cofad: An R package and shiny app for contrast analysis

Abstract Background Meiotic recombination is a vital biological process playing an essential role in genome's structural and functional dynamics. Genomes exhibit highly various recombination profiles along chromosomes associated with several chromatin states. However, eu-heterochromatin boundaries are not available nor easily provided for non-model organisms, especially for newly sequenced ones. Hence, we miss accurate local recombination rates necessary to address evolutionary questions. Results Here, we propose an automated computational tool, based on the Marey maps method, allowing to identify heterochromatin boundaries along chromosomes and estimating local recombination rates. Our method, called BREC (heterochromatin Boundaries and RECombination rate estimates) is non-genome-specific, running even on non-model genomes as long as genetic and physical maps are available. BREC is based on pure statistics and is data-driven, implying that good input data quality remains a strong requirement. Therefore, a data pre-processing module (data quality control and cleaning) is provided. Experiments show that BREC handles different markers' density and distribution issues. Conclusions BREC's heterochromatin boundaries have been validated with cytological equivalents experimentally generated on the fruit fly Drosophila melanogaster genome, for which BREC returns congruent corresponding values. Also, BREC's recombination rates have been compared with previously reported estimates. Based on the promising results, we believe our tool has the potential to help bring data science into the service of genome biology and evolution. We introduce BREC within an R-package and a Shiny web-based user-friendly application yielding a fast, easy-to-use, and broadly accessible resource. The BREC R-package is available at the GitHub repository https://github.com/GenomeStructureOrganization.

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PRISMA2020: an R package and Shiny app for producing PRISMA 2020-compliant flow diagrams, with interactivity for optimised digital transparency and Open Synthesis

10.1101/2021.07.14.21260492 ◽

2021 ◽

Author(s):

Neal R Haddaway ◽

Matthew J Page ◽

Christopher C Pritchard ◽

Luke A McGuinness

Keyword(s):

Systematic Review ◽

Systematic Reviews ◽

Science Communication ◽

Review Process ◽

Evidence Synthesis ◽

R Package ◽

Web Based ◽

Shiny App ◽

Data Files ◽

Flow Diagrams

Background Reporting standards, such as PRISMA aim to ensure that the methods and results of systematic reviews are described in sufficient detail to allow full transparency. Flow diagrams in evidence syntheses allow the reader to rapidly understand the core procedures used in a review and examine the attrition of irrelevant records throughout the review process. Recent research suggests that use of flow diagrams in systematic reviews is poor and of low quality and called for standardised templates to facilitate better reporting in flow diagrams. The increasing options for interactivity provided by the Internet gives us an opportunity to support easy-to-use evidence synthesis tools, and here we report on the development of tools for the production of PRISMA 2020-compliant systematic review flow diagrams. Methods and Findings We developed a free-to-use, Open Source R package and web-based Shiny app to allow users to design PRISMA flow diagrams for their own systematic reviews. Our tools allow users to produce standardised visualisations that transparently document the methods and results of a systematic review process in a variety of formats. In addition, we provide the opportunity to produce interactive, web-based flow diagrams (exported as HTML files), that allow readers to click on boxes of the diagram and navigate to further details on methods, results or data files. We provide an interactive example here; https://driscoll.ntu.ac.uk/prisma/. Conclusions We have developed a user-friendly suite of tools for producing PRISMA 2020-compliant flow diagrams for users with coding experience and, importantly, for users without prior experience in coding by making use of Shiny. These free-to-use tools will make it easier to produce clear and PRISMA 2020-compliant systematic review flow diagrams. Significantly, users can also produce interactive flow diagrams for the first time, allowing readers of their reviews to smoothly and swiftly explore and navigate to further details of the methods and results of a review. We believe these tools will increase use of PRISMA flow diagrams, improve the compliance and quality of flow diagrams, and facilitate strong science communication of the methods and results of systematic reviews by making use of interactivity. We encourage the systematic review community to make use of these tools, and provide feedback to streamline and improve their usability and efficiency.

