scholarly journals BITE: an R package for biodiversity analyses

2017 ◽  
Author(s):  
Marco Milanesi ◽  
Stefano Capomaccio ◽  
Elia Vajana ◽  
Lorenzo Bomba ◽  
José Fernando Garcia ◽  
...  
Keyword(s):  
Data Analysis ◽  
R Package ◽  
Molecular Data ◽  
Third Party ◽  
Biodiversity Data ◽  
Representative Sampling ◽  
Link Type ◽  
Data Analyses ◽  
Snp Data ◽  
User Friendly

AbstractNowadays, molecular data analyses for biodiversity studies often require advanced bioinformatics skills, preventing many life scientists from analyzing their own data autonomously. BITE R package provides complete and user-friendly functions to handle SNP data and third-party software results (i.e. Admixture, TreeMix), facilitating their visualization, interpretation and use. Furthermore, BITE implements additional useful procedures, such as representative sampling and bootstrap for TreeMix, filling the gap in existing biodiversity data analysis tools.Availability:https://github.com/marcomilanesi/BITE

scholarly journals Introducing bdclean: a user friendly biodiversity data cleaning pipeline

2018 ◽  
Vol 2 ◽  
pp. e25564
Author(s):  
Tomer Gueta ◽  
Vijay Barve ◽  
Thiloshon Nagarajah ◽  
Ashwin Agrawal ◽  
Yohay Carmel
Keyword(s):  
Data Cleaning ◽  
Control Process ◽  
R Package ◽  
Modular Approach ◽  
Data Validation ◽  
Biodiversity Data ◽  
Quality Control Process ◽  
Cleaning Procedures ◽  
R Packages ◽  
User Friendly

A new R package for biodiversity data cleaning, 'bdclean', was initiated in the Google Summer of Code (GSoC) 2017 and is available on github. Several R packages have great data validation and cleaning functions, but 'bdclean' provides features to manage a complete pipeline for biodiversity data cleaning; from data quality explorations, to cleaning procedures and reporting. Users are able go through the quality control process in a very structured, intuitive, and effective way. A modular approach to data cleaning functionality should make this package extensible for many biodiversity data cleaning needs. Under GSoC 2018, 'bdclean' will go through a comprehensive upgrade. New features will be highlighted in the demonstration.

MetENP/MetENPWeb: An R package and web application for metabolomics enrichment and pathway analysis in Metabolomics Workbench

2020 ◽  
Author(s):  
Kumari Sonal Choudhary ◽  
Eoin Fahy ◽  
Kevin Coakley ◽  
Manish Sud ◽  
Mano R Maurya ◽  
...  
Keyword(s):  
Pathway Analysis ◽  
Web Application ◽  
Enrichment Analysis ◽  
R Package ◽  
Pathway Enrichment Analysis ◽  
Pathway Enrichment ◽  
Kegg Pathways ◽  
Link Type ◽  
Species Specific ◽  
User Friendly

ABSTRACTWith the advent of high throughput mass spectrometric methods, metabolomics has emerged as an essential area of research in biomedicine with the potential to provide deep biological insights into normal and diseased functions in physiology. However, to achieve the potential offered by metabolomics measures, there is a need for biologist-friendly integrative analysis tools that can transform data into mechanisms that relate to phenotypes. Here, we describe MetENP, an R package, and a user-friendly web application deployed at the Metabolomics Workbench site extending the metabolomics enrichment analysis to include species-specific pathway analysis, pathway enrichment scores, gene-enzyme information, and enzymatic activities of the significantly altered metabolites. MetENP provides a highly customizable workflow through various user-specified options and includes support for all metabolite species with available KEGG pathways. MetENPweb is a web application for calculating metabolite and pathway enrichment analysis.Availability and ImplementationThe MetENP package is freely available from Metabolomics Workbench GitHub: (https://github.com/metabolomicsworkbench/MetENP), the web application, is freely available at (https://www.metabolomicsworkbench.org/data/analyze.php)

scholarly journals ASaiM: a Galaxy-based framework to analyze raw shotgun data from microbiota

2017 ◽  
Author(s):  
Bérénice Batut ◽  
Kévin Gravouil ◽  
Clémence Defois ◽  
Saskia Hiltemann ◽  
Jean-François Brugère ◽  
...  
Keyword(s):  
Technological Progress ◽  
Source Code ◽  
Command Line ◽  
Bioinformatic Tools ◽  
Link Type ◽  
Data Analyses ◽  
The Galaxy ◽  
Sequencing Platforms ◽  
User Friendly ◽  
New Generation

