Kenneylam8704

Under strict cut-offs, LAMPA outperformed HHsearch-mediated runs using intact polyproteins as queries by three measures number of and coverage by identified homologous regions, and number of hit Pfam profiles. Compared to HHsearch, LAMPA identified 507 extra homologous regions in 14.4% of polyproteins. This Pfam-based annotation of RNA virus polyproteins by LAMPA was also superior to RefSeq expert annotation by two measures, region number and annotated length, for 69.3% of RNA virus polyprotein entries. We rationalized the obtained results based on dependencies of HHsearch hit statistical significance for local alignment similarity score from lengths and diversities of query-target pairs in computational experiments. AVAILABILITY LAMPA 1.0.0 R package is placed at github. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online. © The Author(s) 2020. Published by Oxford University Press.AIMS To describe the risks of thromboembolism and major bleeding complications in anticoagulated patients with atrial fibrillation (AF) and native aortic or mitral valvular heart disease using data reflecting clinical practice. METHODS AND RESULTS Descriptive cohort study of anticoagulated patients with incident AF and native aortic or mitral valvular heart disease, identified in nationwide Danish registries from 2000-2018. A total of 10,043 patients were included, of which 5,190 (51.7%) patients had aortic stenosis, 1,788 (17.8%) patients had aortic regurgitation, 327 (3.3%) patients had mitral stenosis, and 2,738 (27.3%) patients had mitral regurgitation. At 1 year after AF diagnosis, the risk of thromboembolism was 4.6% in patients with mitral stenosis taking a VKA, and 2.6% in patients with aortic stenosis taking a VKA or NOAC. For patients with aortic or mitral regurgitation, the risks of thromboembolism ranged between 1.5-1.8% in both treatment groups. For the endpoint of major bleeding, the risk was approximately 5.5% in patients with aortic stenosis or mitral stenosis treated with a VKA, and 3.3-4.0% in patients with aortic or mitral regurgitation. For patients treated with a NOAC, the risk of major bleeding was 3.7% in patients with aortic stenosis and approximately 2.5% in patients with aortic or mitral regurgitation. CONCLUSION When using data reflecting contemporary clinical practice, our observations suggested that one year after a diagnosis of AF, anticoagulated patients with aortic or mitral valvular heart disease had dissimilar risk of thromboembolism and major bleeding complications. Specifically, patients with aortic stenosis or mitral stenosis were high-risk subgroups. This observation may guide clinicians regarding intensity of clinical follow-up. © Published on behalf of the European Society of Cardiology. All rights reserved. © The Author 2020. For permissions, please email journals.permissions@oup.com.SUMMARY Single-cell Hi-C (scHi-C) allows the study of cell-to-cell variability in chromatin structure and dynamics. However, the high level of noise inherent in current scHi-C protocols necessitates careful assessment of data quality before biological conclusions can be drawn. Here we present GiniQC, which quantifies unevenness in the distribution of inter-chromosomal reads in the scHi-C contact matrix to measure the level of noise. Our examples show the utility of GiniQC in assessing the quality of scHi-C data as a complement to existing quality control measures. We also demonstrate how GiniQC can help inform the impact of various data processing steps on data quality. AVAILABILITY Source code and documentation are freely available at https//github.com/4dn-dcic/GiniQC. 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.SUMMARY Computational metabolic models typically encode for graphs of species, reactions, and enzymes. Comparing genome-scale models through topological analysis of multipartite graphs is challenging. However, in many practical cases it is not necessary to compare the full networks. The GEMtractor is a web-based tool to trim models encoded in SBML. It can be used to extract subnetworks, for example focusing on reaction- and enzyme-centric views into the model. AVAILABILITY AND IMPLEMENTATION The GEMtractor is licensed under the terms of GPLv3 and developed at github.com/binfalse/GEMtractor - a public version is available at sbi.uni-rostock.de/gemtractor. © The Author(s) (2020). Published by Oxford University Press. All rights reserved. For Permissions, please email journals.permissions@oup.com.MOTIVATION Identifying correlated epigenetic features and finding differences in correlation between individuals with disease compared to controls can give novel insight into disease biology. Stem Cells antagonist This framework has been successful in analysis of gene expression data, but application to epigenetic data has been limited by the computational cost, lack of scalable software and lack of robust statistical tests. RESULTS Decorate, differential epigenetic correlation test, identifies correlated epigenetic features and finds clusters of features that are differentially correlated between two or more subsets of the data. The software scales to genome-wide datasets of epigenetic assays on hundreds of individuals. We apply decorate to four large-scale datasets of DNA methylation, ATAC-seq and histone modification ChIP-seq. AVAILABILITY decorate R package is available from https//github.com/GabrielHoffman/decorate. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online. © The Author(s) 2020. Published by Oxford University Press.Pancreatic β-cells, residents of the islets of Langerhans, are the unique insulin-producers in the body. Their physiology is a topic of intensive studies aiming to understand the biology of insulin production and its role in diabetes pathology. However, investigations about these cells' subset of secreted proteins, the secretome, are surprisingly scarce and a list describing islet/β-cell secretome upon glucose-stimulation is not yet available. In silico predictions of secretomes are an interesting approach that can be employed to forecast proteins likely to be secreted. In this context, using the rationale behind classical secretion of proteins through the secretory pathway, a Python tool capable of predicting classically secreted proteins was developed. This tool was applied to different available proteomic data (human and rodent islets, isolated β-cells, β-cell secretory granules, and β-cells supernatant), filtering them in order to selectively list only classically secreted proteins. The method presented here can retrieve, organize, search and filter proteomic lists using UniProtKB as a central database.