Dataset Information

CaveCrawler: an interactive analysis suite for cavefish bioinformatics.

ABSTRACT: The growing use of genomics in diverse organisms provides the basis for identifying genomic and transcriptional differences across species and experimental conditions. Databases containing genomic and functional data have played critical roles in the development of numerous genetic models but most emerging models lack such databases. The Mexican tetra, Astyanax mexicanus exists as 2 morphs: surface-dwelling and cave-dwelling. There exist at least 30 cave populations, providing a system to study convergent evolution. We have generated a web-based analysis suite that integrates datasets from different studies to identify how gene transcription and genetic markers of selection differ between populations and across experimental contexts. Results of diverse studies can be analyzed in conjunction with other genetic data (e.g. Gene Ontology information), to enable biological inference from cross-study patterns and identify future avenues of research. Furthermore, the framework that we have built for A. mexicanus can be adapted for other emerging model systems.

SUBMITTER: Perry A

PROVIDER: S-EPMC9339328 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature

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CaveCrawler: an interactive analysis suite for cavefish bioinformatics.

Perry Annabel A McGaugh Suzanne E SE Keene Alex C AC Blackmon Heath H

G3 (Bethesda, Md.) 20220701 8

The growing use of genomics in diverse organisms provides the basis for identifying genomic and transcriptional differences across species and experimental conditions. Databases containing genomic and functional data have played critical roles in the development of numerous genetic models but most emerging models lack such databases. The Mexican tetra, Astyanax mexicanus exists as 2 morphs: surface-dwelling and cave-dwelling. There exist at least 30 cave populations, providing a system to study ...[more]

PMID: 35708643

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Project description:BackgroundHigh-throughput transcriptomic datasets are often examined to discover new actors and regulators of a biological response. To this end, graphical interfaces have been developed and allow a broad range of users to conduct standard analyses from RNA-seq data, even with little programming experience. Although existing solutions usually provide adequate procedures for normalization, exploration or differential expression, more advanced features, such as gene clustering or regulatory network inference, often miss or do not reflect current state of the art methodologies.ResultsWe developed here a user interface called DIANE (Dashboard for the Inference and Analysis of Networks from Expression data) designed to harness the potential of multi-factorial expression datasets from any organisms through a precise set of methods. DIANE interactive workflow provides normalization, dimensionality reduction, differential expression and ontology enrichment. Gene clustering can be performed and explored via configurable Mixture Models, and Random Forests are used to infer gene regulatory networks. DIANE also includes a novel procedure to assess the statistical significance of regulator-target influence measures based on permutations for Random Forest importance metrics. All along the pipeline, session reports and results can be downloaded to ensure clear and reproducible analyses.ConclusionsWe demonstrate the value and the benefits of DIANE using a recently published data set describing the transcriptional response of Arabidopsis thaliana under the combination of temperature, drought and salinity perturbations. We show that DIANE can intuitively carry out informative exploration and statistical procedures with RNA-Seq data, perform model based gene expression profiles clustering and go further into gene network reconstruction, providing relevant candidate genes or signalling pathways to explore. DIANE is available as a web service ( https://diane.bpmp.inrae.fr ), or can be installed and locally launched as a complete R package.

Dataset Information

CaveCrawler: an interactive analysis suite for cavefish bioinformatics.

Publications

CaveCrawler: an interactive analysis suite for cavefish bioinformatics.

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