VirStrain: a strain identification tool for RNA viruses

1AbstractGenome epidemiology, which uses genomic data to analyze the source and spread of infectious diseases, provides important information beyond interview-based methods. Given fast accumulation of sequenced viral genomes, a basic need in genome epidemiology is to identify which reference genomes are identical or closest to the ones in a sequenced sample. Then the associated metadata such as the geographical locations can be utilized to infer the transmission network. In this work, we deliver VirStrain, a fast and accurate tool for conducting strain-level analysis from short reads. By using a greedy covering algorithm, we are able to derive unique k-mer combinations for highly similar reference genomes. VirStrain is able to detect the most possible strain and also multiple strains that may simultaneously infect the same host. We tested VirStrain on three types of RNA viruses whose reference genomes have different similarity distributions. For each types of virus, we assessed VirStrain across multiple benchmark datasets of different properties and complexity. The experimental results on both simulated and real sequencing data show that VirStrain outperforms other strain identification tools.

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Viromics Reveal a Number of Novel RNA Viruses in Swedish Mosquitoes

Viruses ◽

10.3390/v11111027 ◽

2019 ◽

Vol 11 (11) ◽

pp. 1027 ◽

Cited By ~ 6

Author(s):

Pontus Öhlund ◽

Juliette Hayer ◽

Hanna Lundén ◽

Jenny C. Hesson ◽

Anne-Lie Blomström

Keyword(s):

In Silico ◽

Complete Genome ◽

Rna Viruses ◽

Mosquito Species ◽

Geographic Locations ◽

Viral Genomes ◽

Geographic Regions ◽

Reference Genomes

Metagenomic studies of mosquitoes have revealed that their virome is far more diverse and includes many more viruses than just the pathogenic arboviruses vectored by mosquitoes. In this study, the virome of 953 female mosquitoes collected in the summer of 2017, representing six mosquito species from two geographic locations in Mid-Eastern Sweden, were characterized. In addition, the near-complete genome of nine RNA viruses were characterized and phylogenetically analysed. These viruses showed association to the viral orders Bunyavirales, Picornavirales, Articulavirales, and Tymovirales, and to the realm Ribovira. Hence, through this study, we expand the knowledge of the virome composition of different mosquito species in Sweden. In addition, by providing viral reference genomes from wider geographic regions and different mosquito species, future in silico recognition and assembly of viral genomes in metagenomic datasets will be facilitated.

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Improving Metagenomic Classification using Discriminative k-mers from Sequencing Data

10.1101/2020.02.20.957308 ◽

2020 ◽

Cited By ~ 1

Author(s):

D. Storato ◽

M. Comin

Keyword(s):

Reference Genome ◽

Actual Number ◽

Sequencing Data ◽

Viral Genomes ◽

Link Type ◽

Metagenomic Sample ◽

Reference Genomes ◽

F Measure

AbstractThe major problem when analyzing a metagenomic sample is to taxonomically annotate its reads in order to identify the species they contain. Most of the methods currently available focus on the classification of reads using a set of reference genomes and their k-mers. While in terms of precision these methods have reached percentages of correctness close to perfection, in terms of recall (the actual number of classified reads) the performances fall at around 50%. One of the reasons is the fact that the sequences in a sample can be very different from the corresponding reference genome, e.g. viral genomes are highly mutated. To address this issue, in this paper we study the problem of metagenomic reads classification by improving the reference k-mers library with novel discriminative k-mers from the input sequencing reads. We evaluated the performance in different conditions against several other tools and the results showed an improved F-measure, especially when close reference genomes are not available.Availabilityhttps://github.com/davide92/K2Mem.git

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Reference flow: reducing reference bias using multiple population genomes

Genome Biology ◽

10.1186/s13059-020-02229-3 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Nae-Chyun Chen ◽

Brad Solomon ◽

Taher Mun ◽

Sheila Iyer ◽

Ben Langmead

Keyword(s):

Genetic Variation ◽

Reference Genome ◽

Alignment Method ◽

Sequencing Data ◽

Computational Overhead ◽

Reference Flow ◽

Multiple Population ◽

Reference Bias ◽

Flow Alignment ◽

Reference Genomes

AbstractMost sequencing data analyses start by aligning sequencing reads to a linear reference genome, but failure to account for genetic variation leads to reference bias and confounding of results downstream. Other approaches replace the linear reference with structures like graphs that can include genetic variation, incurring major computational overhead. We propose the reference flow alignment method that uses multiple population reference genomes to improve alignment accuracy and reduce reference bias. Compared to the graph aligner vg, reference flow achieves a similar level of accuracy and bias avoidance but with 14% of the memory footprint and 5.5 times the speed.

