A modified GC-specific MAKER gene annotation method reveals improved and novel gene predictions of high and low GC content in Oryza sativa

摘要

BackgroundAccurate structural annotation depends on well-trained gene prediction programs. Training data for gene prediction programs are often chosen randomly from a subset of high-quality genes that ideally represent the variation found within a genome. One aspect of gene variation is GC content, which differs across species and is bimodal in grass genomes. When gene prediction programs are trained on a subset of grass genes with random GC content, they are effectively being trained on two classes of genes at once, and this can be expected to result in poor results when genes are predicted in new genome sequences.