4 Matching Annotations
  1. Nov 2022
    1. The genomes were uniformly of high completeness (Figure S2). Due to the high intrinsic base quality of HiFi reads (Q30 to Q40; from one error in 1000 to one error in 10,000) we were able to distinguish insertions of Wolbachia DNA into the host genome from true components of the Wolbachia genome, and to independently assemble even closely related strains with confidence

      This whole study is really interesting. This is one part that is not entirely clear to me. How do you use this information to distinguish actual Wolbachia DNA from Wolbachia from insertions of Wolbachia DNA into the host genome?

    1. We introduce the use of a specific suite of parameters and protocols that greatly improves the agreement among pipelines developed by diverse organizations.

      From reading the paper it seems like this was done on samples where the SARS-COV-2 in the sample is likely to be genetically uniform. Have you done this at all on samples such as those from wastewater or sewage or environmental swabs where there are likely to be many different variants within a single sample?

    1. Fig. S7 shows results at different MSA depth thresholds. After filtering, there are 104 sequences with MSA depth ≤ 100, 70 sequences with MSA depth ≤ 10, and 22 sequences with MSA depth = 1. Beyond the constraint that no template has TM-score > 0.5, no filtering on the number of templates is performed.

      It would be interesting to know if there is anything in common / shared for the proteins for which you can still not predict structures. For example, are they more likely to come from certain environments or environmental conditions (e.g., low temperature samples, high temperature, high salt, etc)? Also is it possible to take into account any of the environmental conditions in the actual structural prediction? For example if samples came from a hydrothermal vent that was at 90C would this be useful in any of the predictions?

    1. When we expanded this analysis to chromosome-wide gene-gene correlations, we discovered a striking ‘X-shaped’ pattern of gene expression covariance (Fig. 1D). Beyond the expected diagonal reflecting coordinated gene expression at the level of operons, the anti-diagonal reflected correlations between genes at a similar distance from the origin of replication, between the “arms” of the circular chromosome, as well as a correlation between genes at the origin and terminus

      It would be really interesting to compare the patterns you observe here with the X-like genome inversion patterns that we and others have reported (e.g., see https://www.ncbi.nlm.nih.gov/pmc/articles/PMC16139/). These patterns basically show that the distance a gene is from the origin of replication is conserved but the side of the origin it is on is not.

      Those inversion patterns have been seen in some but not all comparisons of closely related bacterial and archaeal genomes. So it seems there are some taxa where the distance a gene is from the origin is not conserved over evolutionary time. It would be interesting to know if these taxa show the X-like patternb you report for gene expression.