scholarly journals MicroPhenoDB Associates Metagenomic Data with Pathogenic Microbes, Microbial Core Genes, and Human Disease Phenotypes

2020 ◽  
Author(s):  
Guocai Yao ◽  
Wenliang Zhang ◽  
Minglei Yang ◽  
Huan Yang ◽  
Jianbo Wang ◽  
...  
Keyword(s):  
Human Disease ◽  
Rapid Identification ◽  
Reciprocal Relationship ◽  
Metagenomic Data ◽  
Web Interfaces ◽  
Disease Phenotypes ◽  
Link Type ◽  
Pathogenic Microbes ◽  
Service Application ◽  
Core Genes

AbstractMicrobes play important roles in human health and disease. The interaction between microbes and hosts is a reciprocal relationship, which remains largely under-explored. Current computational resources lack manually and consistently curated data to connect metagenomic data to pathogenic microbes, microbial core genes, and disease phenotypes. We developed the MicroPhenoDB database by manually curating and consistently integrating microbe-disease association data. MicroPhenoDB provides 5677 non-redundant associations between 1781 microbes and 542 human disease phenotypes across more than 22 human body sites. MicroPhenoDB also provides 696,934 relationships between 27,277 unique clade-specific core genes and 685 microbes. Disease phenotypes are classified and described using the Experimental Factor Ontology (EFO). A refined score model was developed to prioritize the associations based on evidential metrics. The sequence search option in MicroPhenoDB enables rapid identification of existing pathogenic microbes in samples without running the usual metagenomic data processing and assembly. MicroPhenoDB offers data browsing, searching and visualization through user-friendly web interfaces and web service application programming interfaces. MicroPhenoDB is the first database platform to detail the relationships between pathogenic microbes, core genes, and disease phenotypes. It will accelerate metagenomic data analysis and assist studies in decoding microbes related to human diseases. MicroPhenoDB is available through http://www.liwzlab.cn/microphenodb and http://lilab2.sysu.edu.cn/microphenodb.

scholarly journals Microbiome Search Engine 2: a Platform for Taxonomic and Functional Search of Global Microbiomes on the Whole-Microbiome Level

mSystems ◽  
2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Gongchao Jing ◽  
Lu Liu ◽  
Zengbin Wang ◽  
Yufeng Zhang ◽  
Li Qian ◽  
...  
Keyword(s):  
Big Data ◽  
User Interface ◽  
Search Engine ◽  
Functional Similarity ◽  
Metagenomic Data ◽  
Data Sets ◽  
Data Space ◽  
Link Type ◽  
Database Platform ◽  
Microbiome Data

ABSTRACT Metagenomic data sets from diverse environments have been growing rapidly. To ensure accessibility and reusability, tools that quickly and informatively correlate new microbiomes with existing ones are in demand. Here, we introduce Microbiome Search Engine 2 (MSE 2), a microbiome database platform for searching query microbiomes in the global metagenome data space based on the taxonomic or functional similarity of a whole microbiome to those in the database. MSE 2 consists of (i) a well-organized and regularly updated microbiome database that currently contains over 250,000 metagenomic shotgun and 16S rRNA gene amplicon samples associated with unified metadata collected from 798 studies, (ii) an enhanced search engine that enables real-time and fast (<0.5 s per query) searches against the entire database for best-matched microbiomes using overall taxonomic or functional profiles, and (iii) a Web-based graphical user interface for user-friendly searching, data browsing, and tutoring. MSE 2 is freely accessible via http://mse.ac.cn. For standalone searches of customized microbiome databases, the kernel of the MSE 2 search engine is provided at GitHub (https://github.com/qibebt-bioinfo/meta-storms). IMPORTANCE A search-based strategy is useful for large-scale mining of microbiome data sets, such as a bird’s-eye view of the microbiome data space and disease diagnosis via microbiome big data. Here, we introduce Microbiome Search Engine 2 (MSE 2), a microbiome database platform for searching query microbiomes against the existing microbiome data sets on the basis of their similarity in taxonomic structure or functional profile. Key improvements include database extension, data compatibility, a search engine kernel, and a user interface. The new ability to search the microbiome space via functional similarity greatly expands the scope of search-based mining of the microbiome big data.

scholarly journals Erratum: CRISPR-engineered mosaicism rapidly reveals that loss of Kcnj13 function in mice mimics human disease phenotypes

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Hua Zhong ◽  
Yiyun Chen ◽  
Yumei Li ◽  
Rui Chen ◽  
Graeme Mardon
Keyword(s):  
Human Disease ◽  
Disease Phenotypes

scholarly journals The signaling lipid PI(3,5)P2 stabilizes V1–Vo sector interactions and activates the V-ATPase

2014 ◽  
Vol 25 (8) ◽  
pp. 1251-1262 ◽  
Author(s):  
Sheena Claire Li ◽  
Theodore T. Diakov ◽  
Tao Xu ◽  
Maureen Tarsio ◽  
Wandi Zhu ◽  
...  
Keyword(s):  
Salt Stress ◽  
Atpase Activity ◽  
Human Disease ◽  
Proton Pumps ◽  
Disease Phenotypes ◽  
Reversible Dissociation ◽  
Signaling Mechanisms ◽  
Vacuolar Acidification ◽  
Direct Binding

