Moore Notes 12 1 10

Group Call

• PhylOTU paper accepted at PLoS Comp Bio
  • Tom is handling proofs

• Protein Db/AMPHORA 2
  • Guillaume is parsing results
  • Pipeline is fairly stable
    • Testing on GOS data (for Steve's paper) - new code, but old gene list still
    • Parallelized tree building program
  • Working with Aaron on a short read strategy
    • No unassembled reads are getting picked up by BLAST search with 25 representatives for each marker
    • With Illumina data
    • New marker list
    • Tom: it is possible that those markers are not present in the data set - check with Aaron
    • Sam: start with conditions where you do recover the hits, then back off
    • Assembly with Velvet, then run assembled contigs through AMPHORA

• Niche mapping
  • Tom's analysis of correlation between 1/2 degree remote data and observed metadata at sampling sites plots
    • Unit scale is not identical for all pairs (hopefully they are proportional)
    • If either data set had a NULL value, data point was thrown out
      • Shorelines had a lot of NULL values
      • Silicate had a lot of NULL values (Josh: should be similar to phosphate - check)
    • All correlations are significant and positive (except silicate)
      • Temperature and salinity are the strongest
    • Time of sampling is important
      • Positive correlations suggest that averages are robust
      • Should check if variances are correlated too
      • James: Local and long term average are probably both important for determining community structure
    • Did filter MICROBIS for <150m
    • Many unexpected data points in MICROBIS, e.g., very high temperatures might be a units problem
  • Josh added variability of environmental variables as layers in maxent
    • Variance has been shown to impact richness
    • Obtained remote variability data on temperature and salinity
      • Other predictors have variability for some locations, but not good coverage of ocean
    • Re-ran all analyses
      • Maps are not changed much
      • Stronger evidence for high richness in regions with mixing and boundary currents
      • Low diversity off W. Africa and Arabia are still there - these are likely due to low sampling in those environments
  • Verifying richness predictions using locations with a lot of sampling (versus Chao) plots
    • Maxent and Chao are similar for order, class, and phylum. Not great for family
    • Chao is generally below maxent
      • Makes sense since Chao is unbiased for the lower bound of richness
    • One outlier point is probably the same sample in all four plots - check
    • Sites with many reads are right on the correlation line
      • Chao is close to real richness at these locations, but is off when there are not many reads (expected)
    • Previously wanted to use family (based on RDP analysis), but order might be better
    • Can test correlation statistically
      • Use a weighted regression to account for different sampling depths
      • Could throw out samples with too few reads for Chao to be accurate (but this will probably depend on true richness)