scholarly journals GlycanFormatConverter: a conversion tool for translating the complexities of glycans

2018 ◽  
Vol 35 (14) ◽  
pp. 2434-2440 ◽  
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.

scholarly journals 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

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.

scholarly journals DNA Features Viewer: a sequence annotation formatting and plotting library for Python

2020 ◽  
Vol 36 (15) ◽  
pp. 4350-4352 ◽  
Author(s):  
Valentin Zulkower ◽  
Susan Rosser
Keyword(s):  
Computational Biology ◽  
Open Source ◽  
Programming Language ◽  
Source Code ◽  
Supplementary Information ◽  
Use Case ◽  
Sequence Annotation ◽  
Supplementary Data ◽  
Python Programming Language ◽  
Python Programming

Abstract Motivation Although the Python programming language counts many Bioinformatics and Computational Biology libraries; none offers customizable sequence annotation visualizations with layout optimization. Results DNA Features Viewer is a sequence annotation plotting library which optimizes plot readability while letting users tailor other visual aspects (colors, labels, highlights etc.) to their particular use case. Availability and implementation Open-source code and documentation are available on Github under the MIT license (https://github.com/Edinburgh-Genome-Foundry/DnaFeaturesViewer). Supplementary information Supplementary data are available at Bioinformatics 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.

KEC: unique sequence search by K-mer exclusion

2021 ◽  
Author(s):  
Pavel Beran ◽  
Dagmar Stehlíková ◽  
Stephen P Cohen ◽  
Vladislav Čurn
Keyword(s):  
Amino Acid ◽  
Nucleic Acid ◽  
Source Code ◽  
Unique Sequence ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Laptop Computers ◽  
Sequence Search ◽  
Target Sequences ◽  
Cross Reference

Abstract Summary Searching for amino acid or nucleic acid sequences unique to one organism may be challenging depending on size of the available datasets. K-mer elimination by cross-reference (KEC) allows users to quickly and easily find unique sequences by providing target and non-target sequences. Due to its speed, it can be used for datasets of genomic size and can be run on desktop or laptop computers with modest specifications. Availability and implementation KEC is freely available for non-commercial purposes. Source code and executable binary files compiled for Linux, Mac and Windows can be downloaded from https://github.com/berybox/KEC. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals BioCommons: a robust java library for RNA structural bioinformatics

2021 ◽  
Author(s):  
Tomasz Zok
Keyword(s):  
Source Code ◽  
Structural Bioinformatics ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Bioinformatic Tools ◽  
Data Formats ◽  
Central Repository ◽  
Diverse Data ◽  
2D And 3D ◽  
Java Library

Abstract Motivation Biomolecular structures come in multiple representations and diverse data formats. Their incompatibility with the requirements of data analysis programs significantly hinders the analytics and the creation of new structure-oriented bioinformatic tools. Therefore, the need for robust libraries of data processing functions is still growing. Results BioCommons is an open-source, Java library for structural bioinformatics. It contains many functions working with the 2D and 3D structures of biomolecules, with a particular emphasis on RNA. Availability and implementation The library is available in Maven Central Repository and its source code is hosted on GitHub: https://github.com/tzok/BioCommons Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals SVIM-asm: Structural variant detection from haploid and diploid genome assemblies

2020 ◽  
Author(s):  
David Heller ◽  
Martin Vingron
Keyword(s):  
Genetic Information ◽  
Source Code ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Diploid Genome ◽  
Insertions And Deletions ◽  
Structural Variant ◽  
Sequencing Technologies ◽  
Variant Detection ◽  
Genome Assemblies

AbstractMotivationWith the availability of new sequencing technologies, the generation of haplotype-resolved genome assemblies up to chromosome scale has become feasible. These assemblies capture the complete genetic information of both parental haplotypes, increase structural variant (SV) calling sensitivity and enable direct genotyping and phasing of SVs. Yet, existing SV callers are designed for haploid genome assemblies only, do not support genotyping or detect only a limited set of SV classes.ResultsWe introduce our method SVIM-asm for the detection and genotyping of six common classes of SVs from haploid and diploid genome assemblies. Compared against the only other existing SV caller for diploid assemblies, DipCall, SVIM-asm detects more SV classes and reached higher F1 scores for the detection of insertions and deletions on two recently published assemblies of the HG002 individual.Availability and ImplementationSVIM-asm has been implemented in Python and can be easily installed via bioconda. Its source code is available at github.com/eldariont/[email protected] informationSupplementary data are available online.

scholarly journals ExpansionHunter: a sequence-graph-based tool to analyze variation in short tandem repeat regions