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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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Lipid Mini-On: mining and ontology tool for enrichment analysis of lipidomic data

Bioinformatics ◽

10.1093/bioinformatics/btz250 ◽

2019 ◽

Vol 35 (21) ◽

pp. 4507-4508 ◽

Cited By ~ 9

Author(s):

Geremy Clair ◽

Sarah Reehl ◽

Kelly G Stratton ◽

Matthew E Monroe ◽

Malak M Tfaily ◽

...

Keyword(s):

Peat Soil ◽

Enrichment Analysis ◽

R Package ◽

Lipid Classes ◽

Supplementary Information ◽

Mass Spec ◽

Shiny App ◽

Lung Endothelial Cells ◽

Lipid Enrichment ◽

The Individual

Abstract Summary Here we introduce Lipid Mini-On, an open-source tool that performs lipid enrichment analyses and visualizations of lipidomics data. Lipid Mini-On uses a text-mining process to bin individual lipid names into multiple lipid ontology groups based on the classification (e.g. LipidMaps) and other characteristics, such as chain length. Lipid Mini-On provides users with the capability to conduct enrichment analysis of the lipid ontology terms using a Shiny app with options of five statistical approaches. Lipid classes can be added to customize the user’s database and remain updated as new lipid classes are discovered. Visualization of results is available for all classification options (e.g. lipid subclass and individual fatty acid chains). Results are also visualized through an editable network of relationships between the individual lipids and their associated lipid ontology terms. The utility of the tool is demonstrated using biological (e.g. human lung endothelial cells) and environmental (e.g. peat soil) samples. Availability and implementation Rodin (R package: https://github.com/PNNL-Comp-Mass-Spec/Rodin), Lipid Mini-On Shiny app (https://github.com/PNNL-Comp-Mass-Spec/LipidMiniOn) and Lipid Mini-On online tool (https://omicstools.pnnl.gov/shiny/lipid-mini-on/). Supplementary information Supplementary data are available at Bioinformatics online.

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DiffNetFDR: differential network analysis with false discovery rate control

Bioinformatics ◽

10.1093/bioinformatics/btz051 ◽

2019 ◽

Vol 35 (17) ◽

pp. 3184-3186

Author(s):

Xiao-Fei Zhang ◽

Le Ou-Yang ◽

Shuo Yang ◽

Xiaohua Hu ◽

Hong Yan

Keyword(s):

False Discovery Rate ◽

Graphical Models ◽

Biological Significance ◽

R Package ◽

Supplementary Information ◽

Gaussian Graphical Models ◽

Multiple Testing Procedure ◽

False Discovery ◽

Differential Network ◽

Shiny App

Abstract Summary To identify biological network rewiring under different conditions, we develop a user-friendly R package, named DiffNetFDR, to implement two methods developed for testing the difference in different Gaussian graphical models. Compared to existing tools, our methods have the following features: (i) they are based on Gaussian graphical models which can capture the changes of conditional dependencies; (ii) they determine the tuning parameters in a data-driven manner; (iii) they take a multiple testing procedure to control the overall false discovery rate; and (iv) our approach defines the differential network based on partial correlation coefficients so that the spurious differential edges caused by the variants of conditional variances can be excluded. We also develop a Shiny application to provide easier analysis and visualization. Simulation studies are conducted to evaluate the performance of our methods. We also apply our methods to two real gene expression datasets. The effectiveness of our methods is validated by the biological significance of the identified differential networks. Availability and implementation R package and Shiny app are available at https://github.com/Zhangxf-ccnu/DiffNetFDR. Supplementary information Supplementary data are available at Bioinformatics online.

