The Microbe Directory: An annotated, searchable inventory of microbes’ characteristics

The Microbe Directory is a collective research effort to profile and annotate more than 7,500 unique microbial species from the MetaPhlAn2 database that includes bacteria, archaea, viruses, fungi, and protozoa. By collecting and summarizing data on various microbes’ characteristics, the project comprises a database that can be used downstream of large-scale metagenomic taxonomic analyses, allowing one to interpret and explore their taxonomic classifications to have a deeper understanding of the microbial ecosystem they are studying. Such characteristics include, but are not limited to: optimal pH, optimal temperature, Gram stain, biofilm-formation, spore-formation, antimicrobial resistance, and COGEM class risk rating. The database has been manually curated by trained student-researchers from Weill Cornell Medicine and CUNY—Hunter College, and its analysis remains an ongoing effort with open-source capabilities so others can contribute. Available in SQL, JSON, and CSV (i.e. Excel) formats, the Microbe Directory can be queried for the aforementioned parameters by a microorganism’s taxonomy. In addition to the raw database, The Microbe Directory has an online counterpart (https://microbe.directory/) that provides a user-friendly interface for storage, retrieval, and analysis into which other microbial database projects could be incorporated. The Microbe Directory was primarily designed to serve as a resource for researchers conducting metagenomic analyses, but its online web interface should also prove useful to any individual who wishes to learn more about any particular microbe.

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Comprehensive comparison of large-scale tissue expression datasets

10.7287/peerj.preprints.1072v1 ◽

2015 ◽

Cited By ~ 1

Author(s):

Alberto Santos ◽

Kalliopi Tsafou ◽

Christian Stolte ◽

Sune Pletscher-Frankild ◽

Seán I O’Donoghue ◽

...

Keyword(s):

Large Scale ◽

Comprehensive Evaluation ◽

Tissue Expression ◽

Expression Data ◽

Web Interface ◽

Comprehensive Comparison ◽

Single User ◽

The Right ◽

User Friendly ◽

Literature Curation

For tissues to carry out their functions, they rely on the right proteins to be present. Several high-throughput technologies have been used to map out which proteins are expressed in which tissues; however, the data have not previously been systematically compared and integrated. We present a comprehensive evaluation of tissue expression data from a variety of experimental techniques and show that these agree surprisingly well with each other and with results from literature curation and text mining. We further found that most datasets support the assumed but not demonstrated distinction between tissue-specific and ubiquitous expression. By developing comparable confidence scores for all types of evidence, we show that it is possible to improve both quality and coverage by combining the datasets. To facilitate use and visualization of our work, we have developed the TISSUES resource ( http://tissues.jensenlab.org ), which makes all the scored and integrated data available through a single user-friendly web interface.

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Comprehensive comparison of large-scale tissue expression datasets

10.7287/peerj.preprints.1072 ◽

2015 ◽

Author(s):

Alberto Santos ◽

Kalliopi Tsafou ◽

Christian Stolte ◽

Sune Pletscher-Frankild ◽

Seán I O’Donoghue ◽

...

Keyword(s):

Large Scale ◽

Comprehensive Evaluation ◽

Tissue Expression ◽

Expression Data ◽

Web Interface ◽

Comprehensive Comparison ◽

Single User ◽

The Right ◽

User Friendly ◽

Literature Curation

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Computer-assisted initial diagnosis of rare diseases

PeerJ ◽

10.7717/peerj.2211 ◽

2016 ◽

Vol 4 ◽

pp. e2211 ◽

Cited By ~ 10

Author(s):

Rui Alves ◽

Marc Piñol ◽

Jordi Vilaplana ◽

Ivan Teixidó ◽

Joaquim Cruz ◽

...

