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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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2018

rCASC: reproducible Classification Analysis of Single Cell sequencing data

SummarySingle-cell RNA sequencing has emerged as an essential tool to investigate cellular heterogeneity, and highlighting cell sub-population specific signatures. Nowadays, dedicated and user-friendly bioinformatics workflows are required to exploit the deconvolution of single-cells transcriptome. Furthermore, there is a growing need of bioinformatics workflows granting both functional, i.e. saving information about data and analysis parameters, and computation reproducibility, i.e. storing the real image of the computation environment. Here, we present rCASC a modular RNAseq analysis workflow allowing data analysis from counts generation to cell sub-population signatures identification, granting both functional and computation reproducibility.\n\nAvailability and ImplementationrCASC is part of the reproducible bioinfomatics project. rCASC is a docker based application controlled by a R package available at https://github.com/kendomaniac/rCASC.\n\nSupplementary informationSupplementary data are available at rCASC github

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bioRxiv2018

GenPipes: an open-source framework for distributed and scalable genomic analyses

With the decreasing cost of sequencing and the rapid developments in genomics technologies and protocols, the need for validated bioinformatics software that enables efficient large-scale data processing is growing. Here we present GenPipes, a flexible Python-based framework that facilitates the development and deployment of multi-step workflows optimized for High Performance Computing clusters and the cloud. GenPipes already implements 12 validated and scalable pipelines for various genomics applications, including RNA-Seq, ChIP-Seq, DNA-Seq, Methyl-Seq, Hi-C, capture Hi-C, metagenomics and PacBio long read assembly. The software is available under a GPLv3 open source license and is continuously updated to follow recent advances in genomics and bioinformatics. The framework has been already configured on several servers and a docker image is also available to facilitate additional installations. In summary, GenPipes offers genomic researchers a simple method to analyze different types of data, customizable to their needs and resources, as well as the flexibility to create their own workflows.

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bioRxiv2018

A case study on the detailed reproducibility of a human cell atlas project

Reproducibility is a defining feature of a scientific discovery. Reproducibility can be at different levels for different types of study. The purpose of the Human Cell Atlas (HCA) project is to build maps of molecular signatures of all human cell types and states to serve as references for future discoveries. Constructing such a complex reference atlas must involve the assembly and aggregation of data from multiple labs, probably generated with different technologies. It has much higher requirements on reproducibility than individual research projects. To add another layer of complexity, the bioinformatics procedures involved for single-cell data have high flexibility and diversity. There are many factors in the processing and analysis of single-cell RNA-seq data that can shape the final results in different ways. To study what levels of reproducibility can be reached in current practices, we conducted a detailed reproduction study for a well-documented recent publication on the atlas of human blood dendritic cells as an example to break down the bioinformatics steps and factors that are crucial for the reproducibility at different levels. We found that the major scientific discovery can be well reproduced after some efforts, but there are also some differences in some details that may cause uncertainty in the future reference. This study provides a detailed case observation on the on-going discussions of the type of standards the HCA community should take when releasing data and publications to guarantee the reproducibility and reliability of the future atlas.

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bioRxiv2018

Laniakea: an open solution to provide Galaxy \"on-demand\" instances over heterogeneous cloud infrastructures.