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BREC: An R package/Shiny app for automatically identifying heterochromatin boundaries and estimating local recombination rates along chromosomes

10.1101/2020.06.29.178095 ◽

2020 ◽

Author(s):

Yasmine Mansour ◽

Annie Chateau ◽

Anna-Sophie Fiston-Lavier

Keyword(s):

Data Quality ◽

Data Science ◽

Fruit Fly ◽

R Package ◽

Model Organisms ◽

Data Quality Control ◽

Recombination Rates ◽

Functional Dynamics ◽

Shiny App ◽

User Friendly

AbstractMotivationMeiotic recombination is a vital biological process playing an essential role in genomes structural and functional dynamics. Genomes exhibit highly various recombination profiles along chromosomes associated with several chromatin states. However, eu-heterochromatin boundaries are not available nor easily provided for non-model organisms, especially for newly sequenced ones. Hence, we miss accurate local recombination rates, necessary to address evolutionary questions.ResultsHere, we propose an automated computational tool, based on the Marey maps method, allowing to identify heterochromatin boundaries along chromosomes and estimating local recombination rates. Our method, called BREC (heterochromatin Boundaries and RECombination rate estimates) is non-genome-specific, running even on non-model genomes as long as genetic and physical maps are available. BREC is based on pure statistics and is data-driven, implying that good input data quality remains a strong requirement. Therefore, a data pre-processing module (data quality control and cleaning) is provided. Experiments show that BREC handles different markers density and distribution issues. BREC’s heterochromatin boundaries have been validated with cytological equivalents experimentally generated on the fruit fly Drosophila melanogaster genome, for which BREC returns congruent corresponding values. Also, BREC’s recombination rates have been compared with previously reported estimates. Based on the promising results, we believe our tool has the potential to help bring data science into the service of genome biology and evolution. We introduce BREC within an R-package and a Shiny web-based user-friendly application yielding a fast, easy-to-use, and broadly accessible resource.AvailabilityBREC R-package is available at the GitHub repository https://github.com/ymansour21/BREC.

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Crafting for a better MAGIC: systematic design and test for multiparental advanced generation inter-cross population

10.1101/2021.04.27.441636 ◽

2021 ◽

Author(s):

Chin Jian Yang ◽

Rodney N. Edmondson ◽

Hans-Peter Piepho ◽

Wayne Powell ◽

Ian Mackay

Keyword(s):

Complex Traits ◽

Recombinant Inbred Lines ◽

Haplotype Diversity ◽

R Package ◽

Shiny App ◽

New Varieties ◽

Creativity And Innovation ◽

Advanced Generation ◽

Population Design ◽

Magic Population

AbstractMultiparental advanced generation inter-cross (MAGIC) populations are valuable crop resources with a wide array of research uses including genetic mapping of complex traits, management of genetic resources and breeding of new varieties. Multiple founders are crossed to create a rich mosaic of highly recombined founder genomes in the MAGIC recombinant inbred lines (RILs). Many variations of MAGIC population designs exist; however, a large proportion of the currently available populations have been created empirically and based on similar designs. In our evaluations of five MAGIC populations, we found that the choice of designs has a large impact on the recombination landscape in the RILs. The most popular design used in many MAGIC populations has been shown to have a bias in recombinant haplotypes and low level of unique recombinant haplotypes, and therefore is not recommended. To address this problem and provide a remedy for the future, we have developed the “magicdesign” R package for creating and testing any MAGIC population design via simulation. A Shiny app version of the package is available as well. Our “magicdesign” package provides a unifying tool and a framework for creativity and innovation in MAGIC population designs. For example, using this package, we demonstrate that MAGIC population designs can be found which are very effective in creating haplotype diversity without the requirement for very large crossing programmes. Further, we show that interspersing cycles of crossing with cycles of selfing is effective in increasing haplotype diversity. These approaches are applicable in species which are hard to cross or in which resources are limited.

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Criterion-Based Metrics for Range Restriction Corrections

10.31234/osf.io/t8cy3 ◽

2020 ◽

Author(s):

Dustin Fife ◽

Tom O'Kane ◽

Valerie LaMastro

Keyword(s):

