scholarly journals Pavian: Interactive analysis of metagenomics data for microbiomics and pathogen identification

2016 ◽  
Author(s):  
Florian P. Breitwieser ◽  
Steven L. Salzberg
Keyword(s):  
Web Application ◽  
Disease Diagnosis ◽  
Supplementary Information ◽  
Special Focus ◽  
Web Browser ◽  
R Language ◽  
Interactive Analysis ◽  
Link Type ◽  
Metagenomics Data ◽  
Flow Diagrams

AbstractSummaryPavian is a web application for exploring metagenomics classification results, with a special focus on infectious disease diagnosis. Pinpointing pathogens in metagenomics classification results is often complicated by host and laboratory contaminants as well as many non-pathogenic microbiota. With Pavian, researchers can analyze, display and transform results from the Kraken and Centrifuge classifiers using interactive tables, heatmaps and flow diagrams. Pavian also provides an alignment viewer for validation of matches to a particular genome.Availability and implementationPavian is implemented in the R language and based on the Shiny framework. It can be hosted on Windows, Mac OS X and Linux systems, and used with any contemporary web browser. It is freely available under a GPL-3 license from http://github.com/fbreitwieser/pavian. Furthermore a Docker image is provided at https://hub.docker.com/r/florianbw/[email protected] informationSupplementary data is available at Bioinformatics online.

scholarly journals Pavian: interactive analysis of metagenomics data for microbiome studies and pathogen identification

2019 ◽  
Author(s):  
Florian P Breitwieser ◽  
Steven L Salzberg
Keyword(s):  
Web Application ◽  
Pathogen Detection ◽  
R Language ◽  
Interactive Analysis ◽  
Alignment Coverage ◽  
Metagenomics Data ◽  
Flow Diagrams ◽  
Interactive Data ◽  
Web App ◽  
Data Tables

Abstract Summary Pavian is a web application for exploring classification results from metagenomics experiments. With Pavian, researchers can analyze, visualize and transform results from various classifiers—such as Kraken, Centrifuge and MethaPhlAn—using interactive data tables, heatmaps and Sankey flow diagrams. An interactive alignment coverage viewer can help in the validation of matches to a particular genome, which can be crucial when using metagenomics experiments for pathogen detection. Availability and implementation Pavian is implemented in the R language as a modular Shiny web app and is freely available under GPL-3 from http://github.com/fbreitwieser/pavian. Contact [email protected]

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

2016 ◽  
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.

scholarly journals FOCUS2: agile and sensitive classification of metagenomics data using a reduced database

2016 ◽  
Author(s):  
Genivaldo Gueiros Z. Silva ◽  
Bas E. Dutilh ◽  
Robert A. Edwards
Keyword(s):  
Microbial Community ◽  
Dna Sequences ◽  
Computational Method ◽  
Environmental Research ◽  
Supplementary Information ◽  
Sequence Classification ◽  
Computationally Efficient ◽  
Link Type ◽  
Metagenomics Data

ABSTRACTSummaryMetagenomics approaches rely on identifying the presence of organisms in the microbial community from a set of unknown DNA sequences. Sequence classification has valuable applications in multiple important areas of medical and environmental research. Here we introduce FOCUS2, an update of the previously published computational method FOCUS. FOCUS2 was tested with 10 simulated and 543 real metagenomes demonstrating that the program is more sensitive, faster, and more computationally efficient than existing methods.AvailabilityThe Python implementation is freely available at https://edwards.sdsu.edu/FOCUS2.Supplementary informationavailable at Bioinformatics online.

scholarly journals DOSCHEDA: a web application for interactive chemoproteomics data analysis

2017 ◽  
Vol 3 ◽  
pp. e129 ◽  
Author(s):  
Bruno Contrino ◽  
Eric Miele ◽  
Ronald Tomlinson ◽  
M. Paola Castaldi ◽  
Piero Ricchiuto
Keyword(s):  
Data Analysis ◽  
Protein Interactions ◽  
Web Application ◽  
Main Tool ◽  
Bioinformatics Analyses ◽  
User Input ◽  
R Language ◽  
Link Type ◽  
Input Dataset ◽  
Programming Knowledge

Background Mass Spectrometry (MS) based chemoproteomics has recently become a main tool to identify and quantify cellular target protein interactions with ligands/drugs in drug discovery. The complexity associated with these new types of data requires scientists with a limited computational background to perform systematic data quality controls as well as to visualize the results derived from the analysis to enable rapid decision making. To date, there are no readily accessible platforms specifically designed for chemoproteomics data analysis. Results We developed a Shiny-based web application named DOSCHEDA (Down Stream Chemoproteomics Data Analysis) to assess the quality of chemoproteomics experiments, to filter peptide intensities based on linear correlations between replicates, and to perform statistical analysis based on the experimental design. In order to increase its accessibility, DOSCHEDA is designed to be used with minimal user input and it does not require programming knowledge. Typical inputs can be protein fold changes or peptide intensities obtained from Proteome Discover, MaxQuant or other similar software. DOSCHEDA aggregates results from bioinformatics analyses performed on the input dataset into a dynamic interface, it encompasses interactive graphics and enables customized output reports. Conclusions DOSCHEDA is implemented entirely in R language. It can be launched by any system with R installed, including Windows, Mac OS and Linux distributions. DOSCHEDA is hosted on a shiny-server at https://doscheda.shinyapps.io/doscheda and is also available as a Bioconductor package (http://www.bioconductor.org/).

