FuncTree2: an interactive radial tree for functional hierarchies and omics data visualization

Abstract Summary Functional annotations and their hierarchical classification are widely used in omics workflows to build novel insight upon existing biological knowledge. Currently, a plethora of tools is available to explore omics datasets at the level of functional annotations, but there is a lack of feature rich and user-friendly tools that help scientists take advantage of their hierarchical classification for additional and often invaluable insights. Here, we present FuncTree2, a user-friendly web application that turns hierarchical classifications into interactive and highly customizable radial trees, and enables researchers to visualize their data simultaneously on all its levels. FuncTree2 features mapping of data from multiple samples and several navigation features like zooming, panning, re-rooting and collapsing of nodes or levels. Availability and implementation FuncTree2 is freely available at https://bioviz.tokyo/functree2/ as a web application and a REST API. Source code is available on GitHub https://github.com/yamada-lab/functree-ng. Supplementary information Supplementary data are available at Bioinformatics online.

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VCF/Plotein: visualization and prioritization of genomic variants from human exome sequencing projects

Bioinformatics ◽

10.1093/bioinformatics/btz458 ◽

2019 ◽

Vol 35 (22) ◽

pp. 4803-4805 ◽

Cited By ~ 1

Author(s):

Raul Ossio ◽

O Isaac Garcia-Salinas ◽

Diego Said Anaya-Mancilla ◽

Jair S Garcia-Sotelo ◽

Luis A Aguilar ◽

...

Keyword(s):

Exome Sequencing ◽

Web Application ◽

Source Code ◽

Supplementary Information ◽

Sequencing Data ◽

Genomic Variants ◽

Exome Sequencing Data ◽

Variant Filtering ◽

Variant Information ◽

User Friendly

Abstract Motivation Identifying disease-causing variants from exome sequencing projects remains a challenging task that often requires bioinformatics expertise. Here we describe a user-friendly graphical application that allows medical professionals and bench biologists to prioritize and visualize genetic variants from human exome sequencing data. Results We have implemented VCF/Plotein, a graphical, fully interactive web application able to display exome sequencing data in VCF format. Gene and variant information is extracted from Ensembl. Cross-referencing with external databases and application-based gene and variant filtering have also been implemented. All data processing is done locally by the user’s CPU to ensure the security of patient data. Availability and implementation Freely available on the web at https://vcfplotein.liigh.unam.mx. Website implemented in JavaScript using the Vue.js framework, with all major browsers supported. Source code freely available for download at https://github.com/raulossio/VCF-plotein. Supplementary information Supplementary data are available at Bioinformatics online.

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PathScore: a web tool for identifying altered pathways in cancer data

10.1101/067090 ◽

2016 ◽

Cited By ~ 2

Author(s):

Stephen G. Gaffney ◽

Jeffrey P. Townsend

Keyword(s):

Web Application ◽

Somatic Mutations ◽

Supplementary Information ◽

Web Tool ◽

Cancer Data ◽

Link Type ◽

Novel Approach ◽

Supplementary Material ◽

User Friendly ◽

Pathway Effect

ABSTRACTSummaryPathScore quantifies the level of enrichment of somatic mutations within curated pathways, applying a novel approach that identifies pathways enriched across patients. The application provides several user-friendly, interactive graphic interfaces for data exploration, including tools for comparing pathway effect sizes, significance, gene-set overlap and enrichment differences between projects.Availability and ImplementationWeb application available at pathscore.publichealth.yale.edu. Site implemented in Python and MySQL, with all major browsers supported. Source code available at github.com/sggaffney/pathscore with a GPLv3 [email protected] InformationAdditional documentation can be found at http://pathscore.publichealth.yale.edu/faq.

