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The rapidly growing number of sequenced genomes requires fully automated methods for accurate gene structure annotation. With this goal in mind, we have developed BRAKER1 [1], a combination of GeneMark-ET [2] and AUGUSTUS [3], that uses genomic and RNA-Seq data to automatically generate full gene structure annotations in novel genomes.
However, the quality of RNA-Seq data that is available for annotating a novel genome is variable, and in some cases, RNA-Seq data is not available, at all.
BRAKER2 is an extension of BRAKER1 which allows for fully automated training of the gene prediction tools GeneMark-EX [2,4,5] (EX = ES/ET/EP/ETP, all available for download under the name {GeneMark-ES/ET} and AUGUSTUS from RNA-Seq and/or protein homology information, and that integrates the extrinsic evidence from RNA-Seq and protein homology information into the prediction.
In contrast to other available methods that rely on protein homology information, BRAKER2 reaches high gene prediction accuracy even in the absence of the annotation of very closely related species and in the absence of RNA-Seq data.
BRAKER2 can also combine RNA-Seq and protein homology information.
[1] Hoff, K. J., Lange, S., Lomsadze, A., Borodovsky, M., and Stanke, M. (2015). BRAKER1: unsupervised RNA-Seq-based genome annotation with GeneMark-et and AUGUSTUS. Bioinformatics, 32(5):767-769.
[2] Lomsadze, A., Burns, P. D., and Borodovsky, M. (2014). Integration of mapped RNA-Seq reads into automatic training of eukaryotic gene finding algorithm. Nucleic Acids Research, 42(15):e119-e119.
[3] Stanke, M., Diekhans, M., Baertsch, R., and Haussler, D. (2008). Using native and syntenically mapped cDNA alignments to improve de novo gene finding. Bioinformatics, 24(5):637-644.
[4] Lomsadze, A., Ter-Hovhannisyan, V., Chernoff, Y.O. and Borodovsky, M. (2005). Gene identification in novel eukaryotic genomes by self-training algorithm. Nucleic Acids Research, 33(20):6494-6506.
[5] Ter-Hovhannisyan, V., Lomsadze, A., Chernoff, Y.O. and Borodovsky, M. (2008). Gene prediction in novel fungal genomes using an ab initio algorithm with unsupervised training. Genome Research, pages gr-081612.