scholarly journals ensembldb: an R package to create and use Ensembl-based annotation resources

2019 ◽  
Vol 35 (17) ◽  
pp. 3151-3153 ◽  
Author(s):  
Johannes Rainer ◽  
Laurent Gatto ◽  
Christian X Weichenberger
Keyword(s):  
Coordinate System ◽  
Positional Information ◽  
R Package ◽  
Research Data ◽  
Supplementary Information ◽  
Reproducible Research ◽  
Bioconductor Package ◽  
Supplementary Data ◽  
Reference Coordinate System ◽  
Associated Data

Abstract Summary Bioinformatics research frequently involves handling gene-centric data such as exons, transcripts, proteins and their positions relative to a reference coordinate system. The ensembldb Bioconductor package retrieves and stores Ensembl-based genetic annotations and positional information, and furthermore offers identifier conversion and coordinates mappings for gene-associated data. In support of reproducible research, data are tied to Ensembl releases and are kept separately from the software. Premade data packages are available for a variety of genomes and Ensembl releases. Three examples demonstrate typical use cases of this software. Availability and implementation ensembldb is part of Bioconductor (https://bioconductor.org/packages/ensembldb). Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals ngsReports: a Bioconductor package for managing FastQC reports and other NGS related log files

2019 ◽  
Vol 36 (8) ◽  
pp. 2587-2588 ◽  
Author(s):  
Christopher M Ward ◽  
Thu-Hien To ◽  
Stephen M Pederson
Keyword(s):  
Quality Control ◽  
R Package ◽  
Supplementary Information ◽  
Bioconductor Package ◽  
Supplementary Data ◽  
Large Sample ◽  
Log Files ◽  
Shiny App ◽  
Next Generation Sequencing Ngs ◽  
Generation Sequencing

Abstract Motivation High throughput next generation sequencing (NGS) has become exceedingly cheap, facilitating studies to be undertaken containing large sample numbers. Quality control (QC) is an essential stage during analytic pipelines and the outputs of popular bioinformatics tools such as FastQC and Picard can provide information on individual samples. Although these tools provide considerable power when carrying out QC, large sample numbers can make inspection of all samples and identification of systemic bias a challenge. Results We present ngsReports, an R package designed for the management and visualization of NGS reports from within an R environment. The available methods allow direct import into R of FastQC reports along with outputs from other tools. Visualization can be carried out across many samples using default, highly customizable plots with options to perform hierarchical clustering to quickly identify outlier libraries. Moreover, these can be displayed in an interactive shiny app or HTML report for ease of analysis. Availability and implementation The ngsReports package is available on Bioconductor and the GUI shiny app is available at https://github.com/UofABioinformaticsHub/shinyNgsreports. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals DEsingle for detecting three types of differential expression in single-cell RNA-seq data

2017 ◽  
Author(s):  
Zhun Miao ◽  
Ke Deng ◽  
Xiaowo Wang ◽  
Xuegong Zhang
Keyword(s):  
Single Cell ◽  
Differential Expression ◽  
Negative Binomial ◽  
Single Cells ◽  
R Package ◽  
Supplementary Information ◽  
Binomial Model ◽  
Supplementary Data ◽  
Rna Seq ◽  
Real Zeros

AbstractSummaryThe excessive amount of zeros in single-cell RNA-seq data include “real” zeros due to the on-off nature of gene transcription in single cells and “dropout” zeros due to technical reasons. Existing differential expression (DE) analysis methods cannot distinguish these two types of zeros. We developed an R package DEsingle which employed Zero-Inflated Negative Binomial model to estimate the proportion of real and dropout zeros and to define and detect 3 types of DE genes in single-cell RNA-seq data with higher accuracy.Availability and ImplementationThe R package DEsingle is freely available at https://github.com/miaozhun/DEsingle and is under Bioconductor’s consideration [email protected] informationSupplementary data are available at bioRxiv online.

