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258: Correcting GC bias in metagenomes

258: Correcting GC bias in metagenomes

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Holcik L et al., Genomic GC bias correction improves species abundance estimation from metagenomic data. Nature Communications - GuaCAMOLE is an alignment-free algorithm that estimates and removes genomic GC-content-dependent sequencing bias to produce more accurate species abundance estimates from single metagenomic samples. Key terms: GC bias, metagenomics, species abundance, GuaCAMOLE, colorectal cancer.

Study Highlights:
GuaCAMOLE combines Kraken2/Bracken read assignment with per-taxon GC binning and a regularized least-squares estimator to infer GC-dependent sequencing efficiencies and bias-corrected abundances from a single sample. On simulations and mock communities across 28 library protocols it produced near-unbiased estimates and outperformed Bracken and MetaPhlAn4 when GC bias was present. Application to 3,435 gut microbiomes from 33 colorectal cancer studies revealed four distinct protocol-specific GC-bias shapes and systematic underestimation of GC-poor taxa. The tool also filters false-positive taxa by comparing observed and expected GC distributions and can apply inferred efficiencies to correct other tools' outputs.

Conclusion:
Per-sample GC-bias correction with GuaCAMOLE improves accuracy and comparability of metagenomic species abundance estimates across diverse protocols

Music:
Enjoy the music based on this article at the end of the episode.

Reference:
Holcik L., von Haeseler A., Pflug F. G. Genomic GC bias correction improves species abundance estimation from metagenomic data. Nature Communications. 2025;16:10523. https://doi.org/10.1038/s41467-025-65530-4

License:
This episode is based on an open-access article published under the Creative Commons Attribution 4.0 International License (CC BY 4.0) – https://creativecommons.org/licenses/by/4.0/

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Episode Slug: gc-bias-correction-metagenomics

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