AbstractBackgroundNew generation of sequencing platforms coupled to numerous bioinformatics tools has led to rapid technological progress in metagenomics and metatranscriptomics to investigate complex microorganism communities. Nevertheless, a combination of different bioinformatic tools remains necessary to draw conclusions out of microbiota studies. Modular and user-friendly tools would greatly improve such studies.FindingsWe therefore developed ASaiM, an Open-Source Galaxy-based framework dedicated to microbiota data analyses. ASaiM provides a curated collection of tools to explore and visualize taxonomic and functional information from raw amplicon, metagenomic or metatranscriptomic sequences. To guide different analyses, several customizable workflows are included. All workflows are supported by tutorials and Galaxy interactive tours to guide the users through the analyses step by step. ASaiM is implemented as Galaxy Docker flavour. It is scalable to many thousand datasets, but also can be used a normal PC. The associated source code is available under Apache 2 license at https://github.com/ASaiM/framework and documentation can be found online (http://asaim.readthedocs.io/)ConclusionsBased on the Galaxy framework, ASaiM offers sophisticated analyses to scientists without command-line knowledge. ASaiM provides a powerful framework to easily and quickly explore microbiota data in a reproducible and transparent environment.

scholarly journals RNAdetector: a free user-friendly stand-alone and cloud-based system for RNA-Seq data analysis

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Alessandro La Ferlita ◽  
Salvatore Alaimo ◽  
Sebastiano Di Bella ◽  
Emanuele Martorana ◽  
Georgios I. Laliotis ◽  
...  
Keyword(s):  
Data Analysis ◽  
Transcriptome Profiling ◽  
Biological Species ◽  
Third Party ◽  
Rna Seq ◽  
Rna Molecules ◽  
Non Coding Rna ◽  
Cross Platform ◽  
Cloud Environments ◽  
User Friendly

Abstract Background RNA-Seq is a well-established technology extensively used for transcriptome profiling, allowing the analysis of coding and non-coding RNA molecules. However, this technology produces a vast amount of data requiring sophisticated computational approaches for their analysis than other traditional technologies such as Real-Time PCR or microarrays, strongly discouraging non-expert users. For this reason, dozens of pipelines have been deployed for the analysis of RNA-Seq data. Although interesting, these present several limitations and their usage require a technical background, which may be uncommon in small research laboratories. Therefore, the application of these technologies in such contexts is still limited and causes a clear bottleneck in knowledge advancement. Results Motivated by these considerations, we have developed RNAdetector, a new free cross-platform and user-friendly RNA-Seq data analysis software that can be used locally or in cloud environments through an easy-to-use Graphical User Interface allowing the analysis of coding and non-coding RNAs from RNA-Seq datasets of any sequenced biological species. Conclusions RNAdetector is a new software that fills an essential gap between the needs of biomedical and research labs to process RNA-Seq data and their common lack of technical background in performing such analysis, which usually relies on outsourcing such steps to third party bioinformatics facilities or using expensive commercial software.

Flash Card Set as Rehearse Tool for Architecture Students

2018 ◽  
Vol 43 (4) ◽  
pp. 105-115
Author(s):  
Badiossadat Hassanpour ◽  
Adi Irfan Che-Ani ◽  
Nil Paşaoğluları Şahin ◽  
Alireza Tabrizi
Keyword(s):  
Data Analysis ◽  
Case Studies ◽  
Design Process ◽  
Eastern Mediterranean ◽  
Architectural Education ◽  
Smooth Transition ◽  
Educational Methods ◽  
Data Analyses ◽  
Flash Cards ◽  
User Friendly

The main goal of architectural education is to increase the independency level of students in finding design solutions throughout their academic years. Despite numerous educational attempts, the lack of supplementary educational methods or tools is still acknowledged by scholars. The present study aims to help students undergo a smooth transition from being highly dependent to minimally dependent on instructors by developing an auxiliary tool that may be used together with critique sessions in design studios. In this study, the critical stages in the design process adopted by students are identified through interviews with instructors and questionnaires for architecture students at Eastern Mediterranean University (EMU) in Cyprus and Universiti Kebangsaan Malaysia (UKM). Basic theories are aligned with the needs and expectations of the chosen case studies to develop a user-friendly model in flash card format. The data analyses show that students and tutors all agree on the two main critical stages of design, namely data analysis and data development (synthesis) which ends with idea simulation. The developed model and the proposed flash cards attempt to connect these critical stages, which are usually skipped by students. Results show that students need to adopt and be equipped with sequences, priorities and creativity techniques in each step of the design process, and the proposed flash cards can help address this concern.

scholarly journals Correlation AnalyzeR: functional predictions from gene co-expression correlations

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Henry E. Miller ◽  
Alexander J. R. Bishop
Keyword(s):  
Web Application ◽  
Bone Cancer ◽  
R Package ◽  
Limited Range ◽  
Web Interface ◽  
Generating Functional ◽  
Link Type ◽  
Genome Wide ◽  
User Friendly