scholarly journals sangeranalyseR: simple and interactive analysis of Sanger sequencing data in R

2020 ◽  
Author(s):  
Kuan-Hao Chao ◽  
Kirston Barton ◽  
Sarah Palmer ◽  
Robert Lanfear
Keyword(s):  
Sanger Sequencing ◽  
Reference Sequence ◽  
Supplementary Information ◽  
File Format ◽  
Bioconductor Package ◽  
Sequencing Data ◽  
Interactive Analysis ◽  
Link Type ◽  
Online Documentation ◽  
Wide Range

AbstractSummarysangeranalyseR is an interactive R/Bioconductor package and two associated Shiny applications designed for analysing Sanger sequencing from data from the ABIF file format in R. It allows users to go from loading reads to saving aligned contigs in a few lines of R code. sangeranalyseR provides a wide range of options for a number of commonly-performed actions including read trimming, detecting secondary peaks, viewing chromatograms, and detecting indels using a reference sequence. All parameters can be adjusted interactively either in R or in the associated Shiny applications. sangeranalyseR comes with extensive online documentation, and outputs detailed interactive HTML reports.Availability and implementationsangeranalyseR is implemented in R and released under an MIT license. It is available for all platforms on Bioconductor (https://bioconductor.org/packages/sangeranalyseR) and on Github (https://github.com/roblanf/sangeranalyseR)[email protected] informationDocumentation at https://sangeranalyser.readthedocs.io/.

scholarly journals Annotating eukaryotic and toxin-specific signal peptides using Razor

2020 ◽  
Author(s):  
Bikash K. Bhandari ◽  
Paul P. Gardner ◽  
Chun Shen Lim
Keyword(s):  
Signal Peptide ◽  
Web Application ◽  
Protein Transport ◽  
Protein Translocation ◽  
Recombinant Protein Expression ◽  
Disease Diagnosis ◽  
Signal Peptides ◽  
Specific Signal ◽  
Link Type ◽  
Command Line Tool

ABSTRACTMotivationSignal peptides are responsible for protein transport and secretion and are ubiquitous to all forms of life. The annotation of signal peptides is important for understanding protein translocation and toxin secretion, optimising recombinant protein expression, as well as for disease diagnosis and metagenomics.ResultsHere we explore the features of these signal sequences across eukaryotes. We find that different kingdoms have their characteristic distributions of signal peptide residues. Additionally, the signal peptides of secretory toxins have common features across kingdoms. We leverage these subtleties to build Razor, a simple yet powerful tool for annotating signal peptides, which additionally predicts toxin- and fungal-specific signal peptides based on the first 23 N-terminal residues. Finally, we demonstrate the usability of Razor by scanning all reviewed sequences from UniProt. Indeed, Razor is able to identify toxins using their signal peptide sequences only. Strikingly, we discover that many defensive proteins across kingdoms harbour a toxin-like signal peptide; some of these defensive proteins have emerged through convergent evolution, e.g. defensin and defensin-like protein families, and phospholipase families.Availability and implementationRazor is available as a web application (https://tisigner.com/razor) and a command-line tool (https://github.com/Gardner-BinfLab/Razor).

scholarly journals Curatr: a web application for creating, curating, and sharing a mass spectral library

2017 ◽  
Author(s):  
Andrew Palmer ◽  
Prasad Phapale ◽  
Dominik Fay ◽  
Theodore Alexandrov
Keyword(s):  
Mass Spectrometry ◽  
Web Application ◽  
Mass Spectrometry Analysis ◽  
Supplementary Information ◽  
Spectral Library ◽  
Mass Spectral Fragmentation ◽  
Mass Spectral ◽  
Link Type ◽  
Manual Curation

