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

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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EARRINGS: an efficient and accurate adapter trimmer entails no a priori adapter sequences

Bioinformatics ◽

10.1093/bioinformatics/btab025 ◽

2021 ◽

Author(s):

Ting-Hsuan Wang ◽

Cheng-Ching Huang ◽

Jui-Hung Hung

Keyword(s):

Open Source Software ◽

Large Scale ◽

A Priori ◽

Supplementary Information ◽

Supplementary Data ◽

Comparable Accuracy ◽

Meta Analyses ◽

Next Generation Sequencing Ngs ◽

Adapter Trimming ◽

Generation Sequencing

Abstract Motivation Cross-sample comparisons or large-scale meta-analyses based on the next generation sequencing (NGS) involve replicable and universal data preprocessing, including removing adapter fragments in contaminated reads (i.e. adapter trimming). While modern adapter trimmers require users to provide candidate adapter sequences for each sample, which are sometimes unavailable or falsely documented in the repositories (such as GEO or SRA), large-scale meta-analyses are therefore jeopardized by suboptimal adapter trimming. Results Here we introduce a set of fast and accurate adapter detection and trimming algorithms that entail no a priori adapter sequences. These algorithms were implemented in modern C++ with SIMD and multithreading to accelerate its speed. Our experiments and benchmarks show that the implementation (i.e. EARRINGS), without being given any hint of adapter sequences, can reach comparable accuracy and higher throughput than that of existing adapter trimmers. EARRINGS is particularly useful in meta-analyses of a large batch of datasets and can be incorporated in any sequence analysis pipelines in all scales. Availability and implementation EARRINGS is open-source software and is available at https://github.com/jhhung/EARRINGS. Supplementary information Supplementary data are available at Bioinformatics online.

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

Bioinformatics ◽

10.1093/bioinformatics/bty967 ◽

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.

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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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DrawGlycan-SNFG and gpAnnotate: rendering glycans and annotating glycopeptide mass spectra

Bioinformatics ◽

10.1093/bioinformatics/btz819 ◽

2019 ◽

Cited By ~ 4

Author(s):

Kai Cheng ◽

Gabrielle Pawlowski ◽

Xinheng Yu ◽

Yusen Zhou ◽

Sriram Neelamegham

Keyword(s):

Mass Spectrometry ◽

Open Source ◽

Mass Spectra ◽

Supplementary Information ◽

Supplementary Data ◽

International Union ◽

Open Source Program ◽

Source Program ◽

Wide Range ◽

Peptide Modifications

Abstract Summary This manuscript describes an open-source program, DrawGlycan-SNFG (version 2), that accepts IUPAC (International Union of Pure and Applied Chemist)-condensed inputs to render Symbol Nomenclature For Glycans (SNFG) drawings. A wide range of local and global options enable display of various glycan/peptide modifications including bond breakages, adducts, repeat structures, ambiguous identifications etc. These facilities make DrawGlycan-SNFG ideal for integration into various glycoinformatics software, including glycomics and glycoproteomics mass spectrometry (MS) applications. As a demonstration of such usage, we incorporated DrawGlycan-SNFG into gpAnnotate, a standalone application to score and annotate individual MS/MS glycopeptide spectrum in different fragmentation modes. Availability and implementation DrawGlycan-SNFG and gpAnnotate are platform independent. While originally coded using MATLAB, compiled packages are also provided to enable DrawGlycan-SNFG implementation in Python and Java. All programs are available from https://virtualglycome.org/drawglycan; https://virtualglycome.org/gpAnnotate. Contact [email protected] 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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DNA Chisel, a versatile sequence optimizer

Bioinformatics ◽

10.1093/bioinformatics/btaa558 ◽

2020 ◽

Vol 36 (16) ◽

pp. 4508-4509 ◽

Cited By ~ 3

Author(s):

Valentin Zulkower ◽

Susan Rosser

Keyword(s):

Open Source ◽

Dna Sequence ◽

Web Application ◽

Optimization Problems ◽

Supplementary Information ◽

Supplementary Data ◽

Sequence Optimization ◽

Sequence Design ◽

Optimization Framework ◽

Dna Sequence Design

Abstract Motivation Accounting for biological and practical requirements in DNA sequence design often results in challenging optimization problems. Current software solutions are problem-specific and hard to combine. Results DNA Chisel is an easy-to-use, easy-to-extend sequence optimization framework allowing to freely define and combine optimization specifications via Python scripts or Genbank annotations. Availability and implementation The framework is available as a web application (https://cuba.genomefoundry.org/sculpt_a_sequence) or open-source Python library (see at https://github.com/Edinburgh-Genome-Foundry/DNAChisel for code and documentation). Supplementary information Supplementary data are available at Bioinformatics online.

