Annotated sequence data are instrumental in nearly all realms of biology. However, the advent of next-generation sequencing has rapidly facilitated an imbalance between accurate sequence data and accurate annotation data. To increase the annotation accuracy of the Caulobacter vibrioides CB13b1a (CB13) genome, we compared the PGAP and RAST annotations of the CB13 genome. A total of 64 unique genes were identified in the PGAP annotation that were either completely or partially absent in the RAST annotation, and a total of 16 genes were identified in the RAST annotation that were not included in the PGAP annotation. Moreover, PGAP identified 73 frameshifted genes and 22 genes with an internal stop. In contrast, RAST annotated the larger segment of these frameshifted genes without indicating a change in reading frame may have occurred. The RAST annotation did not include any genes with internal stop codons, since it chose start codons that were after the internal stop. To confirm the discrepancies between the two annotations and verify the accuracy of the CB13 genome sequence data, we re-sequenced and re-annotated the entire genome and obtained an identical sequence, except in a small number of homopolymer regions. A genome sequence comparison between the two versions allowed us to determine the correct number of bases in each homopolymer region, which eliminated frameshifts for 31 genes annotated as frameshifted genes and removed 24 pseudogenes from the PGAP annotation. Both annotation systems correctly identified genes that were missed by the other system. In addition, PGAP identified conserved gene fragments that represented the beginning of genes, but it employed no corrective method to adjust the reading frame of frameshifted genes or the start sites of genes harboring an internal stop codon. In doing so, the PGAP annotation identified a large number of pseudogenes, which may reflect evolutionary history but likely do not produce gene products. These results demonstrate that re-sequencing and annotation comparisons can be used to increase the accuracy of genomic data and the corresponding gene annotation.