At each location, two X-fast and two Y-fast data sets were recorded.
See Additional File 1 for GenBank numbers of sequences included in slow vs. fast data sets.
We found this smoothing parameter to be 3.2 for both slow and fast data sets.
A total of three X-fast and three Y-fast data sets were acquired, and the original variance images as well as the results of the motion correction are shown.
For both slow and fast data sets the median branch length, divided by the prior of root age resulted in a value between 0.003 and 0.005 depending on the root age prior; therefore for our preferred estimates we utilized a prior of 0.002 ± 0.002 (standard deviation).
Furthermore, Lep-MAP is faster on large data sets, requires no manual input, and handles genotyping errors better because it estimates genotyping error rates for each marker.
Overall, whereas the fast-evolving data set appears enriched for rapidly evolving genes in D. miranda, this pattern is less pronounced in D. pseudoobscura.
In D. miranda, one gene is significant in our fast-evolving data set, using a Fisher's test (CG7051), whereas CG32527 is significant in D. pseudoobscura.
Compared with randomly selected genes sampled in the same individuals (D. miranda mean πsyn = 0.0060; Bachtrog et al. 2009; D. pseudoobscura πsyn = 0.0171; Jensen JD, Bachtrog D, in preparation), levels of variation are significantly reduced in both species among our "fast-evolving" data set (Wilcoxon two-sample test, D. miranda, P < 7 × 10−4; D. pseudoobscura, P < 1 × 10−4).
Our tool (RegScan) is designed for performing basic linear regression analysis with continuous traits maximally fast on large data sets.
Although the identification of protein interactions by high-throughput (HTP) methods progresses at a fast pace, 'interactome' data sets still suffer from high rates of false positives and low coverage.
