Abstract
GuaCAMOLE is a novel computational method which detects and removes GC bias from metagenomic sequencing data. 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. GuaCAMOLE works regardless of the specific amount or direction of GC-bias present in the data and requires only a single sample. The algorithm reports unbiased abundances and quantifies the amount of bias present in terms of GC-depdendent sequencing efficiencies. Experimental mock community data confirms both estimates to be accurate across a wide range of experimental protocols. In gut microbiomes of colorectal cancer patients we observe a clear bias against GC-poor species in the abundances reported by existing methods. GuaCAMOLE successfully removes this bias and corrects the abundance of clinically relevant GC-poor species such as F. nucleatum (28% GC) by up to a factor of two. GuaCAMOLE thus contributes to a better quantitative understanding of microbial communities by improving the accuracy and comparability of species abundances across experimental setups.
Competing Interest Statement
The authors have declared no competing interest.
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