Clustering huge protein sequence sets in linear time

@inproceedings{Steinegger2018ClusteringHP,
  title={Clustering huge protein sequence sets in linear time},
  author={Martin Steinegger and Johannes S{\"o}ding},
  year={2018}
}
Metagenomic datasets contain billions of protein sequences that could greatly enhance large-scale functional annotation and structure prediction. Utilizing this enormous resource would require reducing its redundancy by similarity clustering. However, clustering hundreds of million of sequences is impractical using current algorithms because their runtimes scale as the input set size N times the number of clusters K, which is typically of similar order as N, resulting in runtimes that increase… CONTINUE READING

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