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Using single-cell cytometry to illustrate integrated multi-perspective evaluation of clustering algorithms using Pareto fronts

Bioinformatics ◽

10.1093/bioinformatics/btab038 ◽

2021 ◽

Author(s):

Givanna H Putri ◽

Irena Koprinska ◽

Thomas M Ashhurst ◽

Nicholas J C King ◽

Mark N Read

Keyword(s):

Single Cell ◽

Performance Metrics ◽

Clustering Algorithms ◽

Latin Hypercube Sampling ◽

Supplementary Information ◽

Sequencing Data ◽

Evaluation Protocol ◽

Benchmark Datasets ◽

Pareto Fronts ◽

Parameter Values

Abstract Motivation Many ‘automated gating’ algorithms now exist to cluster cytometry and single-cell sequencing data into discrete populations. Comparative algorithm evaluations on benchmark datasets rely either on a single performance metric, or a few metrics considered independently of one another. However, single metrics emphasize different aspects of clustering performance and do not rank clustering solutions in the same order. This underlies the lack of consensus between comparative studies regarding optimal clustering algorithms and undermines the translatability of results onto other non-benchmark datasets. Results We propose the Pareto fronts framework as an integrative evaluation protocol, wherein individual metrics are instead leveraged as complementary perspectives. Judged superior are algorithms that provide the best trade-off between the multiple metrics considered simultaneously. This yields a more comprehensive and complete view of clustering performance. Moreover, by broadly and systematically sampling algorithm parameter values using the Latin Hypercube sampling method, our evaluation protocol minimizes (un)fortunate parameter value selections as confounding factors. Furthermore, it reveals how meticulously each algorithm must be tuned in order to obtain good results, vital knowledge for users with novel data. We exemplify the protocol by conducting a comparative study between three clustering algorithms (ChronoClust, FlowSOM and Phenograph) using four common performance metrics applied across four cytometry benchmark datasets. To our knowledge, this is the first time Pareto fronts have been used to evaluate the performance of clustering algorithms in any application domain. Availability and implementation Implementation of our Pareto front methodology and all scripts and datasets to reproduce this article are available at https://github.com/ghar1821/ParetoBench. Supplementary information Supplementary data are available at Bioinformatics online.

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AStrap: identification of alternative splicing from transcript sequences without a reference genome

Bioinformatics ◽

10.1093/bioinformatics/bty1008 ◽

2018 ◽

Vol 35 (15) ◽

pp. 2654-2656 ◽

Cited By ~ 5

Author(s):

Guoli Ji ◽

Wenbin Ye ◽

Yaru Su ◽

Moliang Chen ◽

Guangzao Huang ◽

...

Keyword(s):

Machine Learning ◽

Alternative Splicing ◽

Single Molecule ◽

Reference Genome ◽

De Novo ◽

Supplementary Information ◽

Model Organisms ◽

Sequencing Data ◽

Extensive Evaluation ◽

Reference Genomes

Abstract Summary Alternative splicing (AS) is a well-established mechanism for increasing transcriptome and proteome diversity, however, detecting AS events and distinguishing among AS types in organisms without available reference genomes remains challenging. We developed a de novo approach called AStrap for AS analysis without using a reference genome. AStrap identifies AS events by extensive pair-wise alignments of transcript sequences and predicts AS types by a machine-learning model integrating more than 500 assembled features. We evaluated AStrap using collected AS events from reference genomes of rice and human as well as single-molecule real-time sequencing data from Amborella trichopoda. Results show that AStrap can identify much more AS events with comparable or higher accuracy than the competing method. AStrap also possesses a unique feature of predicting AS types, which achieves an overall accuracy of ∼0.87 for different species. Extensive evaluation of AStrap using different parameters, sample sizes and machine-learning models on different species also demonstrates the robustness and flexibility of AStrap. AStrap could be a valuable addition to the community for the study of AS in non-model organisms with limited genetic resources. Availability and implementation AStrap is available for download at https://github.com/BMILAB/AStrap. Supplementary information Supplementary data are available at Bioinformatics online.

