scholarly journals AlpsNMR: an R package for signal processing of fully untargeted NMR-based metabolomics

2020 ◽  
Vol 36 (9) ◽  
pp. 2943-2945 ◽  
Author(s):  
Francisco Madrid-Gambin ◽  
Sergio Oller-Moreno ◽  
Luis Fernandez ◽  
Simona Bartova ◽  
Maria Pilar Giner ◽  
...  
Keyword(s):  
Signal Processing ◽  
Statistical Analysis ◽  
Processing System ◽  
R Package ◽  
Supplementary Information ◽  
Test Case ◽  
Previous Knowledge ◽  
Spectral Processing ◽  
Computational Tools ◽  
User Friendly

Abstract Summary Nuclear magnetic resonance (NMR)-based metabolomics is widely used to obtain metabolic fingerprints of biological systems. While targeted workflows require previous knowledge of metabolites, prior to statistical analysis, untargeted approaches remain a challenge. Computational tools dealing with fully untargeted NMR-based metabolomics are still scarce or not user-friendly. Therefore, we developed AlpsNMR (Automated spectraL Processing System for NMR), an R package that provides automated and efficient signal processing for untargeted NMR metabolomics. AlpsNMR includes spectra loading, metadata handling, automated outlier detection, spectra alignment and peak-picking, integration and normalization. The resulting output can be used for further statistical analysis. AlpsNMR proved effective in detecting metabolite changes in a test case. The tool allows less experienced users to easily implement this workflow from spectra to a ready-to-use dataset in their routines. Availability and implementation The AlpsNMR R package and tutorial is freely available to download from http://github.com/sipss/AlpsNMR under the MIT license. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals FIREcaller: Detecting Frequently Interacting Regions from Hi-C Data

2019 ◽  
Author(s):  
Cheynna Crowley ◽  
Yuchen Yang ◽  
Yunjiang Qiu ◽  
Benxia Hu ◽  
Armen Abnousi ◽  
...  
Keyword(s):  
Gene Regulation ◽  
Spatial Organization ◽  
R Package ◽  
Specific Gene ◽  
List Type ◽  
Cell Type ◽  
R Software ◽  
Computational Tools ◽  
Cell Type Specific ◽  
User Friendly

AbstractHi-C experiments have been widely adopted to study chromatin spatial organization, which plays an essential role in genome function. We have recently identified frequently interacting regions (FIREs) and found that they are closely associated with cell-type-specific gene regulation. However, computational tools for detecting FIREs from Hi-C data are still lacking. In this work, we present FIREcaller, a stand-alone, user-friendly R package for detecting FIREs from Hi-C data. FIREcaller takes raw Hi-C contact matrices as input, performs within-sample and cross-sample normalization, and outputs continuous FIRE scores, dichotomous FIREs, and super-FIREs. Applying FIREcaller to Hi-C data from various human tissues, we demonstrate that FIREs and super-FIREs identified, in a tissue-specific manner, are closely related to gene regulation, are enriched for enhancer-promoter (E-P) interactions, tend to overlap with regions exhibiting epigenomic signatures of cis-regulatory roles, and aid the interpretation or GWAS variants. The FIREcaller package is implemented in R and freely available at https://yunliweb.its.unc.edu/FIREcaller.Highlights– Frequently Interacting Regions (FIREs) can be used to identify tissue and cell-type-specific cis-regulatory regions.– An R software, FIREcaller, has been developed to identify FIREs and clustered FIREs into super-FIREs.

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

2019 ◽  
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.

An open-source high-content analysis workflow for CFTR function measurements using the forskolin-induced swelling assay

2020 ◽  
Author(s):  
Marne C Hagemeijer ◽  
Annelotte M Vonk ◽  
Nikhil T Awatade ◽  
Iris A L Silva ◽  
Christian Tischer ◽  
...  
Keyword(s):  
Content Analysis ◽  
Statistical Analysis ◽  
Open Source ◽  
R Package ◽  
Supplementary Information ◽  
Image Quantification ◽  
Quantification Method ◽  
Analysis Workflow ◽  
High Content Analysis ◽  
Microscopy Images

Abstract Motivation The forskolin-induced swelling (FIS) assay has become the preferential assay to predict the efficacy of approved and investigational CFTR-modulating drugs for individuals with cystic fibrosis (CF). Currently, no standardized quantification method of FIS data exists thereby hampering inter-laboratory reproducibility. Results We developed a complete open-source workflow for standardized high-content analysis of CFTR function measurements in intestinal organoids using raw microscopy images as input. The workflow includes tools for (i) file and metadata handling; (ii) image quantification and (iii) statistical analysis. Our workflow reproduced results generated by published proprietary analysis protocols and enables standardized CFTR function measurements in CF organoids. Availability All workflow components are open-source and freely available: the htmrenamer R package for file handling https://github.com/hmbotelho/htmrenamer; CellProfiler and ImageJ analysis scripts/pipelines https://github.com/hmbotelho/FIS_image_analysis; the Organoid Analyst application for statistical analysis https://github.com/hmbotelho/organoid_analyst; detailed usage instructions and a demonstration dataset https://github.com/hmbotelho/FIS_analysis. Distributed under GPL v3.0. Supplementary information Supplementary information and a stepwise guide for software installation and data analysis for training purposes are available at Bioinformatics online.

scholarly journals RCytoGPS: An R Package for Reading and Visualizing Cytogenetics Data

2021 ◽  
Author(s):  
Zachary B Abrams ◽  
Dwayne G Tally ◽  
Lynne V Abruzzo ◽  
Kevin R Coombes
Keyword(s):  
Large Scale ◽  
International System ◽  
Genetic Data ◽  
R Package ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Computational Tools ◽  
Text Format ◽  
Karyotype Analyses ◽  
Computational Analyses

