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1.
Chunyan Fan Xiujuan Lei Jiaojiao Tie Yuchen Zhang Fang-Xiang Wu Yi Pan 《基因组蛋白质组与生物信息学报(英文版)》2022,20(3):435-445
With accumulating dysregulated circular RNAs(circRNAs) in pathological processes,the regulatory functions of circRNAs, especially circRNAs as microRNA(miRNA) sponges and their interactions with RNA-binding proteins(RBPs), have been widely validated. However, the collected information on experimentally validated circRNA–disease associations is only preliminary.Therefore, an updated CircR2Disease database providing a comprehensive resource and web tool to clarify the relationships between circRNAs... 相似文献
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Mikaela Koutrouli Evangelos Karatzas Katerina Papanikolopoulou Georgios A. Pavlopoulos 《基因组蛋白质组与生物信息学报(英文版)》2022,20(3):578
The Network Makeup Artist (NORMA) is a web tool for interactive network annotation visualization and topological analysis, able to handle multiple networks and annotations simultaneously. Precalculated annotations (e.g., Gene Ontology, Pathway enrichment, community detection, or clustering results) can be uploaded and visualized in a network, either as colored pie-chart nodes or as color-filled areas in a 2D/3D Venn-diagram-like style. In the case where no annotation exists, algorithms for automated community detection are offered. Users can adjust the network views using standard layout algorithms or allow NORMA to slightly modify them for visually better group separation. Once a network view is set, users can interactively select and highlight any group of interest in order to generate publication-ready figures. Briefly, with NORMA, users can encode three types of information simultaneously. These are 1) the network, 2) the communities or annotations of interest, and 3) node categories or expression values. Finally, NORMA offers basic topological analysis and direct topological comparison across any of the selected networks. NORMA service is available at http://norma.pavlopouloslab.info, whereas the code is available at https://github.com/PavlopoulosLab/NORMA. 相似文献
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Fengxia Zhou Rui Gan Fan Zhang Chunyan Ren Ling Yu Yu Si Zhiwei Huang 《基因组蛋白质组与生物信息学报(英文版)》2022,20(3):508
Phage–microbe interactions are appealing systems to study coevolution, and have also been increasingly emphasized due to their roles in human health, disease, and the development of novel therapeutics. Phage–microbe interactions leave diverse signals in bacterial and phage genomic sequences, defined as phage–host interaction signals (PHISs), which include clustered regularly interspaced short palindromic repeats (CRISPR) targeting, prophage, and protein–protein interaction signals. In the present study, we developed a novel tool phage–host interaction signal detector (PHISDetector) to predict phage–host interactions by detecting and integrating diverse in silico PHISs, and scoring the probability of phage–host interactions using machine learning models based on PHIS features. We evaluated the performance of PHISDetector on multiple benchmark datasets and application cases. When tested on a dataset of 758 annotated phage–host pairs, PHISDetector yields the prediction accuracies of 0.51 and 0.73 at the species and genus levels, respectively, outperforming other phage–host prediction tools. When applied to on 125,842 metagenomic viral contigs (mVCs) derived from 3042 geographically diverse samples, a detection rate of 54.54% could be achieved. Furthermore, PHISDetector could predict infecting phages for 85.6% of 368 multidrug-resistant (MDR) bacteria and 30% of 454 human gut bacteria obtained from the National Institutes of Health (NIH) Human Microbiome Project (HMP). The PHISDetector can be run either as a web server (http://www.microbiome-bigdata.com/PHISDetector/) for general users to study individual inputs or as a stand-alone version (https://github.com/HIT-ImmunologyLab/PHISDetector) to process massive phage contigs from virome studies. 相似文献
4.
Zev N. Kronenberg Edward J. Osborne Kelsey R. Cone Brett J. Kennedy Eric T. Domyan Michael D. Shapiro Nels C. Elde Mark Yandell 《PLoS computational biology》2015,11(12)
Existing methods for identifying structural variants (SVs) from short read datasets are inaccurate. This complicates disease-gene identification and efforts to understand the consequences of genetic variation. In response, we have created Wham (Whole-genome Alignment Metrics) to provide a single, integrated framework for both structural variant calling and association testing, thereby bypassing many of the difficulties that currently frustrate attempts to employ SVs in association testing. Here we describe Wham, benchmark it against three other widely used SV identification tools–Lumpy, Delly and SoftSearch–and demonstrate Wham’s ability to identify and associate SVs with phenotypes using data from humans, domestic pigeons, and vaccinia virus. Wham and all associated software are covered under the MIT License and can be freely downloaded from github (https://github.com/zeeev/wham), with documentation on a wiki (http://zeeev.github.io/wham/). For community support please post questions to https://www.biostars.org/.
