scholarly journals WgLink: reconstructing whole-genome viral haplotypes using L0 + L1-regularization

2020 ◽  
Author(s):  
Chen Cao ◽  
Matthew Greenberg ◽  
Quan Long
Keyword(s):  
Real Data ◽  
Data Sets ◽  
Whole Genome ◽  
Regularized Regression ◽  
Viral Genomes ◽  
Physical Linkage ◽  
Multiple Regions ◽  
Viral Sequences ◽  
Multiple Variants ◽  
Generation Sequencing

AbstractMany tools can reconstruct viral sequences based on next generation sequencing reads. Although existing tools effectively recover local regions, their accuracy suffers when reconstructing the whole viral genomes (strains). Moreover, they consume significant memory when the sequencing coverage is high or when the genome size is large. We present WgLink to meet this challenge. WgLink takes local reconstructions produced by other tools as input and patches the resulting segments together into coherent whole-genome strains. We accomplish this using an L0 + L1-regularized regression synthesizing variant allele frequency data with physical linkage between multiple variants spanning multiple regions simultaneously. WgLink achieves higher accuracy than existing tools both on simulated and real data sets while using significantly less memory (RAM) and fewer CPU hours. Source code and binaries are freely available at https://github.com/theLongLab/wglink.

scholarly journals WgLink: reconstructing whole-genome viral haplotypes using L0 + L1-regularization

2021 ◽  
Author(s):  
Chen Cao ◽  
Matthew Greenberg ◽  
Quan Long
Keyword(s):  
Supplementary Information ◽  
Whole Genome ◽  
Regularized Regression ◽  
Viral Genomes ◽  
Sequencing Coverage ◽  
Physical Linkage ◽  
Multiple Regions ◽  
Viral Sequences ◽  
Multiple Variants ◽  
Generation Sequencing

Abstract Summary Many tools can reconstruct viral sequences based on next-generation sequencing reads. Although existing tools effectively recover local regions, their accuracy suffers when reconstructing the whole viral genomes (strains). Moreover, they consume significant memory when the sequencing coverage is high or when the genome size is large. We present WgLink to meet this challenge. WgLink takes local reconstructions produced by other tools as input and patches the resulting segments together into coherent whole-genome strains. We accomplish this using an L0+L1-regularized regression, synthesizing variant allele frequency data with physical linkage between multiple variants spanning multiple regions simultaneously. WgLink achieves higher accuracy than existing tools both on simulated and on real datasets while using significantly less memory (RAM) and fewer CPU hours. Availability and implementation Source code and binaries are freely available at https://github.com/theLongLab/wglink. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals The effect of variant interference on de novo assembly for viral deep sequencing

2019 ◽  
Author(s):  
Christina J. Castro ◽  
Rachel L. Marine ◽  
Edward Ramos ◽  
Terry Fei Fan Ng
Keyword(s):  
Deep Sequencing ◽  
De Novo ◽  
Gc Content ◽  
Read Length ◽  
Viral Genomes ◽  
Minor Variant ◽  
Main Driver ◽  
Next Generation Sequencing Ngs ◽  
Viral Sequences ◽  
Generation Sequencing

AbstractViruses have high mutation rates and generally exist as a mixture of variants in biological samples. Next-generation sequencing (NGS) approach has surpassed Sanger for generating long viral sequences, yet how variants affect NGS de novo assembly remains largely unexplored. Our results from >15,000 simulated experiments showed that presence of variants can turn an assembly of one genome into tens to thousands of contigs. This “variant interference” (VI) is highly consistent and reproducible by ten most used de novo assemblers, and occurs independent of genome length, read length, and GC content. The main driver of VI is pairwise identities between viral variants. These findings were further supported by in silico simulations, where selective removal of minor variant reads from clinical datasets allow the “rescue” of full viral genomes from fragmented contigs. These results call for careful interpretation of contigs and contig numbers from de novo assembly in viral deep sequencing.

