![]() ![]() ![]() Read moreĪn important class of functions that arise in statistics and other areas are the log-concave functions. Related data, codes and software tools were accessible at the link ∼lg/clustering/. Our aim is to assist bioinformatics users in employing suitable clustering tools effectively to analyze big sequencing data. In this review, we selected several popular clustering tools, briefly explained the key computing principles, analyzed their characters and compared them using two independent benchmark datasets. Understanding the different clustering mechanisms is crucial to understanding the results that they produce. Different software tools can produce diverse results and users can find them difficult to analyze. However, there is often a gap between algorithm developers and bioinformatics users. ribosomal RNA operational taxonomic units are typically used. The challenge is to cluster the sequence data using stable, quick and accurate methods. The latest sequencing techniques have decreased costs and as a result, massive amounts of DNA/RNA sequences are being produced. ![]() Sequence clustering is a basic bioinformatics task that is attracting renewed attention with the development of metagenomics and microbiomics. ![]()
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