BackgroundGalaxy is rapidly becoming the de facto standard among workflow managers for bioinformatics. A rich feature set, its overall flexibility, and a thriving community of enthusiastic users are among the main factors contributing to the popularity of Galaxy and Galaxy based applications. One of the main advantages of Galaxy consists in providing access to sophisticated analysis pipelines, e.g., involving numerous steps and large data sets, even to users lacking computer proficiency, while at the same time improving reproducibility and facilitating teamwork and data sharing among researchers. Although several Galaxy public services are currently available, these resources are often overloaded with a large number of jobs and offer little or no customization options to end users. Moreover, there are scenarios where a private Galaxy instance still constitutes a more viable alternative, including, but not limited to, heavy workloads, data privacy concerns or particular needs of customization. In such cases, a cloud-based virtual Galaxy instance can represent a solution that overcomes the typical burdens of managing the local hardware and software infrastructure needed to run and maintain a production-grade Galaxy service.\n\nResultsHere we present Laniakea, a robust and feature-rich software suite which can be deployed on any scientific or commercial Cloud infrastructure in order to provide a \"Galaxy on demand\" Platform as a Service (PaaS). Laying its foundations on the INDIGO-DataCloud middleware, which has been developed to accommodate the needs of a large number of scientific communities, Laniakea can be deployed and provisioned over multiple architectures by private or public e-infrastructures. The end user interacts with Laniakea through a front-end that allows a general setup of the Galaxy instance, then Laniakea takes charge of the deployment both of the virtual hardware and all the software components. At the end of the process the user has access to a private, production-grade, yet fully customizable, Galaxy virtual instance. Laniakeas supports the deployment of plain or cluster backed Galaxy instances, shared reference data volumes, encrypted data volumes and rapid development of novel Galaxy flavours, that is Galaxy configurations tailored for specific tasks. As a proof of concept, we provide a demo Laniakea instance hosted at an ELIXIR-IT Cloud facility.\n\nConclusionsThe migration of scientific computational services towards virtualization and e-infrastructures is one of the most visible trends of our times. Laniakea provides Cloud administrators with a ready-to-use software suite that enables them to offer Galaxy, a popular workflow manager for bioinformatics, as an on-demand PaaS to their users. We believe that Laniakea can concur in making the many advantages of using Galaxy more accessible to a broader user base by removing most of the burdens involved in running a private instance. Finally, Laniakeas design is sufficiently general and modular that could be easily adapted to support different services and platforms beyond Galaxy.

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bioRxiv2018

Consensus clustering of herpesvirus protein interaction networks provides insights into their evolutionary relationship with bacteriophages

Infections with human herpesviruses are ubiquitous and a public health concern worldwide. Current treatments reduce the morbidity of some manifested symptoms but neither remove the viral reservoir from the infected host nor protect from the recurrent symptom outbreaks that characterize herpetic infections. The difficulty in therapeutically tackling these viral systems stems in part from their remarkably large proteomes and the complex networks of physical and functional associations that they tailor. This study presents our efforts to unravel the complexity of the interactome of herpes simplex virus type 1 (HSV1), the prototypical herpesvirus species. Inspired by our previous work, we present an improved computational pipeline for the protein-protein interaction (PPI) network reconstruction in HSV1, combining both experimentally supported and bioinformatic predictions. Our newly-developed consensus clustering approach allows us to extend the analysis beyond binary physical interactions and reveals higher order functional associations including that of pUS10 with capsid proteins. In-depth bioinformatics sequence analysis unraveled structural features of this 34-36 kDa protein reminiscent of those observed in some capsid-associated proteins in tailed bacteriophages, with which herpesviruses are thought to share a common ancestry. This suggests that pUS10 could represent an evolutionary vestige between these two viral lineages. Using immunoaffinity purification-mass spectrometry we found that pUS10 specifically co-isolated with the inner tegument protein pUL37, which binds cytosolic capsids, contributing to initial tegumentation and eventual virion maturation. In summary, this study unveils new insights at both the system and molecular levels that can help better understand the complexity behind herpesvirus infections.

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bioRxiv2018

Ultra-deep, long-read nanopore sequencing of mock microbial community standards

BackgroundLong sequencing reads are information-rich: aiding de novo assembly and reference mapping, and consequently have great potential for the study of microbial communities. However, the best approaches for analysis of long-read metagenomic data are unknown. Additionally, rigorous evaluation of bioinformatics tools is hindered by a lack of long-read data from validated samples with known composition.\n\nMethodsWe sequenced two commercially-available mock communities containing ten microbial species (ZymoBIOMICS Microbial Community Standards) with Oxford Nanopore GridION and PromethION. Both communities and the ten individual species isolates were also sequenced with Illumina technology.\n\nDataWe generated 14 and 16 Gbp from GridION flowcells and 146 and 148 Gbp from PromethION flowcells for the even and log communities respectively. Read length N50 was 5.3 Kbp and 5.2 Kbp for the even and log community, respectively. Basecalls and corresponding signal data are made available (4.2 TB in total).\n\nResultsAlignment to Illumina-sequenced isolates demonstrated the expected microbial species at anticipated abundances, with the limit of detection for the lowest abundance species below 50 cells (GridION). De novo assembly of metagenomes recovered long contiguous sequences without the need for pre-processing techniques such as binning.\n\nConclusionsWe present ultra-deep, long-read nanopore datasets from a well-defined mock community. These datasets will be useful for those developing bioinformatics methods for long-read metagenomics and for the validation and comparison of current laboratory and software pipelines.

