Related Experiment Video
Updated: Apr 25, 2026
09:43
Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
Published on: November 22, 2019
5.7K
Extracting patterns of database and software usage from the bioinformatics literature.
Geraint Duck1, Goran Nenadic2, Andy Brass2
1School of Computer Science, Manchester Institute of Biotechnology and Computational and Evolutionary Biology, Faculty of Life Sciences, The University of Manchester, Manchester M13 9PL, UK.
Bioinformatics (Oxford, England)
|August 28, 2014
Summary
Bioinformatics resource usage patterns were analyzed by constructing networks of co-occurring databases and software. This reveals community practices and emerging tools in phylogenetics, aiding in identifying scientific best practices.
Area of Science:
- Bioinformatics
- Computational Biology
- Software Engineering
Background:
- Bioinformatics, a computer-based discipline, faces rapid growth in databases and software resources.
- Understanding usage patterns is crucial for tracking developments and community practices in bioinformatics.
- Analyzing resource interconnections can highlight software longevity and the emergence of new tools.
Purpose of the Study:
- To audit bioinformatics database and software usage patterns.
- To provide an overview of developments and common practices within the bioinformatics community.
- To identify scientific best practices through resource co-occurrence analysis.
Main Methods:
- Constructed networks based on the co-occurrence of bioinformatics databases and software.
- Applied network analysis to pairings of phylogenetics software reported in scientific literature.
- Utilized extracted resource data and network generation scripts for analysis.
Main Results:
- Developed networks representing snapshots of common practice in the bioinformatics community.
- Demonstrated the application of this approach to phylogenetics software.
- Identified potential for this method to serve as a basis for identifying scientific best practices.
Conclusions:
- Network analysis of resource co-occurrence provides insights into bioinformatics community practices.
- This methodology can help track the evolution and adoption of bioinformatics tools.
- The approach offers a pathway towards defining scientific best practices in bioinformatics.
