ASAP: assemble species by automatic partitioning

Abstract

Here, we describe Assemble Species by Automatic Partitioning (ASAP), a new method to build species partitions from single locus sequence alignments (i.e., barcode data sets). ASAP is efficient enough to split data sets as large 104 sequences into putative species in several minutes. Although grounded in evolutionary theory, ASAP is the implementation of a hierarchical clustering algorithm that only uses pairwise genetic distances, avoiding the computational burden of phylogenetic reconstruction. Importantly, ASAP proposes species partitions ranked by a new scoring system that uses no biological prior insight of intraspecific diversity. ASAP is a stand‐alone program that can be used either through a graphical web‐interface or that can be downloaded and compiled for local usage. We have assessed its power along with three others programs (ABGD, PTP and GMYC) on 10 real COI barcode data sets representing various degrees of challenge (from small and easy cases to large and complicated data sets). We also used Monte‐Carlo simulations of a multispecies coalescent framework to assess the strengths and weaknesses of ASAP and the other programs. Through these analyses, we demonstrate that ASAP has the potential to become a major tool for taxonomists as it proposes rapidly in a full graphical exploratory interface relevant species hypothesis as a first step of the integrative taxonomy process.

Factors affecting the efficiency of molecular species delimitation in a species‐rich insect family

“…ABGD (Puillandre et al, 2012); iv. ASAP (Puillandre et al, 2020) v. GMYC (Pons et al, 2006;Fujisawa & Barraclough, 2013); vi. the Multi-rate Poisson tree processes (mPTP; Kapli et al, 2017).…”

“…ASAP analyses were run using the program web-interface (https://bioinfo.mnhn.fr/abi/public/asap); K2P (Kimura, 1980) was selected as nucleotide substitution model and other parameters were left as default. ASAP delimitation was defined evaluating both the partitions with first and the second best asap-score according to Puillandre et al, 2020. In order to perform GMYC and mPTP species delimitation analyses, an ultrametric tree was inferred using the software BEAST v 1.8 (Drummond, Suchard, Xie, & Rambaut, 2012) from each haplotype-reduced dataset (Tang et al, 2014). One or more (in the case of the family dataset) orthologous sequences from the appropriate outgroup was included to properly root the tree.…”

“…Several molecular species delimitation methods have been proposed: early methods analysed alloenzymes variability, allele frequency or nuclear genes co-dominance (Highton, 1989;Porter, 1990;Davis & Nixon, 1992;Good & Wake, 1992;Highton, 2000), but, currently, the most widely adopted molecular delimitation methods rely on DNA sequences as markers. The result of species delimitation analyses using nucleotide sequences is more accurate when taking information from more than one marker (Rubinoff & Holland, 2005;Dupuis, Roe, & Sperling, 2012); nevertheless, single-locus species delimitation approach is widely applied on data derived from DNA barcoding studies (DeSalle & Goldstein, 2019;Puillandre, Brouillet, & Achaz, 2020) and is still the only reliable approach for metabarcoding. Even when multiple markers are used for metabarcoding, the origin of the different markers cannot be considered from the same individual and any delimitation analysis has to be performed independently for each marker.…”

iTaxoTools 0.1: Kickstarting a specimen-based software toolkit for taxonomists

“…6) and GMYC (Pons et al 2006;Fujisawa & Barraclough 2013; Python version J. Zhang) which delimit species from single-locus trees; tr2 (Fujisawa et al 2016) and DELINEATE (Sukumaran et al 2020) that use coalescence-based approaches on multiple gene trees; and ABGD (Puillandre et al 2012) (Fig. 7) and ASAP (Puillandre et al 2020) that are alignment-based and rely on calculations of genetic distances. For some of these tools (PTP, GMYC, tr2, DELINEATE) the current pre-release GUI versions are still basic and only run under default settings; options to change and refine parameters will be added to the first complete 6 release.…”

SPART, a versatile and standardized data exchange format for species partition information

“…directly aggregating individuals into species hypotheses; exploratory methods), others statistically compare competing species hypotheses that have been defined a priori (hypothesis-testing methods), and these programs require a species partition as input. SD methods may also assign scores, either to the entire inferred partition (e.g., ASAP-score in the program ASAP; Puillandre et al 2021), to the distinctiveness of each subset from the others (e.g., posterior probabilities in the programs BPP and bPTP; Yang & Rannala 2010; Zhang et al 2013), or to the presence of each individual in a given subset (e.g., probability of placement in calculation of BINs, Ratnasingham & Hebert 2013).…”