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# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!

cff-version: 1.2.0
title: LRGE
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: Michael
    name-particle: B
    family-names: Hall
    affiliation: >-
      Department of Microbiology and Immunology, The
      University of Melbourne, at the Peter Doherty
      Institute for Infection and Immunity, Melbourne,
      Australia
    orcid: 'https://orcid.org/0000-0003-3683-6208'
  - given-names: Chenxi
    family-names: Zhou
    affiliation: >-
      Department of Genetics, The University of Cambridge,
      Cambridge, United Kingdom
    orcid: 'https://orcid.org/0000-0002-1735-2630'
  - given-names: Lachlan
    name-particle: JM
    family-names: Coin
    affiliation: >-
      Department of Microbiology and Immunology, The
      University of Melbourne, at the Peter Doherty
      Institute for Infection and Immunity, Melbourne,
      Australia
    orcid: 'https://orcid.org/0000-0002-4300-455X'
identifiers:
  - type: doi
    value: 10.1093/bioinformatics/btaf593
    description: Journal article describing the work
repository-code: 'https://github.com/mbhall88/lrge'
abstract: >-
  Motivation


  Accurate genome size estimation is an important component
  of genomic analyses such as assembly and coverage
  calculation, though existing tools are primarily optimized
  for short-read data.

  Results


  We present LRGE, a novel tool that uses read-to-read
  overlap information to estimate genome size in a
  reference-free manner. LRGE calculates per-read genome
  size estimates by analysing the expected number of
  overlaps for each read, considering read lengths and a
  minimum overlap threshold. The final size is taken as the
  median of these estimates, ensuring robustness to outliers
  such as reads with no overlaps. Additionally, LRGE
  provides an expected confidence range for the estimate. We
  validate LRGE on a large, diverse bacterial dataset and
  confirm it generalizes to eukaryotic datasets. On
  bacterial genomes, LRGE outperforms k-mer-based methods in
  both accuracy and computational efficiency and produces
  genome size estimates comparable to those from
  assembly-based approaches, like Raven, while using
  significantly less computational resources.

  Availability and implementation


  Our method, LRGE (Long Read-based Genome size Estimation
  from overlaps), is implemented in Rust and is available as
  a precompiled binary for most architectures, a Bioconda
  package, a prebuilt container image, and a crates.io
  package as a binary (lrge) or library (liblrge). The
  source code is available at
  https://github.com/mbhall88/lrge and an archive at
  https://doi.org/10.5281/zenodo.17183812 under an MIT
  license.
keywords:
  - bioinformatics
  - genome size
  - estimation
  - overlaps
license: MIT
preferred-citation:
  type: article
  authors:
    - given-names: Michael
      name-particle: B
      family-names: Hall
      affiliation: >-
        Department of Microbiology and Immunology, The
        University of Melbourne, at the Peter Doherty
        Institute for Infection and Immunity, Melbourne,
        Australia
      orcid: 'https://orcid.org/0000-0003-3683-6208'
    - given-names: Chenxi
      family-names: Zhou
      affiliation: >-
        Department of Genetics, The University of Cambridge,
        Cambridge, United Kingdom
      orcid: 'https://orcid.org/0000-0002-1735-2630'
    - given-names: Lachlan
      name-particle: JM
      family-names: Coin
      affiliation: >-
        Department of Microbiology and Immunology, The
        University of Melbourne, at the Peter Doherty
        Institute for Infection and Immunity, Melbourne,
        Australia
      orcid: 'https://orcid.org/0000-0002-4300-455X'
  doi: "10.1093/bioinformatics/btaf593"
  journal: "Bioinformatics"
  month: 11
  start: btaf593
  title: "Genome size estimation from long read overlaps"
  issue: 11
  volume: 41
  year: 2025