Biology subjects

Puillandre, N.

Publications and source records attributed to Puillandre, N..

4 recordsLinked to original sources

bioRxiv2021

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

While powerful and user-friendly software suites exist for phylogenetics, and an impressive cybertaxomic infrastructure of online species databases has been set up in the past two decades, software specifically targeted at facilitating alpha-taxonomic work, i.e., delimiting and diagnosing species, is still in its infancy. Here we present a project to develop a bioinformatic toolkit for taxonomy, based on open-source Python code, including tools focusing on species delimitation and diagnosis and centered around specimen identifiers. At the core of iTaxoTools is user-friendliness, with numerous autocorrect options for data files and with intuitive graphical user interfaces. Assembled standalone executables for all tools or a suite of tools with a launcher window will be distributed for Windows, Linux, and Mac OS systems, and in the future also implemented on a web server. The alpha version (iTaxoTools 0.1) distributed with this paper contains GUI versions of six species delimitation programs (ABGD, ASAP, DELINEATE, GMYC, PTP, tr2) and a simple threshold-clustering delimitation tool. There are also new Python implementations of existing algorithms, including tools to compute pairwise DNA distances, ultrametric time trees based on non-parametric rate smoothing, species-diagnostic nucleotide positions, and standard morphometric analyses. Other utilities convert among different formats of molecular sequences, geographical coordinates, and units; merge, split and prune sequence files and tables; and perform simple statistical tests. As a future perspective, we envisage iTaxoTools to become part of a bioinformatic pipeline for next-generation taxonomy that accelerates the inventory of life while maintaining high-quality species hypotheses.

evolutionary biology↗

bioRxiv2021

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

A wide range of data types can be used to delimit species and various computer-based tools dedicated to this task are now available. Although these formalized approaches have significantly contributed to increase the objectivity of SD under different assumptions, they are not routinely used by alpha-taxonomists. One obvious shortcoming is the lack of interoperability among the various independently developed SD programs. Given the frequent incongruences between species partitions inferred by different SD approaches, researchers applying these methods often seek to compare these alternative species partitions to evaluate the robustness of the species boundaries. This procedure is excessively time consuming at present, and the lack of a standard format for species partitions is a major obstacle. Here we propose a standardized format, SPART, to enable compatibility between different SD tools exporting or importing partitions. This format reports the partitions and describes, for each of them, the assignment of individuals to the "inferred species". The syntax also allows to optionally report support values, as well as original trees and the full command lines used in the respective SD analyses. Two variants of this format are proposed, overall using the same terminology but presenting the data either optimized for human readability (matricial SPART) or in a format in which each partition forms a separate block (SPART.XML). ABGD, DELINEATE, GMYC, PTP and TR2 have already been adapted to output SPART files and a new version of LIMES has been developed to import, export, merge and split them.

bioinformatics↗

bioRxiv2019

Revisiting use of DNA characters in taxonomy with MolD - a tree independent algorithm to retrieve diagnostic nucleotide characters from monolocus datasets

AO_SCPLOWBSTRACTC_SCPLOWWhile DNA characters are increasingly used for phylogenetic inference, taxa delimitation and identification, their use for formal description of taxa (i.e. providing either a formal description or a diagnosis) remains scarce and inconsistent. The impediments are neither nomenclatural, nor conceptual, but rather methodological issues: lack of agreement of what DNA character should be provided, and lack of a suitable operational algorithm to identify such characters. Furthermore, the reluctance of using DNA data in taxonomy may also be due to the concerns of insufficient reliability of DNA characters as robustness of the DNA based diagnoses has never been thoroughly assessed. Removing these impediments will enhance integrity of systematics, and will enable efficient treatment of traditionally problematic cases, such as for example, cryptic species. We have developed a novel versatile and scalable algorithm MolD to recover diagnostic combinations of nucleotides (DNCs) for pre-defined groups of DNA sequences, corresponding to taxa. We applied MolD to four published monolocus datasets to examine 1) which type of DNA characters compilation allows for more robust diagnosis, and 2) how the robustness of DNA based diagnosis changes depending on the sampled fraction of taxons diversity. We demonstrate that the redundant DNCs, termed herein sDNCs, allow for higher robustness. Furthermore, we show that a reliable DNA-based diagnosis may be obtained when a rather small fraction of the entire data set is available. Based on our results we propose improvements to the existing practices of handling DNA data in taxonomic descriptions, and discuss a workflow of contemporary systematic study, where the integrative taxonomy part precedes the proposition of a DNA based diagnosis and the diagnosis itself can be efficiently used as a DNA barcode. Our analysis fills existing methodological gaps, thus setting stage for a wider use of the DNA data in taxa description.

bioinformatics↗

bioRxiv2018

Lack of signal for the impact of venom gene diversity on speciation rates in cone snails

Understanding why some groups of organisms are more diverse than others is a central goal in macroevolution. Evolvability, or lineages intrinsic capacity for evolutionary change, is thought to influence disparities in species diversity across taxa. Over macroevolutionary time scales, clades that exhibit high evolvability are expected to have higher speciation rates. Cone snails (family: Conidae, >900 spp.) provide a unique opportunity to test this prediction because their venom genes can be used to characterize differences in evolvability between clades. Cone snails are carnivorous, use prey-specific venom (conotoxins) to capture prey, and the genes that encode venom are known and diversify through gene duplication. Theory predicts that higher gene diversity confers a greater potential to generate novel phenotypes for specialization and adaptation. Therefore, if conotoxin gene diversity gives rise to varying levels of evolvability, conotoxin gene diversity should be coupled with macroevolutionary speciation rates. We applied exon capture techniques to recover phylogenetic markers and conotoxin loci across 314 species, the largest venom discovery effort in a single study. We paired a reconstructed timetree using 12 fossil calibrations with species-specific estimates of conotoxin gene diversity and used trait-dependent diversification methods to test the impact of evolvability on diversification patterns. Surprisingly, did not detect any signal for the relationship between conotoxin gene diversity and speciation rates, suggesting that venom evolution may not be the rate-limiting factor controlling diversification dynamics in Conidae. Comparative analyses showed some signal for the impact of diet and larval dispersal strategy on diversification patterns, though whether or not we detected a signal depended on the dataset and the method. If our results remain true with increased sampling in future studies, they suggest that the rapid evolution of Conidae venom may cause other factors to become more critical to diversification, such as ecological opportunity or traits that promote isolation among lineages.

evolutionary biology↗