Genomic GC bias correction improves species abundance estimation from metagenomic data.

ABSTRACT: Metagenomic sequencing measures the species composition of microbial communities and has revealed the crucial role of microbiomes in the etiology of a range of diseases such as colorectal cancer. Quantitative comparisons of microbial communities are, however, affected by GC-content-dependent biases. Here, we present GuaCAMOLE, a computational method to detect and remove GC bias from metagenomic sequencing data. The algorithm relies on comparisons between individual species in a single sample to estimate the sequencing efficiency at levels of GC content, and outputs unbiased species abundances. GuaCAMOLE thus works regardless of the specific amount or direction of GC-bias present in the data and does not rely on calibration experiments or multiple samples. Applying our algorithm to 3435 gut

SUBMITTER: Holcik L 

PROVIDER: S-EPMC12658245 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature