Dataset Information

DRfit: a Java tool for the analysis of discrete data from multi-well plate assays.

ABSTRACT: BACKGROUND:Analyses of replicates in sets of discrete data, typically acquired in multi-well plate formats, is a recurring task in many contemporary areas in the Life Sciences. The availability of accessible cross-platform data analysis tools for such fundamental tasks in varied projects and environments is an important prerequisite to ensuring a reliable and timely turnaround as well as to provide practical analytical tools for student training. RESULTS:We have developed an easy-to-use, interactive software tool for the analysis of multiple data sets comprising replicates of discrete bivariate data points. For each dataset, the software identifies the replicate data points from a defined matrix layout and calculates their means and standard errors. The averaged values are then automatically fitted using either a linear or a logistic dose response function. CONCLUSIONS:DRfit is a practical and convenient tool for the analysis of one or multiple sets of discrete data points acquired as replicates from multi-well plate assays. The design of the graphical user interface and the built-in analysis features make it a flexible and useful tool for a wide range of different assays.

SUBMITTER: Hofmann A

PROVIDER: S-EPMC6528253 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

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DRfit: a Java tool for the analysis of discrete data from multi-well plate assays.

Hofmann Andreas A Preston Sarah S Cross Megan M Herath H M P Dilrukshi HMPD Simon Anne A Gasser Robin B RB

BMC bioinformatics 20190521 1

<h4>Background</h4>Analyses of replicates in sets of discrete data, typically acquired in multi-well plate formats, is a recurring task in many contemporary areas in the Life Sciences. The availability of accessible cross-platform data analysis tools for such fundamental tasks in varied projects and environments is an important prerequisite to ensuring a reliable and timely turnaround as well as to provide practical analytical tools for student training.<h4>Results</h4>We have developed an easy- ...[more]

PMID: 31113359

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Project description:BackgroundLactobacillus species are used as probiotics and play an important role in fermented food production. However, use of 16S rRNA gene sequences as standard markers for the differentiation of Lactobacillus species offers a very limited scope, as several species of Lactobacillus share similar 16S rRNA gene sequences. In this study, we developed a rapid and accurate method based on comparative genomic analysis for the identification of 37 Lactobacillus species that are commonly used in probiotics and fermented foods.ResultsTo select species-specific sequences or genes, a total of 180 Lactobacillus genome sequences were compared using Python scripts. In 14 out of 37 species, species-specific sequences could not be found due to the similarity of the 16S-23S rRNA gene. Selected unique genes were obtained using comparative genomic analysis and all genes were confirmed to be specific for 52,478,804 genomes via in silico analysis; they were found not to be strain-specific, but to exist in all strains of the same species. Species-specific primer pairs were designed from the selected 16S-23S rRNA gene sequences or unique genes of species. The specificity of the species-specific primer pairs was confirmed using reference strains, and the accuracy and efficiency of the polymerase chain reaction (PCR) with the standard curve were confirmed. The PCR method developed in this study is able to accurately differentiate species that were not distinguishable using the 16S rRNA gene alone. This PCR assays were designed to detect and identify 37 Lactobacillus species. The developed method was then applied in the monitoring of 19 probiotics and 12 dairy products. The applied tests confirmed that the species detected in 17 products matched those indicated on their labels, whereas the remaining products contained species other than those appearing on the label.ConclusionsThe method developed in this study is able to rapidly and accurately distinguish different species of Lactobacillus, and can be used to monitor specific Lactobacillus species in foods such as probiotics and dairy products.

Project description:BackgroundDNA microarrays open up a new horizon for studying the genetic determinants of disease. The high throughput nature of these arrays creates an enormous wealth of information, but also poses a challenge to data analysis. Inferential problems become even more pronounced as experimental designs used to collect data become more complex. An important example is multigroup data collected over different experimental groups, such as data collected from distinct stages of a disease process. We have developed a method specifically addressing these issues termed Bayesian ANOVA for microarrays (BAM). The BAM approach uses a special inferential regularization known as spike-and-slab shrinkage that provides an optimal balance between total false detections and total false non-detections. This translates into more reproducible differential calls. Spike and slab shrinkage is a form of regularization achieved by using information across all genes and groups simultaneously.ResultsBAMarray is a graphically oriented Java-based software package that implements the BAM method for detecting differentially expressing genes in multigroup microarray experiments (up to 256 experimental groups can be analyzed). Drop-down menus allow the user to easily select between different models and to choose various run options. BAMarraycan also be operated in a fully automated mode with preselected run options. Tuning parameters have been preset at theoretically optimal values freeing the user from such specifications. BAMarray provides estimates for gene differential effects and automatically estimates data adaptive, optimal cutoff values for classifying genes into biological patterns of differential activity across experimental groups. A graphical suite is a core feature of the product and includes diagnostic plots for assessing model assumptions and interactive plots that enable tracking of prespecified gene lists to study such things as biological pathway perturbations. The user can zoom in and lasso genes of interest that can then be saved for downstream analyses.ConclusionBAMarray is user friendly platform independent software that effectively and efficiently implements the BAM methodology. Classifying patterns of differential activity is greatly facilitated by a data adaptive cutoff rule and a graphical suite. BAMarray is licensed software freely available to academic institutions. More information can be found at http://www.bamarray.com.

Dataset Information

DRfit: a Java tool for the analysis of discrete data from multi-well plate assays.

Publications

DRfit: a Java tool for the analysis of discrete data from multi-well plate assays.

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