scholarly journals Screening Metagenomic Data for Viruses Using the E-Probe Diagnostic Nucleic Acid Assay

2014 ◽  
Vol 104 (10) ◽  
pp. 1125-1129 ◽  
Author(s):  
A. H. Stobbe ◽  
W. L. Schneider ◽  
P. R. Hoyt ◽  
U. Melcher
Keyword(s):  
Nucleic Acid ◽  
Mosaic Virus ◽  
Yellow Mosaic Virus ◽  
Metagenomic Data ◽  
Data Sets ◽  
Virus Species ◽  
Data Set ◽  
Golden Yellow ◽  
Nucleic Acid Assay ◽  
Ngs Data

Next generation sequencing (NGS) is not used commonly in diagnostics, in part due to the large amount of time and computational power needed to identify the taxonomic origin of each sequence in a NGS data set. By using the unassembled NGS data sets as the target for searches, pathogen-specific sequences, termed e-probes, could be used as queries to enable detection of specific viruses or organisms in plant sample metagenomes. This method, designated e-probe diagnostic nucleic acid assay, first tested with mock sequence databases, was tested with NGS data sets generated from plants infected with a DNA (Bean golden yellow mosaic virus, BGYMV) or an RNA (Plum pox virus, PPV) virus. In addition, the ability to detect and differentiate among strains of a single virus species, PPV, was examined by using probe sets that were specific to strains. The use of probe sets for multiple viruses determined that one sample was dually infected with BGYMV and Bean golden mosaic virus.

scholarly journals An Extensive Meta-Metagenomic Search Identifies SARS-CoV-2-Homologous Sequences in Pangolin Lung Viromes

mSphere ◽  
2020 ◽  
Vol 5 (3) ◽  
Author(s):  
Lamia Wahba ◽  
Nimit Jain ◽  
Andrew Z. Fire ◽  
Massa J. Shoura ◽  
Karen L. Artiles ◽  
...  
Keyword(s):  
Nucleic Acid ◽  
High Speed ◽  
High Throughput Sequencing ◽  
Biological Significance ◽  
Metagenomic Data ◽  
Data Sets ◽  
Sequencing Data ◽  
Data Set ◽  
Link Type ◽  
Recent Emergence

ABSTRACT In numerous instances, tracking the biological significance of a nucleic acid sequence can be augmented through the identification of environmental niches in which the sequence of interest is present. Many metagenomic data sets are now available, with deep sequencing of samples from diverse biological niches. While any individual metagenomic data set can be readily queried using web-based tools, meta-searches through all such data sets are less accessible. In this brief communication, we demonstrate such a meta-metagenomic approach, examining close matches to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in all high-throughput sequencing data sets in the NCBI Sequence Read Archive accessible with the “virome” keyword. In addition to the homology to bat coronaviruses observed in descriptions of the SARS-CoV-2 sequence (F. Wu, S. Zhao, B. Yu, Y. M. Chen, et al., Nature 579:265–269, 2020, https://doi.org/10.1038/s41586-020-2008-3; P. Zhou, X. L. Yang, X. G. Wang, B. Hu, et al., Nature 579:270–273, 2020, https://doi.org/10.1038/s41586-020-2012-7), we note a strong homology to numerous sequence reads in metavirome data sets generated from the lungs of deceased pangolins reported by Liu et al. (P. Liu, W. Chen, and J. P. Chen, Viruses 11:979, 2019, https://doi.org/10.3390/v11110979). While analysis of these reads indicates the presence of a similar viral sequence in pangolin lung, the similarity is not sufficient to either confirm or rule out a role for pangolins as an intermediate host in the recent emergence of SARS-CoV-2. In addition to the implications for SARS-CoV-2 emergence, this study illustrates the utility and limitations of meta-metagenomic search tools in effective and rapid characterization of potentially significant nucleic acid sequences. IMPORTANCE Meta-metagenomic searches allow for high-speed, low-cost identification of potentially significant biological niches for sequences of interest.

scholarly journals Detecting bean golden yellow mosaic virus in bean breeding lines and in the common legume weed Macroptilium lathyroides in Puerto Rico.