scholarly journals CPS analysis: self-contained validation of biomedical data clustering

2020 ◽  
Vol 36 (11) ◽  
pp. 3516-3521 ◽  
Author(s):  
Lixiang Zhang ◽  
Lin Lin ◽  
Jia Li
Keyword(s):  
Data Clustering ◽  
State Of The Art ◽  
R Package ◽  
Research Community ◽  
Supplementary Information ◽  
Biomedical Data ◽  
Data Generation ◽  
Supplementary Data ◽  
Point Set ◽  
Class Labels

Abstract Motivation Cluster analysis is widely used to identify interesting subgroups in biomedical data. Since true class labels are unknown in the unsupervised setting, it is challenging to validate any cluster obtained computationally, an important problem barely addressed by the research community. Results We have developed a toolkit called covering point set (CPS) analysis to quantify uncertainty at the levels of individual clusters and overall partitions. Functions have been developed to effectively visualize the inherent variation in any cluster for data of high dimension, and provide more comprehensive view on potentially interesting subgroups in the data. Applying to three usage scenarios for biomedical data, we demonstrate that CPS analysis is more effective for evaluating uncertainty of clusters comparing to state-of-the-art measurements. We also showcase how to use CPS analysis to select data generation technologies or visualization methods. Availability and implementation The method is implemented in an R package called OTclust, available on CRAN. Contact [email protected] or [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

RMTL: an R library for multi-task learning

2018 ◽  
Vol 35 (10) ◽  
pp. 1797-1798 ◽  
Author(s):  
Han Cao ◽  
Jiayu Zhou ◽  
Emanuel Schwarz
Keyword(s):  
Biological Networks ◽  
Simulated Data ◽  
R Package ◽  
Low Rank ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Software Environment ◽  
Machine Learning Technique ◽  
Task Learning ◽  
Learning Technique

Abstract Motivation Multi-task learning (MTL) is a machine learning technique for simultaneous learning of multiple related classification or regression tasks. Despite its increasing popularity, MTL algorithms are currently not available in the widely used software environment R, creating a bottleneck for their application in biomedical research. Results We developed an efficient, easy-to-use R library for MTL (www.r-project.org) comprising 10 algorithms applicable for regression, classification, joint predictor selection, task clustering, low-rank learning and incorporation of biological networks. We demonstrate the utility of the algorithms using simulated data. Availability and implementation The RMTL package is an open source R package and is freely available at https://github.com/transbioZI/RMTL. RMTL will also be available on cran.r-project.org. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals AssessORF: combining evolutionary conservation and proteomics to assess prokaryotic gene predictions

2019 ◽  
Author(s):  
Deepank R Korandla ◽  
Jacob M Wozniak ◽  
Anaamika Campeau ◽  
David J Gonzalez ◽  
Erik S Wright
Keyword(s):  
R Package ◽  
Evolutionary Conservation ◽  
Supplementary Information ◽  
Bioconductor Package ◽  
Gene Finding ◽  
Proteomics Data ◽  
Protein Coding ◽  
New Approach ◽  
Protein Coding Genes ◽  
Clear Winner

Abstract Motivation A core task of genomics is to identify the boundaries of protein coding genes, which may cover over 90% of a prokaryote's genome. Several programs are available for gene finding, yet it is currently unclear how well these programs perform and whether any offers superior accuracy. This is in part because there is no universal benchmark for gene finding and, therefore, most developers select their own benchmarking strategy. Results Here, we introduce AssessORF, a new approach for benchmarking prokaryotic gene predictions based on evidence from proteomics data and the evolutionary conservation of start and stop codons. We applied AssessORF to compare gene predictions offered by GenBank, GeneMarkS-2, Glimmer and Prodigal on genomes spanning the prokaryotic tree of life. Gene predictions were 88–95% in agreement with the available evidence, with Glimmer performing the worst but no clear winner. All programs were biased towards selecting start codons that were upstream of the actual start. Given these findings, there remains considerable room for improvement, especially in the detection of correct start sites. Availability and implementation AssessORF is available as an R package via the Bioconductor package repository. Supplementary information Supplementary data are available at Bioinformatics online.

