Sensitive clustering of protein sequences at tree-of-life scale using DIAMOND DeepClust

TY - UNPB

T1 - Sensitive clustering of protein sequences at tree-of-life scale using DIAMOND DeepClust

AU - Buchfink, Benjamin

AU - Ashkenazy, Haim

AU - Reuter, Klaus

AU - Kennedy, John A.

AU - Drost, Hajk-Georg

PY - 2023/2/7

Y1 - 2023/2/7

N2 - The biosphere genomics era is transforming life science research, but existing methods struggle to efficiently reduce the vast dimensionality of the protein universe. We present DIAMOND DeepClust, an ultra-fast cascaded clustering method optimized to cluster the 19 billion protein sequences currently defining the protein biosphere. As a result, we detect 1.7 billion clusters of which 32% hold more than one sequence. This means that 544 million clusters represent 94% of all known proteins, illustrating that clustering across the tree of life can significantly accelerate comparative studies in the Earth BioGenome era.

AB - The biosphere genomics era is transforming life science research, but existing methods struggle to efficiently reduce the vast dimensionality of the protein universe. We present DIAMOND DeepClust, an ultra-fast cascaded clustering method optimized to cluster the 19 billion protein sequences currently defining the protein biosphere. As a result, we detect 1.7 billion clusters of which 32% hold more than one sequence. This means that 544 million clusters represent 94% of all known proteins, illustrating that clustering across the tree of life can significantly accelerate comparative studies in the Earth BioGenome era.

U2 - 10.1101/2023.01.24.525373

DO - 10.1101/2023.01.24.525373

M3 - Preprint

BT - Sensitive clustering of protein sequences at tree-of-life scale using DIAMOND DeepClust

PB - BioRxiv

ER -