Lawrence Berkeley National Laboratory; University of California, Berkeley, United States of America
Identifying similar protein sequences is a core step in many computational biology pipelines such as detection of homologous protein sequences, generation of similarity protein graphs for downstream analysis, functional annotation and gene location. Performance and scalability of protein similarity searches have proven to be a bottleneck in many bioinformatics pipelines due to increases in cheap and abundant sequencing data. This work presents a new distributed-memory software, PASTIS. PASTIS relies on sparse matrix computations for efficient identification of possibly similar proteins. We use distributed sparse matrices for scalability and show that the sparse matrix infrastructure is a great fit for protein similarity searches when coupled with a fully-distributed dictionary of sequences that allows remote sequence requests to be fulfilled. Our algorithm incorporates the unique bias in amino acid sequence substitution in searches without altering the basic sparse matrix model, and in turn, achieves ideal scaling up to millions of protein sequences.