Benjamin Linard1,2,3, Krister Swenson1,4, Fabio Pardi1,4. 1. LIRMM, University of Montpellier, CNRS, Montpellier, France. 2. ISEM, University of Montpellier, CNRS, IRD, EPHE, CIRAD, INRAP, Montpellier, France. 3. AGAP, University of Montpellier, CIRAD, INRA, Montpellier Supagro, Montpellier, France. 4. Institut de Biologie Computationnelle, Montpellier, France.
Abstract
MOTIVATION: Taxonomic classification is at the core of environmental DNA analysis. When a phylogenetic tree can be built as a prior hypothesis to such classification, phylogenetic placement (PP) provides the most informative type of classification because each query sequence is assigned to its putative origin in the tree. This is useful whenever precision is sought (e.g. in diagnostics). However, likelihood-based PP algorithms struggle to scale with the ever-increasing throughput of DNA sequencing. RESULTS: We have developed RAPPAS (Rapid Alignment-free Phylogenetic Placement via Ancestral Sequences) which uses an alignment-free approach, removing the hurdle of query sequence alignment as a preliminary step to PP. Our approach relies on the precomputation of a database of k-mers that may be present with non-negligible probability in relatives of the reference sequences. The placement is performed by inspecting the stored phylogenetic origins of the k-mers in the query, and their probabilities. The database can be reused for the analysis of several different metagenomes. Experiments show that the first implementation of RAPPAS is already faster than competing likelihood-based PP algorithms, while keeping similar accuracy for short reads. RAPPAS scales PP for the era of routine metagenomic diagnostics. AVAILABILITY AND IMPLEMENTATION: Program and sources freely available for download at https://github.com/blinard-BIOINFO/RAPPAS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: Taxonomic classification is at the core of environmental DNA analysis. When a phylogenetic tree can be built as a prior hypothesis to such classification, phylogenetic placement (PP) provides the most informative type of classification because each query sequence is assigned to its putative origin in the tree. This is useful whenever precision is sought (e.g. in diagnostics). However, likelihood-based PP algorithms struggle to scale with the ever-increasing throughput of DNA sequencing. RESULTS: We have developed RAPPAS (Rapid Alignment-free Phylogenetic Placement via Ancestral Sequences) which uses an alignment-free approach, removing the hurdle of query sequence alignment as a preliminary step to PP. Our approach relies on the precomputation of a database of k-mers that may be present with non-negligible probability in relatives of the reference sequences. The placement is performed by inspecting the stored phylogenetic origins of the k-mers in the query, and their probabilities. The database can be reused for the analysis of several different metagenomes. Experiments show that the first implementation of RAPPAS is already faster than competing likelihood-based PP algorithms, while keeping similar accuracy for short reads. RAPPAS scales PP for the era of routine metagenomic diagnostics. AVAILABILITY AND IMPLEMENTATION: Program and sources freely available for download at https://github.com/blinard-BIOINFO/RAPPAS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Guillaume E Scholz; Benjamin Linard; Nikolai Romashchenko; Eric Rivals; Fabio Pardi Journal: Bioinformatics Date: 2020-12-17 Impact factor: 6.937