RCytoGPS: An R Package for Reading and Visualizing Cytogenetics Data

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.

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RCytoGPS: An R Package for Reading and Visualizing Cytogenetics Data

10.1101/2021.03.16.389791 ◽

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.

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dbgap2x: An R package to explore and extract data from the database of Genotypes and Phenotypes (dbGaP)

Bioinformatics ◽

10.1093/bioinformatics/btz680 ◽

2019 ◽

Cited By ~ 1

Author(s):

Grégoire Versmée ◽

Laura Versmée ◽

Mikaël Dusenne ◽

Niloofar Jalali ◽

Paul Avillach

Keyword(s):

Data Sharing ◽

Large Scale ◽

Genomic Data ◽

R Package ◽

National Institutes Of Health ◽

Supplementary Information ◽

Supplementary Data ◽

Complex Procedure ◽

Range Of Functions ◽

The Relationship

Abstract Summary Based on the Genomic Data Sharing Policy issued in August 2007, the National Institutes of Health (NIH) has supported several repositories such as the database of Genotypes and Phenotypes (dbGaP). dbGaP is an online repository that provides access to large-scale genetic and phenotypic datasets with more than 1,000 studies. However, navigating the website and understanding the relationship between the studies are not easy tasks. Moreover, the decryption of the files is a complex procedure. In this study we propose the dbgap2x R package that covers a broad range of functions for searching dbGaP studies, exploring the characteristics of a study and easily decrypting the files from dbGaP. Availability and implementation dbgap2x is an R package with the code available at https://github.com/gversmee/dbgap2x. A containerized version including the package, a Jupyter server and with a Notebook example is available at https://hub.docker.com/r/gversmee/dbgap2x. Supplementary information Supplementary data are available at Bioinformatics online.

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EARRINGS: an efficient and accurate adapter trimmer entails no a priori adapter sequences

Bioinformatics ◽

10.1093/bioinformatics/btab025 ◽

2021 ◽

Author(s):

Ting-Hsuan Wang ◽

Cheng-Ching Huang ◽

Jui-Hung Hung

Keyword(s):

Open Source Software ◽

Large Scale ◽

A Priori ◽

Supplementary Information ◽

Supplementary Data ◽

Comparable Accuracy ◽

Meta Analyses ◽

Next Generation Sequencing Ngs ◽

Adapter Trimming ◽

Generation Sequencing

Abstract Motivation Cross-sample comparisons or large-scale meta-analyses based on the next generation sequencing (NGS) involve replicable and universal data preprocessing, including removing adapter fragments in contaminated reads (i.e. adapter trimming). While modern adapter trimmers require users to provide candidate adapter sequences for each sample, which are sometimes unavailable or falsely documented in the repositories (such as GEO or SRA), large-scale meta-analyses are therefore jeopardized by suboptimal adapter trimming. Results Here we introduce a set of fast and accurate adapter detection and trimming algorithms that entail no a priori adapter sequences. These algorithms were implemented in modern C++ with SIMD and multithreading to accelerate its speed. Our experiments and benchmarks show that the implementation (i.e. EARRINGS), without being given any hint of adapter sequences, can reach comparable accuracy and higher throughput than that of existing adapter trimmers. EARRINGS is particularly useful in meta-analyses of a large batch of datasets and can be incorporated in any sequence analysis pipelines in all scales. Availability and implementation EARRINGS is open-source software and is available at https://github.com/jhhung/EARRINGS. Supplementary information Supplementary data are available at Bioinformatics online.