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YeastSpotter: accurate and parameter-free web segmentation for microscopy images of yeast cells

Bioinformatics ◽

10.1093/bioinformatics/btz402 ◽

2019 ◽

Vol 35 (21) ◽

pp. 4525-4527 ◽

Cited By ~ 10

Author(s):

Alex X Lu ◽

Taraneh Zarin ◽

Ian S Hsu ◽

Alan M Moses

Keyword(s):

Image Analysis ◽

Web Application ◽

Single Cells ◽

Yeast Cells ◽

Supplementary Information ◽

Supplementary Data ◽

User Friendly ◽

Microscopy Images

Abstract Summary We introduce YeastSpotter, a web application for the segmentation of yeast microscopy images into single cells. YeastSpotter is user-friendly and generalizable, reducing the computational expertise required for this critical preprocessing step in many image analysis pipelines. Availability and implementation YeastSpotter is available at http://yeastspotter.csb.utoronto.ca/. Code is available at https://github.com/alexxijielu/yeast_segmentation. Supplementary information Supplementary data are available at Bioinformatics online.

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DiscoRhythm: an easy-to-use web application and R package for discovering rhythmicity

Bioinformatics ◽

10.1093/bioinformatics/btz834 ◽

2019 ◽

Cited By ~ 2

Author(s):

Matthew Carlucci ◽

Algimantas Kriščiūnas ◽

Haohan Li ◽

Povilas Gibas ◽

Karolis Koncevičius ◽

...

Keyword(s):

Web Application ◽

Statistical Significance ◽

R Package ◽

Biological Data ◽

Supplementary Information ◽

Statistical Knowledge ◽

Health And Disease ◽

Phase Amplitude ◽

Almost All ◽

User Friendly

Abstract Motivation Biological rhythmicity is fundamental to almost all organisms on Earth and plays a key role in health and disease. Identification of oscillating signals could lead to novel biological insights, yet its investigation is impeded by the extensive computational and statistical knowledge required to perform such analysis. Results To address this issue, we present DiscoRhythm (Discovering Rhythmicity), a user-friendly application for characterizing rhythmicity in temporal biological data. DiscoRhythm is available as a web application or an R/Bioconductor package for estimating phase, amplitude, and statistical significance using four popular approaches to rhythm detection (Cosinor, JTK Cycle, ARSER, and Lomb-Scargle). We optimized these algorithms for speed, improving their execution times up to 30-fold to enable rapid analysis of -omic-scale datasets in real-time. Informative visualizations, interactive modules for quality control, dimensionality reduction, periodicity profiling, and incorporation of experimental replicates make DiscoRhythm a thorough toolkit for analyzing rhythmicity. Availability and Implementation The DiscoRhythm R package is available on Bioconductor (https://bioconductor.org/packages/DiscoRhythm), with source code available on GitHub (https://github.com/matthewcarlucci/DiscoRhythm) under a GPL-3 license. The web application is securely deployed over HTTPS (https://disco.camh.ca) and is freely available for use worldwide. Local instances of the DiscoRhythm web application can be created using the R package or by deploying the publicly available Docker container (https://hub.docker.com/r/mcarlucci/discorhythm). Supplementary information Supplementary data are available at Bioinformatics online.

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G4Killer web application: a tool to design G-quadruplex mutations

Bioinformatics ◽

10.1093/bioinformatics/btaa057 ◽

2020 ◽

Vol 36 (10) ◽

pp. 3246-3247

Author(s):

Vaclav Brazda ◽

Jan Kolomaznik ◽

Jean-Louis Mergny ◽

Jiri Stastny

Keyword(s):

