FastTree 2 – Approximately Maximum-Likelihood Trees for Large Alignments

Abstract

BackgroundWe recently described FastTree, a tool for inferring phylogenies for alignments with up to hundreds of thousands of sequences. Here, we describe improvements to FastTree that improve its accuracy without sacrificing scalability.Methodology/Principal FindingsWhere FastTree 1 used nearest-neighbor interchanges (NNIs) and the minimum-evolution criterion to improve the tree, FastTree 2 adds minimum-evolution subtree-pruning-regrafting (SPRs) and maximum-likelihood NNIs. FastTree 2 uses heuristics to restrict the search for better trees and estimates a rate of evolution for each site (the “CAT” approximation). Nevertheless, for both simulated and genuine alignments, FastTree 2 is slightly more accurate than a standard implementation of maximum-likelihood NNIs (PhyML 3 with default settings). Although FastTree 2 is not quite as accurate as methods that use maximum-likelihood SPRs, most of the splits that disagree are poorly supported, and for large alignments, FastTree 2 is 100–1,000 times faster. FastTree 2 inferred a topology and likelihood-based local support values for 237,882 distinct 16S ribosomal RNAs on a desktop computer in 22 hours and 5.8 gigabytes of memory.Conclusions/SignificanceFastTree 2 allows the inference of maximum-likelihood phylogenies for huge alignments. FastTree 2 is freely available at http://www.microbesonline.org/fasttree.

Finding the pond through the weeds: eDNA reveals underestimated diversity of pondweeds

“…Each of the three fragments were analyzed using distances calculated with the Tamura–Nei model in Geneious version 9.1.5 (Tamura and Nei, 1993) and approximated with a maximum likelihood tree using the Fast Tree algorithm (Price et al., 2010) (Figs. 1, 2, 3, Appendix S3).…”

“…Unresolved groups of species are labeled as complexes. Trees were generated in Fast Tree (Price et al, 2010). …”

Gut microflora may facilitate adaptation to anthropic habitat: A comparative study in Rattus

“…We constructed rarefaction plots to determine adequacy of the 16s sequencing procedure. We aligned all representative sequences using the PyNAST algorithm (Caporaso, Bittinger et al., 2010), filtered the alignment using a lane mask file, and constructed phylogeny with FastTree (Price, Dehal, & Arkin, 2010), which was then visualized using the Topiary Explorer (Pirrung et al., 2011). Finally, we filtered reads that were unclassified, unalignable, and singletons from the OTU table.…”

Re-evaluating the phylogeny of Sipuncula through transcriptomics

“…The selected orthogroups for each matrix (675 and 141, respectively) were aligned individually using MUSCLE version 3.6 (Edgar, 2004). To account for alignment uncertainty and increase the signal-to-noise ratio of the data, we applied a probabilistic character masking to each alignment with ZORRO , using default parameters and FastTree 2.1.4 (Price et al, 2010) to construct guide trees. In all of the alignments, positions that were assigned a confidence score below the threshold of 5 by ZORRO were discarded, using a custom Python script.…”