BMDx: a graphical Shiny application to perform Benchmark Dose analysis for transcriptomics data

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

Motivation

The analysis of dose-dependent effects on the gene expression is gaining attention in the field of toxicogenomics. Currently available computational methods are usually limited to specific omics platforms or biological annotations and are able to analyse only one experiment at a time.

Results

We developed the software BMDx with a graphical user interface for the Benchmark Dose (BMD) analysis of transcriptomics data. We implemented an approach based on the fitting of multiple models and the selection of the optimal model based on the Akaike Information Criterion. The BMDx tool takes as an input a gene expression matrix and a phenotype table, computes the BMD, its related values, and IC50/EC50 estimations. It reports interactive tables and plots that the user can investigate for further details of the fitting, dose effects and functional enrichment. BMDx allows a fast and convenient comparison of the BMD values of a transcriptomics experiment at different time points and an effortless way to interpret the results. Furthermore, BMDx allows to analyse and to compare multiple experiments at once.

Availability and implementation

BMDx is implemented as an R/Shiny software and is available at https://github.com/Greco-Lab/BMDx/.

Supplementary information

Supplementary data are available at Bioinformatics online.

Related collections

Most cited references 5

Record: found
Abstract: found
Article: not found

Introduction to benchmark dose methods and U.S. EPA's benchmark dose software (BMDS) version 2.1.1.

J Allen Davis, Jeffrey S. Gift, Q Zhao (2011)

Traditionally, the No-Observed-Adverse-Effect-Level (NOAEL) approach has been used to determine the point of departure (POD) from animal toxicology data for use in human health risk assessments. However, this approach is subject to substantial limitations that have been well defined, such as strict dependence on the dose selection, dose spacing, and sample size of the study from which the critical effect has been identified. Also, the NOAEL approach fails to take into consideration the shape of the dose-response curve and other related information. The benchmark dose (BMD) method, originally proposed as an alternative to the NOAEL methodology in the 1980s, addresses many of the limitations of the NOAEL method. It is less dependent on dose selection and spacing, and it takes into account the shape of the dose-response curve. In addition, the estimation of a BMD 95% lower bound confidence limit (BMDL) results in a POD that appropriately accounts for study quality (i.e., sample size). With the recent advent of user-friendly BMD software programs, including the U.S. Environmental Protection Agency's (U.S. EPA) Benchmark Dose Software (BMDS), BMD has become the method of choice for many health organizations world-wide. This paper discusses the BMD methods and corresponding software (i.e., BMDS version 2.1.1) that have been developed by the U.S. EPA, and includes a comparison with recently released European Food Safety Authority (EFSA) BMD guidance.

0 comments Cited 50 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

Benchmark dose (BMD) modeling: current practice, issues, and challenges.

Lynne T Haber, Michael L Dourson, Bruce Allen … (2018)

Benchmark dose (BMD) modeling is now the state of the science for determining the point of departure for risk assessment. Key advantages include the fact that the modeling takes account of all of the data for a particular effect from a particular experiment, increased consistency, and better accounting for statistical uncertainties. Despite these strong advantages, disagreements remain as to several specific aspects of the modeling, including differences in the recommendations of the US Environmental Protection Agency (US EPA) and the European Food Safety Authority (EFSA). Differences exist in the choice of the benchmark response (BMR) for continuous data, the use of unrestricted models, and the mathematical models used; these can lead to differences in the final BMDL. It is important to take confidence in the model into account in choosing the BMDL, rather than simply choosing the lowest value. The field is moving in the direction of model averaging, which will avoid many of the challenges of choosing a single best model when the underlying biology does not suggest one, but additional research would be useful into methods of incorporating biological considerations into the weights used in the averaging. Additional research is also needed regarding the interplay between the BMR and the UF to ensure appropriate use for studies supporting a lower BMR than default values, such as for epidemiology data. Addressing these issues will aid in harmonizing methods and moving the field of risk assessment forward.

0 comments Cited 47 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: found

Is Open Access

BMDExpress: a software tool for the benchmark dose analyses of genomic data

Longlong Yang, Bruce Allen, Russell S. Thomas (2007)

Background Dose-dependent processes are common within biological systems and include phenotypic changes following exposures to both endogenous and xenobiotic molecules. The use of microarray technology to explore the molecular signals that underlie these dose-dependent processes has become increasingly common; however, the number of software tools for quantitatively analyzing and interpreting dose-response microarray data has been limited. Results We have developed BMDExpress, a Java application that combines traditional benchmark dose methods with gene ontology classification in the analysis of dose-response data from microarray experiments. The software application is designed to perform a stepwise analysis beginning with a one-way analysis of variance to identify the subset of genes that demonstrate significant dose-response behavior. The second step of the analysis involves fitting the gene expression data to a selection of standard statistical models (linear, 2° polynomial, 3° polynomial, and power models) and selecting the model that best describes the data with the least amount of complexity. The model is then used to estimate the benchmark dose at which the expression of the gene significantly deviates from that observed in control animals. Finally, the software application summarizes the statistical modeling results by matching each gene to its corresponding gene ontology categories and calculating summary values that characterize the dose-dependent behavior for each biological process and molecular function. As a result, the summary values represent the dose levels at which genes in the corresponding cellular process show transcriptional changes. Conclusion The application of microarray technology together with the BMDExpress software tool represents a useful combination in characterizing dose-dependent transcriptional changes in biological systems. The software allows users to efficiently analyze large dose-response microarray studies and identify reference doses at which particular cellular processes are altered. The software is freely available at and is distributed under the MIT Public License.

0 comments Cited 47 times – based on 0 reviews      Review now

Bookmark

All references

Author and article information

Journal

Title: Bioinformatics

Publisher: Oxford University Press (OUP)

ISSN (Print): 1367-4803

ISSN (Electronic): 1460-2059

Publication date Created: May 01 2020

Publication date Created: January 17 2020

Publication date Other: May 01 2020

Publication date (Print): May 01 2020

Publication date (Electronic): January 17 2020

Volume: 36

Issue: 9

Pages: 2932-2933

Affiliations

[1 ]Faculty of Medicine and Health Technology, Tampere University, Tampere 33100, Finland

[2 ]BioMediTech Institute, Tampere University, Tampere 33100, Finland

[3 ]Institute of Biotechnology, University of Helsinki, Helsinki 00014, Finland

Article

DOI: 10.1093/bioinformatics/btaa030

PubMed ID: 31950985

SO-VID: bc2a88eb-5e4b-4c1e-a33e-5e5127fc918a

License:

https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model

BMDx: a graphical Shiny application to perform Benchmark Dose analysis for transcriptomics data

Read this article at

Abstract

Motivation

Results

Availability and implementation

Supplementary information

Related collections

Software for SAXS correction and analysis

Most cited references 5

Introduction to benchmark dose methods and U.S. EPA's benchmark dose software (BMDS) version 2.1.1.

Benchmark dose (BMD) modeling: current practice, issues, and challenges.

BMDExpress: a software tool for the benchmark dose analyses of genomic data

Author and article information

Journal

Affiliations

Article

History

Comments

Comment on this article

Similar content 149

Cited by 10

Most referenced authors 54