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.

Input: Consider Input: 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.

Output: Bayesian mixture analysis for metagenomic community profiling


Input: Consider Input: Hepatitis C virus (HCV) is thought to be involved in the pathogenesis of autoimmune hepatitis (AIH) type 2, which is defined by the presence of type I antiliver kidney microsome autoantibodies directed mainly against cytochrome P450 (CYP)2D6 and by autoreactive liver infiltrating T cells. Virus-specific CD8 ϩ cytotoxic T lymphocytes (CTLs) that recognize infected cells and contribute to viral clearance and tissue injury during HCV infection could be involved in the induction of AIH. To explore whether the antiviral cellular immunity may turn against self-antigens, we characterized the primary CTL response against an HLA-A * 0201-restricted HCV-derived epitope, i.e., HCV core 178-187, which shows sequence homology with human CYP2A6 and CYP2A7 8-17. To determine the relevance of these homologies for the pathogenesis of HCV-associated AIH, we used synthetic peptides to induce primary CTL responses in peripheral blood mononuclear cells of healthy blood donors and patients with chronic HCV infection. We found that the naive CTL repertoire of both groups contains cross-reactive CTLs inducible by the HCV peptide recognizing both CYP2A6 and CYP2A7 peptides as well as endogenously processed CYP2A6 protein. Importantly, we failed to induce CTLs with the CYPderived peptides that showed a lower capacity to form stable complexes with the HLA-A2 molecule. These findings demonstrate the potential of HCV to induce autoreactive CD8 ϩ CTLs by molecular mimicry, possibly contributing to virus-associated autoimmunity.

Output: Molecular Mimicry of Human Cytochrome P450 by Hepatitis C Virus at the Level of Cytotoxic T Cell Recognition


Input: Consider Input: The threat of an influenza A virus pandemic stems from continual virus spillovers from reservoir species, a tiny fraction of which spark sustained transmission in humans. To date, no pandemic emergence of a new influenza strain has been preceded by detection of a closely related precursor in an animal or human. Nonetheless, influenza surveillance efforts are expanding, prompting a need for tools to assess the pandemic risk posed by a detected virus. The goal would be to use genetic sequence and/or biological assays of viral traits to identify those non-human influenza viruses with the greatest risk of evolving into pandemic threats, and/or to understand drivers of such evolution, to prioritize pandemic prevention or response measures. We describe such efforts, identify progress and ongoing challenges, and discuss three specific traits of influenza viruses (hemagglutinin receptor binding specificity, hemagglutinin pH of activation, and polymerase complex efficiency) that contribute to pandemic risk.


Output: Viral factors in influenza pandemic risk assessment