Dogan Mccall (layerdegree08)

Background The first step in understanding ecological community diversity and dynamics is quantifying community membership. An increasingly common method for doing so is through metagenomics. Because of the rapidly increasing popularity of this approach, a large number of computational tools and pipelines are available for analysing metagenomic data. However, the majority of these tools have been designed and benchmarked using highly accurate short read data (i.e. Illumina), with few studies benchmarking classification accuracy for long error-prone reads (PacBio or Oxford Nanopore). In addition, few tools have been benchmarked for non-microbial communities. Results Here we compare simulated long reads from Oxford Nanopore and Pacific Biosciences (PacBio) with high accuracy Illumina read sets to systematically investigate the effects of sequence length and taxon type on classification accuracy for metagenomic data from both microbial and non-microbial communities. We show that very generally, classification accuracy is far lower for non-microbial communities, even at low taxonomic resolution (e.g. family rather than genus). We then show that for two popular taxonomic classifiers, long reads can significantly increase classification accuracy, and this is most pronounced for non-microbial communities. Conclusions This work provides insight on the expected accuracy for metagenomic analyses for different taxonomic groups, and establishes the point at which read length becomes more important than error rate for assigning the correct taxon.Background DNA methylation is a major epigenetic modification involved in regulating gene expression. The effects of DNA methylation on gene expression differ by genomic location and vary across kingdoms, species and environmental conditions. To identify the functional regulatory roles of DNA methylation, the correlation between DNA methylation changes and alterations in gene expression is crucial. With the advance of next-generation sequencing, genome-wide methylation and gene expression profiling have become feasible. Current bioinformatics tools for investigating such correlation are designed to the assessment of DNA methylation at CG sites. The correlation of non-CG methylation and gene expression is very limited. Some bioinformatics databases allow correlation analysis, but they are limited to specific genomes such as that of humans and do not allow user-provided data. Results Here, we developed a bioinformatics web tool, MethGET (Methylation and Gene Expression Teller), that is specialized to analyse tha and discovered that CHH methylation in the gene body region may play a role in the tissue culture process, which demonstrates the capability of MethGET for use in epigenomic research. Conclusions MethGET is a Python software that correlates DNA methylation and gene expression. Its web interface is publicly available at https//paoyang.ipmb.sinica.edu.tw/Software.html. The stand-alone version and source codes are available on GitHub at https//github.com/Jason-Teng/MethGET.Background Cerebral stroke occurs following ischemic and hemorrhagic lesions in the brain. Survival and recovery of stroke patients depend on the severity of the initial injury but also the therapeutic approaches applied for emergent lifesaving and continuing post-stroke management. Dl-3-n-Butylphthalide (NBP), an active compound derived from Chinese celery seeds, has shown clinical efficacy in the treatment of ischemic cerebral stroke. Results In the present study we explored the therapeutic effect of NBP in a rat model of intracerebral hemorrhage (ICH), focusing on its potential role in promoting neovascularization in the perihemorrhagic zone. ICH was induced in male Sprague-Dawley rats by unilateral injection of autologous blood into the globus pallidus, with sham-operated (Sham group), vehicle-treated (ICH) and NBP-treated (at 10 and 25 mg/kg/Bid, p.o., ICH + NBP10 and ICH + NBP25, respectively) groups examined behaviora