ECHO: an application for detection and analysis of oscillators identifies metabolic regulation on genome-wide circadian output

Abstract Motivation Time courses utilizing genome scale data are a common approach to identifying the biological pathways that are controlled by the circadian clock, an important regulator of organismal fitness. However, the methods used to detect circadian oscillations in these datasets are not able to accommodate changes in the amplitude of the oscillations over time, leading to an underestimation of the impact of the clock on biological systems. Results We have created a program to efficaciously identify oscillations in large-scale datasets, called the Extended Circadian Harmonic Oscillator application, or ECHO. ECHO utilizes an extended solution of the fixed amplitude oscillator that incorporates the amplitude change coefficient. Employing synthetic datasets, we determined that ECHO outperforms existing methods in detecting rhythms with decreasing oscillation amplitudes and in recovering phase shift. Rhythms with changing amplitudes identified from published biological datasets revealed distinct functions from those oscillations that were harmonic, suggesting purposeful biologic regulation to create this subtype of circadian rhythms. Availability and implementation ECHO’s full interface is available at https://github.com/delosh653/ECHO. An R package for this functionality, echo.find, can be downloaded at https://CRAN.R-project.org/package=echo.find. Supplementary information Supplementary data are available at Bioinformatics online.

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ECHO: an Application for Detection and Analysis of Oscillators Identifies Metabolic Regulation on Genome-Wide Circadian Output

10.1101/690941 ◽

2019 ◽

Cited By ~ 1

Author(s):

Hannah De los Santos ◽

Emily J. Collins ◽

Catherine Mann ◽

April W. Sagan ◽

Meaghan S. Jankowski ◽

...

Keyword(s):

Metabolic Regulation ◽

Large Scale ◽

R Package ◽

Supplementary Information ◽

Link Type ◽

Time Courses ◽

Synthetic Datasets ◽

Mass Spring ◽

Genome Scale ◽

The Impact

AbstractMotivationTime courses utilizing genome scale data are a common approach to identifying the biological pathways that are controlled by the circadian clock, an important regulator of organismal fitness. However, the methods used to detect circadian oscillations in these datasets are not able to accommodate changes in the amplitude of the oscillations over time, leading to an underestimation of the impact of the clock on biological systems.ResultsWe have created a program to efficaciously identify oscillations in large-scale datasets, called the Extended Circadian Harmonic Oscillator application, or ECHO. ECHO utilizes an extended solution of the fixed amplitude mass-spring oscillator that incorporates the amplitude change coefficient. Employing synthetic datasets, we determined that ECHO outperforms existing methods in detecting rhythms with decreasing oscillation amplitudes and recovering phase shift. Rhythms with changing amplitudes identified from published biological datasets revealed distinct functions from those oscillations that were harmonic, suggesting purposeful biologic regulation to create this subtype of circadian rhythms.AvailabilityECHO’s full interface is available athttps://github.com/delosh653/ECHO. An R package for this functionality, echo.find, can be downloaded athttps://CRAN.R-project.org/[email protected] informationSupplementary data are available

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decorate: differential epigenetic correlation test

Bioinformatics ◽

10.1093/bioinformatics/btaa067 ◽

2020 ◽

Vol 36 (9) ◽

pp. 2856-2861

Author(s):

Gabriel E Hoffman ◽

Jaroslav Bendl ◽

Kiran Girdhar ◽

Panos Roussos

Keyword(s):

Large Scale ◽

Statistical Tests ◽

Computational Cost ◽

R Package ◽

Supplementary Information ◽

Expression Data ◽

Correlation Test ◽

Disease Biology ◽

Genome Wide ◽

Insight Into

Abstract Motivation Identifying correlated epigenetic features and finding differences in correlation between individuals with disease compared to controls can give novel insight into disease biology. This framework has been successful in analysis of gene expression data, but application to epigenetic data has been limited by the computational cost, lack of scalable software and lack of robust statistical tests. Results Decorate, differential epigenetic correlation test, identifies correlated epigenetic features and finds clusters of features that are differentially correlated between two or more subsets of the data. The software scales to genome-wide datasets of epigenetic assays on hundreds of individuals. We apply decorate to four large-scale datasets of DNA methylation, ATAC-seq and histone modification ChIP-seq. Availability and implementation decorate R package is available from https://github.com/GabrielHoffman/decorate. Supplementary information Supplementary data are available at Bioinformatics online.