1969 ◽  
Vol 85 (3-4) ◽  
pp. 165-176
Author(s):  
Lydia I. Rivera-Vargas ◽  
Vilmaris Bracero-Acosta ◽  
James S. Beaver ◽  
Dan E. Purcifull ◽  
Jane E. Polston ◽  
...  
Keyword(s):  
Puerto Rico ◽  
Mosaic Virus ◽  
Yellow Mosaic Virus ◽  
Phaseolus Vulgaris L ◽  
Breeding Lines ◽  
Golden Yellow ◽  
Enzymelinked Immunosorbent Assay ◽  
The Common ◽  
Polymerase Chain ◽  
Macroptilium Lathyroides

Bean golden yellow mosaic virus (BGYMV) is a geminivirus transmitted by whiteflies (Genus: Bemisia). This virus causes significant fosses in common bean (Phaseolus vulgaris L.). Serological techniques such as enzymelinked immunosorbent assay (ELISA) have been widely used for detection of viruses. We evaluated existing monoclonal antibodies (3F7,2G5 and 5C5) for the detection of BGYMV isolates in bean fines in Puerto Rico. Monoclonal antibody 3F7 was the most effective in detecting the virus in tissues of line DOR 364 and susceptible cuftivars Top Crop and Quest. However, it was not effective in the detection of BGYMV in lines of DOR 303, which showed typical symptoms. Sampfes from Macroptilium lathyroides, a weed that might be a possible reservoir of the virus, were also tested for viraf infection. ELISA tests were inconclusive for detection of geminiviruses in M. lathyroides. Polymerase Chain Reaction (PCR) was also used to complement BGYMV diagnosis in M. lathyroides and in bean lines that showed symptoms but were negative for the ELfSA test. Two sets of primers, specific for Begomovirus such as BGYMV, were used in PCR experiments. Using PCR, we were able to detect the virus in the line DOR 303 and in M. lathyroides tissues.

scholarly journals METAnnotatorX2: a Comprehensive Tool for Deep and Shallow Metagenomic Data Set Analyses

mSystems ◽  
2021 ◽  
Vol 6 (3) ◽  
Author(s):  
Christian Milani ◽  
Gabriele Andrea Lugli ◽  
Federico Fontana ◽  
Leonardo Mancabelli ◽  
Giulia Alessandri ◽  
...  
Keyword(s):  
Sensitivity And Specificity ◽  
Metagenomic Data ◽  
Data Sets ◽  
Data Set

We developed a novel tool, i.e., METAnnotatorX2, that includes a number of new advanced features for analysis of deep and shallow metagenomic data sets and is accompanied by (regularly updated) customized databases for archaea, bacteria, fungi, protists, and viruses. Both software and databases were developed so as to maximize sensitivity and specificity while including support for shallow metagenomic data sets.

scholarly journals Novel Cell-Virus-Virophage Tripartite Infection Systems Discovered in the Freshwater Lake Dishui Lake in Shanghai, China

2020 ◽  
Vol 94 (11) ◽  
Author(s):  
Shengzhong Xu ◽  
Liang Zhou ◽  
Xiaosha Liang ◽  
Yifan Zhou ◽  
Hao Chen ◽  
...  
Keyword(s):  
Green Algae ◽  
Green Alga ◽  
Phylogenetic Analyses ◽  
Freshwater Lake ◽  
Metagenomic Data ◽  
Data Sets ◽  
Data Set ◽  
Dna Viruses ◽  
Oligonucleotide Frequency ◽  
Dsdna Viruses

ABSTRACT Virophages are small parasitic double-stranded DNA (dsDNA) viruses of giant dsDNA viruses infecting unicellular eukaryotes. Except for a few isolated virophages characterized by parasitization mechanisms, features of virophages discovered in metagenomic data sets remain largely unknown. Here, the complete genomes of seven virophages (26.6 to 31.5 kbp) and four large DNA viruses (190.4 to 392.5 kbp) that coexist in the freshwater lake Dishui Lake, Shanghai, China, have been identified based on environmental metagenomic investigation. Both genomic and phylogenetic analyses indicate that Dishui Lake virophages (DSLVs) are closely related to each other and to other lake virophages, and Dishui Lake large DNA viruses are affiliated with the micro-green alga-infecting Prasinovirus of the Phycodnaviridae (named Dishui Lake phycodnaviruses [DSLPVs]) and protist (protozoan and alga)-infecting Mimiviridae (named Dishui Lake large alga virus [DSLLAV]). The DSLVs possess more genes with closer homology to that of large alga viruses than to that of giant protozoan viruses. Furthermore, the DSLVs are strongly associated with large green alga viruses, including DSLPV4 and DSLLAV1, based on codon usage as well as oligonucleotide frequency and correlation analyses. Surprisingly, a nonhomologous CRISPR-Cas like system is found in DSLLAV1, which appears to protect DSLLAV1 from the parasitization of DSLV5 and DSLV8. These results suggest that novel cell-virus-virophage (CVv) tripartite infection systems of green algae, large green alga virus (Phycodnaviridae- and Mimiviridae-related), and virophage exist in Dishui Lake, which will contribute to further deep investigations of the evolutionary interaction of virophages and large alga viruses as well as of the essential roles that the CVv plays in the ecology of algae. IMPORTANCE Virophages are small parasitizing viruses of large/giant viruses. To our knowledge, the few isolated virophages all parasitize giant protozoan viruses (Mimiviridae) for propagation and form a tripartite infection system with hosts, here named the cell-virus-virophage (CVv) system. However, the CVv system remains largely unknown in environmental metagenomic data sets. In this study, we systematically investigated the metagenomic data set from the freshwater lake Dishui Lake, Shanghai, China. Consequently, four novel large alga viruses and seven virophages were discovered to coexist in Dishui Lake. Surprisingly, a novel CVv tripartite infection system comprising green algae, large green alga viruses (Phycodnaviridae- and Mimiviridae-related), and virophages was identified based on genetic link, genomic signature, and CRISPR system analyses. Meanwhile, a nonhomologous CRISPR-like system was found in Dishui Lake large alga viruses, which appears to protect the virus host from the infection of Dishui Lake virophages (DSLVs). These findings are critical to give insight into the potential significance of CVv in global evolution and ecology.

