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MorphoBank: phylophenomics in the "cloud".

Maureen A O'Leary1, Seth Kaufman2

  • 1Department of Anatomical Sciences, HSC T-8 (040), Stony Brook University, Stony Brook, NY 11794-8081, USA.

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Summary
This summary is machine-generated.

MorphoBank is a web application enabling real-time, collaborative construction of phenomic data matrices for phylogenetics. This cloud-based tool facilitates team-based research, integrating media and ontologies for large-scale species classification.

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Area of Science:

  • Systematic biology
  • Computational phylogenetics
  • Bioinformatics

Background:

  • Genomic data infrastructure for phylogenetics has advanced rapidly, but phenomic data tools lag.
  • Classifying Earth's millions of species requires large-scale, team-based phenomic data matrix construction.
  • Existing desktop software is inadequate for collaborative, media-rich phenomic data management.

Purpose of the Study:

  • To introduce MorphoBank, a web application and database for constructing phenomic data matrices.
  • To provide a cloud-based, collaborative platform for phylogenetic research.
  • To enable the integration and management of media and ontologies within phenomic datasets.

Main Methods:

  • Development of MorphoBank as a web application using software-as-a-service and cloud computing.
  • Implementation of features for real-time, team-based matrix construction.
  • Integration of tools for character and media ontology creation and visualization as directed acyclic graphs.

Main Results:

  • MorphoBank enables secure, real-time collaboration for matrix development with linked media.
  • The platform supports the creation of character and media ontologies, defining relationships between characters and media.
  • MorphoBank has facilitated a significant increase in phylophenomic data collection, exemplified by a large collaborative project.

Conclusions:

  • MorphoBank addresses the critical need for collaborative, web-based tools in phylophenomics.
  • The application enhances the ability to manage and analyze complex phenomic datasets, including media.
  • It supports large-scale systematic biology research by enabling efficient data sharing and matrix construction.