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GWASpro: a high-performance genome-wide association analysis server

Bioinformatics ◽

10.1093/bioinformatics/bty989 ◽

2018 ◽

Vol 35 (14) ◽

pp. 2512-2514 ◽

Cited By ~ 4

Author(s):

Bongsong Kim ◽

Xinbin Dai ◽

Wenchao Zhang ◽

Zhaohong Zhuang ◽

Darlene L Sanchez ◽

...

Keyword(s):

High Performance ◽

Large Scale ◽

Linear Mixed Model ◽

Association Studies ◽

Learning Curves ◽

Experimental Designs ◽

Genome Wide Association ◽

Supplementary Information ◽

Genome Wide Association Studies ◽

Genome Wide

Abstract Summary We present GWASpro, a high-performance web server for the analyses of large-scale genome-wide association studies (GWAS). GWASpro was developed to provide data analyses for large-scale molecular genetic data, coupled with complex replicated experimental designs such as found in plant science investigations and to overcome the steep learning curves of existing GWAS software tools. GWASpro supports building complex design matrices, by which complex experimental designs that may include replications, treatments, locations and times, can be accounted for in the linear mixed model. GWASpro is optimized to handle GWAS data that may consist of up to 10 million markers and 10 000 samples from replicable lines or hybrids. GWASpro provides an interface that significantly reduces the learning curve for new GWAS investigators. Availability and implementation GWASpro is freely available at https://bioinfo.noble.org/GWASPRO. Supplementary information Supplementary data are available at Bioinformatics online.

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bGWAS: an R package to perform Bayesian genome wide association studies

Bioinformatics ◽

10.1093/bioinformatics/btaa549 ◽

2020 ◽

Vol 36 (15) ◽

pp. 4374-4376

Author(s):

Ninon Mounier ◽

Zoltán Kutalik

Keyword(s):

Mendelian Randomization ◽

Causal Effect ◽

Association Studies ◽

R Package ◽

Genome Wide Association ◽

Supplementary Information ◽

Genome Wide Association Studies ◽

Biological Mechanisms ◽

Genome Wide ◽

Related Risk

Abstract Summary Increasing sample size is not the only strategy to improve discovery in Genome Wide Association Studies (GWASs) and we propose here an approach that leverages published studies of related traits to improve inference. Our Bayesian GWAS method derives informative prior effects by leveraging GWASs of related risk factors and their causal effect estimates on the focal trait using multivariable Mendelian randomization. These prior effects are combined with the observed effects to yield Bayes Factors, posterior and direct effects. The approach not only increases power, but also has the potential to dissect direct and indirect biological mechanisms. Availability and implementation bGWAS package is freely available under a GPL-2 License, and can be accessed, alongside with user guides and tutorials, from https://github.com/n-mounier/bGWAS. Supplementary information Supplementary data are available at Bioinformatics online.

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Multi-SNP mediation intersection-union test

Bioinformatics ◽

10.1093/bioinformatics/btz285 ◽

2019 ◽

Vol 35 (22) ◽

pp. 4724-4729 ◽

Cited By ~ 4

Author(s):

Wujuan Zhong ◽

Cassandra N Spracklen ◽

Karen L Mohlke ◽

Xiaojing Zheng ◽

Jason Fine ◽

...