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bioRxiv2018

Accelerating structure-function mapping using the ViVa webtool to mine natural variation

Thousands of sequenced genomes are now publicly available capturing a significant amount of natural variation within plant species; yet, much of this data remains inaccessible to researchers without significant bioinformatics experience. Here, we present a webtool called ViVa (Visualizing Variation) which aims to empower any researcher to take advantage of the amazing genetic resource collected in the Arabidopsis thaliana 1001 Genomes Project (http://1001genomes.org). ViVa facilitates data mining on the gene, gene family or gene network level. To test the utility and accessibility of ViVa, we assembled a team with a range of expertise within biology and bioinformatics to analyze the natural variation within the well-studied nuclear auxin signaling pathway. Our analysis has provided further confirmation of existing knowledge and has also helped generate new hypotheses regarding this well studied pathway. These results highlight how natural variation could be used to generate and test hypotheses about less studied gene families and networks, especially when paired with biochemical and genetic characterization. ViVa is also readily extensible to databases of interspecific genetic variation in plants as well as other organisms, such as the 3,000 Rice Genomes Project (http://snp-seek.irri.org/) and human genetic variation (https://www.ncbi.nlm.nih.gov/clinvar/).

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bioRxiv2018

Anacapa Toolkit: an environmental DNA toolkit for processing multilocus metabarcode datasets

1. Environmental DNA (eDNA) metabarcoding is a promising method to monitor species and community diversity that is rapid, affordable, and non-invasive. Longstanding needs of the eDNA community are modular informatics tools, comprehensive and customizable reference databases, flexibility across high-throughput sequencing platforms, fast multilocus metabarcode processing, and accurate taxonomic assignment. As bioinformatics tools continue to improve, addressing each of these demands within a single bioinformatics toolkit is becoming a reality.\n\n2. We present the modular metabarcode sequence toolkit Anacapa (https://github.com/limey-bean/Anacapa/), which addresses the above needs, allowing users to build comprehensive reference databases and assign taxonomy to raw multilocus metabarcode sequence data A novel aspect of Anacapa is our database building module, Creating Reference libraries Using eXisting tools (CRUX), which generates comprehensive reference databases for specific user-defined metabarcode loci. The Quality Control and Dereplication module sorts and processes multiple metabarcode loci and processes merged, unmerged and unpaired reads maximizing recovered diversity. Followed by amplicon sequence variants (ASVs) detection using DADA2. The Anacapa Classifier module aligns these ASVs to CRUX-generated reference databases using Bowtie2. Taxonomy is assigned to ASVs with confidence scores using a Bayesian Lowest Common Ancestor (BLCA) method. The Anacapa Toolkit also includes an R package, ranacapa, for automated results exploration through standard biodiversity statistical analysis.\n\n3. We performed a series of benchmarking tests to verify that the Anacapa Toolkit generates comprehensive reference databases that capture wide taxonomic diversity and that it can assign high-quality taxonomy to both MiSeq-length and Hi-Seq length sequence data. We demonstrate the value of the Anacapa Toolkit to assigning taxonomy to eDNA sequences from seawater samples from southern California including capability of this tool kit to process multilocus metabarcoding data.\n\n4. The Anacapa Toolkit broadens the exploration of eDNA and assists in biodiversity assessment and management by generating metabarcode specific databases, processing multilocus data, retaining all read types, and expanding non-traditional eDNA targets. Anacapa software and source code are open and available in a virtual container to ease installation.

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bioRxiv2018

PhyloSuite: an integrated and scalable desktop platform for streamlined molecular sequence data management and evolutionary phylogenetics studies

Multi-gene and genomic datasets have become commonplace in the field of phylogenetics, but many of the existing tools are not designed for such datasets, which makes the analysis time-consuming and tedious. We therefore present PhyloSuite, a user-friendly workflow desktop platform dedicated to streamlining molecular sequence data management and evolutionary phylogenetics studies. It employs a plugin-based system that integrates a number of useful phylogenetic and bioinformatic tools, thereby streamlining the entire procedure, from data acquisition to phylogenetic tree annotation, with the following features: (i) point-and-click and drag-and-drop graphical user interface, (ii) a workspace to manage and organize molecular sequence data and results of analyses, (iii) GenBank entries extraction and comparative statistics, (iv) a phylogenetic workflow with batch processing capability, (v) elaborate bioinformatic analysis for mitochondrial genomes. The aim of PhyloSuite is to enable researchers to spend more time playing with scientific questions, instead of wasting it on conducting standard analyses. The compiled binary of PhyloSuite is available under the GPL license at https://github.com/dongzhang0725/PhyloSuite/releases, implemented in Python and runs on Windows, Mac OSX and Linux.