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TADOSS: computational estimation of tandem domain swap stability

Bioinformatics ◽

10.1093/bioinformatics/bty974 ◽

2018 ◽

Vol 35 (14) ◽

pp. 2507-2508 ◽

Cited By ~ 2

Author(s):

Aleix Lafita ◽

Pengfei Tian ◽

Robert B Best ◽

Alex Bateman

Keyword(s):

Large Scale ◽

Domain Swapping ◽

Coarse Grained ◽

Supplementary Information ◽

Simulation Studies ◽

Computational Tools ◽

Domain Swap ◽

Computational Estimation ◽

High Propensity ◽

The Stability

Abstract Summary Proteins with highly similar tandem domains have shown an increased propensity for misfolding and aggregation. Several molecular explanations have been put forward, such as swapping of adjacent domains, but there is a lack of computational tools to systematically analyze them. We present the TAndem DOmain Swap Stability predictor (TADOSS), a method to computationally estimate the stability of tandem domain-swapped conformations from the structures of single domains, based on previous coarse-grained simulation studies. The tool is able to discriminate domains susceptible to domain swapping and to identify structural regions with high propensity to form hinge loops. TADOSS is a scalable method and suitable for large scale analyses. Availability and implementation Source code and documentation are freely available under an MIT license on GitHub at https://github.com/lafita/tadoss. Supplementary information Supplementary data are available at Bioinformatics online.

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

10.1101/173997 ◽

2017 ◽

Cited By ~ 1

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.

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OpenBioLink: a benchmarking framework for large-scale biomedical link prediction

Bioinformatics ◽

10.1093/bioinformatics/btaa274 ◽

2020 ◽

Vol 36 (13) ◽

pp. 4097-4098 ◽

Cited By ~ 3

Author(s):

Anna Breit ◽

Simon Ott ◽

Asan Agibetov ◽

Matthias Samwald

Keyword(s):

Link Prediction ◽

Large Scale ◽

Source Code ◽

Machine Learning Algorithms ◽

Knowledge Networks ◽

Supplementary Information ◽

Supplementary Data ◽

Biomedical Knowledge ◽

High Quality ◽

Baseline Evaluation

Abstract Summary Recently, novel machine-learning algorithms have shown potential for predicting undiscovered links in biomedical knowledge networks. However, dedicated benchmarks for measuring algorithmic progress have not yet emerged. With OpenBioLink, we introduce a large-scale, high-quality and highly challenging biomedical link prediction benchmark to transparently and reproducibly evaluate such algorithms. Furthermore, we present preliminary baseline evaluation results. Availability and implementation Source code and data are openly available at https://github.com/OpenBioLink/OpenBioLink. Supplementary information Supplementary data are available at Bioinformatics online.

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

Bioinformatics ◽

10.1093/bioinformatics/btz937 ◽

2019 ◽

Vol 36 (8) ◽

pp. 2587-2588 ◽

Cited By ~ 10

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.

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CPS analysis: self-contained validation of biomedical data clustering

Bioinformatics ◽

10.1093/bioinformatics/btaa165 ◽

2020 ◽

Vol 36 (11) ◽

pp. 3516-3521 ◽

Cited By ~ 1

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.

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RMTL: an R library for multi-task learning

Bioinformatics ◽

10.1093/bioinformatics/bty831 ◽

2018 ◽

Vol 35 (10) ◽

pp. 1797-1798 ◽

Cited By ~ 2

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.

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PyMethylProcess—convenient high-throughput preprocessing workflow for DNA methylation data

Bioinformatics ◽

10.1093/bioinformatics/btz594 ◽

2019 ◽

Vol 35 (24) ◽

pp. 5379-5381 ◽

Cited By ~ 2

Author(s):

Joshua J Levy ◽

Alexander J Titus ◽

Lucas A Salas ◽

Brock C Christensen

Keyword(s):

Large Scale ◽

Supplementary Information ◽

Scale Production ◽

Methylation Data ◽

Supplementary Data ◽

Data Preparation ◽

Methylation Array ◽

Project Home Page ◽

Large Scale Production ◽

Set Up

Abstract Summary Performing highly parallelized preprocessing of methylation array data using Python can accelerate data preparation for downstream methylation analyses, including large scale production-ready machine learning pipelines. We present a highly reproducible, scalable pipeline (PyMethylProcess) that can be quickly set-up and deployed through Docker and PIP. Availability and implementation Project Home Page: https://github.com/Christensen-Lab-Dartmouth/PyMethylProcess. Available on PyPI (pymethylprocess), Docker (joshualevy44/pymethylprocess). Supplementary information Supplementary data are available at Bioinformatics online.

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