Keyword(s):

Rare Disease ◽

Rare Diseases ◽

Large Scale ◽

Initial Diagnosis ◽

Computer Assisted ◽

Web Interface ◽

Genetic Origin ◽

Data Set ◽

User Friendly ◽

Current Database

Introduction.Most documented rare diseases have genetic origin. Because of their low individual frequency, an initial diagnosis based on phenotypic symptoms is not always easy, as practitioners might never have been exposed to patients suffering from the relevant disease. It is thus important to develop tools that facilitate symptom-based initial diagnosis of rare diseases by clinicians. In this work we aimed at developing a computational approach to aid in that initial diagnosis. We also aimed at implementing this approach in a user friendly web prototype. We call this tool Rare Disease Discovery. Finally, we also aimed at testing the performance of the prototype.Methods.Rare Disease Discovery uses the publicly available ORPHANET data set of association between rare diseases and their symptoms to automatically predict the most likely rare diseases based on a patient’s symptoms. We apply the method to retrospectively diagnose a cohort of 187 rare disease patients with confirmed diagnosis. Subsequently we test the precision, sensitivity, and global performance of the system under different scenarios by running large scale Monte Carlo simulations. All settings account for situations where absent and/or unrelated symptoms are considered in the diagnosis.Results.We find that this expert system has high diagnostic precision (≥80%) and sensitivity (≥99%), and is robust to both absent and unrelated symptoms.Discussion.The Rare Disease Discovery prediction engine appears to provide a fast and robust method for initial assisted differential diagnosis of rare diseases. We coupled this engine with a user-friendly web interface and it can be freely accessed athttp://disease-discovery.udl.cat/. The code and most current database for the whole project can be downloaded fromhttps://github.com/Wrrzag/DiseaseDiscovery/tree/no_classifiers.

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EuRBPDB: a comprehensive resource for annotation, functional and oncological investigation of eukaryotic RNA binding proteins (RBPs)

10.1101/713164 ◽

2019 ◽

Author(s):

Jian-You Liao ◽

Bing Yang ◽

Yu-Chan Zhang ◽

Xiao-Juan Wang ◽

Yushan Ye ◽

...

Keyword(s):

Cancer Biology ◽

Large Scale ◽

Binding Proteins ◽

Rna Binding ◽

Rna Binding Proteins ◽

Web Interface ◽

Binding Domains ◽

Large Protein Family ◽

Almost All ◽

User Friendly

ABSTRACTRNA binding proteins (RBPs) are a large protein family that plays important roles at almost all levels of gene regulation through interacting with RNAs, and contributes to numerous biological processes. However, the complete list of eukaryotic RBPs including human is still unavailable. In this study, we systematically identified RBPs in 162 eukaryotic species based on both computational analysis of RNA binding domains (RBDs) and large-scale RNA binding proteomic (RBPome) data, and established a comprehensive eukaryotic RBP database, EuRBPDB (http://EuRBPDB.syshospital.org:8081). We identified a total of 311,571 RBPs with RBDs and 3,639 non-canonical RBPs without known RBDs. EuRBPDB provides detailed annotations for each RBP, including basic information and functional annotation. Moreover, we systematically investigated RBPs in the context of cancer biology based on published literatures and large-scale omics data. To facilitate the exploration of the clinical relevance of RBPs, we additionally designed a cancer web interface to systematically and interactively display the biological features of RBPs in various types of cancers. EuRBPDB has a user-friendly web interface with browse and search functions, as well as data downloading function. We expect that EuRBPDB will be a widely-used resource and platform for the RNA biology community.

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Comprehensive comparison of large-scale tissue expression datasets

10.1101/010975 ◽

2014 ◽

Author(s):

Alberto Santos ◽

Kalliopi Tsafou ◽

Christian Stolte ◽

Sune Frankild ◽

Seán O'Donoghue ◽

...

Keyword(s):

Large Scale ◽

Comprehensive Evaluation ◽

Tissue Expression ◽

Expression Data ◽

Web Interface ◽

Comprehensive Comparison ◽

Single User ◽

The Right ◽

User Friendly ◽

Literature Curation

For tissues to carry out their functions, they rely on the right proteins to be present. Several high-throughput technologies have been used to map out which proteins are expressed in which tissues; however, the data have not previously been systematically compared and integrated. We present a comprehensive evaluation of tissue expression data from a variety of experimental techniques and show that these agree surprisingly well with each other and with results from literature curation and text mining. We further found that most datasets support the assumed but not demonstrated distinction between tissue-specific and ubiquitous expression. By developing comparable confidence scores for all types of evidence, we show that it is possible to improve both quality and coverage by combining the datasets. To facilitate use and visualization of our work, we have developed the TISSUES resource (http://tissues.jensenlab.org), which makes all the scored and integrated data available through a single user-friendly web interface.