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GlycanFormatConverter: a conversion tool for translating the complexities of glycans

Bioinformatics ◽

10.1093/bioinformatics/bty990 ◽

2018 ◽

Vol 35 (14) ◽

pp. 2434-2440 ◽

Cited By ~ 7

Author(s):

Shinichiro Tsuchiya ◽

Issaku Yamada ◽

Kiyoko F Aoki-Kinoshita

Keyword(s):

Open Source ◽

Source Code ◽

Supplementary Information ◽

Biological Processes ◽

Supplementary Data ◽

Unique Representation ◽

Open Source Tool ◽

Living Organisms ◽

Conversion Tool ◽

Complex Glycan

Abstract Motivation Glycans are biomolecules that take an important role in the biological processes of living organisms. They form diverse, complicated structures such as branched and cyclic forms. Web3 Unique Representation of Carbohydrate Structures (WURCS) was proposed as a new linear notation for uniquely representing glycans during the GlyTouCan project. WURCS defines rules for complex glycan structures that other text formats did not support, and so it is possible to represent a wide variety glycans. However, WURCS uses a complicated nomenclature, so it is not human-readable. Therefore, we aimed to support the interpretation of WURCS by converting WURCS to the most basic and widely used format IUPAC. Results In this study, we developed GlycanFormatConverter and succeeded in converting WURCS to the three kinds of IUPAC formats (IUPAC-Extended, IUPAC-Condensed and IUPAC-Short). Furthermore, we have implemented functionality to import IUPAC-Extended, KEGG Chemical Function (KCF) and LinearCode formats and to export WURCS. We have thoroughly tested our GlycanFormatConverter and were able to show that it was possible to convert all the glycans registered in the GlyTouCan repository, with exceptions owing only to the limitations of the original format. The source code for this conversion tool has been released as an open source tool. Availability and implementation https://github.com/glycoinfo/GlycanFormatConverter.git Supplementary information Supplementary data are available at Bioinformatics online.

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CNN-PepPred: An open-source tool to create convolutional NN models for the discovery of patterns in peptide sets. Application to peptide-MHC class II binding prediction

Bioinformatics ◽

10.1093/bioinformatics/btab687 ◽

2021 ◽

Author(s):

Valentin Junet ◽

Xavier Daura

Keyword(s):

Neural Networks ◽

Open Source ◽

Mhc Class Ii ◽

Convolutional Neural Networks ◽

Operating Systems ◽

Class Ii ◽

Supplementary Information ◽

Supplementary Data ◽

Binding Prediction ◽

Open Source Tool

Abstract Summary The ability to unveil binding patterns in peptide sets has important applications in several biomedical areas, including the development of vaccines. We present an open-source tool, CNN-PepPred, that uses convolutional neural networks to discover such patterns, along with its application to peptide-HLA class II binding prediction. The tool can be used locally on different operating systems, with CPUs or GPUs, to train, evaluate, apply and visualize models. Availability and Implementation CNN-PepPred is freely available as a Python tool with a detailed User’s Guide at: https://github.com/ComputBiol-IBB/CNN-PepPred Supplementary information Supplementary data are available at Bioinformatics online.

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Triplet-based similarity score for fully multilabeled trees with poly-occurring labels

Bioinformatics ◽

10.1093/bioinformatics/btaa676 ◽

2020 ◽

Author(s):

Simone Ciccolella ◽

Giulia Bernardini ◽

Luca Denti ◽

Paola Bonizzoni ◽

Marco Previtali ◽

...

Keyword(s):

Open Source ◽

Evolutionary History ◽

Similarity Measures ◽

Real Data ◽

Similarity Score ◽

Supplementary Information ◽

Supplementary Data ◽

Wide Range ◽

Golden Standard ◽

History Of

Abstract Motivation The latest advances in cancer sequencing, and the availability of a wide range of methods to infer the evolutionary history of tumors, have made it important to evaluate, reconcile and cluster different tumor phylogenies. Recently, several notions of distance or similarities have been proposed in the literature, but none of them has emerged as the golden standard. Moreover, none of the known similarity measures is able to manage mutations occurring multiple times in the tree, a circumstance often occurring in real cases. Results To overcome these limitations, in this article, we propose MP3, the first similarity measure for tumor phylogenies able to effectively manage cases where multiple mutations can occur at the same time and mutations can occur multiple times. Moreover, a comparison of MP3 with other measures shows that it is able to classify correctly similar and dissimilar trees, both on simulated and on real data. Availability and implementation An open source implementation of MP3 is publicly available at https://github.com/AlgoLab/mp3treesim. Supplementary information Supplementary data are available at Bioinformatics online.

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