Dna Sequences ◽

Web Application ◽

Rational Design ◽

Therapeutic Potential ◽

Supplementary Information ◽

Web Tool ◽

Web Based ◽

Dna Structures ◽

G Quadruplex ◽

User Friendly

Abstract Motivation G-quadruplexes (G4) are important regulatory non-B DNA structures with therapeutic potential. A tool for rational design of mutations leading to decreased propensity for G4 formation should be useful in studying G4 functions. Although tools exist for G4 prediction, no easily accessible tool for the rational design of G4 mutations has been available. Results We developed a web-based tool termed G4Killer that is based on the G4Hunter algorithm. This new tool is a platform-independent and user-friendly application to design mutations crippling G4 propensity in a parsimonious way (i.e., keeping the primary sequence as close as possible to the original one). The tool is integrated into our DNA analyzer server and allows for generating mutated DNA sequences having the desired lowered G4Hunter score with minimal mutation steps. Availability and implementation The G4Killer web tool can be accessed at: http://bioinformatics.ibp.cz. Supplementary information Supplementary data are available at Bioinformatics online.

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Temporal network alignment via GoT-WAVE

Bioinformatics ◽

10.1093/bioinformatics/btz119 ◽

2019 ◽

Vol 35 (18) ◽

pp. 3527-3529 ◽

Cited By ~ 3

Author(s):

David Aparício ◽

Pedro Ribeiro ◽

Tijana Milenković ◽

Fernando Silva

Keyword(s):

User Interface ◽

State Of The Art ◽

Source Code ◽

Network Alignment ◽

Supplementary Information ◽

Temporal Network ◽

Temporal Networks ◽

Supplementary Data ◽

Node Similarity ◽

User Friendly

Abstract Motivation Network alignment (NA) finds conserved regions between two networks. NA methods optimize node conservation (NC) and edge conservation. Dynamic graphlet degree vectors are a state-of-the-art dynamic NC measure, used within the fastest and most accurate NA method for temporal networks: DynaWAVE. Here, we use graphlet-orbit transitions (GoTs), a different graphlet-based measure of temporal node similarity, as a new dynamic NC measure within DynaWAVE, resulting in GoT-WAVE. Results On synthetic networks, GoT-WAVE improves DynaWAVE’s accuracy by 30% and speed by 64%. On real networks, when optimizing only dynamic NC, the methods are complementary. Furthermore, only GoT-WAVE supports directed edges. Hence, GoT-WAVE is a promising new temporal NA algorithm, which efficiently optimizes dynamic NC. We provide a user-friendly user interface and source code for GoT-WAVE. Availability and implementation http://www.dcc.fc.up.pt/got-wave/ Supplementary information Supplementary data are available at Bioinformatics online.

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Interactive exploration of heterogeneous biological networks with Biomine Explorer

Bioinformatics ◽

10.1093/bioinformatics/btz509 ◽

2019 ◽

Vol 35 (24) ◽

pp. 5385-5388

Author(s):

Vid Podpečan ◽

Živa Ramšak ◽

Kristina Gruden ◽

Hannu Toivonen ◽

Nada Lavrač

Keyword(s):

Biological Networks ◽

Web Application ◽

Biological Knowledge ◽

Biological Databases ◽

Scientific Publications ◽

Public And Private ◽

Interactive Exploration ◽

Probabilistic Network ◽

Private Network ◽

User Friendly

Abstract Summary Biomine Explorer is a web application that enables interactive exploration of large heterogeneous biological networks constructed from selected publicly available biological knowledge sources. It is built on top of Biomine, a system which integrates cross-references from several biological databases into a large heterogeneous probabilistic network. Biomine Explorer offers user-friendly interfaces for search, visualization, exploration and manipulation as well as public and private storage of discovered subnetworks with permanent links suitable for inclusion into scientific publications. A JSON-based web API for network search queries is also available for advanced users. Availability and implementation Biomine Explorer is implemented as a web application, which is publicly available at https://biomine.ijs.si. Registration is not required but registered users can benefit from additional features such as private network repositories.

