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

Background: Accurate 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. Results: We find that gene prediction programs trained on grass genes with random GC content do not completely predict all grass genes with extreme GC content. We show that gene prediction programs that are trained with grass genes with high or low GC content can make both better and unique gene predictions compared to gene prediction programs that are trained on genes with random GC content. By separately training gene prediction programs with genes from multiple GC ranges and using the programs within the MAKER genome annotation pipeline, we were able to improve the annotation of the Oryza sativa genome compared to using the standard MAKER annotation protocol. Gene structure was improved in over 13% of genes, and 651 novel genes were predicted by the GC-specific MAKER protocol. Conclusions: We present a new GC-specific MAKER annotation protocol to predict new and improved gene models and assess the biological significance of this method in Oryza sativa. We expect that this protocol will also be beneficial for gene prediction in any organism with bimodal or other unusual gene GC content.6hmms_makerStandard_noTEs.gff3Genome annotation for Six HMMs gene predictions in rice.6hmms_makerStandard_noTEs_proteins.fastaPredicted proteins from the Six HMMs gene predictions in rice.6hmms_makerStandard_noTEs_transcripts.fastaPredicted transcripts from Six HMMs gene predictions in rice.novel_high_maker_standard_genes.gff3Gene annotations of novel genes predicted by the high GC HMM in rice.novel_high_maker_standard_proteins.fastaPredicted proteins of novel genes predicted by the high GC HMM in rice.novel_high_maker_standard_transcripts.fastaPredicted transcripts of novel genes predicted by the high GC HMM in rice.novel_low_maker_standard_genes.gff3Gene annotations of novel genes predicted by the low GC HMM in rice.novel_low_maker_standard_proteins.fastaPredicted proteins of novel genes predicted by the low GC HMM in rice.novel_low_maker_standard_transcripts.fastaPredicted transcripts of novel genes predicted by the low GC HMM in rice.md5_valuesThe md5 values for the files in this data submission.Funding provided by: National Science FoundationCrossref Funder Registry ID: http://dx.doi.org/10.13039/100000001Award Number: IOS-112699