scholarly journals PyRice: a Python package for querying Oryza sativa databases

2020 ◽  
Author(s):  
Quan Do ◽  
Ho Bich Hai ◽  
Pierre Larmande
Keyword(s):  
Oryza Sativa ◽  
Heterogeneous Data ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Web Based ◽  
Domain Experts ◽  
Heterogeneous Data Sources ◽  
Query System ◽  
Gene Information ◽  
Python Package

Abstract Summary Currently, gene information available for Oryza sativa species is located in various online heterogeneous data sources. Moreover, methods of access are also diverse, mostly web-based and sometimes query APIs, which might not always be straightforward for domain experts. The challenge is to collect information quickly from these applications and combine it logically, to facilitate scientific research. We developed a Python package named PyRice, a unified programing API to access all supported databases at the same time with consistent output. PyRice design is modular and implements a smart query system, which fits the computing resources to optimize the query speed. As a result, PyRice is easy to use and produces intuitive results. Availability and implementation https://github.com/SouthGreenPlatform/PyRice. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals PyRice: a Python package for querying Oryza Sativa databases

2020 ◽  
Author(s):  
Quan Do ◽  
Ho Bich Hai ◽  
Pierre Larmande
Keyword(s):  
Oryza Sativa ◽  
Heterogeneous Data ◽  
Supplementary Information ◽  
Web Based ◽  
Domain Experts ◽  
Link Type ◽  
Heterogeneous Data Sources ◽  
Query System ◽  
Gene Information ◽  
Python Package

AbstractSummaryCurrently, gene information available for Oryza sativa species is located in various online heterogeneous data sources. Moreover, methods of access are also diverse, mostly web-based and sometimes query APIs, which might not always be straightforward for domain experts. The challenge is to collect information quickly from these applications and combine it logically, to facilitate scientific research. We developed a Python package named PyRice, a unified programming API to access all supported databases at the same time with consistent output. PyRice design is modular and implements a smart query system which fits the computing resources to optimize the query speed. As a result, PyRice is easy to use and produces intuitive results.Availability and implementationhttps://github.com/SouthGreenPlatform/PyRiceDocumentationhttps://[email protected] informationMITSupplementary informationSupplementary data are available online.

scholarly journals ccNetViz: a WebGL-based JavaScript library for visualization of large networks

2020 ◽  
Vol 36 (16) ◽  
pp. 4527-4529
Author(s):  
Ales Saska ◽  
David Tichy ◽  
Robert Moore ◽  
Achilles Rasquinha ◽  
Caner Akdas ◽  
...  
Keyword(s):  
Systems Biology ◽  
Complex Networks ◽  
Open Source ◽  
High Speed ◽  
A Priori ◽  
Supplementary Information ◽  
Network Visualization ◽  
Supplementary Data ◽  
Web Based ◽  
Flow Of Information

Abstract Summary Visualizing a network provides a concise and practical understanding of the information it represents. Open-source web-based libraries help accelerate the creation of biologically based networks and their use. ccNetViz is an open-source, high speed and lightweight JavaScript library for visualization of large and complex networks. It implements customization and analytical features for easy network interpretation. These features include edge and node animations, which illustrate the flow of information through a network as well as node statistics. Properties can be defined a priori or dynamically imported from models and simulations. ccNetViz is thus a network visualization library particularly suited for systems biology. Availability and implementation The ccNetViz library, demos and documentation are freely available at http://helikarlab.github.io/ccNetViz/. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Epidemiological modeling in StochSS Live!

2021 ◽  
Author(s):  
Richard Jiang ◽  
Bruno Jacob ◽  
Matthew Geiger ◽  
Sean Matthew ◽  
Bryan Rumsey ◽  
...  
Keyword(s):  
Stochastic Model ◽  
Epidemiological Model ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Web Based ◽  
Epidemiological Modeling ◽  
Modeling Simulation ◽  
Wide Range ◽  
Biochemical Systems

Abstract Summary We present StochSS Live!, a web-based service for modeling, simulation and analysis of a wide range of mathematical, biological and biochemical systems. Using an epidemiological model of COVID-19, we demonstrate the power of StochSS Live! to enable researchers to quickly develop a deterministic or a discrete stochastic model, infer its parameters and analyze the results. Availability and implementation StochSS Live! is freely available at https://live.stochss.org/ Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals BIOLITMAP: a web-based geolocated, temporal and thematic visualization of the evolution of bioinformatics publications