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DamageProfiler: Fast damage pattern calculation for ancient DNA

Bioinformatics ◽

10.1093/bioinformatics/btab190 ◽

2021 ◽

Author(s):

Judith Neukamm ◽

Alexander Peltzer ◽

Kay Nieselt

Keyword(s):

Ancient Dna ◽

Source Code ◽

Supplementary Information ◽

Command Line ◽

Central Importance ◽

Command Line Interface ◽

Analysis Pipeline ◽

File Formats ◽

Programming Knowledge ◽

User Friendly

Abstract Motivation In ancient DNA research, the authentication of ancient samples based on specific features remains a crucial step in data analysis. Because of this central importance, researchers lacking deeper programming knowledge should be able to run a basic damage authentication analysis. Such software should be user-friendly and easy to integrate into an analysis pipeline. Results DamageProfiler is a Java based, stand-alone software to determine damage patterns in ancient DNA. The results are provided in various file formats and plots for further processing. DamageProfiler has an intuitive graphical as well as command line interface that allows the tool to be easily embedded into an analysis pipeline. Availability All of the source code is freely available on GitHub (https://github.com/Integrative-Transcriptomics/DamageProfiler). Supplementary information Supplementary data are available at Bioinformatics online.

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CellTracker (not only) for dummies

Bioinformatics ◽

10.1093/bioinformatics/btv686 ◽

2015 ◽

Vol 32 (6) ◽

pp. 955-957 ◽

Cited By ~ 46

Author(s):

Filippo Piccinini ◽

Alexa Kiss ◽

Peter Horvath

Keyword(s):

Graphical User Interface ◽

Open Source Software ◽

Phase Contrast ◽

Cell Tracking ◽

Source Code ◽

Software Tool ◽

Time Lapse ◽

Supplementary Information ◽

Differential Interference Contrast ◽

User Friendly

Abstract Motivation: Time-lapse experiments play a key role in studying the dynamic behavior of cells. Single-cell tracking is one of the fundamental tools for such analyses. The vast majority of the recently introduced cell tracking methods are limited to fluorescently labeled cells. An equally important limitation is that most software cannot be effectively used by biologists without reasonable expertise in image processing. Here we present CellTracker, a user-friendly open-source software tool for tracking cells imaged with various imaging modalities, including fluorescent, phase contrast and differential interference contrast (DIC) techniques. Availability and implementation: CellTracker is written in MATLAB (The MathWorks, Inc., USA). It works with Windows, Macintosh and UNIX-based systems. Source code and graphical user interface (GUI) are freely available at: http://celltracker.website/. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

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VarFish - Collaborative and Comprehensive Variant Analysis for Diagnosis and Research

10.1101/2020.01.27.921965 ◽

2020 ◽

Author(s):

Manuel Holtgrewe ◽

Oliver Stolpe ◽

Mikko Nieminen ◽

Stefan Mundlos ◽

Alexej Knaus ◽

...

Keyword(s):

Web Application ◽

Close Collaboration ◽

Variant Call ◽

Medical Practitioners ◽

Genome Browsers ◽

Collaborative Analysis ◽

Variant Analysis ◽

User Friendly ◽

Multiple Samples ◽

Variant Assessment

ABSTRACTVarFish is a user-friendly web application for the quality control, filtering, prioritization, analysis, and user-based annotation of panel and exome variant data for rare disease genetics. It is capable of processing variant call files with single or multiple samples. The variants are automatically annotated with population frequencies, molecular impact, and presence in databases such as ClinVar. Further, it provides support for pathogenicity scores including CADD, MutationTaster, and phenotypic similarity scores. Users can filter variants based on these annotations and presumed inheritance pattern and sort the results by these scores. Filtered variants are listed with their annotations and many useful link-outs to genome browsers, other gene/variant data portals, and external tools for variant assessment. VarFish allows user to create their own annotations including support for variant assessment following ACMG-AMP guidelines. In close collaboration with medical practitioners, VarFish was designed for variant analysis and prioritization in diagnostic and research settings as described in the software’s extensive manual. The user interface has been optimized for supporting these protocols. Users can install VarFish on their own in-house servers where it provides additional lab notebook features for collaborative analysis and allows re-analysis of cases, e.g., after update of genotype or phenotype databases.

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