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bioRxiv2019

Integrating past, present and future wheat research with Pretzel

MotivationMajor advances have been made in the assembly of complex genomes such as wheat. Remaining challenges include linking these new resources to legacy research, as well as lowering the bar to entry for members of the research community who may lack specific bioinformatics skills.\n\nResultsPretzel aims to solve these problems by providing an interactive, online environment for data visualisation and analysis which, when loaded with appropriately curated data, can enable researchers with no bioinformatics training to exploit the latest genomic resources. We demonstrate that Pretzel can be used to answer common questions asked by researchers as well as for advanced curation of pseudomolecule structure.\n\nAvailabilityPretzel is implemented in JavaScript and is freely available under a GPLv3 license online at: https://github.com/plantinformatics/pretzel.\n\nContactgabriel.keeble-gagnere@ecodev.vic.gov.au

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bioRxiv2019

Speed, accuracy, sensitivity and quality control choices for detecting clinically relevant microbes in whole blood from patients.

Infections are a serious health concern worldwide, particularly in vulnerable populations such as the immunocompromised, elderly, and young. Advances in metagenomic sequencing availability, speed, and decreased cost offer the opportunity to supplement or replace culture-based identification of pathogens with DNA sequence-based diagnostics. Adopting metagenomic analysis for clinical use requires that all aspects of the pipeline are optimized and tested, including data analysis. We tested the accuracy, sensitivity, and resource requirements of Centrifuge within the context of clinically relevant bacteria. Binary mixtures of bacteria showed Centrifuge reliably identified organisms down to 0.1% relative abundance. A staggered mock bacterial community showed Centrifuge outperformed CLARK while requiring less computing resources. Shotgun metagenomes obtained from whole blood in three febrile neutropenia patients showed Centrifuge could identify both bacteria and viruses as part of a culture-free workflow. Finally, Centrifuge results changed minimally by eliminating time-consuming read quality control and host screening steps. AUTHOR SUMMARYImmunocompromised patients, such as those with febrile neutropenia (FN), are susceptible to infections, yet cultures fail to identify causative organisms ~80% of the time. High-throughput metagenomic sequencing offers a promising approach for identifying pathogens in clinical samples. Mining through metagenomes can be difficult given the volume of reads, overwhelming human contamination, and lack of well-defined bioinformatics methods. The goal of our study was to assess Centrifuge, a leading tool for the identification and quantitation of microbes, and provide a streamlined bioinformatics workflow real-word data from FN patient blood samples. To ensure the accuracy of the workflow we carefully examined each step using known bacterial mixtures that varied by genetic distance and abundance. We show that Centrifuge reliably identifies microbes present at just 1% relative abundance and requires substantially less computer time and resource than CLARK. Moreover, we found that Centrifuge results changed minimally by quality control and host-screening allowing for further reduction in compute time. Next, we leveraged Centrifuge to identify viruses and bacteria in blood draws for three FN patients, and confirmed suspected pathogens using genome coverage plots. We developed a web-based tool in iMicrobe and detailed protocols to promote re-use.

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bioRxiv2019

SquiggleKit: A toolkit for manipulating nanopore signal data

The management of raw nanopore sequencing data poses a challenge that must be overcome to accelerate the development of new bioinformatics algorithms predicated on signal analysis. SquiggleKit is a toolkit for manipulating and interrogating nanopore data that simplifies file handling, data extraction, visualisation, and signal processing. Its modular tools can be used to reduce file numbers and memory footprint, identify poly-A tails, target barcodes, adapters, and find nucleotide sequence motifs in raw nanopore signal, amongst other applications. SquiggleKit serves as a bioinformatics portal into signal space, for novice and experienced users alike. It is comprehensively documented, simple to use, cross-platform compatible and freely available from (https://github.com/Psy-Fer/SquiggleKit).