2018 ◽  
Vol 35 (14) ◽  
pp. 2518-2520
Author(s):  
Adrián Bazaga ◽  
Alfonso Valencia ◽  
María- JoséRementeria
Keyword(s):  
General Public ◽  
Fast Growth ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Web Based ◽  
Research Publications

Abstract Motivation The fast growth of bioinformatics adds a significant difficulty to assess the contribution, geographical and thematic distribution of the research publications. Results To help researchers, grant agencies and general public to assess the progress in bioinformatics, we have developed BIOLITMAP, a web-based geolocation system that allows an easy and sensible exploration of the publications by institution, year and topic. Availability and implementation BIOLITMAP is available at http://socialanalytics.bsc.es/biolitmap and the sources have been deposited at https://github.com/inab/BIOLITMAP. Supplementary information Supplementary data are available at Bioinformatics online.

A Web Query System for Heterogeneous Government Data

2008 ◽  
pp. 775-789
Author(s):  
Nancy Weigand ◽  
Isabel F. Cruz ◽  
Naijun Zhou ◽  
William Sunna
Keyword(s):  
Heterogeneous Data ◽  
Schema Integration ◽  
Data Discovery ◽  
Web Based ◽  
Rewrite Systems ◽  
Query System ◽  
The Difference ◽  
Government Data ◽  
Semantically Heterogeneous ◽  
Xml Web

This paper describes a Web-based query system for semantically heterogeneous government-produced data. Geospatial Web-based information systems and portals currently are being developed by various levels of government along with the GIS community. Typically, these sites provide data discovery and download capabilities but do not include the ability to pose DBMS-type queries. One of the main problems in querying distributed government data sources is the difference in semantics used by various jurisdictions. We extend work in schema integration by focusing on resolving semantics at the value level in addition to the schema or attribute level. We illustrate our method using land use data, but the method can be used to query across other heterogeneous sets of values. Our work starts from an XML Web-based DBMS and adds functionality to accommodate heterogeneous data between jurisdictions. Our ontology and query rewrite systems use mappings to enable querying across distributed heterogeneous data.

scholarly journals aCLImatise: automated generation of tool definitions for bioinformatics workflows

2020 ◽  
Author(s):  
Michael Milton ◽  
Natalie Thorne
Keyword(s):  
Source Code ◽  
Supplementary Information ◽  
Command Line ◽  
Supplementary Data ◽  
Automated Generation ◽  
Base Camp ◽  
Python Package ◽  
Bioinformatics Workflow ◽  
Bioinformatics Workflows

Abstract Summary aCLImatise is a utility for automatically generating tool definitions compatible with bioinformatics workflow languages, by parsing command-line help output. aCLImatise also has an associated database called the aCLImatise Base Camp, which provides thousands of pre-computed tool definitions. Availability and implementation The latest aCLImatise source code is available within a GitHub organisation, under the GPL-3.0 license: https://github.com/aCLImatise. In particular, documentation for the aCLImatise Python package is available at https://aclimatise.github.io/CliHelpParser/, and the aCLImatise Base Camp is available at https://aclimatise.github.io/BaseCamp/. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals INDRA-IPM: interactive pathway modeling using natural language with automated assembly

2019 ◽  
Vol 35 (21) ◽  
pp. 4501-4503 ◽  
Author(s):  
Petar V Todorov ◽  
Benjamin M Gyori ◽  
John A Bachman ◽  
Peter K Sorger
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Web Service ◽  
Language Processing ◽  
Source Code ◽  
Supplementary Information ◽  
Expression Data ◽  
Supplementary Data ◽  
Automated Assembly ◽  
Web Based

Abstract Summary INDRA-IPM (Interactive Pathway Map) is a web-based pathway map modeling tool that combines natural language processing with automated model assembly and visualization. INDRA-IPM contextualizes models with expression data and exports them to standard formats. Availability and implementation INDRA-IPM is available at: http://pathwaymap.indra.bio. Source code is available at http://github.com/sorgerlab/indra_pathway_map. The underlying web service API is available at http://api.indra.bio:8000. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Knot_pull—python package for biopolymer smoothing and knot detection

