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 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 Biological sequence modeling with convolutional kernel networks

2019 ◽  
Vol 35 (18) ◽  
pp. 3294-3302 ◽  
Author(s):  
Dexiong Chen ◽  
Laurent Jacob ◽  
Julien Mairal
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Hybrid Approach ◽  
Training Data ◽  
Supplementary Information ◽  
Small Scale ◽  
Biological Sequences ◽  
Homology Detection ◽  
Binding Prediction ◽  
Sequence Modeling

Abstract Motivation The growing number of annotated biological sequences available makes it possible to learn genotype-phenotype relationships from data with increasingly high accuracy. When large quantities of labeled samples are available for training a model, convolutional neural networks can be used to predict the phenotype of unannotated sequences with good accuracy. Unfortunately, their performance with medium- or small-scale datasets is mitigated, which requires inventing new data-efficient approaches. Results We introduce a hybrid approach between convolutional neural networks and kernel methods to model biological sequences. Our method enjoys the ability of convolutional neural networks to learn data representations that are adapted to a specific task, while the kernel point of view yields algorithms that perform significantly better when the amount of training data is small. We illustrate these advantages for transcription factor binding prediction and protein homology detection, and we demonstrate that our model is also simple to interpret, which is crucial for discovering predictive motifs in sequences. Availability and implementation Source code is freely available at https://gitlab.inria.fr/dchen/CKN-seq. 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.

scholarly journals Efficient implementation of convolutional neural networks in the data processing of two-photon in vivo imaging

2019 ◽  
Vol 35 (17) ◽  
pp. 3208-3210 ◽  
Author(s):  
Yangzhen Wang ◽  
Feng Su ◽  
Shanshan Wang ◽  
Chaojuan Yang ◽  
Yonglu Tian ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Detection Methods ◽  
Supplementary Information ◽  
Imaging Data ◽  
Two Photon ◽  
Processing Power ◽  
Fluctuation Method ◽  
The Brain

Abstract Motivation Functional imaging at single-neuron resolution offers a highly efficient tool for studying the functional connectomics in the brain. However, mainstream neuron-detection methods focus on either the morphologies or activities of neurons, which may lead to the extraction of incomplete information and which may heavily rely on the experience of the experimenters. Results We developed a convolutional neural networks and fluctuation method-based toolbox (ImageCN) to increase the processing power of calcium imaging data. To evaluate the performance of ImageCN, nine different imaging datasets were recorded from awake mouse brains. ImageCN demonstrated superior neuron-detection performance when compared with other algorithms. Furthermore, ImageCN does not require sophisticated training for users. Availability and implementation ImageCN is implemented in MATLAB. The source code and documentation are available at https://github.com/ZhangChenLab/ImageCN. Supplementary information Supplementary data are available at Bioinformatics online.

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

2019 ◽  
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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