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UID:submissions.supercomputing.org_SC20_sess157_pap572@linklings.com
SUMMARY:Distributed Many-to-Many Protein Sequence Alignment using Sparse M
 atrices
DESCRIPTION:Paper\n\nDistributed Many-to-Many Protein Sequence Alignment u
 sing Sparse Matrices\n\nSelvitopi, Ekanayake, Guidi, Pavlopoulos, Azad...\
 n\nIdentifying similar protein sequences is a core step in many computatio
 nal biology pipelines such as detection of homologous protein sequences, g
 eneration of similarity protein graphs for downstream analysis, functional
  annotation and gene location. Performance and scalability of protein simi
 larity searches have proven to be a bottleneck in many bioinformatics pipe
 lines due to increases in cheap and abundant sequencing data. This work pr
 esents a new distributed-memory software, PASTIS. PASTIS relies on sparse 
 matrix computations for efficient identification of possibly similar prote
 ins. We use distributed sparse matrices for scalability and show that the 
 sparse matrix infrastructure is a great fit for protein similarity searche
 s when coupled with a fully-distributed dictionary of sequences that allow
 s remote sequence requests to be fulfilled. Our algorithm incorporates the
  unique bias in amino acid sequence substitution in searches without alter
 ing the basic sparse matrix model, and in turn, achieves ideal scaling up 
 to millions of protein sequences.\n\nTag: Applications, Linear Algebra, Sc
 alable Computing\n\nRegistration Category: Tech Program Reg Pass
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