schex avoids overplotting for large single-cell RNA-sequencing datasets

2019 ◽  
Vol 36 (7) ◽  
pp. 2291-2292 ◽  
Author(s):  
Saskia Freytag ◽  
Ryan Lister
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
R Package ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing

Abstract Summary Due to the scale and sparsity of single-cell RNA-sequencing data, traditional plots can obscure vital information. Our R package schex overcomes this by implementing hexagonal binning, which has the additional advantages of improving speed and reducing storage for resulting plots. Availability and implementation schex is freely available from Bioconductor via http://bioconductor.org/packages/release/bioc/html/schex.html and its development version can be accessed on GitHub via https://github.com/SaskiaFreytag/schex. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals GladiaTOX: GLobal Assessment of Dose-IndicAtor in TOXicology

2019 ◽  
Vol 35 (20) ◽  
pp. 4190-4192 ◽  
Author(s):  
Vincenzo Belcastro ◽  
Stephane Cano ◽  
Diego Marescotti ◽  
Stefano Acali ◽  
Carine Poussin ◽  
...  
Keyword(s):  
Quality Control ◽  
Data Processing ◽  
Web Service ◽  
Biomedical Research ◽  
Global Assessment ◽  
R Package ◽  
Supplementary Information ◽  
High Content Screening ◽  
Supplementary Data ◽  
Severity Scores

Abstract Summary GladiaTOX R package is an open-source, flexible solution to high-content screening data processing and reporting in biomedical research. GladiaTOX takes advantage of the ‘tcpl’ core functionalities and provides a number of extensions: it provides a web-service solution to fetch raw data; it computes severity scores and exports ToxPi formatted files; furthermore it contains a suite of functionalities to generate PDF reports for quality control and data processing. Availability and implementation GladiaTOX R package (bioconductor). Also available via: git clone https://github.com/philipmorrisintl/GladiaTOX.git. Supplementary information Supplementary 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

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.

Issues of Z-factor and an approach to avoid them for quality control in high-throughput screening studies

2020 ◽  
Author(s):  
Xiaohua Douglas Zhang ◽  
Dandan Wang ◽  
Shixue Sun ◽  
Heping Zhang
Keyword(s):  
Quality Control ◽  
High Throughput ◽  
High Throughput Screening ◽  
Theoretical Basis ◽  
Sampling Error ◽  
R Package ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Automation Technology ◽  
General Public License

Abstract Motivation High-throughput screening (HTS) is a vital automation technology in biomedical research in both industry and academia. The well-known Z-factor has been widely used as a gatekeeper to assure assay quality in an HTS study. However, many researchers and users may not have realized that Z-factor has major issues. Results In this article, the following four major issues are explored and demonstrated so that researchers may use the Z-factor appropriately. First, the Z-factor violates the Pythagorean theorem of statistics. Second, there is no adjustment of sampling error in the application of the Z-factor for quality control (QC) in HTS studies. Third, the expectation of the sample-based Z-factor does not exist. Fourth, the thresholds in the Z-factor-based criterion lack a theoretical basis. Here, an approach to avoid these issues was proposed and new QC criteria under homoscedasticity were constructed so that researchers can choose a statistically grounded criterion for QC in the HTS studies. We implemented this approach in an R package and demonstrated its utility in multiple CRISPR/CAS9 or siRNA HTS studies. Availability and implementation The R package qcSSMDhomo is freely available from GitHub: https://github.com/Karena6688/qcSSMDhomo. The file qcSSMDhomo_1.0.0.tar.gz (for Windows) containing qcSSMDhomo is also available at Bioinformatics online. qcSSMDhomo is distributed under the GNU General Public License. Supplementary information Supplementary data are available at Bioinformatics online.

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.

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