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bioRxiv2019

Patterns, Profiles, and Parsimony: dissecting transcriptional signatures from minimal single-cell RNA-seq output with SALSA

Single-cell RNA sequencing (scRNA-seq) technologies have precipitated the development of bioinformatic tools to reconstruct cell lineage specification and differentiation processes with single-cell precision. However, start-up costs and data volumes currently required for statistically reproducible insight remain prohibitively expensive, preventing scRNA-seq technologies from becoming mainstream. Here, we introduce single-cell amalgamation by latent semantic analysis (SALSA), a versatile workflow to address those issues from a data science perspective. SALSA is an integrative and systematic methodology that introduces matrix focusing, a parametric frequentist approach to identify fractions of statistically significant and robust data within single-cell expression matrices. SALSA then transforms the focused matrix into an imputable mix of data-positive and data-missing information, projects it into a latent variable space using generalized linear modelling, and extracts patterns of enrichment. Last, SALSA leverages multivariate analyses, adjusted for rates of library-wise transcript detection and cluster-wise gene representation across latent patterns, to assign individual cells under distinct transcriptional profiles via unsupervised hierarchical clustering. In SALSA, cell type assignment relies exclusively on genes expressed both robustly, relative to sequencing noise, and differentially, among latent patterns, which represent best-candidates for confirmatory validation assays. To benchmark how SALSA performs in experimental settings, we used the publicly available 10X Genomics PBMC 3K dataset, a pre-curated silver standard comprising 2,700 single-cell barcodes from human frozen peripheral blood with transcripts aligned to 16,634 genes. SALSA identified at least 7 distinct transcriptional profiles in PBMC 3K based on <500 differentially expressed Profiler genes determined agnostically, which matched expected frequencies of dominant cell types in peripheral blood. We confirmed that each transcriptional profile inferred by SALSA matched known expression signatures of blood cell types based on surveys of 15 landmark genes and other supplemental markers. SALSA was able to resolve transcriptional profiles from only [~]9% of the total count data accrued, spread across <0.5% of the PBMC 3K expression matrix real estate (16,634 genes x 2,700 cells). In conclusion, SALSA amalgamates scRNA-seq data in favor of reproducible findings. Furthermore, by extracting statistical insight at lower experimental costs and computational workloads than previously reported, SALSA represents an alternative bioinformatics strategy to make single-cell technologies affordable and widespread.

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bioRxiv2019

In Silico Analysis and Modeling of Novel Pathogenic Single Nucleotide Polymorphisms (SNPs) in Human CD40LG Gene

AbstractO_ST_ABSBackgroundC_ST_ABSThe X-linked hyper-immunoglobulin M syndrome (XHIGM) is a rare, inherited immune deficiency disorder. It is more common in males. Characterized by elevated serum IgM levels and low to undetectable levels of serum IgG, IgA and IgE. Hyper-IgM syndrome is caused by mutations in the CD40LG gene. Located in human Xq26. CD40LG acts as an immune modulator in activated T cells. MethodWe used different bioinformatics tools to predict the effect of each SNP on the structure and function of the protein. Result8 novel SNPs out of 233 were found to have most deleterious effect on the protein structure and function. While modeling of nsSNPs was studied by Project HOPE software. ConclusionBetter understanding of Hyper-IgM syndrome caused by mutations in CD40LG gene was achieved using in silico analysis. This is the first in silico functional analysis of CD40LG gene and 8 novel mutations were found using different bioinformatics tools, and they could be used as diagnostic markers for hyper-IgM syndrome. These 8 novel SNPs may be important candidates for the cause of different types of human diseases by CD40LG gene.

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bioRxiv2019

fastq-pair: efficient synchronization of paired-end fastq files

Paired end DNA sequencing provides additional information about the sequence data that is used in sequence assembly, mapping, and other downstream bioinformatics analysis. Paired end reads are usually provided as two fastq-format files, with each file representing one end of the read. Many commonly used downstream tools require that the sequence reads appear in each file in the same order, and reads that do not have a pair in the corresponding file are placed in a separate file of singletons. Although most sequencing instruments capable of generating paired end reads produce files where each read has a corresponding mate, many downstream bioinformatics manipulations break the one-to-one correspondence between reads, and paired-end sequence files loose synchronicity, and contain either unordered sequences or sequences in one or other file without a mate. Trivial solutions to this problem require reading one or both of the DNA sequence files into memory but quickly become limited by computational resources for moderate to large sized sequence files that are common nowadays. Here, we introduce a fast and memory efficient solution, written in C for portability, that synchronizes paired-end fastq files for subsequent analysis and places unmatched reads into singleton files. Fastq-pair is freely available from https://github.com/linsalrob/fastq-pair and is released under the MIT license.