Keyword(s):

Association Studies ◽

R Package ◽

Alternative Methods ◽

Supplementary Information ◽

Genome Wide Association Studies ◽

Mediation Effects ◽

Coding Regions ◽

Genome Wide ◽

Plasma Adiponectin Level ◽

Intersection Union Test

Abstract Summary Tens of thousands of reproducibly identified GWAS (Genome-Wide Association Studies) variants, with the vast majority falling in non-coding regions resulting in no eventual protein products, call urgently for mechanistic interpretations. Although numerous methods exist, there are few, if any methods, for simultaneously testing the mediation effects of multiple correlated SNPs via some mediator (e.g. the expression of a gene in the neighborhood) on phenotypic outcome. We propose multi-SNP mediation intersection-union test (SMUT) to fill in this methodological gap. Our extensive simulations demonstrate the validity of SMUT as well as substantial, up to 92%, power gains over alternative methods. In addition, SMUT confirmed known mediators in a real dataset of Finns for plasma adiponectin level, which were missed by many alternative methods. We believe SMUT will become a useful tool to generate mechanistic hypotheses underlying GWAS variants, facilitating functional follow-up. Availability and implementation The R package SMUT is publicly available from CRAN at https://CRAN.R-project.org/package=SMUT. Supplementary information Supplementary data are available at Bioinformatics online.

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RCytoGPS: An R Package for Reading and Visualizing Cytogenetics Data

Bioinformatics ◽

10.1093/bioinformatics/btab683 ◽

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.

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FALCONET: an R package to accelerate automatic visualisation of genome scale metabolic models

10.1101/662056 ◽

2019 ◽

Author(s):

Hongzhong Lu ◽

Zhengming Zhu ◽

Eduard J Kerkhoven ◽

Jens Nielsen

Keyword(s):

Metabolic Networks ◽

R Package ◽

Network Size ◽

Research Community ◽

Supplementary Information ◽

Large Network ◽

Strain Design ◽

Scale Models ◽

Genome Scale ◽

Integrative Omics

AbstractSummaryFALCONET (FAst visuaLisation of COmputational NETworks) enables the automatic for-mation and visualisation of metabolic maps from genome-scale models with R and CellDesigner, readily facilitating the visualisation of multi-layers omics datasets in the context of metabolic networks.MotivationUntil now, numerous GEMs have been reconstructed and used as scaffolds to conduct integrative omics analysis and in silico strain design. Due to the large network size of GEMs, it is challenging to produce and visualize these networks as metabolic maps for further in-depth analyses.ResultsHere, we presented the R package - FALCONET, which facilitates drawing and visualizing metabolic maps in an automatic manner. This package will benefit the research community by allowing a wider use of GEMs in systems biology.Availability and implementationFALCONET is available on https://github.com/SysBioChalmers/FALCONET and released under the MIT [email protected] informationSupplementary data are available online.

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genuMet: distinguish genuine untargeted metabolic features without quality control samples

10.1101/837260 ◽

2019 ◽

Cited By ~ 1

Author(s):

L Cao ◽

C Clish ◽

FB Hu ◽

MA Martínez-González ◽

C Razquin ◽

...

Keyword(s):

Quality Control ◽

Large Scale ◽

Control Method ◽

R Package ◽

True Positive Rate ◽

Supplementary Information ◽

True Negative ◽

True Negative Rate ◽

Positive Rate ◽

Measurement Artifacts

AbstractMotivationLarge-scale untargeted metabolomics experiments lead to detection of thousands of novel metabolic features as well as false positive artifacts. With the incorporation of pooled QC samples and corresponding bioinformatics algorithms, those measurement artifacts can be well quality controlled. However, it is impracticable for all the studies to apply such experimental design.ResultsWe introduce a post-alignment quality control method called genuMet, which is solely based on injection order of biological samples to identify potential false metabolic features. In terms of the missing pattern of metabolic signals, genuMet can reach over 95% true negative rate and 85% true positive rate with suitable parameters, compared with the algorithm utilizing pooled QC samples. genu-Met makes it possible for studies without pooled QC samples to reduce false metabolic signals and perform robust statistical analysis.Availability and implementationgenuMet is implemented in a R package and available on https://github.com/liucaomics/genuMet under GPL-v2 license.ContactLiming Liang: [email protected] informationSupplementary data are available at ….

