In this task, you're given a paragraph from the research paper and your task is to generate a suitable title for the research paper based on the given paper. Under 100 words is a good title length.

[Q]: Genome Detective is a web-based, user-friendly software application to quickly and accurately assemble all known virus genomes from next generation sequencing datasets. This application allows the identification of phylogenetic clusters and genotypes from assembled genomes in FASTA format. Since its release in 2019, we have produced a number of typing tools for emergent viruses that have caused large outbreaks, such as Zika and Yellow Fever Virus in Brazil. Here, we present The Genome Detective Coronavirus Typing Tool that can accurately identify the novel severe acute respiratory syndrome (SARS) related coronavirus (SARS-CoV-2) sequences isolated in China and around the world. The tool can accept up to 2,000 sequences per submission and the analysis of a new whole genome sequence will take approximately one minute. The tool has been tested and validated with hundreds of whole genomes from ten coronavirus species, and correctly classified all of the SARS-related coronavirus (SARSr-CoV) and all of the available public data for SARS-CoV-2. The tool also allows tracking of new viral mutations as the outbreak expands globally, which may help to accelerate the development of novel diagnostics, drugs and vaccines to stop the COVID-19 disease.
[A]: Genome Detective Coronavirus Typing Tool for rapid identification and characterization of novel coronavirus genomes


[Q]: Estimating temporal changes in a target population from phylogenetic or count data is an important problem in ecology and epidemiology. Reliable estimates can provide key insights into the climatic and biological drivers influencing the diversity or structure of that population and evidence hypotheses concerning its future growth or decline. In infectious disease applications, the individuals infected across an epidemic form the target population. The renewal model estimates the effective reproduction number, R, of the epidemic from counts of its observed cases. The skyline model infers the effective population size, N, underlying a phylogeny of sequences sampled from that epidemic. Practically, R measures ongoing epidemic growth while N informs on historical caseload. While both models solve distinct problems, the reliability of their estimates depends on p-dimensional piecewise-constant functions. If p is misspecified, the model might underfit significant changes or overfit noise and promote a spurious understanding of the epidemic, which might misguide intervention policies or misinform forecasts. Surprisingly, no transparent yet principled approach for optimising p exists. Usually, p is heuristically set, or obscurely controlled via complex algorithms. We present a computable and interpretable p-selection method based on the minimum description length (MDL) formalism of information theory. Unlike many standard model selection techniques, MDL accounts for the additional statistical complexity induced by how parameters interact. As a result, our method optimises p so that R and N estimates properly adapt to the available data. It also outperforms comparable Akaike and Bayesian information criteria on several classification problems. Our approach requires some knowledge of the parameter space and exposes the similarities between renewal and skyline models.
[A]: Adaptive Estimation for Epidemic Renewal and Phylogenetic Skyline Models


[Q]: An extremely affordable virus concentration method based on adsorption-elution to glass wool and subsequent reconcentration through polyethylene glycol 6000 (PEG) precipitation was optimized to recover not only non-enveloped viruses but also enveloped viruses. Hepatitis A virus (HAV) and transmissible gastroenteritis virus (TGEV) were employed as surrogates for naked and enveloped viruses, respectively, to set up the methodology. Initial experimentation in small-volume samples showed that both types of particles readily adsorbed to the positively charged glass wool but were poorly detached from it through standard elution with 0.05 M glycine with 3% of beef extract buffer, pH 9.5, with elution efficiencies of 7.2% and 2.6%, for HAV and TGEV, respectively. To improve the recovery of enveloped viruses, several modifications in the elution were assayed: increasing the elution pH, extending glass wool and eluent contact time, adding a detergent, or performing the elution by recirculation or under agitation. Considering practicability and performance, recircularization of the eluent at pH 11.0 for 20 min was the elution procedure of choice, with efficiencies of 25.7% and 18.8% for HAV and TGEV in 50 L of water. Additionally, employing 20% PEG instead of 10% for virus reconcentration improved recoveries up to 47% and 51%, respectively. The optimized procedure was applied to detect naturally occurring HAV and coronaviruses in surface water of Wadi Hanifa, Riyadh. HAV was detected in 38% of the samples, while one sample was positive for an alphacoronavirus. This cheap virus detection system enables the comprehensive surveillance of viruses present in water samples.
[A]:
Glass Wool Concentration Optimization for the Detection of Enveloped and Non-enveloped Waterborne Viruses