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bioRxiv2019

In silico analysis of IDH3A gene revealed Novel mutations associated with Retinitis Pigmentosa

BackgroundRetinitis Pigmentosa (RP) refers to a group of inherited disorders characterized by the death of photoreceptor cells leading to blindness. The aim of this study is to identify the pathogenic SNPs in the IDH3A gene and their effect on the structure and function of the protein. Methodwe used different bioinformatics tools to predict the effect of each SNP on the structure and function of the protein. Result20 deleterious SNPs out of 178 were found to have a damaging effect on the protein structure and function. Conclusionthis is the first in silico analysis of IDH3A gene and 20 novel mutations were found using different bioinformatics tools, and they could be used as diagnostic markers for Retinitis Pigmentosa.

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bioRxiv2019

Exploration of interaction scoring criteria in the CANDO platform

BackgroundDrug discovery is an arduous process that requires many years and billions of dollars before approval for patient use. However, there are a number of drugs and human ingestibles approved for a variety of indications/diseases that can be potentially repurposed as new treatments for others, decreasing the time and cost required.\n\nMethodsCANDO (Computational Analysis of Novel Drug Opportunities) is a platform for shotgun, multitarget drug discovery and repurposing. The CANDO platform scores interactions between 46,784 proteins structures and 3,733 human use compounds using a bioinformatic docking protocol to generate compound-proteome interaction signatures that are then compared to identify candidates for repurposing. Benchmarking of the platform is accomplished by comparing the compound-proteome interaction signatures and determining whether signatures corresponding to pairs of drugs approved for the same indication fall within particular cutoffs.\n\nResultsWe have altered the scoring function of bioinformatic docking protocol in the newest version of our platform (v1.5) to use the best OBscore for each compound-protein interaction, resulting in an increased benchmarking accuracy from 11.7% in v1 to 12.8% in v1.5 for the top10 cutoff, the most stringent one used, and correspondingly from 24.9% to 31.2% for the top100 cutoff.\n\nConclusionsThe change in the interaction scoring and other bug fixes in CANDO v1.5 have resulted in improved benchmarking performance, making the platform more effective at predicting novel, therapeutic drug-indication pairs.

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bioRxiv2019

Influenza Classification from Short Reads with VAPOR Facilitates Robust Mapping Pipelines and Zoonotic Strain Detection for Routine Surveillance Applications

BackgroundInfluenza viruses are associated with a significant global public health burden. The segmented RNA genome of influenza changes continually due to mutation, and the accumulation of these changes within the antigenic recognition sites of haemagglutinin (HA) and neuraminidase (NA) in turn leads to annual epidemics. Influenza A is also zoonotic, allowing for exchange of segments between human and non-human viruses, resulting in new strains with pandemic potential. These processes necessitate a global surveillance system for influenza monitoring. To this end, whole-genome sequencing (WGS) has begun to emerge as a useful tool. However, due to the diversity and mutability of the influenza genome, and noise in short-read data, bioinformatics processing can present challenges.\n\nResultsConventional mapping approaches can be insufficient when a sub-optimal reference strain is chosen. For short-read datasets simulated from influenza H1N1 HA sequences, read recovery after single-reference mapping was routinely as low as 90% for human-origin influenza sequences, and often lower than 10% for those from avian hosts. To this end, we developed a de Bruijn Graph (DBG)-based classifier of influenza WGS datasets: VAPOR. In real data benchmarking using 257 WGS read sets with corresponding de novo assemblies, VAPOR provided classifications for all samples with a mean of >99.8% identity to assembled contigs. This resulted in an increase in the number of mapped reads by 6.8% on average, up to a maximum of 13.3%. Additionally, using simulations, we demonstrate that classification from reads may be applied to detection of reassorted strains.\n\nConclusionsVAPOR has potential to simplify bioinformatics pipelines for surveillance, providing a novel method for detection of influenza strains of human and non-human origin directly from reads, minimization of potential data loss and bias associated with conventional mapping, and allowing visualization of alignments that would otherwise require slow de novo assembly. Whilst with expertise and time these pitfalls can largely be avoided, with pre-classification they are remedied in a single step. Furthermore, our algorithm could be adapted in future to surveillance of other RNA viruses. VAPOR is available at https://github.com/connor-lab/vapor.

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