sized spheres with compact surface. The DSC measurements showed that the capsules had melting temperature in the range of about 39-60 °C and latent heat energy storage capacity in the range of about 138-152 J/g. The results of TGA showed that sublimit temperature values regarding the first degradation steps of both capsules were quite over the phase change or working temperatures of encapsulated paraffins. The thermal cycling test exhibited that the capsules had good thermal reliability and chemical stability. Additionally, the prepared capsules had reasonably high thermal conductivity.