Computational approaches in viral ecology

Khot, Varada; Strous, Marc; Hawley, Alyse K.

doi:10.1016/j.csbj.2020.06.019

Cited by 22 publications

(26 citation statements)

References 86 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Vigneron et al found that these (putative host) groups were among the most abundant in the summer [ 17 ]. However, the validity of this approach to identifying host groups and assigning taxonomy is questionable as it is unable to account for the mosaicism of viral genomes and assumes a common origin for viruses that may have arisen independently [ 62 ]. In addition, most of the UViGs from this study have deep branches indicating a distant homology with classified viruses which is likely a reflection of the paucity of viruses in the database when compared to the true viral diversity, and the novelty of the viruses in thermokarst lakes.…”

Section: Resultsmentioning

confidence: 99%

Seasonal Regime Shift in the Viral Communities of a Permafrost Thaw Lake

Girard

Langlois

Vigneron

et al. 2020

Viruses

View full text Add to dashboard Cite

Permafrost thaw lakes including thermokarst lakes and ponds are ubiquitous features of Subarctic and Arctic landscapes and are hotspots of microbial activity. Input of terrestrial organic matter into the planktonic microbial loop of these lakes may greatly amplify global greenhouse gas emissions. This microbial loop, dominated in the summer by aerobic microorganisms including phototrophs, is radically different in the winter, when metabolic processes shift to the anaerobic degradation of organic matter. Little is known about the viruses that infect these microbes, despite evidence that viruses can control microbial populations and influence biogeochemical cycling in other systems. Here, we present the results of a metagenomics-based study of viruses in the larger than 0.22 µm fraction across two seasons (summer and winter) in a permafrost thaw lake in Subarctic Canada. We uncovered 351 viral populations (vOTUs) in the surface waters of this lake, with diversity significantly greater during the summer. We also identified and characterized several phage genomes and prophages, which were mostly present in the summer. Finally, we compared the viral community of this waterbody to other habitats and found unexpected similarities with distant bog lakes in North America.

show abstract

Section: Resultsmentioning

confidence: 99%

Seasonal Regime Shift in the Viral Communities of a Permafrost Thaw Lake

Girard

Langlois

Vigneron

et al. 2020

Viruses

View full text Add to dashboard Cite

show abstract

“…During the current pandemic and until now, many virus detection techniques have been extensively utilized. These methods include amplifying and sequencing virus-related genes coding, particularly pathogenic proteins [ 19 , 20 ], detecting the virus in host cells lysates [ 21 ], etc. However, these approaches have certain drawbacks that limit their usage as routine point-of-care tests.…”

Section: Detection Methodsmentioning

confidence: 99%

An Overview on SARS-CoV-2 (COVID-19) and Other Human Coronaviruses and Their Detection Capability via Amplification Assay, Chemical Sensing, Biosensing, Immunosensing, and Clinical Assays

et al. 2020

View full text Add to dashboard Cite

A novel coronavirus of zoonotic origin (SARS-CoV-2) has recently been recognized in patients with acute respiratory disease. COVID-19 causative agent is structurally and genetically similar to SARS and bat SARS-like coronaviruses. The drastic increase in the number of coronavirus and its genome sequence have given us an unprecedented opportunity to perform bioinformatics and genomics analysis on this class of viruses. Clinical tests like PCR and ELISA for rapid detection of this virus are urgently needed for early identification of infected patients. However, these techniques are expensive and not readily available for point-of-care (POC) applications. Currently, lack of any rapid, available, and reliable POC detection method gives rise to the progression of COVID-19 as a horrible global problem. To solve the negative features of clinical investigation, we provide a brief introduction of the general features of coronaviruses and describe various amplification assays, sensing, biosensing, immunosensing, and aptasensing for the determination of various groups of coronaviruses applied as a template for the detection of SARS-CoV-2. All sensing and biosensing techniques developed for the determination of various classes of coronaviruses are useful to recognize the newly immerged coronavirus, i.e., SARS-CoV-2. Also, the introduction of sensing and biosensing methods sheds light on the way of designing a proper screening system to detect the virus at the early stage of infection to tranquilize the speed and vastity of spreading. Among other approaches investigated among molecular approaches and PCR or recognition of viral diseases, LAMP-based methods and LFAs are of great importance for their numerous benefits, which can be helpful to design a universal platform for detection of future emerging pathogenic viruses.

