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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2020

Overlapping Pathogenic Signalling Pathways and Biomarkers in Preeclampsia and Cardiovascular Disease

ObjectivesPreeclampsia is a cardiovascular pregnancy complication which occurs in 5-10% of pregnancies that can lead to a number of pregnancy complications including maternal and foetal death. Long-term, preeclampsia is associated with up to 8-fold increased risk of cardiovascular disease (CVD) for both mothers and their offspring. The lack of mechanistic data in relation to the causes or consequences of preeclampsia has prevented the development of effective therapeutic or monitoring strategies. Study designThis study investigates common underlying mechanisms of preeclampsia and CVD, specifically hypertension and heart failure with preserved ejection fraction (HFpEF) using "in silico" approach of publicly available datasets. Integrated techniques were designed to mine data repositories and identify relevant biomarkers associated with these three conditions. Main outcomes measuresThe knowledge base tools were employed that enabled the analysis of these biomarkers to discover potential molecular and biological links between these three conditions. ResultsOur bioinformatics "in silico" analyses of the publically available datasets identified 76 common biomarkers between preeclampsia, hypertension and HFpEF. These biomarkers were representative of 29 pathways commonly enriched across the three conditions which were largely related to inflammation, metabolism, angiogenesis, remodelling, haemostasis, apoptosis, endoplasmic reticulum (ER) stress signalling and the renin-angiotensin-aldosterone (RAAS) system. ConclusionsThis bioinformatics approach which uses the wealth of scientific data available in public repositories can be helpful to gain a deeper understanding of the overlapping pathogenic mechanisms of associated diseases, which could be explored as biomarkers or targets to prevent long-term cardiovascular complications such as hypertension and HFpEF following preeclampsia. HighlightsO_LIWomen with preeclampsia have increased risk of cardiovascular disease later in life but the mechanism is poorly understood. C_LIO_LI"In silico" analyses of publically available datasets provided overlapping biomarkers and pathogenic pathways between preeclampsia, hypertension and heart failure with preserved ejection fraction (HFpEF). C_LIO_LIThese data could be utilised in the future studies that may lead to the development of better risk stratification strategies or preventative treatments for women post preeclampsia. C_LI

bioinformatics↗
bioRxiv2020

A Novel Riboswitch Classification based on Imbalanced Sequences achieved by Machine Learning

Riboswitch, a part of mRNA (50-250nt in length), has two main classes: aptamer and expression platform. One of the main challenges raised during the classification of riboswitch is imbalanced data. That is a circumstance in which the records of a dataset of one group are very small compared to the others. Such circumstances lead classifier to ignore minority group and emphasize on majority class, that resulting with a skewed classification. We considered sixteen riboswitch families, to be in accord with recent riboswitch classification work, that contain imbalanced dataset ranging from 4,826 instances (RF00174) to 39 (RF01051) instances. The dataset was divided into training and test set using new developed pipeline. From 5460 k-mers, 156 features were produced calculated based on CfsSubsetEval and BestFirst. Statistically tested result was significantly difference between balanced and imbalanced dataset (p < 0.05). Besides, each algorithm also showed a significant difference in sensitivity, specificity, accuracy, and macro F-score when used in both groups (p < 0.05). Several k-mers clustered from heat map were discovered to have biological functions and motifs at the different positions like interior loops, terminal loops and helices. They were validated to have a biological function and some are riboswitch motifs. The analysis has discovered the importance of solving the challenges of majority bias analysis and overfitting. Presented results were generalized evaluation of both balanced and imbalanced models, which implies their ability of classifying novel riboswitches. The scientific community can use python source code at https://github.com/Seasonsling/riboswitch, which can contribute to the process of developing software packages. Author SummaryMachine learning application has been used in many ways in bioinformatics and computational biology. Its use in riboswitch classification is still limited and existing attempt showed challenges due to imbalanced dataset. Algorithms classify dataset with majority and minority group, but they tend to ignore minority group and emphasize on majority class, consequential return a skewed classification We used new pipeline including SMOTE for balancing datasets that showed better classified riboswitch as well as improved performance of algorithms selected. Statistically significant difference observed between balanced and imbalanced in sensitivity, specificity, accuracy and F-score, this proved balanced dataset better for classification of riboswitch. Biological functions and motif search of k-mers in riboswitch families revealed their presence in interior loops, terminal loops and helices, some of the k-mers were reported to be riboswitch motifs of aptamer domains and critical for metabolite binding. The pipeline can be used in machine learning and deep learning study in other domains of bioinformatics and computational biology suffering from imbalanced dataset. Finally, scientific community can use python source code, the work done and flow to develop packages.

