Mapping-based Genome Size Estimation

Mapping-based Genome Size Estimation maps sequencing reads to an existing genome assembly and uses coverage-based analysis to estimate genome size.

Key Features:

• Assembly-based read mapping: Maps short or long reads onto a high-contiguity genome assembly to relate mapped coverage to genomic copy number.
• Coverage-based inference: Uses coverage (sequencing depth) of mapped reads to reveal true copy number of sequences that may be collapsed in assemblies.
• Low-complexity collapse awareness: Accounts for assembly collapse of low-complexity sequences by using mapped-read coverage to adjust size estimates.
• Minimal coverage requirement: Produces reliable estimates with minimal sequencing coverage, specifically around 5-fold coverage.
• Read type compatibility: Operates with both short reads and long reads.
• Cross-dataset validation: Has been validated across diverse biological datasets beyond plant genomes.

Scientific Applications:

• Plant genome size estimation: Applied to model and crop plants including Arabidopsis thaliana, Beta vulgaris, Oryza sativa, Brachypodium distachyon, Solanum lycopersicum, Vitis vinifera, and Zea mays.
• Non-plant genome size estimation: Applied to non-plant genomes such as Escherichia coli, Saccharomyces cerevisiae, and Caenorhabditis elegans.
• Cross-taxa genome analyses: Enables genome size estimation across diverse taxa and datasets by leveraging mapped-read coverage against assemblies.

Methodology:

Map short or long reads to a high-contiguity assembly and analyze coverage (sequencing depth) of mapped reads to infer sequence copy number and calculate an estimated genome size, with reliable results achievable at ~5× coverage.