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Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group
Published on: August 16, 2017
Protein sequence redundancy reduction: comparison of various method
Bioinformation
|March 3, 2011
Summary
Creating non-redundant protein datasets is crucial in bioinformatics. This study compares popular tools, finding moderate similarity between different programs, advising the use of multiple applications for robust results.
Area of Science:
- Bioinformatics
- Computational Biology
- Proteomics
Background:
- Non-redundant protein datasets are essential for various bioinformatics applications, requiring the removal of highly similar sequences.
- Several computational tools exist for generating non-redundant protein datasets, including 'Decrease redundancy', 'cd-hit', 'Pisces', 'BlastClust', and 'SkipRedundant'.
- The degree of similarity between non-redundant datasets produced by different tools remains an important consideration.
Purpose of the Study:
- To systematically compare the outputs of different non-redundant protein dataset generation programs.
- To assess the extent of similarity between non-redundant datasets created by various bioinformatics tools using identical input data and similarity thresholds.
Main Methods:
- Utilized subsets of the UniProt database as input for multiple redundancy reduction programs.
- Performed a systematic comparison of the features and outputs generated by 'Decrease redundancy', 'cd-hit', 'Pisces', 'BlastClust', and 'SkipRedundant'.
- Analyzed the overlap and similarity levels between the resulting non-redundant datasets based on varying identity thresholds.
Main Results:
- High overlap was observed between non-redundant datasets generated by the same program with different identity thresholds.
- Moderate similarity was found between non-redundant datasets produced by different programs using the same identity threshold.
- The choice of program significantly influences the composition of the final non-redundant protein dataset.
Conclusions:
- Non-redundant protein dataset generation tools exhibit variability in their output.
- Users should be aware of potential differences in datasets produced by distinct software.
- Employing multiple computational applications is recommended to ensure comprehensive and reliable non-redundant protein datasets.
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