This is PLOS Computational Biology software paper.相似文献
5.
Background
Multifactor dimensionality reduction (MDR) is widely used to analyze interactions of genes to determine the complex relationship between diseases and polymorphisms in humans. However, the astronomical number of high-order combinations makes MDR a highly time-consuming process which can be difficult to implement for multiple tests to identify more complex interactions between genes. This study proposes a new framework, named fast MDR (FMDR), which is a greedy search strategy based on the joint effect property.Results
Six models with different minor allele frequencies (MAFs) and different sample sizes were used to generate the six simulation data sets. A real data set was obtained from the mitochondrial D-loop of chronic dialysis patients. Comparison of results from the simulation data and real data sets showed that FMDR identified significant gene–gene interaction with less computational complexity than the MDR in high-order interaction analysis.Conclusion
FMDR improves the MDR difficulties associated with the computational loading of high-order SNPs and can be used to evaluate the relative effects of each individual SNP on disease susceptibility. FMDR is freely available at http://bioinfo.kmu.edu.tw/FMDR.rar.Electronic supplementary material
The online version of this article (doi:10.1186/s12864-015-1717-8) contains supplementary material, which is available to authorized users. 相似文献6.
Rapidly improving high-throughput sequencing technologies provide unprecedented opportunities for carrying out population-genomic studies with various organisms. To take full advantage of these methods, it is essential to correctly estimate allele and genotype frequencies, and here we present a maximum-likelihood method that accomplishes these tasks. The proposed method fully accounts for uncertainties resulting from sequencing errors and biparental chromosome sampling and yields essentially unbiased estimates with minimal sampling variances with moderately high depths of coverage regardless of a mating system and structure of the population. Moreover, we have developed statistical tests for examining the significance of polymorphisms and their genotypic deviations from Hardy–Weinberg equilibrium. We examine the performance of the proposed method by computer simulations and apply it to low-coverage human data generated by high-throughput sequencing. The results show that the proposed method improves our ability to carry out population-genomic analyses in important ways. The software package of the proposed method is freely available from https://github.com/Takahiro-Maruki/Package-GFE. 相似文献
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Sarah M. Hird 《PloS one》2012,7(10)
Genomic enrichment methods and next-generation sequencing produce uneven coverage for the portions of the genome (the loci) they target; this information is essential for ascertaining the suitability of each locus for further analysis. lociNGS is a user-friendly accessory program that takes multi-FASTA formatted loci, next-generation sequence alignments and demographic data as input and collates, displays and outputs information about the data. Summary information includes the parameters coverage per locus, coverage per individual and number of polymorphic sites, among others. The program can output the raw sequences used to call loci from next-generation sequencing data. lociNGS also reformats subsets of loci in three commonly used formats for multi-locus phylogeographic and population genetics analyses – NEXUS, IMa2 and Migrate. lociNGS is available at https://github.com/SHird/lociNGS and is dependent on installation of MongoDB (freely available at http://www.mongodb.org/downloads). lociNGS is written in Python and is supported on MacOSX and Unix; it is distributed under a GNU General Public License. 相似文献
10.
Andrei L. Lomize Kevin A. Schnitzer Spencer C. Todd Stanislav Cherepanov Carlos Outeiral Charlotte M. Deane Irina D. Pogozheva 《Protein science : a publication of the Protein Society》2022,31(5)
The Membranome database provides comprehensive structural information on single‐pass (i.e., bitopic) membrane proteins from six evolutionarily distant organisms, including protein–protein interactions, complexes, mutations, experimental structures, and models of transmembrane α‐helical dimers. We present a new version of this database, Membranome 3.0, which was significantly updated by revising the set of 5,758 bitopic proteins and incorporating models generated by AlphaFold 2 in the database. The AlphaFold models were parsed into structural domains located at the different membrane sides, modified to exclude low‐confidence unstructured terminal regions and signal sequences, validated through comparison with available experimental structures, and positioned with respect to membrane boundaries. Membranome 3.0 was re‐developed to facilitate visualization and comparative analysis of multiple 3D structures of proteins that belong to a specified family, complex, biological pathway, or membrane type. New tools for advanced search and analysis of proteins, their interactions, complexes, and mutations were included. The database is freely accessible at https://membranome.org. 相似文献
11.