Abstract Background Co-expression correlations provide the ability to predict gene functionality within specific biological contexts, such as different tissue and disease conditions. However, current gene co-expression databases generally do not consider biological context. In addition, these tools often implement a limited range of unsophisticated analysis approaches, diminishing their utility for exploring gene functionality and gene relationships. Furthermore, they typically do not provide the summary visualizations necessary to communicate these results, posing a significant barrier to their utilization by biologists without computational skills. Results We present Correlation AnalyzeR, a user-friendly web interface for exploring co-expression correlations and predicting gene functions, gene–gene relationships, and gene set topology. Correlation AnalyzeR provides flexible access to its database of tissue and disease-specific (cancer vs normal) genome-wide co-expression correlations, and it also implements a suite of sophisticated computational tools for generating functional predictions with user-friendly visualizations. In the usage example provided here, we explore the role of BRCA1-NRF2 interplay in the context of bone cancer, demonstrating how Correlation AnalyzeR can be effectively implemented to generate and support novel hypotheses. Conclusions Correlation AnalyzeR facilitates the exploration of poorly characterized genes and gene relationships to reveal novel biological insights. The database and all analysis methods can be accessed as a web application at https://gccri.bishop-lab.uthscsa.edu/correlation-analyzer/ and as a standalone R package at https://github.com/Bishop-Laboratory/correlationAnalyzeR.

scholarly journals MutSpot: detection of non-coding mutation hotspots in cancer genomes

2019 ◽  
Author(s):  
Yu Amanda Guo ◽  
Mei Mei Chang ◽  
Anders Jacobsen Skanderup
Keyword(s):  
Somatic Mutations ◽  
R Package ◽  
Supplementary Information ◽  
Patient Specific ◽  
Supplementary Data ◽  
Link Type ◽  
Genome Wide ◽  
Cancer Genomes ◽  
User Friendly ◽  
Regulatory Dna

AbstractSummaryRecurrence and clustering of somatic mutations (hotspots) in cancer genomes may indicate positive selection and involvement in tumorigenesis. MutSpot performs genome-wide inference of mutation hotspots in non-coding and regulatory DNA of cancer genomes. MutSpot performs feature selection across hundreds of epigenetic and sequence features followed by estimation of position and patient-specific background somatic mutation probabilities. MutSpot is user-friendly, works on a standard workstation, and scales to thousands of cancer genomes.Availability and implementationMutSpot is implemented as an R package and is available at https://github.com/skandlab/MutSpot/Supplementary informationSupplementary data are available at https://github.com/skandlab/MutSpot/

scholarly journals webMCP-counter: a web interface for transcriptomics-based quantification of immune and stromal cells in heterogeneous human or murine samples

2020 ◽  
Author(s):  
Maxime Meylan ◽  
Etienne Becht ◽  
Catherine Sautès-Fridman ◽  
Aurélien de Reyniès ◽  
Wolf H. Fridman ◽  
...  
Keyword(s):  
Stromal Cells ◽  
R Package ◽  
Web Interface ◽  
Transcriptomic Data ◽  
R Programming Language ◽  
Link Type ◽  
Precise Estimation ◽  
R Packages ◽  
R Programming ◽  
User Friendly

AbstractSummaryWe previously reported MCP-counter and mMCP-counter, methods that allow precise estimation of the immune and stromal composition of human and murine samples from bulk transcriptomic data, but they were only distributed as R packages. Here, we report webMCP-counter, a user-friendly web interface to allow all users to use these methods, regardless of their proficiency in the R programming language.Availability and ImplementationFreely available from http://134.157.229.105:3838/webMCP/. Website developed with the R package shiny. Source code available from GitHub: https://github.com/FPetitprez/webMCP-counter.

scholarly journals dsSwissKnife: An R package for federated data analysis

2020 ◽  
Author(s):  
Iulian Dragan ◽  
Thomas Sparsø ◽  
Dmitry Kuznetsov ◽  
Roderick Slieker ◽  
Mark Ibberson
Keyword(s):  
Type 2 Diabetes ◽  
Data Analysis ◽  
General Public ◽  
Biomarker Discovery ◽  
R Package ◽  
Link Type ◽  
General Public License

ABSTRACTSummarydsSwissKnife is an R package that enables several powerful analyses to be performed on federated datasets. The package works alongside DataSHIELD and extends its functionality. We have developed and implemented dsSwissKnife in a large IMI project on type 2 diabetes, RHAPSODY, where data from 10 observational cohorts have been harmonised and federated in CDISC SDTM format and made available for biomarker discovery.Availability and implementationdsSwissKnife is freely available online at https://github.com/sib-swiss/dsSwissKnife. The package is distributed under the GNU General Public License version [email protected]

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