Sequence Clustering is a software tool that groups similar sequences based on their common features such as length, similarity, or structure. It is commonly used in bioinformatics and machine learning applications to analyze large datasets and identify patterns among sequences.
Currently, Sheafer provides two clustering methods - Diversity-Ambiguity Optimization (DAO) and Contrast Entropy Optimization (CEO). DAO can be further comprehended with DAO.pdf. It is worth mentioning that the software is built upon Boost C++ libraries, an expat XML parser, and EO Evolutionary Computation Framework.
What sets Sheafer apart from other clustering software is its accuracy, versatility, and speed. With its range of features and powerful algorithms, users can quickly obtain actionable insights into their protein sequence data.
Version 1.0 Alpha 1: N/A