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Impact of sustained TGFβ receptor inhibition on chromatin accessibility and gene expression in cultured human endometrial MSC

10.1101/2020.05.01.073346 ◽

2020 ◽

Author(s):

Raffaella Lucciola ◽

Pavle Vrljicak ◽

Caitlin Filby ◽

Saeedeh Darzi ◽

Shanti Gurung ◽

...

Keyword(s):

Gene Expression ◽

Retinoic Acid ◽

Gene Networks ◽

Large Scale ◽

Transforming Growth Factor ◽

Retinoic Acid Receptor ◽

Human Endometrium ◽

Acid Receptor ◽

Genome Wide ◽

The Impact

AbstractEndometrial mesenchymal stem cells (eMSC) drive the extraordinary regenerative capacity of the human endometrium. Clinical application of eMSC for therapeutic purposes is hampered by spontaneous differentiation and cellular senescence upon large-scale expansion in vitro. A83-01, a selective transforming growth factor-β receptor (TGFβ-R) inhibitor, promotes pharmacological expansion of eMSC in culture by blocking differentiation and senescence, but the underlying mechanisms are incompletely understood. In this study, we combined RNA-seq and ATAC-seq to study the impact of sustained TGFβ-R inhibition on gene expression and chromatin architecture of eMSC. Treatment of primary eMSC with A83-01 for 5 weeks resulted in differential expression of 1,463 genes. Gene ontology analysis showed enrichment of genes implicated in cell growth whereas extracellular matrix genes and genes involved in cell fate commitment were downregulated. ATAC-seq analysis demonstrated that sustained TGFβ-R inhibition results in opening and closure of 3,555 and 2,412 chromatin loci, respectively. Motif analysis revealed marked enrichment of retinoic acid receptor (RAR) binding sites, which was paralleled by the induction of RARB, encoding retinoic acid receptor beta (RARβ). Selective RARβ inhibition attenuated proliferation and clonogenicity of A83-01 treated eMSC. Taken together, our study provides new insights into the gene networks and genome-wide chromatin changes that underpin maintenance of an undifferentiated phenotype of eMSC in prolonged culture.Significance statementCycling human endometrium is a rich source of adult stem/progenitor cells that could be exploited for clinical purposes. Small molecules, such as A83-01, that modulate cell identity may open new avenues to maintain the functional properties of eMSC upon expansion in culture. By integrating complementary genome-wide profiling techniques, we mapped the dynamic changes in chromatin landscape and gene expression in response to prolonged A83-01 treatment of eMSC. Our findings provide new insights into the mechanisms of action of TGFβ-R inhibition that may lead to the development of more targeted pharmacological approaches for MSC expansion.

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Mercator: An R Package for Visualization of Distance Matrices

10.1101/733261 ◽

2019 ◽

Author(s):

Zachary B. Abrams ◽

Caitlin E. Coombes ◽

Suli Li ◽

Kevin R. Coombes

Keyword(s):

Large Scale ◽

R Package ◽

Supplementary Information ◽

Data Sets ◽

Data Types ◽

Vast Number ◽

Large Scale Data ◽

Scientific Disciplines ◽

Compare And Contrast ◽

Scale Data

AbstractSummaryUnsupervised data analysis in many scientific disciplines is based on calculating distances between observations and finding ways to visualize those distances. These kinds of unsupervised analyses help researchers uncover patterns in large-scale data sets. However, researchers can select from a vast number of different distance metrics, each designed to highlight different aspects of different data types. There are also numerous visualization methods with their own strengths and weaknesses. To help researchers perform unsupervised analyses, we developed the Mercator R package. Mercator enables users to see important patterns in their data by generating multiple visualizations using different standard algorithms, making it particularly easy to compare and contrast the results arising from different metrics. By allowing users to select the distance metric that best fits their needs, Mercator helps researchers perform unsupervised analyses that use pattern identification through computation and visual inspection.Availability and ImplementationMercator is freely available at the Comprehensive R Archive Network (https://cran.r-project.org/web/packages/Mercator/index.html)[email protected] informationSupplementary data are available at Bioinformatics online.

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