Mutations in the presenilin (PSEN) encoding genes (PSEN1 and PSEN2) occur in most early onset familial Alzheimer’s Disease. Despite the identification of the involvement of PSEN in Alzheimer’s Disease (AD) ∼20 years ago, the underlying role of PSEN in AD is not fully understood. To gain insight into the biological function of PSEN, we investigated the role of the PSEN homolog SEL-12 in Caenorhabditis elegans. Using genetic, cell biological, and pharmacological approaches, we demonstrate that mutations in sel-12 result in defects in calcium homeostasis, leading to mitochondrial dysfunction. Moreover, consistent with mammalian PSEN, we provide evidence that SEL-12 has a critical role in mediating endoplasmic reticulum (ER) calcium release. Furthermore, we found that in SEL-12-deficient animals, calcium transfer from the ER to the mitochondria leads to fragmentation of the mitochondria and mitochondrial dysfunction. Additionally, we show that the impact that SEL-12 has on mitochondrial function is independent of its role in Notch signaling, γ-secretase proteolytic activity, and amyloid plaques. Our results reveal a critical role for PSEN in mediating mitochondrial function by regulating calcium transfer from the ER to the mitochondria. 相似文献
12.
Mehmet Kemal Samur 《PloS one》2014,9(9)
Background & Objective
Managing data from large-scale projects (such as The Cancer Genome Atlas (TCGA)) for further analysis is an important and time consuming step for research projects. Several efforts, such as the Firehose project, make TCGA pre-processed data publicly available via web services and data portals, but this information must be managed, downloaded and prepared for subsequent steps. We have developed an open source and extensible R based data client for pre-processed data from the Firehouse, and demonstrate its use with sample case studies. Results show that our RTCGAToolbox can facilitate data management for researchers interested in working with TCGA data. The RTCGAToolbox can also be integrated with other analysis pipelines for further data processing.Availability and implementation
The RTCGAToolbox is open-source and licensed under the GNU General Public License Version 2.0. All documentation and source code for RTCGAToolbox is freely available at http://mksamur.github.io/RTCGAToolbox/ for Linux and Mac OS X operating systems. 相似文献13.
Ellsworth M. Campbell Anthony Boyles Anupama Shankar Jay Kim Sergey Knyazev Roxana Cintron William M. Switzer 《PLoS computational biology》2021,17(9)
Outbreak investigations use data from interviews, healthcare providers, laboratories and surveillance systems. However, integrated use of data from multiple sources requires a patchwork of software that present challenges in usability, interoperability, confidentiality, and cost. Rapid integration, visualization and analysis of data from multiple sources can guide effective public health interventions. We developed MicrobeTrace to facilitate rapid public health responses by overcoming barriers to data integration and exploration in molecular epidemiology. MicrobeTrace is a web-based, client-side, JavaScript application (https://microbetrace.cdc.gov) that runs in Chromium-based browsers and remains fully operational without an internet connection. Using publicly available data, we demonstrate the analysis of viral genetic distance networks and introduce a novel approach to minimum spanning trees that simplifies results. We also illustrate the potential utility of MicrobeTrace in support of contact tracing by analyzing and displaying data from an outbreak of SARS-CoV-2 in South Korea in early 2020. MicrobeTrace is developed and actively maintained by the Centers for Disease Control and Prevention. Users can email vog.cdc@ecarteborcim for support. The source code is available at https://github.com/cdcgov/microbetrace. 相似文献
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Michael I Klein Vincent L Cannataro Jeffrey P Townsend Scott Newman David F Stern Hongyu Zhao 《Molecular systems biology》2021,17(3)
Identifying cooperating modules of driver alterations can provide insights into cancer etiology and advance the development of effective personalized treatments. We present Cancer Rule Set Optimization (CRSO) for inferring the combinations of alterations that cooperate to drive tumor formation in individual patients. Application to 19 TCGA cancer types revealed a mean of 11 core driver combinations per cancer, comprising 2–6 alterations per combination and accounting for a mean of 70% of samples per cancer type. CRSO is distinct from methods based on statistical co‐occurrence, which we demonstrate is a suboptimal criterion for investigating driver cooperation. CRSO identified well‐studied driver combinations that were not detected by other approaches and nominated novel combinations that correlate with clinical outcomes in multiple cancer types. Novel synergies were identified in NRAS‐mutant melanomas that may be therapeutically relevant. Core driver combinations involving NFE2L2 mutations were identified in four cancer types, supporting the therapeutic potential of NRF2 pathway inhibition. CRSO is available at https://github.com/mikekleinsgit/CRSO/. 相似文献
16.
Shanmugam Anandakumar Saravanan Vijayakumar Nagarajan Arumugam M Michael Gromiha 《Bioinformation》2015,11(11):512-513
Mammalian Mitochondrial ncRNA is a web-based database, which provides specific information on non-coding RNA in mammals.
This database includes easy searching, comparing with BLAST and retrieving information on predicted structure and its function
about mammalian ncRNAs.
Availability
The database is available for free at http://www.iitm.ac.in/bioinfo/mmndb/ 相似文献17.
