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.

Motivation: Deep sequencing of clinical samples is now an established tool for the detection of infectious pathogens, with direct medical applications. The large amount of data generated produces an opportunity to detect species even at very low levels, provided that computational tools can effectively profile the relevant metagenomic communities. Data interpretation is complicated by the fact that short sequencing reads can match multiple organisms and by the lack of completeness of existing databases, in particular for viral pathogens. Here we present metaMix, a Bayesian mixture model framework for resolving complex metagenomic mixtures. We show that the use of parallel Monte Carlo Markov chains for the exploration of the species space enables the identification of the set of species most likely to contribute to the mixture. Results: We demonstrate the greater accuracy of metaMix compared with relevant methods, particularly for profiling complex communities consisting of several related species. We designed metaMix specifically for the analysis of deep transcriptome sequencing datasets, with a focus on viral pathogen detection; however, the principles are generally applicable to all types of metagenomic mixtures. Availability and implementation: metaMix is implemented as a user friendly R package, freely available on CRAN: http://cran.r-project.org/web/packages/metaMix Contact: sofia.morfopoulou.10@ucl.ac.uk Supplementary information: Supplementary data are available at Bionformatics online.
Bayesian mixture analysis for metagenomic community profiling

Shigella dysenteriae causing shigellosis is one of the diseases that threaten the health of human society in the developing countries. In Shigella, IpaD gene is one of the key pathogenic genes causing strong mucosal immune system reactions. Anthrax disease is caused by Bacillus anthracis. PA protective antigen is one of the subunits in anthrax toxin complex responsible for the transfer of other subunits into the cytosol of host cells. The 20 kDa subunit of PA (PA20) has the property of immunogenicity. CTxB or B subunit of Vibrio cholerae toxin (CT) is a non-toxic protein and has the function to transfer toxic subunit into cytosol of the host cells by binding to GM1 receptor. The aim of this study was to fuse PA20, ipaD and CTxB and transform tomato plants by this cassette in order to produce an oral vaccine against shigellosis, anthrax and cholera. CTxB was used for these two antigens as an immune adjuvant. IpaD and PA20 genes were cloned in pBI121 containing the CTxB gene and Extensin signal peptide. In order to evaluate the transient expression of Shigellosis, Anthrax and Cholera antigens, agro-infiltrated tomato tissues were inoculated with Agrobacterium tumefaciens containing the gene cassette. Cloning was confirmed by PCR, enzymatic digestion and sequencing techniques. Expression of the antigens was examined by SDS-PAGE, dot blot and ELISA. Maturate green fruits demonstrated the highest expression of the recombinant proteins. The first phase of this study was carried out for cloning and expressing of CtxB, ipaD and PA20 antigens in tomato. In the next phase, we aim to analyze the immunogenicity of this vaccine candidate in laboratory animals.
Constructing and transient expression of a gene cassette containing edible vaccine elements and shigellosis, anthrax and cholera recombinant antigens in tomato

Background: As the outbreak of Coronavirus disease 2019 (COVID-19) is a worldwide pandemic, and it is rapidly expanding in Iran, real-time analyses of epidemiological data are needed to increase situational awareness and inform interventions. In this study, we built a predictive model based on the cumulative trend of new cases and deaths for the top five provinces. We will also look at modeling the trends for confirmed cases, deaths and recovered for the whole country. Method: In this study, we have chosen to apply the exponential smoothing model to iteratively forecast future values of a regular time series from weighted averages of past daily values. This method is exponential because the value of each level is influenced by every preceding actual value to an exponentially decreasing degree -more recent values are given greater weight. The available data is too small to identify seasonal patterns and make a predictable variation in value, such as annual fluctuation in temperature related to the season. The trend is a tendency of the data to increase or decrease over time.
Title: Modeling the Corona Virus Outbreak in IRAN