The EPA algorithm can optionally use the nonparametric bootstrap (Felsenstein 1985) to account for uncertainty in the placement of the QS. An example for this is shown in Fig. 3. Thus, a QS might be placed repeatedly onto different edges of the RT with various levels of support. For the bootstrap procedure, we introduce additional heuristics to accelerate the insertion process. During the insertions onto the RT using the original alignment, we keep track of the insertion scores for all QS into all edges of the RT. For every QS, we can then sort the insertion edges by their scores and for each bootstrap replicate only conduct insertions for a specific QS into 10% of the best-scoring insertion edges on the RT. This reduces the number of insertion scores to be computed per QS on each bootstrap replicate by 90% and therefore approximately yields a 10-fold speedup for the bootstrapping procedure.
-G enable the ML-based evolutionary placement algorithm heuristics by specifying a threshold value (fraction of insertion branches to be evaluated using thorough insertions under ML).
Bootstrapping in the EPA has been deprecated a long time ago, the
likelihood weights work much better for obtaining support for
placements, in particular because the query sequence tend to be short.
Hi Josh,
Thanks for the helpful response.
:-)
Bootstrapping in the EPA has been deprecated a long time ago, the
likelihood weights work much better for obtaining support for
placements, in particular because the query sequence tend to be short.
We're actually using EPA to get fast placement results for sequences
which may be (up to) equal length with the reference sequence set, about
10kb.
Interesting :-)
Would this lead to problems in terms of the placement support?
I guess no, the longer the query sequences, the better the placement signal should be, except if you have chimeric sequences in there or some sort of lateral gene transfer, or species tree gene tree incongruence. Nontheless, with such long sequences it could be possible to even detect these effects using placement weights.
Alexis
Josh
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Alexandros (Alexis) Stamatakis
Research Group Leader, Heidelberg Institute for Theoretical Studies
Full Professor, Dept. of Informatics, Karlsruhe Institute of Technology
Adjunct Professor, Dept. of Ecology and Evolutionary Biology, University
of Arizona at Tucson
www.exelixis-lab.org
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