Distributed Many-to-Many Protein Sequence Alignment using Sparse Matrices

TL;DR

This paper introduces PASTIS, a distributed software leveraging sparse matrix computations to efficiently perform large-scale protein sequence similarity searches, addressing scalability bottlenecks in bioinformatics pipelines.

Contribution

The work presents a novel distributed-memory approach using sparse matrices for scalable protein similarity searches, incorporating amino acid substitution biases without changing the core model.

Findings

Achieves ideal scaling up to millions of protein sequences

Utilizes distributed sparse matrices for efficient computation

Incorporates amino acid substitution bias effectively

Abstract

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.