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Accurate Reconstruction of Microbial Strains from Metagenomic Sequencing Using Representative Reference Genomes

10.1101/215707 ◽

2017 ◽

Cited By ~ 2

Author(s):

Zhemin Zhou ◽

Nina Luhmann ◽

Nabil-Fareed Alikhan ◽

Christopher Quince ◽

Mark Achtman

Keyword(s):

Evaluation Studies ◽

Species Level ◽

Metagenomic Sequencing ◽

Sequencing Data ◽

Reference Databases ◽

Microbial Strains ◽

Taxonomic Assignments ◽

Taxonomic Groups ◽

Reference Genomes ◽

Recent Evaluation

AbstractExploring the genetic diversity of microbes within the environment through metagenomic sequencing first requires classifying these reads into taxonomic groups. Current methods compare these sequencing data with existing biased and limited reference databases. Several recent evaluation studies demonstrate that current methods either lack sufficient sensitivity for species-level assignments or suffer from false positives, overestimating the number of species in the metagenome. Both are especially problematic for the identification of low-abundance microbial species, e. g. detecting pathogens in ancient metagenomic samples. We present a new method, SPARSE, which improves taxonomic assignments of metagenomic reads. SPARSE balances existing biased reference databases by grouping reference genomes into similarity-based hierarchical clusters, implemented as an efficient incremental data structure. SPARSE assigns reads to these clusters using a probabilistic model, which specifically penalizes non-specific mappings of reads from unknown sources and hence reduces false-positive assignments. Our evaluation on simulated datasets from two recent evaluation studies demonstrated the improved precision of SPARSE in comparison to other methods for species-level classification. In a third simulation, our method successfully differentiated multiple co-existing Escherichia coli strains from the same sample. In real archaeological datasets, SPARSE identified ancient pathogens with ≤ 0.02% abundance, consistent with published findings that required additional sequencing data. In these datasets, other methods either missed targeted pathogens or reported non-existent ones. SPARSE and all evaluation scripts are available at https://github.com/zheminzhou/SPARSE.

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An R Package for Divergence Analysis of Omics Data

10.1101/720391 ◽

2019 ◽

Author(s):

Wikum Dinalankara ◽

Qian Ke ◽

Donald Geman ◽

Luigi Marchionni

Keyword(s):

High Throughput Sequencing ◽

R Package ◽

The Cancer Genome Atlas ◽

High Dimensional ◽

Omics Data ◽

Sequencing Data ◽

High Throughput Sequencing Data ◽

Ternary Code ◽

Cancer Genome Atlas ◽

Level Analysis

AbstractGiven the ever-increasing amount of high-dimensional and complex omics data becoming available, it is increasingly important to discover simple but effective methods of analysis. Divergence analysis transforms each entry of a high-dimensional omics profile into a digitized (binary or ternary) code based on the deviation of the entry from a given baseline population. This is a novel framework that is significantly different from existing omics data analysis methods: it allows digitization of continuous omics data at the univariate or multivariate level, facilitates sample level analysis, and is applicable on many different omics platforms. The divergence package, available on the R platform through the Bioconductor repository collection, provides easy-to-use functions for carrying out this transformation. Here we demonstrate how to use the package with sample high throughput sequencing data from the Cancer Genome Atlas.

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EagleImp: Fast and Accurate Genome-wide Phasing and Imputation in a Single Tool

10.1101/2022.01.11.475810 ◽

2022 ◽

Author(s):

Lars Wienbrandt ◽

David Ellinghaus

Keyword(s):

Memory Management ◽

Imputation Accuracy ◽

Simulated Data ◽

Genotype Imputation ◽

Whole Genome Sequencing Data ◽

Common Variants ◽

Sequencing Data ◽

1000 Genomes ◽

Genome Wide ◽

Reference Genomes

Background: Reference-based phasing and genotype imputation algorithms have been developed with sublinear theoretical runtime behaviour, but runtimes are still high in practice when large genome-wide reference datasets are used. Methods: We developed EagleImp, a software with algorithmic and technical improvements and new features for accurate and accelerated phasing and imputation in a single tool. Results: We compared accuracy and runtime of EagleImp with Eagle2, PBWT and prominent imputation servers using whole-genome sequencing data from the 1000 Genomes Project, the Haplotype Reference Consortium and simulated data with more than 1 million reference genomes. EagleImp is 2 to 10 times faster (depending on the single or multiprocessor configuration selected) than Eagle2/PBWT, with the same or better phasing and imputation quality in all tested scenarios. For common variants investigated in typical GWAS studies, EagleImp provides same or higher imputation accuracy than the Sanger Imputation Service, Michigan Imputation Server and the newly developed TOPMed Imputation Server, despite larger (not publicly available) reference panels. It has many new features, including automated chromosome splitting and memory management at runtime to avoid job aborts, fast reading and writing of large files, and various user-configurable algorithm and output options. Conclusions: Due to the technical optimisations, EagleImp can perform fast and accurate reference-based phasing and imputation for future very large reference panels with more than 1 million genomes. EagleImp is freely available for download from https://github.com/ikmb/eagleimp.