AbstractMotivationIdentification from metabolomics mass spectrometry experiments requires comparison of fragmentation spectra from experimental samples to spectra from analytical standards. As the quality of identification depends directly on the quality of the reference spectra, manual curation is routine during the selection of reference spectra to include in a spectral library. Whilst building our own in-house spectral library we realised that there is currently no vendor neutral open access tool for for facilitating manual curation of spectra from raw LC-MS data into a custom spectral library.ResultsWe developed a web application curatr for the rapid generation of high quality mass spectral fragmentation libraries for liquid-chromatography mass spectrometry analysis. Curatr handles datasets from single or multiplexed standards, automatically extracting chromatographic profiles and potential fragmentation spectra for multiple adducts. These are presented through an intuitive interface for manual curation before being documented in a custom spectral library. Searchable molecular information and the providence of each standard is stored along with metadata on the experimental protocol. Curatr support the export of spectral libraries in several standard formats for easy use with third party software or submission to community databases, maximising the return on investment for these costly measurements. We demonstrate the use of curatr to generate the EMBL Metabolomics Core Facility spectral library which is publicly available at http://curatr.mcf.embl.de.AvailabilityThe source code is freely available at http://github.com/alexandrovteam/curatr/ along with example data.Supplementary informationA step-by step user manual is available in the supplementary information

scholarly journals DNA Chisel, a versatile sequence optimizer

2019 ◽  
Author(s):  
Valentin Zulkower ◽  
Susan Rosser
Keyword(s):  
Open Source ◽  
Dna Sequence ◽  
Web Application ◽  
Optimization Problems ◽  
Supplementary Information ◽  
Sequence Optimization ◽  
Sequence Design ◽  
Optimization Framework ◽  
Link Type ◽  
Dna Sequence Design

AbstractMotivationAccounting 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.ResultsDNA 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.Availabilityas a web application (https://cuba.genomefoundry.org/sculpt_a_sequence) or open-source Python library (code and documentation at https://github.com/Edinburgh-Genome-Foundry/DNAChisel)[email protected] informationattached.

scholarly journals COVID-19 Knowledge Graph: a computable, multi-modal, cause-and-effect knowledge model of COVID-19 pathophysiology

2020 ◽  
Author(s):  
Daniel Domingo-Fernández ◽  
Shounak Baksi ◽  
Bruce Schultz ◽  
Yojana Gadiya ◽  
Reagon Karki ◽  
...  
Keyword(s):  
Web Application ◽  
Research Community ◽  
Supplementary Information ◽  
Knowledge Graph ◽  
The Novel ◽  
Knowledge Model ◽  
Global Response ◽  
Cause And Effect ◽  
Link Type ◽  
Novel Coronavirus

AbstractSummaryThe past few weeks have witnessed a worldwide mobilization of the research community in response to the novel coronavirus (COVID-19). This global response has led to a burst of publications on the pathophysiology of the virus, yet without coordinated efforts to organize this knowledge, it can remain hidden away from individual research groups. By extracting and formalizing this knowledge in a structured and computable form, as in the form of a knowledge graph, researchers can readily reason and analyze this information on a much larger scale. Here, we present the COVID-19 Knowledge Graph, an expansive cause-and-effect network constructed from scientific literature on the new coronavirus that aims to provide a comprehensive view of its pathophysiology. To make this resource available to the research community and facilitate its exploration and analysis, we also implemented a web application and released the KG in multiple standard formats.AvailabilityThe COVID-19 Knowledge Graph is publicly available under CC-0 license at https://github.com/covid19kg and https://bikmi.covid19-knowledgespace.de.Contactalpha.tom.kodamullil@scai.fraunhofer.deSupplementary informationSupplementary data are available online.

scholarly journals CRISPRAnalyzeR: Interactive analysis, annotation and documentation of pooled CRISPR screens

2017 ◽  
Author(s):  
Jan Winter ◽  
Marc Schwering ◽  
Oliver Pelz ◽  
Benedikt Rauscher ◽  
Tianzuo Zhan ◽  
...  
Keyword(s):  
Web Application ◽  
Systematic Investigation ◽  
Cellular Processes ◽  
Interactive Analysis ◽  
Link Type ◽  
External Data ◽  
Versatile Tool ◽  
Interactive Visualizations ◽  
Meta Information ◽  
Data Tables

AbstractPooled CRISPR/Cas9 screens are a powerful and versatile tool for the systematic investigation of cellular processes in a variety of organisms. Such screens generate large amounts of data that present a new challenge to analyze and interpret. Here, we developed a web application to analyze, document and explore pooled CRISR/Cas9 screens using a unified single workflow. The end-to-end analysis pipeline features eight different hit calling strategies based on state-of-the-art methods, including DESeq2, MAGeCK, edgeR, sgRSEA, Z-Ratio, Mann-Whitney test, ScreenBEAM and BAGEL. Results can be compared with interactive visualizations and data tables. CRISPRAnalyzeR integrates meta-information from 26 external data resources, providing a wide array of options for the annotation and documentation of screens. The application was developed with user experience in mind, requiring no previous knowledge in bioinformatics. All modern operating systems are supported.Availability and online documentation: The source code, a pre-configured docker application, sample data and a documentation can be found on our GitHub page (http://www.github.com/boutroslab/CRISPRAnalyzeR). A tutorial video can be found at http://www.crispr-analyzer.org.

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