2019 ◽  
Vol 35 (22) ◽  
pp. 4754-4756 ◽  
Author(s):  
Egor Dolzhenko ◽  
Viraj Deshpande ◽  
Felix Schlesinger ◽  
Peter Krusche ◽  
Roman Petrovski ◽  
...  
Keyword(s):  
Tandem Repeat ◽  
Broad Class ◽  
Source Code ◽  
Computational Method ◽  
Supplementary Information ◽  
Dna Repeats ◽  
Supplementary Data ◽  
Sequence Graph ◽  
Version 2.0 ◽  
Short Tandem

Abstract Summary We describe a novel computational method for genotyping repeats using sequence graphs. This method addresses the long-standing need to accurately genotype medically important loci containing repeats adjacent to other variants or imperfect DNA repeats such as polyalanine repeats. Here we introduce a new version of our repeat genotyping software, ExpansionHunter, that uses this method to perform targeted genotyping of a broad class of such loci. Availability and implementation ExpansionHunter is implemented in C++ and is available under the Apache License Version 2.0. The source code, documentation, and Linux/macOS binaries are available at https://github.com/Illumina/ExpansionHunter/. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals LIONS: analysis suite for detecting and quantifying transposable element initiated transcription from RNA-seq

2019 ◽  
Vol 35 (19) ◽  
pp. 3839-3841 ◽  
Author(s):  
Artem Babaian ◽  
I Richard Thompson ◽  
Jake Lever ◽  
Liane Gagnier ◽  
Mohammad M Karimi ◽  
...  
Keyword(s):  
Transposable Elements ◽  
Transposable Element ◽  
Test Data ◽  
Source Code ◽  
Supplementary Information ◽  
Transcriptional Networks ◽  
Supplementary Data ◽  
Rna Seq ◽  
Transcriptional Initiation ◽  
Instruction Manual

Abstract Summary Transposable elements (TEs) influence the evolution of novel transcriptional networks yet the specific and meaningful interpretation of how TE-derived transcriptional initiation contributes to the transcriptome has been marred by computational and methodological deficiencies. We developed LIONS for the analysis of RNA-seq data to specifically detect and quantify TE-initiated transcripts. Availability and implementation Source code, container, test data and instruction manual are freely available at www.github.com/ababaian/LIONS. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals OpenBioLink: a benchmarking framework for large-scale biomedical link prediction

2020 ◽  
Vol 36 (13) ◽  
pp. 4097-4098 ◽  
Author(s):  
Anna Breit ◽  
Simon Ott ◽  
Asan Agibetov ◽  
Matthias Samwald
Keyword(s):  
Link Prediction ◽  
Large Scale ◽  
Source Code ◽  
Machine Learning Algorithms ◽  
Knowledge Networks ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Biomedical Knowledge ◽  
High Quality ◽  
Baseline Evaluation

Abstract Summary Recently, novel machine-learning algorithms have shown potential for predicting undiscovered links in biomedical knowledge networks. However, dedicated benchmarks for measuring algorithmic progress have not yet emerged. With OpenBioLink, we introduce a large-scale, high-quality and highly challenging biomedical link prediction benchmark to transparently and reproducibly evaluate such algorithms. Furthermore, we present preliminary baseline evaluation results. Availability and implementation Source code and data are openly available at https://github.com/OpenBioLink/OpenBioLink. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Capybara: equivalence ClAss enumeration of coPhylogenY event-BAsed ReconciliAtions

2020 ◽  
Vol 36 (14) ◽  
pp. 4197-4199
Author(s):  
Yishu Wang ◽  
Arnaud Mary ◽  
Marie-France Sagot ◽  
Blerina Sinaimeri
Keyword(s):  
Source Code ◽  
Supplementary Information ◽  
Optimal Solutions ◽  
Supplementary Data ◽  
Huge Number ◽  
Tree Reconciliation ◽  
Class Enumeration ◽  
Suboptimal Solutions ◽  
The Many ◽  
Event Based

Abstract Motivation Phylogenetic tree reconciliation is the method of choice in analyzing host-symbiont systems. Despite the many reconciliation tools that have been proposed in the literature, two main issues remain unresolved: (i) listing suboptimal solutions (i.e. whose score is ‘close’ to the optimal ones) and (ii) listing only solutions that are biologically different ‘enough’. The first issue arises because the optimal solutions are not always the ones biologically most significant; providing many suboptimal solutions as alternatives for the optimal ones is thus very useful. The second one is related to the difficulty to analyze an often huge number of optimal solutions. In this article, we propose Capybara that addresses both of these problems in an efficient way. Furthermore, it includes a tool for visualizing the solutions that significantly helps the user in the process of analyzing the results. Availability and implementation The source code, documentation and binaries for all platforms are freely available at https://capybara-doc.readthedocs.io/. Contact [email protected] or [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

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