Auto Tag: Institute for Genome Sciences

Without knowledge of their genomic sequences, it is impossible to make functional models of the bacteria that make up human and animal microbiota. Unfortunately, the vast majority of publicly available genomes are only working drafts, an incompleteness that causes numerous problems and constitutes a major obstacle to genotypic and phenotypic interpretation. In this work, we began with an example from the class Bacteroidia in the phylum Bacteroidetes, which is preponderant among human orodigestive microbiota. We successfully identify the genetic loci responsible for assembly breaks and misassemblies and demonstrate the importance and usefulness of long-read sequencing and curated reannotation.We showed that the fragmentation in Bacteroidia draft genomes assembled from massively parallel sequencing linearly correlates with genomic repeats of the same or greater size than the reads. We also demonstrated that some of these repeats, especially the long ones, correspond to misassembled loci in three reference Porphyromonas gingivalis genomes marked as circularized (thus complete or finished). We prove that even at modest coverage (30X), long-read resequencing together with PCR contiguity verification (rrn operons and an integrative and conjugative element or ICE) can be used to identify and correct the wrongly combined or assembled regions. Finally, although time-consuming and labor-intensive, consistent manual biocuration of three P. gingivalis strains allowed us to compare and correct the existing genomic annotations, resulting in a more accurate interpretation of the genomic differences among these strains.In this study, we demonstrate the usefulness and importance of long-read sequencing in verifying published genomes (even when complete) and generating assemblies for new bacterial strains/species with high genomic plasticity. We also show that when combined with biological validation processes and diligent biocurated annotation, this strategy helps reduce the propagation of errors in shared databases, thus limiting false conclusions based on incomplete or misleading information.