bioinformatics↗
bioRxiv2020

canvasDesigner2: An enhanced interactive publication-quality composite figure designing tool

BackgroundMany fit-for-purpose bioinformatics tools generate plots to investigate data quality and illustrate findings. However, assembling individual plots in different formats from various sources into one high-resolution figure requires mastery of commercial tools or even programming skills. In addition, it is a time-consuming and sometimes frustrating process even for scientists with modest computational skills. ResultsWe developed figureComposer, a web-based bioinformatics tool that interactively arranges high-resolution images in various formats, mainly SVG to produce one multi-panel publication-quality composite figure in both PDF and interactive HTML formats in a user-friendly matter, requiring no programming skills. ConclusionsfigureComposer is open-source and publicly available web tool that can be accessed online at https://baohongz.github.io/figureComposer while the source code is provided at https://github.com/baohongz/figureComposer.

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bioRxiv2020

The influence of the phylogenetic inference pipeline on murine antibody repertoire sequencing data following viral infection

Understanding B cell evolution following vaccination or infection is crucial for instructing targeted immunotherapies when searching for potential therapeutic or virus-neutralizing antibodies. Antibody phylogenetics holds the potential to quantify both clonal selection and somatic hypermutation, two key players shaping B cell evolution. A wide range of bioinformatic pipelines and phylogenetic inference methods have been utilized on antibody repertoire sequencing datasets to delineate B cell evolution. Although the majority of B cell repertoire studies incorporate some aspect of antibody evolution, how the chosen computational methods affect the results is largely ignored. Therefore, we performed an extensive computational analysis on time-resolved antibody repertoire sequencing data to better characterize how commonly employed bioinformatic practices influence conclusions regarding antibody selection and evolution. Our findings reveal that different combinations of clonal lineage assignment strategies, phylogenetic inference methods, and biological sampling affect the inferred size, mutation rates, and topologies of B cell lineages in response to virus infection.

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bioRxiv2020

Accuracy and efficiency of germline variant calling pipelines for human genome data

Advances in next-generation sequencing technology has enabled whole genome sequencing (WGS) to be widely used for identification of causal variants in a spectrum of genetic-related disorders, and provided new insight into how genetic polymorphisms affect disease phenotypes. The development of different bioinformatics pipelines has continuously improved the variant analysis of WGS data, however there is a necessity for a systematic performance comparison of these pipelines to provide guidance on the application of WGS-based scientific and clinical genomics. In this study, we evaluated the performance of three variant calling pipelines (GATK, DRAGEN and DeepVariant) using Genome in a Bottle Consortium, "synthetic-diploid" and simulated WGS datasets. DRAGEN and DeepVariant show a better accuracy in SNPs and indels calling, with no significant differences in their F1-score. DRAGEN platform offers accuracy, flexibility and a highly-efficient running speed, and therefore superior advantage in the analysis of WGS data on a large scale. The combination of DRAGEN and DeepVariant also provides a good balance of accuracy and efficiency as an alternative solution for germline variant detection in further applications. Our results facilitate the standardization of benchmarking analysis of bioinformatics pipelines for reliable variant detection, which is critical in genetics-based medical research and clinical application.

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bioRxiv2020

Real-time biomedical knowledge synthesis of the exponentially growing world wide web using unsupervised neural networks

Decoding disease mechanisms for addressing unmet clinical need demands the rapid assimilation of the exponentially growing biomedical knowledge. These are either inherently unstructured and non-conducive to current computing paradigms or siloed into structured databases requiring specialized bioinformatics. Despite the recent renaissance in unsupervised neural networks for deciphering unstructured natural languages and the availability of numerous bioinformatics resources, a holistic platform for real-time synthesis of the scientific literature and seamless triangulation with deep omic insights and real-world evidence has not been advanced. Here, we introduce the nferX platform that makes the highly unstructured biomedical knowledge computable and supports the seamless visual triangulation with statistical inference from diverse structured databases. The nferX platform will accelerate and amplify the research potential of subject-matter experts as well as non-experts across the life science ecosystem (https://academia.nferx.com/).