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Utilizing the VirIdAl Pipeline to Search for Viruses in the Metagenomic Data of Bat Samples

Viruses ◽

10.3390/v13102006 ◽

2021 ◽

Vol 13 (10) ◽

pp. 2006

Author(s):

Anna Y Budkina ◽

Elena V Korneenko ◽

Ivan A Kotov ◽

Daniil A Kiselev ◽

Ilya V Artyushin ◽

...

Keyword(s):

Large Scale ◽

High Throughput Sequencing ◽

Metagenomic Data ◽

Sequencing Data ◽

Viral Pathogens ◽

Genomic Databases ◽

Bioinformatic Pipeline ◽

Viral Genomes ◽

Sequencing Technologies ◽

Viral Screening

According to various estimates, only a small percentage of existing viruses have been discovered, naturally much less being represented in the genomic databases. High-throughput sequencing technologies develop rapidly, empowering large-scale screening of various biological samples for the presence of pathogen-associated nucleotide sequences, but many organisms are yet to be attributed specific loci for identification. This problem particularly impedes viral screening, due to vast heterogeneity in viral genomes. In this paper, we present a new bioinformatic pipeline, VirIdAl, for detecting and identifying viral pathogens in sequencing data. We also demonstrate the utility of the new software by applying it to viral screening of the feces of bats collected in the Moscow region, which revealed a significant variety of viruses associated with bats, insects, plants, and protozoa. The presence of alpha and beta coronavirus reads, including the MERS-like bat virus, deserves a special mention, as it once again indicates that bats are indeed reservoirs for many viral pathogens. In addition, it was shown that alignment-based methods were unable to identify the taxon for a large proportion of reads, and we additionally applied other approaches, showing that they can further reveal the presence of viral agents in sequencing data. However, the incompleteness of viral databases remains a significant problem in the studies of viral diversity, and therefore necessitates the use of combined approaches, including those based on machine learning methods.

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The global and local distribution of RNA structure throughout the SARS-CoV-2 genome

Journal of Virology ◽

10.1128/jvi.02190-20 ◽

2020 ◽

Cited By ~ 2

Author(s):

Rafael de Cesaris Araujo Tavares ◽

Gandhar Mahadeshwar ◽

Han Wan ◽

Nicholas C. Huston ◽

Anna Marie Pyle

Keyword(s):

In Silico ◽

Rna Structure ◽

Drug Targets ◽

Rna Folding ◽

Rna Viruses ◽

Viral Agent ◽

Rna Structures ◽

Viral Rnas ◽

Viral Genomes ◽

Rna Genome

SARS-CoV-2 is the causative viral agent of COVID-19, the disease at the center of the current global pandemic. While knowledge of highly structured regions is integral for mechanistic insights into the viral infection cycle, very little is known about the location and folding stability of functional elements within the massive, ∼30kb SARS-CoV-2 RNA genome. In this study, we analyze the folding stability of this RNA genome relative to the structural landscape of other well-known viral RNAs. We present an in-silico pipeline to predict regions of high base pair content across long genomes and to pinpoint hotspots of well-defined RNA structures, a method that allows for direct comparisons of RNA structural complexity within the several domains in SARS-CoV-2 genome. We report that the SARS-CoV-2 genomic propensity for stable RNA folding is exceptional among RNA viruses, superseding even that of HCV, one of the most structured viral RNAs in nature. Furthermore, our analysis suggests varying levels of RNA structure across genomic functional regions, with accessory and structural ORFs containing the highest structural density in the viral genome. Finally, we take a step further to examine how individual RNA structures formed by these ORFs are affected by the differences in genomic and subgenomic contexts, which given the technical difficulty of experimentally separating cellular mixtures of sgRNA from gRNA, is a unique advantage of our in-silico pipeline. The resulting findings provide a useful roadmap for planning focused empirical studies of SARS-CoV-2 RNA biology, and a preliminary guide for exploring potential SARS-CoV-2 RNA drug targets. Importance The RNA genome of SARS-CoV-2 is among the largest and most complex viral genomes, and yet its RNA structural features remain relatively unexplored. Since RNA elements guide function in most RNA viruses, and they represent potential drug targets, it is essential to chart the architectural features of SARS-CoV-2 and pinpoint regions that merit focused study. Here we show that RNA folding stability of SARS-CoV-2 genome is exceptional among viral genomes and we develop a method to directly compare levels of predicted secondary structure across SARS-CoV-2 domains. Remarkably, we find that coding regions display the highest structural propensity in the genome, forming motifs that differ between the genomic and subgenomic contexts. Our approach provides an attractive strategy to rapidly screen for candidate structured regions based on base pairing potential and provides a readily interpretable roadmap to guide functional studies of RNA viruses and other pharmacologically relevant RNA transcripts.

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