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

  1. Xiao Hu
  2. Iddo Friedberg

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Abstract

Background

Gene 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.

Findings

Here 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.

Conclusions

SwiftOrtho enables the accurate comparative genomic analyses of thousands of genomes using low-memory computers. SwiftOrtho is available at https://github.com/Rinoahu/SwiftOrtho

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  1. GigaScience

    Now published in GigaScience doi: 10.1093/gigascience/giz118

    Xiao Hu Find this author on Google ScholarFind this author on PubMedSearch for this author on this site

    A version of this preprint has been published in the Open Access journal GigaScience (see paper https://doi.org/10.1093/gigascience/giz118 ), where the paper and peer reviews are published openly under a CC-BY 4.0 license.

    These peer reviews were as follows:

    Reviewer 1: http://dx.doi.org/10.5524/REVIEW.101954 Reviewer 2: http://dx.doi.org/10.5524/REVIEW.101955 Reviewer 3: http://dx.doi.org/10.5524/REVIEW.101956

  2. Version published to 10.1093/gigascience/giz118
  3. Version published to 10.1101/543223v2 on bioRxiv
  4. Version published to 10.1101/543223v1 on bioRxiv