2019 ◽  
Author(s):  
Aleksandra I Jarmolinska ◽  
Anna Gambin ◽  
Joanna I Sulkowska
Keyword(s):  
Learning Curve ◽  
Source Code ◽  
Supplementary Information ◽  
Command Line ◽  
Supplementary Data ◽  
Steep Learning Curve ◽  
Independent Source ◽  
Python Package

Abstract Summary The biggest hurdle in studying topology in biopolymers is the steep learning curve for actually seeing the knots in structure visualization. Knot_pull is a command line utility designed to simplify this process—it presents the user with a smoothing trajectory for provided structures (any number and length of protein, RNA or chromatin chains in PDB, CIF or XYZ format), and calculates the knot type (including presence of any links, and slipknots when a subchain is specified). Availability and implementation Knot_pull works under Python >=2.7 and is system independent. Source code and documentation are available at http://github.com/dzarmola/knot_pull under GNU GPL license and include also a wrapper script for PyMOL for easier visualization. Examples of smoothing trajectories can be found at: https://www.youtube.com/watch?v=IzSGDfc1vAY. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Alin: improving interactive ontology matching by interactively revising mapping suggestions

2020 ◽  
Vol 35 ◽  
Author(s):  
Jomar Da Silva ◽  
Kate Revoredo ◽  
Fernanda Baião ◽  
Jérôme Euzenat
Keyword(s):  
State Of The Art ◽  
Heterogeneous Data ◽  
Data Sources ◽  
Ontology Matching ◽  
New Techniques ◽  
Domain Experts ◽  
Matching Process ◽  
Heterogeneous Data Sources ◽  
Expert Answer ◽  
Comparable Quality

Abstract Ontology matching aims at discovering mappings between the entities of two ontologies. It plays an important role in the integration of heterogeneous data sources that are described by ontologies. Interactive ontology matching involves domain experts in the matching process. In some approaches, the expert provides feedback about mappings between ontology entities, that is, these approaches select mappings to present to the expert who replies which of them should be accepted or rejected, so taking advantage of the knowledge of domain experts towards finding an alignment. In this paper, we present Alin, an interactive ontology matching approach which uses expert feedback not only to approve or reject selected mappings but also to dynamically improve the set of selected mappings, that is, to interactively include and to exclude mappings from it. This additional use for expert answers aims at increasing in the benefit brought by each expert answer. For this purpose, Alin uses four techniques. Two techniques were used in the previous versions of Alin to dynamically select concept and attribute mappings. Two new techniques are introduced in this paper: one to dynamically select relationship mappings and another one to dynamically reject inconsistent selected mappings using anti-patterns. We compared Alin with state-of-the-art tools, showing that it generates alignment of comparable quality.

scholarly journals PyIOmica: longitudinal omics analysis and trend identification

2019 ◽  
Vol 36 (7) ◽  
pp. 2306-2307 ◽  
Author(s):  
Sergii Domanskyi ◽  
Carlo Piermarocchi ◽  
George I Mias
Keyword(s):  
Source Code ◽  
Temporal Trends ◽  
Enrichment Analysis ◽  
Supplementary Information ◽  
Data Normalization ◽  
Supplementary Data ◽  
Visibility Graphs ◽  
Trend Identification ◽  
Microsoft Windows ◽  
Python Package

Abstract Summary PyIOmica is an open-source Python package focusing on integrating longitudinal multiple omics datasets, characterizing and categorizing temporal trends. The package includes multiple bioinformatics tools including data normalization, annotation, categorization, visualization and enrichment analysis for gene ontology terms and pathways. Additionally, the package includes an implementation of visibility graphs to visualize time series as networks. Availability and implementation PyIOmica is implemented as a Python package (pyiomica), available for download and installation through the Python Package Index (https://pypi.python.org/pypi/pyiomica), and can be deployed using the Python import function following installation. PyIOmica has been tested on Mac OS X, Unix/Linux and Microsoft Windows. The application is distributed under an MIT license. Source code for each release is also available for download on Zenodo (https://doi.org/10.5281/zenodo.3548040). Supplementary information Supplementary data are available at Bioinformatics

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