GuiPeng Li Ming Li YiWei Zhang Dong Wang Rong Li Roger Guimerà Juntao Tony Gao Michael Q. Zhang 《PloS one》2014,9(5)
Rapidly increasing amounts of (physical and genetic) protein-protein interaction (PPI) data are produced by various high-throughput techniques, and interpretation of these data remains a major challenge. In order to gain insight into the organization and structure of the resultant large complex networks formed by interacting molecules, using simulated annealing, a method based on the node connectivity, we developed ModuleRole, a user-friendly web server tool which finds modules in PPI network and defines the roles for every node, and produces files for visualization in Cytoscape and Pajek. For given proteins, it analyzes the PPI network from BioGRID database, finds and visualizes the modules these proteins form, and then defines the role every node plays in this network, based on two topological parameters Participation Coefficient and Z-score. This is the first program which provides interactive and very friendly interface for biologists to find and visualize modules and roles of proteins in PPI network. It can be tested online at the website http://www.bioinfo.org/modulerole/index.php, which is free and open to all users and there is no login requirement, with demo data provided by “User Guide” in the menu Help. Non-server application of this program is considered for high-throughput data with more than 200 nodes or user’s own interaction datasets. Users are able to bookmark the web link to the result page and access at a later time. As an interactive and highly customizable application, ModuleRole requires no expert knowledge in graph theory on the user side and can be used in both Linux and Windows system, thus a very useful tool for biologist to analyze and visualize PPI networks from databases such as BioGRID.
Availability
ModuleRole is implemented in Java and C, and is freely available at http://www.bioinfo.org/modulerole/index.php. Supplementary information (user guide, demo data) is also available at this website. API for ModuleRole used for this program can be obtained upon request. 相似文献18.
Verena Schm?kel Nadin Memar Anne Wiekenberg Martin Trotzmüller Ralf Schnabel Frank D?ring 《Genetics》2016,202(3):1071-1083
Lipids play a pivotal role in embryogenesis as structural components of cellular membranes, as a source of energy, and as signaling molecules. On the basis of a collection of temperature-sensitive embryonic lethal mutants, a systematic database search, and a subsequent microscopic analysis of >300 interference RNA (RNAi)–treated/mutant worms, we identified a couple of evolutionary conserved genes associated with lipid storage in Caenorhabditis elegans embryos. The genes include cpl-1 (cathepsin L–like cysteine protease), ccz-1 (guanine nucleotide exchange factor subunit), and asm-3 (acid sphingomyelinase), which is closely related to the human Niemann-Pick disease–causing gene SMPD1. The respective mutant embryos accumulate enlarged droplets of neutral lipids (cpl-1) and yolk-containing lipid droplets (ccz-1) or have larger genuine lipid droplets (asm-3). The asm-3 mutant embryos additionally showed an enhanced resistance against C band ultraviolet (UV-C) light. Herein we propose that cpl-1, ccz-1, and asm-3 are genes required for the processing of lipid-containing droplets in C. elegans embryos. Owing to the high levels of conservation, the identified genes are also useful in studies of embryonic lipid storage in other organisms. 相似文献
19.
Pol Castellano-Escuder Raúl Gonzlez-Domínguez Francesc Carmona-Pontaque Cristina Andrs-Lacueva Alex Snchez-Pla 《PLoS computational biology》2021,17(7)
Metabolomics and proteomics, like other omics domains, usually face a data mining challenge in providing an understandable output to advance in biomarker discovery and precision medicine. Often, statistical analysis is one of the most difficult challenges and it is critical in the subsequent biological interpretation of the results. Because of this, combined with the computational programming skills needed for this type of analysis, several bioinformatic tools aimed at simplifying metabolomics and proteomics data analysis have emerged. However, sometimes the analysis is still limited to a few hidebound statistical methods and to data sets with limited flexibility. POMAShiny is a web-based tool that provides a structured, flexible and user-friendly workflow for the visualization, exploration and statistical analysis of metabolomics and proteomics data. This tool integrates several statistical methods, some of them widely used in other types of omics, and it is based on the POMA R/Bioconductor package, which increases the reproducibility and flexibility of analyses outside the web environment. POMAShiny and POMA are both freely available at https://github.com/nutrimetabolomics/POMAShiny and https://github.com/nutrimetabolomics/POMA, respectively. 相似文献
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Ruth Lynfield Richard Davey Dominic E. Dwyer Marcelo H. Losso Deborah Wentworth Alessandro Cozzi-Lepri Kathy Herman-Lamin Grazyna Cholewinska Daniel David Stefan Kuetter Zelalem Ternesgen Timothy M. Uyeki H. Clifford Lane Jens Lundgren James D. Neaton for the INSIGHT Influenza Study Group 《PloS one》2014,9(7)