Fast and accurate distance‐based phylogenetic placement using divide and conquer

Abstract

Phylogenetic placement of query samples on an existing phylogeny is increasingly used in molecular ecology, including sample identification and microbiome environmental sampling. As the size of available reference trees used in these analyses continues to grow, there is a growing need for methods that place sequences on ultra‐large trees with high accuracy. Distance‐based placement methods have recently emerged as a path to provide such scalability while allowing flexibility to analyse both assembled and unassembled environmental samples. In this study, we introduce a distance‐based phylogenetic placement method, APPLES‐2, that is more accurate and scalable than existing distance‐based methods and even some of the leading maximum‐likelihood methods. This scalability is owed to a divide‐and‐conquer technique that limits distance calculation and phylogenetic placement to parts of the tree most relevant to each query. The increased scalability and accuracy enables us to study the effectiveness of APPLES‐2 for placing microbial genomes on a data set of 10,575 microbial species using subsets of 381 marker genes. APPLES‐2 has very high accuracy in this setting, placing 97% of query genomes within three branches of the optimal position in the species tree using 50 marker genes. Our proof‐of‐concept results show that APPLES‐2 can quickly place metagenomic scaffolds on ultra‐large backbone trees with high accuracy as long as a scaffold includes tens of marker genes. These results pave the path for a more scalable and widespread use of distance‐based placement in various areas of molecular ecology.

DEPP: Deep Learning Enables Extending Species Trees using Single Genes

“…We now compare accuracy of DEPP to distance-based APPLES-II (Balaban, Jiang, et al, 2021) used with the standard Jukes Cantor (JC) model, maximum likelihood method EPA-ng (Barbera et al, 2019), and the quartet-based discordant-aware method INSTRAL (Rabiee and Mirarab, 2020). Note that APPLES-JC and EPA-ng are not designed for discordant placement using a single gene, and INSTRAL is designed only for datasets with many genes (at least two but ideally many more).…”

“…Other hyperparameters are fixed to their defaults (Table S2) unless otherwise specified. DEPP is trained on the reference tree and is used to compute distances that are then fed to APPLES-II (Balaban, Jiang, et al, 2021). APPLES-II is used identically to APPLES-II+JC (see below).…”

Recent progress on methods for estimating and updating large phylogenies

“…APPLES-2 is an improvement on APPLES with respect to accuracy and running time, and also scales to at least 200 000 sequences. Recent studies [81,85,86] show that APPLES and APPLES-2 can run on trees with 200 000 leaves and are much faster than both pplacer and EPA-ng; however, even APPLES-2 does not match the accuracy of pplacer. UShER is parsimony-based and very fast, but has not been compared to pplacer, APPLES, or APPLES-2, while RAPPAS, which is based on k-mers, is very fast but not as accurate as EPA-ng or pplacer [83]).…”

<strong></strong> Recent Progress on Methods for Estimating and Updating Large Phylogenies

“…Other phylogenetic placement methods have been developed that seek to improve scalability to larger trees or reduce running time (e.g., UShER (Turakhia et al, 2021), RAPPAS (Linard et al, 2019), EPA-ng (Barbera et al, 2019), APPLES (Balaban et al, 2020), and APPLES-2 (Balaban et al, 2021)). EPA-ng is likelihood-based and has been optimized for "batch processing" of query sequences (so that the cost of performing phylogenetic placement of a large number of query sequences is much less than the cost of placing them one-by-one).…”

“…APPLES-2 is an improvement on APPLES with respect to accuracy and running time, and also scales to at least 200,000 sequences. Recent studies (Balaban et al, 2020(Balaban et al, , 2021Wedell et al, 2021) show that APPLES and APPLES-2 can run on trees with 200,000 leaves and are much faster than both pplacer and EPA-ng; however, even APPLES-2 does not match the accuracy of pplacer. UShER is parsimony-based and very fast, but has not been compared to pplacer, APPLES, or APPLES-2, while RAPPAS, which is based on k-mers, is very fast but not as accurate as EPA-ng or pplacer (Linard et al, 2019)).…”