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bioRxiv2020

Biological function polarity prediction of missense variants using machine learning

Functional interpretation is crucial when facing on average 20,000 missense variants per human exome, as the great majority are not associated with any underlying disease. In silico bioinformatics tools can predict the deleteriousness of variants or assess their functional impact by assigning scores, but they cannot predict whether the variant in question results in gain or loss of function at the protein level. Here, we show that machine learning can effectively predict this biological function polarity of missense variants. The new method adapts weighted gradient boosting machine approach on a set of damaging variants (1,288 loss of function and 218 gain of function variants) as annotated by the tools SIFT, PolyPhen2 and CADD. Area under the ROC curve of 0.85 illustrates high discriminative power of the classifier. Predictive performance of the classifier remains consistent against an independent set of damaging variants as highlighted by the area under the ROC curve of 0.83. This new approach may help to guide biological experiments on the clinical relevance of damaging genetic variants. Author summaryMissense variant occurs when a single genetic alteration in DNA takes place and as a result a new amino acid is translated into the protein. This amino acid change can inactivate the existing protein function causing loss-of-function or produce a new function causing gain-of-function. Therefore, it is very important to interpret these functional consequences of missense variants as they often turn out to be disease causing. Each individuals genome sequence has thousands of missense variants, out of which very few are actually associated with any underlying disease. Various computational tools have been developed to predict whether missense variants are damaging or not, but none of them can actually predict whether the damaging missense variants cause gain-of-function or loss-of-function. We have developed a new ensemble classifier to predict this biological function polarity at the protein level. The classifier combines the prediction scores of three existing bioinformatics tools and applies machine learning to make effective predictions. We have validated our classifier against an independent data set to show its high predictive power and robustness. The predictions made by our machine learning tool can be used as indicators of biological function polarity, but with further evidence on pathogenicity.

bioinformatics↗
bioRxiv2020

BSAseq: an interactive and integrated web-based workflow for identification of causal mutations in bulked F2 populations

SummaryWith the advance of next-generation sequencing (NGS) technologies and reductions in the costs of these techniques, bulked segregant analysis (BSA) has become not only a powerful tool for mapping quantitative trait loci (QTL) but also a useful way to identify causal gene mutations underlying phenotypes of interest. However, due to the presence of background mutations and errors in sequencing, genotyping, and reference assembly, it is often difficult to distinguish true causal mutations from background mutations. In this study, we developed the BSAseq workflow, which includes an automated bioinformatics analysis pipeline with a probabilistic model for estimating the segregation region and an interactive Shiny web application for visualizing the results. We deeply sequenced a male sterile parental line (ms8) to capture the majority of background mutations in our bulked F2 data. We applied the workflow to 11 bulked F2 populations and identified the true causal mutation in each population. The workflow is intuitive and straightforward, facilitating its adoption by users without bioinformatics analysis skills. We anticipate that BSAseq will be broadly applicable to the identification of causal mutations for many phenotypes of interest. AvailabilityBSAseq is freely available on https://www.sciapps.org/page/bsa Contactliya.wang@cshl.edu, ware@cshl.edu, zhanguo.xin@ars.usda.gov

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bioRxiv2020

New alignment-based sequence extraction software (ALiBaSeq) and its utility for deep level phylogenetics

Despite many bioinformatic solutions for analyzing sequencing data, few options exist for targeted sequence retrieval from whole genomic sequencing (WGS) data. Available tools especially struggle at deep phylogenetic levels and necessitate amino-acid space searches, increasing rates of false positive results. Many such tools also suffer from difficult installation processes and lack adequate user resources. Here, we describe a program using freely available similarity search tools to find homologs in assembled WGS data with unparalleled freedom to modify parameters. We evaluate its performance as well as that of other utilized bioinformatics tools on two divergent insect species (>200 My) for which annotated genomes exist, as well as on one large set each of highly conserved and more variable loci. Our software is capable of retrieving orthologs from well-curated, low and high depth shotgun, and target capture assemblies as well or better than other software as assessed by finding the most genes with maximal coverage and with a low rate of false positives throughout all datasets. The software (implemented in Python), tutorials, and manual are freely available at https://github.com/AlexKnyshov/alibaseq.

