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

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

Bioinformatics ◽

10.1093/bioinformatics/btaa559 ◽

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.

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Epidemiological modeling in StochSS Live!

Bioinformatics ◽

10.1093/bioinformatics/btab061 ◽

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.

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

Bioinformatics ◽

10.1093/bioinformatics/btz289 ◽

2019 ◽

Vol 35 (21) ◽

pp. 4501-4503 ◽

Cited By ~ 9

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.

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A fast and memory-efficient implementation of the transfer bootstrap

Bioinformatics ◽

10.1093/bioinformatics/btz874 ◽

2019 ◽

Vol 36 (7) ◽

pp. 2280-2281 ◽

Cited By ~ 2

Author(s):

Sarah Lutteropp ◽

Alexey M Kozlov ◽

Alexandros Stamatakis

Keyword(s):

General Public ◽

Efficient Implementation ◽

Supplementary Information ◽

Bootstrap Support ◽

Supplementary Data ◽

Original Algorithm ◽

Parallel Version ◽

Branch Support ◽

General Public License ◽

Memory Efficient

Abstract Motivation Recently, Lemoine et al. suggested the transfer bootstrap expectation (TBE) branch support metric as an alternative to classical phylogenetic bootstrap support for taxon-rich datasets. However, the original TBE implementation in the booster tool is compute- and memory-intensive. Results We developed a fast and memory-efficient TBE implementation. We improve upon the original algorithm by Lemoine et al. via several algorithmic and technical optimizations. On empirical as well as on random tree sets with varying taxon counts, our implementation is up to 480 times faster than booster. Furthermore, it only requires memory that is linear in the number of taxa, which leads to 10× to 40× memory savings compared with booster. Availability and implementation Our implementation has been partially integrated into pll-modules and RAxML-NG and is available under the GNU Affero General Public License v3.0 at https://github.com/ddarriba/pll-modules and https://github.com/amkozlov/raxml-ng. The parallel version that also computes additional TBE-related statistics is available at: https://github.com/lutteropp/raxml-ng/tree/tbe. Supplementary information Supplementary data are available at Bioinformatics online.

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SolupHred: a server to predict the pH-dependent aggregation of intrinsically disordered proteins

Bioinformatics ◽

10.1093/bioinformatics/btaa909 ◽

2020 ◽

Author(s):

Carlos Pintado ◽

Jaime Santos ◽

Valentín Iglesias ◽

Salvador Ventura

Keyword(s):

Intrinsically Disordered Proteins ◽

Disordered Proteins ◽

Supplementary Information ◽

Supplementary Data ◽

Solution Ph ◽

Predictive Tool ◽

Web Based ◽

Intrinsically Disordered ◽

Dependent Protein ◽

Ph Dependent

Abstract Summary Polypeptides are exposed to changing environmental conditions that modulate their intrinsic aggregation propensities. Intrinsically disordered proteins (IDPs) constitutively expose their aggregation determinants to the solvent, thus being especially sensitive to its fluctuations. However, solvent conditions are often disregarded in computational aggregation predictors. We recently developed a phenomenological model to predict IDPs' solubility as a function of the solution pH, which is based on the assumption that both protein lipophilicity and charge depend on this parameter. The model anticipated solubility changes in different IDPs accurately. In this application note, we present SolupHred, a web-based interface that implements the aforementioned theoretical framework into a predictive tool able to compute IDPs aggregation propensities as a function of pH. SolupHred is the first dedicated software for the prediction of pH-dependent protein aggregation. Availability and implementation The SolupHred web server is freely available for academic users at: https://ppmclab.pythonanywhere.com/SolupHred. It is platform-independent and does not require previous registration. Supplementary information Supplementary data are available at Bioinformatics online.

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GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database

Bioinformatics ◽

10.1093/bioinformatics/btz848 ◽

2019 ◽

Cited By ~ 161

Author(s):

Pierre-Alain Chaumeil ◽

Aaron J Mussig ◽

Philip Hugenholtz ◽

Donovan H Parks

Keyword(s):

General Public ◽

Source Code ◽

Supplementary Information ◽

Supplementary Data ◽

Computationally Efficient ◽

Taxonomic Assignments ◽

General Public License

Abstract Summary The GTDB Toolkit (GTDB-Tk) provides objective taxonomic assignments for bacterial and archaeal genomes based on the Genome Taxonomy Database (GTDB). GTDB-Tk is computationally efficient and able to classify thousands of draft genomes in parallel. Here we demonstrate the accuracy of the GTDB-Tk taxonomic assignments by evaluating its performance on a phylogenetically diverse set of 10,156 bacterial and archaeal metagenome-assembled genomes. Availability GTDB-Tk is implemented in Python and licensed under the GNU General Public License v3.0. Source code and documentation are available at: https://github.com/ecogenomics/gtdbtk Supplementary information Supplementary data are available at Bioinformatics online.

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ARBitR: an overlap-aware genome assembly scaffolder for linked reads

Bioinformatics ◽

10.1093/bioinformatics/btaa975 ◽

2020 ◽

Author(s):

Markus Hiltunen ◽

Martin Ryberg ◽

Hanna Johannesson

Keyword(s):

Genome Assembly ◽

General Public ◽

Source Code ◽

Draft Genome ◽

Supplementary Information ◽

Genomic Sequencing ◽

Supplementary Data ◽

Genome Assemblies ◽

General Public License

Abstract Summary Linked genomic sequencing reads contain information that can be used to join sequences together into scaffolds in draft genome assemblies. Existing software for this purpose performs the scaffolding by joining sequences with a gap between them, not considering potential overlaps of contigs. We developed ARBitR to create scaffolds where overlaps are taken into account and show that it can accurately recreate regions where draft assemblies are broken. Availability and implementation ARBitR is written and implemented in Python3 for Unix-based operative systems. All source code is available at https://github.com/markhilt/ARBitR under the GNU General Public License v3. Supplementary information Supplementary data are available at Bioinformatics online.

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PyRice: a Python package for querying Oryza sativa databases

Bioinformatics ◽

10.1093/bioinformatics/btaa694 ◽

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.

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GAPIN: Grouped and Aligned Protein Interface Networks

10.1101/520833 ◽

2019 ◽

Author(s):

Biharck M. Araújo ◽

Aline L. Coelho ◽

Sabrina A. Silveira ◽

João P. R. Romanelli ◽

Raquel C. de Melo-Minardi ◽

...

Keyword(s):

Interaction Network ◽

Supplementary Information ◽

Protein Interface ◽

Supplementary Data ◽

Web Interface ◽

Web Based ◽

Structural Interaction ◽

Peptidase Inhibitor ◽

Supplementary Material ◽

Hydrophobic Patterns

AbstractSummaryGAPIN is a web-based application for structural interaction network analysis among any type of PDB molecules, regardless of whether their interfaces are between chain-chain or chain-ligand. A special emphasis is given to graph clustering, allowing users to scrutinize target contexts for ligand candidates. We show how GAPIN can be used to unveil underlying hydrophobic patterns on a set of peptidase-inhibitor complexes. In another experiment, we show there is a positive correlation between cluster sizes and the presence of druggable spots, indicating that the clustering may discriminate the higher complexity of these hot subnetworks.Availability and implementationGAPIN is freely available as an easy-to-use web interface at https://[email protected], [email protected] informationSupplementary data are available online.

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