Greenwood Dideriksen (packetborder7)

We anticipated that DNA4mC-LIP could serve as a powerful computational technique for identifying 4mC sites and facilitate the interpretation of 4mC mechanism. AVAILABILITY http//i.uestc.edu.cn/DNA4mC-LIP/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online. © The Author(s) (2020). Published by Oxford University Press. All rights reserved. For Permissions, please email journals.permissions@oup.com.BACKGROUND Conventional analytic approaches for studying diet patterns assume no dietary synergy, which can lead to bias if incorrectly modeled. Machine learning algorithms can overcome these limitations. OBJECTIVES We estimated associations between fruit and vegetable intake relative to total energy intake and adverse pregnancy outcomes using targeted maximum likelihood estimation (TMLE) paired with the ensemble machine learning algorithm Super Learner, and compared these with results generated from multivariable logistic regression. learn more METHODS We used data from 7572 women in the Nulliparous Pregnancy Outcomes Study monitoring mothers-to-be. Usual daily periconceptional intake of total fruits and total vegetables was estimated from an FFQ. We calculated the marginal risk of preterm birth, small-for-gestational-age (SGA) birth, gestational diabetes, and pre-eclampsia according to density of fruits and vegetables (cups/1000 kcal) ≥80th percentile compared with less then 80th percentile using multivariable logistnovative methodology for analyzing dietary data has the potential to advance the study of diet patterns. Copyright © The Author(s) 2020.SUMMARY We developed 2DImpute, an imputation method for correcting false zeros (known as dropouts) in single-cell RNA sequencing (scRNA-seq) data. It features preventing excessive correction by predicting the false zeros and imputing their values by making use of the interrelationships between both genes and cells in the expression matrix. We showed that 2DImpute outperforms several leading imputation methods by applying it on datasets from various scRNA-seq protocols. AVAILABILITY AND IMPLEMENTATION The R package of 2DImpute is freely available at GitHub (https//github.com/zky0708/2DImpute). SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online. © The Author(s) 2020. Published by Oxford University Press.SUMMARY 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. AVAILABILITY https//. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online. © The Author(s) 2020. Published by Oxford University Press.MOTIVATION Many methods for microbial protein subcellular localization (SCL) prediction exist, however none is readily available for analysis of metagenomic sequence data,