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bioRxiv2020

Benchmarking available bacterial promoter prediction tools: potentialities and limitations

BackgroundThe promoter region is a key element required for the production of RNA in bacteria. While new high-throughput technology allows massive mapping of promoter elements, we still mainly relay on bioinformatic tools to predict such elements in bacterial genomes. Additionally, despite many different prediction tools have become popular to identify bacterial promoters, there is no systematic comparison of such tools. ResultsHere, we performed a systematic comparison between several widely used promoter prediction tools (BPROM, bTSSfinder, BacPP, CNNProm, IBBP, Virtual Footprint, IPro70-FMWin, 70ProPred, iPromoter-2L and MULTiPly) using well-defined sequence data sets and standardized metrics to determine how well those tools performed related to each other. For this, we used datasets of experimentally validated promoters from Escherichia coli and a control dataset composed by randomly generated sequences with similar nucleotide distributions. We compared the performance of the tools using metrics such as specificity, sensibility, accuracy and Matthews Correlation Coefficient (MCC). We show that the widely used BPROM presented the worse performance among compared tools, while four tools (CNNProm, IPro70-FMWin, 70ProPreda and iPromoter-2L) offered high predictive power. From these, iPro70-FMWin exhibited the best results for most of the metrics used. ConclusionsTherefore, we exploit here some potentials and limitations of available tools and hope future works can be built upon our effort to systematically characterize such quite useful class of bioinformatics tools.

bioinformatics↗
bioRxiv2020

VIRTUS: a pipeline for comprehensive virus analysis from conventional RNA-seq data

The possibility that RNA transcripts from clinical samples contain plenty of virus RNAs has not been pursued actively so far. We here developed a new tool for analyzing virus-transcribed mRNAs, not virus copy numbers, in the data of conventional and single-cell RNA-sequencing of human cells. Our pipeline, named VIRTUS (VIRal Transcript Usage Sensor), was able to detect 763 viruses including herpesviruses, retroviruses, and even SARS-CoV-2 (COVID-19), and quantify their transcripts in the sequence data. This tool thus enabled simultaneously detecting infected cells, the composition of multiple viruses within the cell, and the endogenous host gene expression profile of the cell. This bioinformatics method would be instrumental in addressing the possible effects of covertly infecting viruses on certain diseases and developing new treatments to target such viruses. Availability and implementationVIRTUS is implemented using Common Workflow Language and Docker under a CC-NC license. VIRTUS is freely available at https://github.com/yyoshiaki/VIRTUS. Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗
bioRxiv2020

Advanced Research Infrastructure for Experimentation in genomicS (ARIES): a lustrum of Galaxy experience

Background: With the introduction of Next Generation Sequencing (NGS) and Whole-Genome Sequencing (WGS) in microbiology and molecular epidemiology, the development of an information system for the collection of genomic and epidemiological data and subsequent transparent and reproducible data analysis became indispensable. Further requirements for the system included accessibility and ease of use by bioinformatics as well as command line profane scientists. Findings: The ARIES (Advanced Research Infrastructure for Experimentation in genomicS, https://aries.iss.it) platform has been implemented in 2015 as an instance of the Galaxy framework specific for use of WGS in molecular epidemiology. Here, the experience with ARIES is reported. Conclusions: During its five years existence, ARIES has grown into a well-established reality not only as a web service but as well as a workflow engine for the Integrated Rapid Infectious Disease Analysis (IRIDA) platform. In fact, an environment has been created with the implementation of complex bioinformatic tools in an easy-to-use context allowing scientists to concentrate on what to do instead of how to do it.