scholarly journals Two Newly Described Begomoviruses of Macroptilium lathyroides and Common Bean

2003 ◽  
Vol 93 (7) ◽  
pp. 774-783 ◽  
Author(s):  
A. M. Idris ◽  
E. Hiebert ◽  
J. Bird ◽  
J. K. Brown
Keyword(s):  
Common Bean ◽  
Mosaic Virus ◽  
Dna Sequences ◽  
Western Hemisphere ◽  
Yellow Mosaic Virus ◽  
Nucleotide Identity ◽  
Caribbean Region ◽  
Golden Yellow ◽  
The Caribbean ◽  
Macroptilium Lathyroides

Macroptilium lathyroides, a perennial weed in the Caribbean region and Central America, is a host of Macroptilium yellow mosaic Florida virus (MaYMFV) and Macroptilium mosaic Puerto Rico virus (MaMPRV). The genomes of MaYMFV and MaMPRV were cloned from M. lathyroides and/or field-infected bean and the DNA sequences were determined. Cloned A and B components for both viruses were infectious when inoculated to M. lathyroides and common bean. Comparison of the DNA sequences for cloned A and B components with well-studied begomovirus indicated that MaMPRV (bean and M. lathyroides) and MaYMFV (M. lathyroides) are unique, previously undescribed begomo-viruses from the Western Hemisphere. Phylogenetic analysis of viral A components indicated that the closest relative of MaYMFV are members of the Bean golden yellow mosaic virus (BGYMV) group, at 76 to 78% nucleotide identity, whereas the closest relative for the A component of MaMPRV was Rhynchosia golden mosaic virus at 78% nucleotide identity. In contrast, BGYMV is the closest relative for the B component of both MaYMFV and MaMPRV, with which they share ≈68.0 and ≈72% identity, respectively. The incongruent taxonomic placement for the bipartite components for MaMPRV indicates that they did not evolve entirely along a common path. MaYMFV and MaMPRV caused distinctive symptoms in bean and M. lathyroides and were transmissible by the whitefly vector and by grafting; however, only MaYMFV was mechanically transmissible. The experimental host range for the two viruses was similar and included species within the families Fabaceae and Malvaceae, but only MaYMFV infected Malva parviflora and soybean. These results collectively indicate that MaMPRV and MaYMFV are new, previously undescribed species of the BGYMV group, a clade previously known to contain only strains and isolates of BGYMV from the Caribbean region that infect Phaseolus spp. Both MaYMFV and MaMPRV may pose an economic threat to bean production in the region.

scholarly journals DNA Analysis Confirms Macroptilium lathyroides as Alternative Host of Bean golden yellow mosaic virus

Plant Disease ◽  
2003 ◽  
Vol 87 (9) ◽  
pp. 1022-1025 ◽  
Author(s):  
V. Bracero ◽  
L. I. Rivera ◽  
J. S. Beaver
Keyword(s):  
Sequence Analysis ◽  
Nucleotide Sequence ◽  
Mosaic Virus ◽  
Yellow Mosaic Virus ◽  
Nucleotide Sequence Analysis ◽  
Alternative Host ◽  
Degenerate Primers ◽  
Viral Dna ◽  
Golden Yellow ◽  
Macroptilium Lathyroides

