SwiftOrtho: A fast, memory-efficient, multiple genome orthology classifier.

Xiao Hu,Iddo Friedberg

doi:10.1093/gigascience/giz118

Abstract

BackgroundGene homology type classification is required for many types of genome analyses, including comparative genomics, phylogenetics, and protein function annotation. Consequently, a large variety of tools have been developed to perform homology classification across genomes of different species. However, when applied to large genomic data sets, these tools require high memory and CPU usage, typically available only in computational clusters.FindingsHere we present a new graph-based orthology analysis tool, SwiftOrtho, which is optimized for speed and memory usage when applied to large-scale data. SwiftOrtho uses long k-mers to speed up homology search, while using a reduced amino acid alphabet and spaced seeds to compensate for the loss of sensitivity due to long k-mers. In addition, it uses an affinity propagation algorithm to reduce the memory usage when clustering large-scale orthology relationships into orthologous groups. In our tests, SwiftOrtho was the only tool that completed orthology analysis of proteins from 1,760 bacterial genomes on a computer with only 4 GB RAM. Using various standard orthology data sets, we also show that SwiftOrtho has a high accuracy.ConclusionsSwiftOrtho enables the accurate comparative genomic analyses of thousands of genomes using low-memory computers. SwiftOrtho is available at https://github.com/Rinoahu/SwiftOrtho

Highlights

Gene homology type classification is a requisite for many types of genome analyses, including comparative genomics, phylogenetics, and protein function annotation
The orthology analysis consists of homology search, orthology inference, and cluster analysis
The results show that for most tools replacing BLASTP with SwiftOrtho’s built-in homology search module does not significantly reduce the recall (Figure 4)

Summary

Introduction

Gene homology type classification is a requisite for many types of genome analyses, including comparative genomics, phylogenetics, and protein function annotation. When applied to large genomic datasets, these tools require high memory and CPU usage, typically available only in costly computational clusters. To address this problem, we developed a new graph-based orthology analysis tool, SwiftOrtho, which is optimized for speed and memory usage when applied to large-scale data. While the proper inference of homology type involves tracing gene history using phylogenetic trees [1], several proxy methods have been developed over the years. RBH states the following: when two proteins that are encoded by two genes, each in a different genome, find each other as the best scoring match, they are considered to be orthologs [2, 3]

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Conclusion