Vacuolar proton-translocating ATPases (V-ATPases) are highly conserved, ATP-driven proton pumps regulated by reversible dissociation of its cytosolic, peripheral V1 domain from the integral membrane Vo domain. Multiple stresses induce changes in V1-Vo assembly, but the signaling mechanisms behind these changes are not understood. Here we show that certain stress-responsive changes in V-ATPase activity and assembly require the signaling lipid phosphatidylinositol 3,5-bisphosphate (PI(3,5)P2). V-ATPase activation through V1-Vo assembly in response to salt stress is strongly dependent on PI(3,5)P2 synthesis. Purified Vo complexes preferentially bind to PI(3,5)P2 on lipid arrays, suggesting direct binding between the lipid and the membrane sector of the V-ATPase. Increasing PI(3,5)P2 levels in vivo recruits the N-terminal domain of Vo-sector subunit Vph1p from cytosol to membranes, independent of other subunits. This Vph1p domain is critical for V1-Vo interaction, suggesting that interaction of Vph1p with PI(3,5)P2-containing membranes stabilizes V1-Vo assembly and thus increases V-ATPase activity. These results help explain the previously described vacuolar acidification defect in yeast fab1∆ and vac14∆ mutants and suggest that human disease phenotypes associated with PI(3,5)P2 loss may arise from compromised V-ATPase stability and regulation.

scholarly journals SpacePHARER: Sensitive identification of phages from CRISPR spacers in prokaryotic hosts

2020 ◽  
Author(s):  
R. Zhang ◽  
M. Mirdita ◽  
E. Levy Karin ◽  
C. Norroy ◽  
C. Galiez ◽  
...  
Keyword(s):  
Open Source ◽  
Protein Level ◽  
De Novo ◽  
False Positives ◽  
Metagenomic Data ◽  
Command Line ◽  
Link Type ◽  
Host Relationships ◽  
User Friendly

SummarySpacePHARER (CRISPR Spacer Phage-Host Pair Finder) is a sensitive and fast tool for de novo prediction of phage-host relationships via identifying phage genomes that match CRISPR spacers in genomic or metagenomic data. SpacePHARER gains sensitivity by comparing spacers and phages at the protein-level, optimizing its scores for matching very short sequences, and combining evidences from multiple matches, while controlling for false positives. We demonstrate SpacePHARER by searching a comprehensive spacer list against all complete phage genomes.Availability and implementationSpacePHARER is available as an open-source (GPLv3), user-friendly command-line software for Linux and macOS at spacepharer.soedinglab.org.

scholarly journals GENVISAGE: Rapid Identification of Discriminative and Explainable Feature Pairs for Genomic Analysis

2020 ◽  
Author(s):  
Silu Huang ◽  
Charles Blatti ◽  
Saurabh Sinha ◽  
Aditya Parameswaran
Keyword(s):  
Large Scale ◽  
Genomic Analysis ◽  
Rapid Identification ◽  
Biological Data ◽  
Data Sets ◽  
Chemotherapy Drugs ◽  
Link Type ◽  
Discriminative Feature ◽  
Exploration Tool ◽  
High Dimensional Datasets

AbstractMotivationA common but critical task in genomic data analysis is finding features that separate and thereby help explain differences between two classes of biological objects, e.g., genes that explain the differences between healthy and diseased patients. As lower-cost, high-throughput experimental methods greatly increase the number of samples that are assayed as objects for analysis, computational methods are needed to quickly provide insights into high-dimensional datasets with tens of thousands of objects and features.ResultsWe develop an interactive exploration tool called Genvisage that rapidly discovers the most discriminative feature pairs that best separate two classes in a dataset, and displays the corresponding visualizations. Since quickly finding top feature pairs is computationally challenging, especially when the numbers of objects and features are large, we propose a suite of optimizations to make Genvisage more responsive and demonstrate that our optimizations lead to a 400X speedup over competitive baselines for multiple biological data sets. With this speedup, Genvisage enables the exploration of more large-scale datasets and alternate hypotheses in an interactive and interpretable fashion. We apply Genvisage to uncover pairs of genes whose transcriptomic responses significantly discriminate treatments of several chemotherapy drugs.AvailabilityFree webserver at http://genvisage.knoweng.org:443/ with source code at https://github.com/KnowEnG/Genvisage

scholarly journals A Computational Toolset for Rapid Identification of SARS-CoV-2, other Viruses, and Microorganisms from Sequencing Data

2020 ◽  
Author(s):  
Shifu Chen ◽  
Changshou He ◽  
Yingqiang Li ◽  
Zhicheng Li ◽  
Charles E Melançon
Keyword(s):  
Rapid Identification ◽  
Experimental Results ◽  
Large Set ◽  
Sequencing Data ◽  
Short Read ◽  
Microbial Genomes ◽  
Extension Method ◽  
Link Type ◽  
Long Read ◽  
Adapter Trimming