show abstract

“…The process of characterizing MGE diversity presents two major challenges: (i) accurate and sensitive detection of MGEs, and (ii) MGE classification. An increasingly large number of tools have been developed to search genomic sequence data for putative MGEs using distinct computational approaches (18). Each of these individual approaches to MGE prediction has advantages and limitations.…”

Section: Importance Introductionmentioning

confidence: 99%

“…Genome alignment tools identify all non-core genomic elements, which include MGEs but can also include other accessory genomic features, such as recombination hotspots (20), cell surface modification loci (20,21), and CRISPR-Cas systems (22). Machine learning and deep learning-based tools may require large dataset inputs or customized training data, which may not be available for most microbial systems (18,23,24). Furthermore, MGE identification is most thoroughly tested on clinically-relevant and well-studied host microbes, especially those in the phylum Proteobacteria (19,(23)(24)(25)(26)(27), and is largely tailored to identifying integrated viruses (i.e., prophages), neglecting the numerous other types of MGEs (18,28).…”

Section: Importance Introductionmentioning

confidence: 99%

“…Machine learning and deep learning-based tools may require large dataset inputs or customized training data, which may not be available for most microbial systems (18,23,24). Furthermore, MGE identification is most thoroughly tested on clinically-relevant and well-studied host microbes, especially those in the phylum Proteobacteria (19,(23)(24)(25)(26)(27), and is largely tailored to identifying integrated viruses (i.e., prophages), neglecting the numerous other types of MGEs (18,28). Although testing these tools on lesser-understood microbial hosts and MGEs is intrinsically difficult, the current lack of representation across host and MGE diversity has resulted in biases in MGE prediction (27).…”

Section: Importance Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Interrogation of the integrated mobile genetic elements in gut-associated Bacteroidaceae with a consensus prediction approach

Campbell

Leigh

Kim

et al. 2021

Preprint

View full text Add to dashboard Cite

Exploration of mobile genetic element (MGE) diversity and relatedness is vital to understanding microbial communities, especially the gut microbiome, where the mobilization of antibiotic resistance and pathogenicity genes has important clinical consequences. Current MGE prediction tools are biased toward elements similar to previously-identified MGEs, especially tailed phages of proteobacterial hosts. Further, there is a need for methods to examine relatedness and gene sharing among MGEs. We present VICSIN, a consensus approach for MGE prediction and clustering of predictions to provide classification. Testing of VICSIN on datasets of Pseudomonas aeruginosa and Bacteroides fragilis genomes suggests VICSIN is the optimal approach to predict integrated MGEs from poorly-explored host taxa, because of its increased sensitivity and accuracy. We applied VICSIN to a dataset of gut-associated B acteroidaceae genomes, identifying 816 integrated MGEs falling into 95 clusters, most of which are novel. VICSIN's fast and simple network-building scheme revealed a high degree of gene sharing within and between related MGE clusters. Shared gene functions across MGEs include core mobilization functions and accessory gene content, such as type VI secretion systems and antibiotic resistance genes. The MGEs identified here encode a large portion of unknown gene content, emphasizing the fact that the full diversity of MGEs and the factors they encode remain very poorly understood. Together, this work motivates more exploration of the gut mobilome, which is likely one of the most potent drivers of microbial evolution in the human microbiome.

show abstract

Computational approaches in viral ecology

Abstract: Graphical abstract

Cited by 22 publications

References 86 publications

Seasonal Regime Shift in the Viral Communities of a Permafrost Thaw Lake

Seasonal Regime Shift in the Viral Communities of a Permafrost Thaw Lake

An Overview on SARS-CoV-2 (COVID-19) and Other Human Coronaviruses and Their Detection Capability via Amplification Assay, Chemical Sensing, Biosensing, Immunosensing, and Clinical Assays

Interrogation of the integrated mobile genetic elements in gut-associated Bacteroidaceae with a consensus prediction approach

Contact Info

Product

Resources

About