Multiple Imputation ◽

R Package ◽

Optimal Selection ◽

Selection Methods ◽

Range Restriction ◽

Intuitive Statistics ◽

Selection Decisions ◽

Shiny App ◽

Mechanical Selection ◽

The Cost

For decades, it has been suggested organizations utilize mechanical decisions. Unfortunately, organizations continue to rely on holistic judgments. Perhaps part of the reasons organizations continue to rely on judgment when mak- ing decisions is because the reported statistics associated with mechanical judgments (e.g., R2) are not intuitive to stakeholders. For example, it is unclear in terms of turnover whether an R2 change from 0.23 under the old (holistic) system to an R2 of 0.27 under the (mechanical) system is enough of an improvement to justify the cost of changing selection methods. In this paper, we argue that researchers instead report changes in criteria of interest (e.g., absenteeism rate before versus after utilizing a mechanical selection sys- tem). This can be accomplished by utilizing a multiple imputation algorithm that simulates optimal selection decisions. Additionally, we provide both an R-package, as well as a point-and-click Shiny app that allows researchers to easily estimate intuitive statistics (e.g., improvement in turnover, proportion of applicants for which an optimal and holistic system agree).

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Mapfuser: an integrative toolbox for consensus map construction and Marey maps

10.1101/200311 ◽

2017 ◽

Author(s):

Dennis van Muijen ◽

Ram K. Basnet ◽

Nathalie N.J. Dek ◽

Chris Maliepaard ◽

Evert W. Gutteling

Keyword(s):

R Package ◽

Reproducible Research ◽

Consensus Map ◽

Link Type ◽

Map Construction ◽

Shiny App ◽

Data Function ◽

High Level ◽

Automated Quality Control ◽

Map Analysis

AbstractMotivationWhere standalone tools for genetic map visualisation, consensus map construction, and Marey map analysis offer useful analyses for research and breeding, no single tool combines the three. Manual data curation is part of each of these analyses, which is difficult to standardize and consequently error prone.ResultsMapfuser provides a high-level interface for common analyses in breeding programs and quantitative genetics. Combined with interactive visualisations and automated quality control, mapfuser provides a standardized and flexible toolbox and is available as Shiny app for biologists. Reproducible research in the R package is facilitated by storage of raw data, function parameters, and results in an R object. In the shiny app a rmarkdown report is available.AvailabilityMapfuser is available as R package at https://github.com/dmuijen/mapfuser under the GPL-3 License and is available for public use as Shiny application at https://plantbreeding.shinyapps.io/mapfuser. Documentation for the R package is available as package vignette. Shiny app documentation is integrated in the [email protected]

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Crafting for a better MAGIC: systematic design and test for multiparental advanced generation inter-cross population

G3 Genes|Genome|Genetics ◽

10.1093/g3journal/jkab295 ◽

2021 ◽

Author(s):

Chin Jian Yang ◽

Rodney N Edmondson ◽

Hans-Peter Piepho ◽

Wayne Powell ◽

Ian Mackay

Keyword(s):

Complex Traits ◽

Recombinant Inbred Lines ◽

Haplotype Diversity ◽

R Package ◽

Shiny App ◽

New Varieties ◽

Creativity And Innovation ◽

Advanced Generation ◽

Population Design ◽

Magic Population

Abstract Multiparental advanced generation inter-cross (MAGIC) populations are valuable crop resources with a wide array of research uses including genetic mapping of complex traits, management of genetic resources and breeding of new varieties. Multiple founders are crossed to create a rich mosaic of highly recombined founder genomes in the MAGIC recombinant inbred lines (RILs). Many variations of MAGIC population designs exist; however, a large proportion of the currently available populations have been created empirically and based on similar designs. In our evaluations of five MAGIC populations, we found that the choice of designs has a large impact on the recombination landscape in the RILs. The most popular design used in many MAGIC populations has been shown to have a bias in recombinant haplotypes and low level of unique recombinant haplotypes, and therefore is not recommended. To address this problem and provide a remedy for the future, we have developed the “magicdesign” R package for creating and testing any MAGIC population design via simulation. A Shiny app version of the package is available as well. Our “magicdesign” package provides a unifying tool and a framework for creativity and innovation in MAGIC population designs. For example, using this package, we demonstrate that MAGIC population designs can be found which are very effective in creating haplotype diversity without the requirement for very large crossing programs. Further, we show that interspersing cycles of crossing with cycles of selfing is effective in increasing haplotype diversity. These approaches are applicable in species which are hard to cross or in which resources are limited.

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