ABSTRACTIn this paper, we present a toolset and related resources for rapid identification of viruses and microorganisms from short-read or long-read sequencing data. We present fastv as an ultra-fast tool to detect microbial sequences present in sequencing data, identify target microorganisms, and visualize coverage of microbial genomes. This tool is based on the k-mer mapping and extension method. K-mer sets are generated by UniqueKMER, another tool provided in this toolset. UniqueKMER can generate complete sets of unique k-mers for each genome within a large set of viral or microbial genomes. For convenience, unique k-mers for microorganisms and common viruses that afflict humans have been generated and are provided with the tools. As a lightweight tool, fastv accepts FASTQ data as input, and directly outputs the results in both HTML and JSON formats. Prior to the k-mer analysis, fastv automatically performs adapter trimming, quality pruning, base correction, and other pre-processing to ensure the accuracy of k-mer analysis. Specifically, fastv provides built-in support for rapid SARS-CoV-2 identification and typing. Experimental results showed that fastv achieved 100% sensitivity and 100% specificity for detecting SARS-CoV-2 from sequencing data; and can distinguish SARS-CoV-2 from SARS, MERS, and other coronaviruses. This toolset is available at: https://github.com/OpenGene/fastv.

scholarly journals The population-specific impact of Neandertal introgression on human disease

2020 ◽  
Author(s):  
Michael Dannemann
Keyword(s):  
Prostate Cancer ◽  
Human Disease ◽  
European Ancestry ◽  
Summary Statistics ◽  
Behavioral Traits ◽  
Disease Phenotypes ◽  
Disease Associations ◽  
Specific Impact ◽  
People Today

Abstract Since the discovery of admixture between modern humans and Neandertals, multiple studies investigated the effect of Neandertal-derived DNA on human disease and non-disease phenotypes. These studies have linked Neandertal ancestry to skin and hair related phenotypes, immunity, neurological and behavioral traits. However, these inferences have so far been limited to cohorts with participants of European ancestry. Here, I analyze summary statistics from 40 disease GWAS cohorts of ∼212,000 individuals provided by the Biobank Japan Project for phenotypic effects of Neandertal DNA. I show that Neandertal DNA is associated with autoimmune diseases, prostate cancer and type 2 diabetes. Many of these disease associations are linked to population-specific Neandertal DNA, highlighting the importance of studying a wider range of ancestries to characterize the phenotypic legacy of Neandertals in people today.

scholarly journals pTuneos: prioritizing tumor neoantigens from next-generation sequencing data

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Chi Zhou ◽  
Zhiting Wei ◽  
Zhanbing Zhang ◽  
Biyu Zhang ◽  
Chenyu Zhu ◽  
...  
Keyword(s):  
Cytotoxic T Cells ◽  
Rapid Identification ◽  
The Cancer Genome Atlas ◽  
Next Generation Sequencing Data ◽  
Survival Prediction ◽  
Next Generation ◽  
Sequencing Data ◽  
Melanoma Cancer ◽  
Link Type ◽  
Cohort Data

Abstract Background Cancer neoantigens are expressed only in cancer cells and presented on the tumor cell surface in complex with major histocompatibility complex (MHC) class I proteins for recognition by cytotoxic T cells. Accurate and rapid identification of neoantigens play a pivotal role in cancer immunotherapy. Although several in silico tools for neoantigen prediction have been presented, limitations of these tools exist. Results We developed pTuneos, a computational pipeline for prioritizing tumor neoantigens from next-generation sequencing data. We tested the performance of pTuneos on the melanoma cancer vaccine cohort data and tumor-infiltrating lymphocyte (TIL)-recognized neopeptide data. pTuneos is able to predict the MHC presentation and T cell recognition ability of the candidate neoantigens, and the actual immunogenicity of single-nucleotide variant (SNV)-based neopeptides considering their natural processing and presentation, surpassing the existing tools with a comprehensive and quantitative benchmark of their neoantigen prioritization performance and running time. pTuneos was further tested on The Cancer Genome Atlas (TCGA) cohort data as well as the melanoma and non-small cell lung cancer (NSCLC) cohort data undergoing checkpoint blockade immunotherapy. The overall neoantigen immunogenicity score proposed by pTuneos is demonstrated to be a powerful and pan-cancer marker for survival prediction compared to traditional well-established biomarkers. Conclusions In summary, pTuneos provides the state-of-the-art one-stop and user-friendly solution for prioritizing SNV-based candidate neoepitopes, which could help to advance research on next-generation cancer immunotherapies and personalized cancer vaccines. pTuneos is available at https://github.com/bm2-lab/pTuneos, with a Docker version for quick deployment at https://cloud.docker.com/u/bm2lab/repository/docker/bm2lab/ptuneos.

Human Disease Phenotypes Associated With Mutations in TREX1

2015 ◽  
Vol 35 (3) ◽  
pp. 235-243 ◽  
Author(s):  
Gillian I. Rice ◽  
Mathieu P. Rodero ◽  
Yanick J. Crow
Keyword(s):  
Human Disease ◽  
Disease Phenotypes
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