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Statistical Package for Growth Rates Made Easy.

ABSTRACT: Growth rates are an important tool in microbiology because they provide high throughput fitness measurements. The release of GrowthRates, a program that uses the output of plate reader files to automatically calculate growth rates, has facilitated experimental procedures in many areas. However, many sources of variation within replicate growth rate data exist and can decrease data reliability. We have developed a new statistical package, CompareGrowthRates (CGR), to enhance the program GrowthRates and accurately measure variation in growth rate data sets. We define a metric, Variability-score (V-score), that can help determine if variation within a data set might result in false interpretations. CGR also uses the bootstrap method to determine the fraction of bootstrap replicates in which a strain will grow the fastest. We illustrate the usage of CGR with growth rate data sets similar to those in Mira, Meza, et al. (Adaptive landscapes of resistance genes change as antibiotic concentrations change. Mol Biol Evol. 32(10): 2707-2715). These statistical methods are compatible with the analytic methods described in Growth Rates Made Easy and can be used with any set of growth rate output from GrowthRates.

SUBMITTER: Mira P

PROVIDER: S-EPMC5850790 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature

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Statistical Package for Growth Rates Made Easy.

Mira Portia P Barlow Miriam M Meza Juan C JC Hall Barry G BG

Molecular biology and evolution 20171201 12

Growth rates are an important tool in microbiology because they provide high throughput fitness measurements. The release of GrowthRates, a program that uses the output of plate reader files to automatically calculate growth rates, has facilitated experimental procedures in many areas. However, many sources of variation within replicate growth rate data exist and can decrease data reliability. We have developed a new statistical package, CompareGrowthRates (CGR), to enhance the program GrowthRat ...[more]

PMID: 29029174

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Project description:The analysis and comparison of genomes rely on diﬀerent tools for tasks such as annotation, orthology prediction, and phylogenetic inference. Most tools are specialized for a single task, and additional eﬀorts are necessary to integrate and visualize the results. To ﬁll this gap, we developed zDB, an application integrating a Nextﬂow analysis pipeline and a Python visualization platform built on the Django framework. The application is available on GitHub (https://github.com/metagenlab/zDB) and from the bioconda channel. Starting from annotated Genbank ﬁles, zDB identiﬁes orthologs and infers a phylogeny for each orthogroup. A species phylogeny is also constructed from shared single-copy orthologs. The results can be enriched with Pfam protein domain prediction, Cluster of Orthologs Genes and Kyoto Encyclopedia of Genes and Genomes annotations, and Swissprot homologs. The web application allows searching for speciﬁc genes or annotations, running Blast queries, and comparing genomic regions and whole genomes. The metabolic capacities of organisms can be compared at either the module or pathway levels. Finally, users can run queries to examine the conservation of speciﬁc genes or annotations across a chosen subset of genomes and display the results as a list of genes, Venn diagram, or heatmaps. Those features make zDB useful for both bioinformaticians and researchers more accustomed to laboratory research.IMPORTANCEGenome comparison and analysis rely on many independent tools, leaving to scientists the burden to integrate and visualize their results for interpretation. To alleviate this burden, we have built zDB, a comparative genomics tool that includes both an analysis pipeline and a visualization platform. The analysis pipeline automates gene annotation, orthology prediction, and phylogenetic inference, while the visualization platform allows scientists to easily explore the results in a web browser. Among other features, the interface allows users to visually compare whole genomes and targeted regions, assess the conservation of genes or metabolic pathways, perform Blast searches, or look for speciﬁc annotations. Altogether, this tool will be useful for a broad range of applications in comparative studies between two and hundred genomes. Furthermore, it is designed to allow sharing of data sets easily at a local or international scale, thereby supporting exploratory analyses for non-bioinformaticians on the genome of their favorite organisms.

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Statistical Package for Growth Rates Made Easy.

Publications

Statistical Package for Growth Rates Made Easy.

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