Human adenovirus penton base and encapsidation sequences detected in Pelodiscus sinensis by next generation sequencing

2019 ◽  
Vol 14 (7) ◽  
pp. 453-460
Author(s):  
Cheng Xu ◽  
Jiehao Xu ◽  
Jiating Liu ◽  
Yu Chen ◽  
Øystein Evensen ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Human Adenovirus ◽  
South East Asia ◽  
Next Generation ◽  
Penton Base ◽  
Pelodiscus Sinensis ◽  
Viral Genomes ◽  
Viral Sequences ◽  
Generation Sequencing

The Chinese soft-shelled turtle ( Pelodiscus sinensis) has become one of the leading cultured organisms in China and South East Asia. The objectives of the present study were to use next generation sequencing to identify viral genomes present in liver tissues from Chinese soft-shelled turtle in China. BLAST analysis of viral sequences from liver samples showed high homology with the human adenovirus (HAdV) penton base and encapsidation proteins. This homology points to possible existence of HAdV in freshwater environments used for the culture of soft-shelled turtles. Therefore, our findings merit further investigations to determine possible contamination of HAdV in aquaculture environments and the possible role of the Chinese soft-shelled turtle in transmitting HAdV to humans.

scholarly journals virMine: automated detection of viral sequences from complex metagenomic samples

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6695 ◽  
Author(s):  
Andrea Garretto ◽  
Thomas Hatzopoulos ◽  
Catherine Putonti
Keyword(s):  
Software Tool ◽  
Metagenomic Data ◽  
Data Sets ◽  
Data Repositories ◽  
Viral Genomes ◽  
Manual Curation ◽  
Public Data ◽  
Control Assembly ◽  
Signature Sequences ◽  
Viral Sequences

Metagenomics has enabled sequencing of viral communities from a myriad of different environments. Viral metagenomic studies routinely uncover sequences with no recognizable homology to known coding regions or genomes. Nevertheless, complete viral genomes have been constructed directly from complex community metagenomes, often through tedious manual curation. To address this, we developed the software tool virMine to identify viral genomes from raw reads representative of viral or mixed (viral and bacterial) communities. virMine automates sequence read quality control, assembly, and annotation. Researchers can easily refine their search for a specific study system and/or feature(s) of interest. In contrast to other viral genome detection tools that often rely on the recognition of viral signature sequences, virMine is not restricted by the insufficient representation of viral diversity in public data repositories. Rather, viral genomes are identified through an iterative approach, first omitting non-viral sequences. Thus, both relatives of previously characterized viruses and novel species can be detected, including both eukaryotic viruses and bacteriophages. Here we present virMine and its analysis of synthetic communities as well as metagenomic data sets from three distinctly different environments: the gut microbiota, the urinary microbiota, and freshwater viromes. Several new viral genomes were identified and annotated, thus contributing to our understanding of viral genetic diversity in these three environments.

scholarly journals Benchmarking metagenomics classifiers on ancient viral DNA: a simulation study

2021 ◽  
Author(s):  
Yami Ommar Arizmendi C&aacuterdenas ◽  
Samuel Neuenschwander ◽  
Anna-Sapfo Malaspinas
Keyword(s):  
Simulation Study ◽  
Ancient Dna ◽  
Real Data ◽  
Sequencing Error ◽  
Short Reads ◽  
Origin And Evolution ◽  
Viral Genomes ◽  
Technological Advances ◽  
Specific Virus ◽  
Viral Sequences

Owing to technological advances in ancient DNA, it is now possible to sequence viruses from the past to track down their origin and evolution. However, ancient DNA data is considerably more degraded and contaminated than modern data making the identification of ancient viral genomes particularly challenging. Several methods to characterise the modern microbiome (and, within this, the virome) have been developed. Many of them assign sequenced reads to specific taxa to characterise the organisms present in a sample of interest. While these existing tools are routinely used in modern data, their performance when applied to ancient virome data remains unknown. In this work, we conduct an extensive simulation study using public viral sequences to establish which tool is the most suitable for ancient virome studies. We compare the performance of four widely used classifiers, namely Centrifuge, Kraken2, DIAMOND and MetaPhlAn2, in correctly assigning sequencing reads to the corresponding viruses. To do so, we simulate reads by adding noise typical of ancient DNA to a randomly chosen set of publicly available viral sequences and to the human genome. We fragment the DNA into different lengths, add sequencing error and C to T and G to A deamination substitutions at the read termini. Then we measure the resulting precision and sensitivity for all classifiers. Across most simulations, 119 out of the 120 simulated viruses are recovered by Centrifuge, Kraken2 and DIAMOND in contrast to MetaPhlAn2 which recovers only around one third. While deamination damage has little impact on the performance of the classifiers, DIAMOND and Kraken2 cannot classify very short reads. For data with longer fragments, if precision is strongly favoured over sensitivity, DIAMOND performs best. However, since Centrifuge can handle short reads and since it achieves the highest sensitivity and precision at the species level, it is our recommended tool overall. Regardless of the tool used, our simulations indicate that, for ancient human studies, users should use strict filters to remove all reads of potential human origin. Finally, if the goal is to detect a specific virus, given the high variability observed among tested viral sequences, a simulation study to determine if a given tool can recover the virus of interest should be conducted prior to analysing real data.