The leguminous weed Macroptilium lathyroides is considered a potential host of the Bean golden yellow mosaic virus (BGYMV; BGMV = Mesoamerican isolates). To determine if M. lathyroides could be a host for BGYMV, an infectivity cycle was established between this weed and Phaseolus vulgaris. Virus transmission was carried out using the whitefly, Bemisia argentifolli, as a vector. Inoculated plants of both species were examined for symptoms such as mosaic, stunting, and leaf distortion. P. vulgaris and M. lathyroides showed golden yellow mosaic symptoms during all infectivity cycle stages. Symptomatic plants of both species were tested for BGYMV using polymerase chain reaction (PCR) and nucleotide sequence analysis. Two degenerate primers sets were used for PCR to detect viral DNA: PAL1v1978/PAR1c715 and PCRc2/PBL12039. PCR analysis using primers PCRc2/PBL12039 amplified viral DNA for component B from both plant species. Nucleotide sequence analysis revealed a 93% identity between the virus isolated from M. lathyroides and the Puerto Rican isolate of BGYMV. These results confirmed that M. lathyroides could serve as an alternative host of BGYMV and that an infectivity cycle of BGYMV could possibly occur between P. vulgaris and M. lathyroides in Puerto Rico.

scholarly journals A Novel Method to Detect Bias in Short Read NGS Data

2017 ◽  
Vol 14 (3) ◽  
Author(s):  
Jamie Alnasir ◽  
Hugh P. Shanahan
Keyword(s):  
Biological Significance ◽  
Gc Content ◽  
Next Generation Sequencing Data ◽  
Data Sets ◽  
Sequencing Data ◽  
Data Set ◽  
Short Read ◽  
Novel Method ◽  
Type Data ◽  
Ngs Data

AbstractDetecting sources of bias in transcriptomic data is essential to determine signals of Biological significance. We outline a novel method to detect sequence specific bias in short read Next Generation Sequencing data. This is based on determining intra-exon correlations between specific motifs. This requires a mild assumption that short reads sampled from specific regions from the same exon will be correlated with each other. This has been implemented on Apache Spark and used to analyse two D. melanogaster eye-antennal disc data sets generated at the same laboratory. The wild type data set in drosophila indicates a variation due to motif GC content that is more significant than that found due to exon GC content. The software is available online and could be applied for cross-experiment transcriptome data analysis in eukaryotes.

scholarly journals Nonpareil 3: Fast Estimation of Metagenomic Coverage and Sequence Diversity

mSystems ◽  
2018 ◽  
Vol 3 (3) ◽  
Author(s):  
Luis M. Rodriguez-R ◽  
Santosh Gunturu ◽  
James M. Tiedje ◽  
James R. Cole ◽  
Konstantinos T. Konstantinidis
Keyword(s):  
Microbial Community ◽  
Shannon Index ◽  
Sequence Diversity ◽  
Metagenomic Data ◽  
Reference Database ◽  
Rrna Gene ◽  
Data Sets ◽  
Data Set ◽  
Genomic Study ◽  
Culture Independent

ABSTRACT Estimations of microbial community diversity based on metagenomic data sets are affected, often to an unknown degree, by biases derived from insufficient coverage and reference database-dependent estimations of diversity. For instance, the completeness of reference databases cannot be generally estimated since it depends on the extant diversity sampled to date, which, with the exception of a few habitats such as the human gut, remains severely undersampled. Further, estimation of the degree of coverage of a microbial community by a metagenomic data set is prohibitively time-consuming for large data sets, and coverage values may not be directly comparable between data sets obtained with different sequencing technologies. Here, we extend Nonpareil, a database-independent tool for the estimation of coverage in metagenomic data sets, to a high-performance computing implementation that scales up to hundreds of cores and includes, in addition, a k -mer-based estimation as sensitive as the original alignment-based version but about three hundred times as fast. Further, we propose a metric of sequence diversity ( N d ) derived directly from Nonpareil curves that correlates well with alpha diversity assessed by traditional metrics. We use this metric in different experiments demonstrating the correlation with the Shannon index estimated on 16S rRNA gene profiles and show that N d additionally reveals seasonal patterns in marine samples that are not captured by the Shannon index and more precise rankings of the magnitude of diversity of microbial communities in different habitats. Therefore, the new version of Nonpareil, called Nonpareil 3, advances the toolbox for metagenomic analyses of microbiomes. IMPORTANCE Estimation of the coverage provided by a metagenomic data set, i.e., what fraction of the microbial community was sampled by DNA sequencing, represents an essential first step of every culture-independent genomic study that aims to robustly assess the sequence diversity present in a sample. However, estimation of coverage remains elusive because of several technical limitations associated with high computational requirements and limiting statistical approaches to quantify diversity. Here we described Nonpareil 3, a new bioinformatics algorithm that circumvents several of these limitations and thus can facilitate culture-independent studies in clinical or environmental settings, independent of the sequencing platform employed. In addition, we present a new metric of sequence diversity based on rarefied coverage and demonstrate its use in communities from diverse ecosystems.

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