Abstract Summary Cytogenetics data, or karyotypes, are among the most common clinically used forms of genetic data. Karyotypes are stored as standardized text strings using the International System for Human Cytogenomic Nomenclature (ISCN). Historically, these data have not been used in large-scale computational analyses due to limitations in the ISCN text format and structure. Recently developed computational tools such as CytoGPS have enabled large-scale computational analyses of karyotypes. To further enable such analyses, we have now developed RCytoGPS, an R package that takes JSON files generated from CytoGPS.org and converts them into objects in R. This conversion facilitates the analysis and visualizations of karyotype data. In effect this tool streamlines the process of performing large-scale karyotype analyses, thus advancing the field of computational cytogenetic pathology. Availability and Implementation Freely available at https://CRAN.R-project.org/package=RCytoGPS. The code for the underlying CytoGPS software can be found at https://github.com/i2-wustl/CytoGPS. Supplementary information There is no supplementary data.

scholarly journals dittoSeq: universal user-friendly single-cell and bulk RNA sequencing visualization toolkit

2020 ◽  
Author(s):  
Daniel G Bunis ◽  
Jared Andrews ◽  
Gabriela K Fragiadakis ◽  
Trevor D Burt ◽  
Marina Sirota
Keyword(s):  
Single Cell ◽  
R Package ◽  
Color Blindness ◽  
Ease Of Use ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Rnaseq Data ◽  
Visualization Toolkit ◽  
User Friendly ◽  
Publication Quality

Abstract Summary A visualization suite for major forms of bulk and single-cell RNAseq data in R. dittoSeq is color blindness-friendly by default, robustly documented to power ease-of-use and allows highly customizable generation of both daily-use and publication-quality figures. Availability and implementation dittoSeq is an R package available through Bioconductor via an open source MIT license. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals bWGR: Bayesian whole-genome regression

2019 ◽  
Author(s):  
Alencar Xavier ◽  
William M Muir ◽  
Katy M Rainey
Keyword(s):  
Bayesian Methods ◽  
Expectation Maximization ◽  
Complex Traits ◽  
Hierarchical Models ◽  
R Package ◽  
Supplementary Information ◽  
Whole Genome ◽  
Regression Methods ◽  
Genome Wide ◽  
User Friendly

AbstractMotivationWhole-genome regressions methods represent a key framework for genome-wide prediction, cross-validation studies and association analysis. The bWGR offers a compendium of Bayesian methods with various priors available, allowing users to predict complex traits with different genetic architectures.ResultsHere we introduce bWGR, an R package that enables users to efficient fit and cross-validate Bayesian and likelihood whole-genome regression methods. It implements a series of methods referred to as the Bayesian alphabet under the traditional Gibbs sampling and optimized expectation-maximization. The package also enables fitting efficient multivariate models and complex hierarchical models. The package is user-friendly and computational efficient.Availability and implementationbWGR is an R package available in the CRAN repository. It can be installed in R by typing: install.packages(‘bWGR’).Supplementary informationSupplementary data are available at Bioinformatics online.

scholarly journals RCytoGPS: An R Package for Reading and Visualizing Cytogenetics Data

2021 ◽  
Author(s):  
Zachary B. Abrams ◽  
Dwayne G. Tally ◽  
Lynne V. Abruzzo ◽  
Kevin R. Coombes
Keyword(s):  
Large Scale ◽  
International System ◽  
Genetic Data ◽  
R Package ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Computational Tools ◽  
Text Format ◽  
Karyotype Analyses ◽  
Computational Analyses

AbstractSummaryCytogenetics data, or karyotypes, are among the most common clinically used forms of genetic data. Karyotypes are stored as standardized text strings using the International System for Human Cytogenomic Nomenclature (ISCN). Historically, these data have not been used in large-scale computational analyses due to limitations in the ISCN text format and structure. Recently developed computational tools such as CytoGPS have enabled large-scale computational analyses of karyotypes. To further enable such analyses, we have now developed RCytoGPS, an R package that takes JSON files generated from CytoGPS.org and converts them into objects in R. This conversion facilitates the analysis and visualizations of karyotype data. In effect this tool streamlines the process of performing large-scale karyotype analyses, thus advancing the field of computational cytogenetic pathology.Availability and ImplementationFreely available at https://CRAN.R-project.org/package=RCytoGPSSupplementary informationSupplementary data are available at Bioinformatics online.

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 rCASC: reproducible Classification Analysis of Single Cell sequencing data

2018 ◽  
Author(s):  
Luca Alessandrì ◽  
Marco Beccuti ◽  
Maddalena Arigoni ◽  
Martina Olivero ◽  
Greta Romano ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
R Package ◽  
Cellular Heterogeneity ◽  
Supplementary Information ◽  
Sequencing Data ◽  
Single Cell Sequencing ◽  
Analysis Workflow ◽  
User Friendly ◽  
Bioinformatics Workflows

AbstractSummarySingle-cell RNA sequencing has emerged as an essential tool to investigate cellular heterogeneity, and highlighting cell sub-population specific signatures. Nowadays, dedicated and user-friendly bioinformatics workflows are required to exploit the deconvolution of single-cells transcriptome. Furthermore, there is a growing need of bioinformatics workflows granting both functional, i.e. saving information about data and analysis parameters, and computation reproducibility, i.e. storing the real image of the computation environment. Here, we present rCASC a modular RNAseq analysis workflow allowing data analysis from counts generation to cell sub-population signatures identification, granting both functional and computation reproducibility.Availability and ImplementationrCASC is part of the reproducible bioinfomatics project. rCASC is a docker based application controlled by a R package available at https://github.com/kendomaniac/rCASC.Supplementary informationSupplementary data are available at rCASC github

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