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Microcomputer Simulations of Presidential Elections

News for Teachers of Political Science ◽

10.1017/s0197901900005110 ◽

1983 ◽

Vol 38 ◽

pp. 20-20

Author(s):

Robert S. Ross

Keyword(s):

Social Sciences ◽

Large Scale ◽

Role Playing ◽

Science Students ◽

Instructional Programs ◽

Santa Barbara ◽

Large Scale Systems ◽

The Social ◽

User Friendly ◽

Machine Interaction

Simulations have been an important adjunct to instructional programs for some time. These have ranged from games, or role playing exercises, such as SIMSOC or Internation Simulation, to student-machine interaction, such as the inter-school simulation run out of University of California, Santa Barbara in the early 70's, to the all machine activities found in some of the early SETUPS. Having social science students use the mainframe computer, however, always posed problems: it definitely was not user-friendly and most instructors had little if any training or interest in the use of large scale systems.The wide-spread use of the micro computer is not only revolutionizing areas traditionally relying upon the computer, but is going to have an impact on the social sciences as well.

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fastER: a user-friendly tool for ultrafast and robust cell segmentation in large-scale microscopy

Bioinformatics ◽

10.1093/bioinformatics/btx107 ◽

2017 ◽

Vol 33 (13) ◽

pp. 2020-2028 ◽

Cited By ~ 28

Author(s):

Oliver Hilsenbeck ◽

Michael Schwarzfischer ◽

Dirk Loeffler ◽

Sotiris Dimopoulos ◽

Simon Hastreiter ◽

...

Keyword(s):

Large Scale ◽

Cell Segmentation ◽

User Friendly

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IO/EAM: An Input-Output Economic Assessment Model

Journal of Agricultural and Applied Economics ◽

10.1017/s0081305200016915 ◽

1984 ◽

Vol 16 (2) ◽

pp. 145-150 ◽

Cited By ~ 1

Author(s):

Jeffrey L. Jordan ◽

Rusty Brooks

Keyword(s):

Research Effort ◽

Economic Assessment ◽

Assessment Model ◽

Input Output ◽

County Extension Agents ◽

University Of Georgia ◽

Microcomputer Software ◽

The University ◽

User Friendly ◽

The Impact

AbstractThis paper describes a microcomputer software package, IO/EAM: An Input-Output Economic Assessment Model, which was developed at the University of Georgia through a joint extension-research effort. The package is a menudriven, user friendly program that is designed to be used by county extension agents, researchers, and other extension personnel to estimate the economic impact of changes in county economies. The program employs an input-output model that provides users with a locally based, easily updated source of economic intelligence. Use of input-output techniques allows users to estimate the impact of changes in county economies on an industry-by-industry basis and to account for the interdependencies among these industries in a county.

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MetaADEDB 2.0: a comprehensive database on adverse drug events

Bioinformatics ◽

10.1093/bioinformatics/btaa973 ◽

2020 ◽

Author(s):

Zhuohang Yu ◽

Zengrui Wu ◽

Weihua Li ◽

Guixia Liu ◽

Yun Tang

Keyword(s):

Safety Assessment ◽

Adverse Drug Events ◽

Adverse Event Reporting System ◽

Adverse Event Reporting ◽

Supplementary Information ◽

Online Database ◽

Web Interface ◽

Drug Discovery And Development ◽

Comprehensive Information ◽

User Friendly

Abstract Summary MetaADEDB is an online database we developed to integrate comprehensive information on adverse drug events (ADEs). The first version of MetaADEDB was released in 2013 and has been widely used by researchers. However, it has not been updated for more than seven years. Here, we reported its second version by collecting more and newer data from the U.S. FDA Adverse Event Reporting System (FAERS) and Canada Vigilance Adverse Reaction Online Database, in addition to the original three sources. The new version consists of 744 709 drug–ADE associations between 8498 drugs and 13 193 ADEs, which has an over 40% increase in drug–ADE associations compared to the previous version. Meanwhile, we developed a new and user-friendly web interface for data search and analysis. We hope that MetaADEDB 2.0 could provide a useful tool for drug safety assessment and related studies in drug discovery and development. Availability and implementation The database is freely available at: http://lmmd.ecust.edu.cn/metaadedb/. Supplementary information Supplementary data are available at Bioinformatics online.

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