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bioRxiv2020

COVID-Align: Accurate online alignment of hCoV-19 genomes using a profile HMM

MotivationThe first cases of the COVID-19 pandemic emerged in December 2019. Until the end of February 2020, the number of available genomes was below 1,000, and their multiple alignment was easily achieved using standard approaches. Subsequently, the availability of genomes has grown dramatically. Moreover, some genomes are of low quality with sequencing/assembly errors, making accurate re-alignment of all genomes nearly impossible on a daily basis. A more efficient, yet accurate approach was clearly required to pursue all subsequent bioinformatics analyses of this crucial data. ResultshCoV-19 genomes are highly conserved, with very few indels and no recombination. This makes the profile HMM approach particularly well suited to align new genomes, add them to an existing alignment and filter problematic ones. Using a core of [~]2,500 high quality genomes, we estimated a profile using HMMER, and implemented this profile in COVID-Align, a user-friendly interface to be used online or as standalone via Docker. The alignment of 1,000 genomes requires less than 20mn on our cluster. Moreover, COVID-Align provides summary statistics, which can be used to determine the sequencing quality and evolutionary novelty of input genomes (e.g. number of new mutations and indels). Availabilityhttps://covalign.pasteur.cloud, hub.docker.com/r/evolbioinfo/covid-align Contactsolivier.gascuel@pasteur.fr, frederic.lemoine@pasteur.fr Supplementary informationSupplementary information is available at Bioinformatics online.

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bioRxiv2020

Taxallnomy: Closing gaps in the NCBI Taxonomy

NCBI Taxonomy is the main taxonomic source for several bioinformatics tools and databases since all organisms with sequence accessions deposited on INSDC are organized in its hierarchical structure. Despite the extensive use and application of this data source, taking advantage of its taxonomic tree could be challenging because (1) some taxonomic ranks are missing in some lineages and (2) some nodes in the tree do not have a taxonomic rank assigned (referred to as "no rank"). To address this issue, we developed an algorithm that takes the tree structure from NCBI Taxonomy and generates a hierarchically complete taxonomic tree. The procedures performed by the algorithm consist of attempting to assign a taxonomic rank to "no rank" nodes and of creating/deleting nodes throughout the tree. The algorithm also creates a name for the new nodes by borrowing the names from its ranked child or, if there is no child, from its ranked parent node. The new hierarchical structure was named taxallnomy and it contains 33 hierarchical levels corresponding to the 33 taxonomic ranks currently used in the NCBI Taxonomy database. From taxallnomy, users can obtain the complete taxonomic lineage with 33 nodes of all taxa available in the NCBI Taxonomy database. Taxallnomy is applicable to several bioinformatics analyses that depend on NCBI Taxonomy data. In this work, we demonstrate its applicability by embedding taxonomic information of a specified rank into a phylogenetic tree; and by making metagenomics profiles. Taxallnomy algorithm was written in PERL and all its resources are available at bioinfo.icb.ufmg.br/taxallnomy. Database URL: http://bioinfo.icb.ufmg.br/taxallnomy

bioinformatics↗
bioRxiv2020

A single-cell RNA-seq Training and Analysis Suite using the Galaxy Framework

BackgroundThe vast ecosystem of single-cell RNA-seq tools has until recently been plagued by an excess of diverging analysis strategies, inconsistent file formats, and compatibility issues between different software suites. The uptake of 10x Genomics datasets has begun to calm this diversity, and the bioinformatics community leans once more towards the large computing requirements and the statistically-driven methods needed to process and understand these ever-growing datasets. ResultsHere we outline several Galaxy workflows and learning resources for scRNA-seq, with the aim of providing a comprehensive analysis environment paired with a thorough user learning experience that bridges the knowledge gap between the computational methods and the underlying cell biology. The Galaxy reproducible bioinformatics framework provides tools, workflows and trainings that not only enable users to perform one-click 10x preprocessing, but also empowers them to demultiplex raw sequencing from custom tagged and full-length sequencing protocols. The downstream analysis supports a wide range of high-quality interoperable suites separated into common stages of analysis: inspection, filtering, normalization, confounder removal and clustering. The teaching resources cover an assortment of different concepts from computer science to cell biology. Access to all resources is provided at the singlecell.usegalaxy.eu portal. ConclusionsThe reproducible and training-oriented Galaxy framework provides a sustainable HPC environment for users to run flexible analyses on both 10x and alternative platforms. The tutorials from the Galaxy Training Network along with the frequent training workshops hosted by the Galaxy Community provide a means for users to learn, publish and teach scRNA-seq analysis. Key PointsO_LISingle-cell RNA-seq has stabilised towards 10x Genomics datasets. C_LIO_LIGalaxy provides rich and reproducible scRNA-seq workflows with a wide range of robust tools. C_LIO_LIThe Galaxy Training Network provides tutorials for the processing of both 10x and non-10x datasets. C_LI

bioinformatics↗
bioRxiv2020

RPRH: a web-based tool for the inference of potential roles of plant microRNAs in human body