scholarly journals Viral dark matter and virus–host interactions resolved from publicly available microbial genomes

eLife ◽  
2015 ◽  
Vol 4 ◽  
Author(s):  
Simon Roux ◽  
Steven J Hallam ◽  
Tanja Woyke ◽  
Matthew B Sullivan
Keyword(s):  
Sequence Space ◽  
Data Sets ◽  
Microbial Genomes ◽  
Bacterial Phyla ◽  
Viral Genomes ◽  
Host Interactions ◽  
Public Data ◽  
Ecological Importance ◽  
Genomic Recombination ◽  
Viral Sequences

The ecological importance of viruses is now widely recognized, yet our limited knowledge of viral sequence space and virus–host interactions precludes accurate prediction of their roles and impacts. In this study, we mined publicly available bacterial and archaeal genomic data sets to identify 12,498 high-confidence viral genomes linked to their microbial hosts. These data augment public data sets 10-fold, provide first viral sequences for 13 new bacterial phyla including ecologically abundant phyla, and help taxonomically identify 7–38% of ‘unknown’ sequence space in viromes. Genome- and network-based classification was largely consistent with accepted viral taxonomy and suggested that (i) 264 new viral genera were identified (doubling known genera) and (ii) cross-taxon genomic recombination is limited. Further analyses provided empirical data on extrachromosomal prophages and coinfection prevalences, as well as evaluation of in silico virus–host linkage predictions. Together these findings illustrate the value of mining viral signal from microbial genomes.

scholarly journals gmos: Rapid detection of genome mosaicism over short evolutionary distances

2016 ◽  
Author(s):  
Mirjana Domazet-Lošo ◽  
Tomislav Domazet-Lošo
Keyword(s):  
Real Data ◽  
Mosaic Structure ◽  
Local Alignment ◽  
Data Sets ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Multiple Sequence Alignments ◽  
Viral Genomes ◽  
Alignment Free ◽  
Query Region

AbstractProkaryotic and viral genomes are often altered by recombination and horizontal gene transfer. The existing methods for detecting recombination are primarily aimed at viral genomes or sets of loci, since the expensive computation of underlying statistical models often hinders the comparison of complete prokaryotic genomes. As an alternative, alignment-free solutions are more efficient, but cannot map (align) a query to subject genomes. To address this problem, we have developed gmos (Genome MOsaic Structure), a new program that determines the mosaic structure of query genomes when compared to a set of closely related subject genomes. The program first computes local alignments between query and subject genomes and then reconstructs the query mosaic structure by choosing the best local alignment for each query region. To accomplish the analysis quickly, the program mostly relies on pairwise alignments and constructs multiple sequence alignments over short overlapping subject regions only when necessary. This fine-tuned implementation achieves an efficiency comparable to an alignment-free tool. The program performs well for simulated and real data sets of closely related genomes and can be used for fast recombination detection; for instance, when a new prokaryotic pathogen is discovered. As an example, gmos was used to detect genome mosaicism in a pathogenic Enterococcus faecium strain compared to seven closely related genomes. The analysis took less than two minutes on a single 2.1 GHz processor. The output is available in fasta format and can be visualized using an accessory program, gmosDraw (freely available with gmos).

scholarly journals Transforming variables to central normality

2021 ◽  
Author(s):  
Jakob Raymaekers ◽  
Peter J. Rousseeuw
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimator ◽  
Simulation Study ◽  
Real Data ◽  
Data Sets ◽  
Transformation Parameter ◽  
Likelihood Estimator ◽  
Extensive Simulation ◽  
Highly Sensitive

AbstractMany real data sets contain numerical features (variables) whose distribution is far from normal (Gaussian). Instead, their distribution is often skewed. In order to handle such data it is customary to preprocess the variables to make them more normal. The Box–Cox and Yeo–Johnson transformations are well-known tools for this. However, the standard maximum likelihood estimator of their transformation parameter is highly sensitive to outliers, and will often try to move outliers inward at the expense of the normality of the central part of the data. We propose a modification of these transformations as well as an estimator of the transformation parameter that is robust to outliers, so the transformed data can be approximately normal in the center and a few outliers may deviate from it. It compares favorably to existing techniques in an extensive simulation study and on real data.

Sign in / Sign up

Export Citation Format

Share Document