FastTree: computing large minimum evolution trees with profiles instead of a distance matrix.
Gene families are growing rapidly, but standard methods for inferring phylogenies do not scale to alignments with over 10,000 sequences. We present FastTree, a method for constructing large phylogenies and for estimating their reliability. Instead of storing a distance matrix, FastTree stores sequence profiles of internal nodes in the tree. FastTree uses these profiles to implement Neighbor-Joining and uses heuristics to quickly identify candidate joins. FastTree then uses nearest neighbor interchanges to reduce the length of the tree. For an alignment with N sequences, L sites, and a different characters, a distance matrix requires O(N(2)) space and O(N(2)L) time, but FastTree requires just O(NLa + N ) memory and O(N log (N)La) time. To estimate the tree's reliability, FastTree uses local bootstrapping, which gives another 100-fold speedup over a distance matrix. For example, FastTree computed a tree and support values for 158,022 distinct 16S ribosomal RNAs in 17 h and 2.4 GB of memory. Just computing pairwise Jukes-Cantor distances and storing them, without inferring a tree or bootstrapping, would require 17 h and 50 GB of memory. In simulations, FastTree was slightly more accurate than Neighbor-Joining, BIONJ, or FastME; on genuine alignments, FastTree's topologies had higher likelihoods. FastTree is available at http://microbesonline.org/fasttree.
Physical Biosciences Division, Lawrence Berkeley National Laboratory, CA, USA. morgannprice@yahoo.com
Morgan N
MN
Price
Paramvir S
PS
Dehal
Adam P
AP
Arkin
D000465:Algorithms; D019143:Evolution, Molecular; D008957:Models, Genetic; D010802:Phylogeny; D011506:Proteins / Q000235:genetics*; D016415:Sequence Alignment / Q000379:methods*
D011506:Proteins