Recently, increasing studies reported that plant microRNAs (miRNAs) from food could be absorbed through gastrointestinal tract to regulate the physiological processes of animals. These novel cross-kingdom regulatory roles of plant miRNAs open a new field in miRNA study and drug discovery, but the lack of corresponding bioinformatics tools limit the development of this field. In this study, we built a web-based tool, RPRH (Role of Plant microRNA in Human), to infer the potential roles of plant miRNAs in human and to profile the landscape of all documented plant miRNAs regulatory functions in human. Totally, RPRH included 10414 miRNAs from 82 plant species and 18062 annotation terms like diseases, pathway, gene ontology (GO), and drugs. RPRH represents a bioinformatics resource for further investigation of cross-kingdom regulations of plant miRNAs. Availabilityhttp://www.rnanut.net/rprh/

bioinformatics↗
bioRxiv2020

Bio-informed Protein Sequence Generation for Multi-class Virus Mutation Prediction

Viral pandemics are emerging as a serious global threat to public health, like the recent outbreak of COVID-19. Viruses, especially those belonging to a large family of +ssRNA viruses, have a high possibility of mutating by inserting, deleting, or substituting one or multiple genome segments. It is of great importance for human health worldwide to predict the possible virus mutations, which can effectively avoid the potential second outbreak. In this work, we develop a GAN-based multi-class protein sequence generative model, named ProteinSeqGAN. Given the viral species, the generator is modeled on RNNs to predict the corresponding antigen epitope sequences synthesized by viral genomes. Additionally, a Graphical Protein Autoencoder (GProAE) built upon VAE is proposed to featurize proteins bioinformatically. GProAE, as a multi-class discriminator, also learns to evaluate the goodness of protein sequences and predict the corresponding viral species. Further experiments show that our ProteinSeqGAN model can generate valid antigen protein sequences from both bioinformatics and statistics perspectives, which can be promising predictions of virus mutations.

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bioRxiv2020

Multi Locus View : An Extensible Web Based Tool for the Analysis of Genomic Data

MotivationTracking and understanding data quality, analysis and reproducibility are critical concerns in the biological sciences. This is especially true in genomics where Next Generation Sequencing (NGS) based technologies such as ChIP-seq, RNA-seq and ATAC-seq are generating a flood of genome-scale data. These data-types are extremely high level and complex with single experiments capable of mapping ten to hundreds of thousands of biologically meaningful events across the genome. However, such data are usually processed with automated tools and pipelines, generating tabular outputs and static visualizations. These are difficult to interact with and require substantial bioinformatic skills to manipulate and query. Similarly, interpretation is normally made at a high level without the ability to visualise the underlying data in detail and so the complexity and quality of the real underlying biological signal is lost. Also genomics datasets require integration with other genomics datasets to be properly interpreted and this integration with multiple tracks again requires substantial bioinformatics skills and is difficult to visualise across multiple pertinent datasets. Conventional genome browsers do allow for the detailed visualisation of multiple tracks but are limited to browsing single locations and do not allow for interactions with the dataset as a whole. MLV has been developed to allow users to fluidly interact with genomics datasets at multiple scales, from complete metadata labelled and clustered populations to detailed representations of individual elements. It has inbuilt tools to integrate signals across multiple datasets and to perform dimensionality reduction and clustering analysis based on the extracted signal, allowing for the high-level analysis of complex datasets while maintaining visualisation of the fine grain structure of the data. MLVs ability to visualise clustering within the data combined with efficient tools for large-scale tagging of individual elements makes it a unique tool for the generation of annotated datasets for modern machine learning approaches. ResultsMulti Locus View (MLV) is a web based tool for the visualisation, analysis and annotation of Next Generation Sequencing data sets. The user is able to browse the raw data, cluster, and combine the data with other analysis. Intuitive filtering and visualisation then enables the user to quickly locate and annotate regions of interest. User datasets can then be shared with other users or made public for quick assessment from the academic community. MLV is publically available at https://mlv.molbiol.ox.ac.uk and the source code is available at https://github.com/Hughes-Genome-Group/mlv

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