In metagenomics datasets, it is standard practice to correct samples for (a) differences in sequencing effort (library size) and (b) normalise gene counts based on the total annotated hits per sample to obtain relative abundances
However, most databases on functional genes such as SEED or KEGG are biased, such that genes involved in central metabolism are better annotated. Hence, categories such as Carbohydrate metabolism and protein synthesis often dominate function profiles as result of this bias. Most articles do not correct for this database bias.
What are the common ways of accounting for this bias?