SMILE

Stochastic Models for the Inference of Life Evolution

Bibtex

@article{puillandre_abgd_2012,
Author = {Puillandre, N. and Lambert, A. and Brouillet, S. and
Achaz, G.},
Title = {{ABGD} {Automatic} Barcode} {Gap} {Discovery} for
primary species delimitation},
Journal = {Molecular Ecology},
Volume = {21},
Number = {8},
Pages = {1864--1877},
abstract = {Within uncharacterized groups, DNA barcodes, short DNA
sequences that are present in a wide range of species,
can be used to assign organisms into species. We
propose an automatic procedure that sorts the sequences
into hypothetical species based on the barcode gap,
which can be observed whenever the divergence among
organisms belonging to the same species is smaller than
divergence among organisms from different species. We
use a range of prior intraspecific divergence to infer
from the data a model-based one-sided confidence limit
for intraspecific divergence. The method, called
Automatic Barcode Gap Discovery (ABGD), then detects
the barcode gap as the first significant gap beyond
this limit and uses it to partition the data. Inference
of the limit and gap detection are then recursively
applied to previously obtained groups to get finer
partitions until there is no further partitioning.
Using six published data sets of metazoans, we show
that ABGD is computationally efficient and performs
well for standard prior maximum intraspecific
divergences (a few per cent of divergence for the five
data sets), except for one data set where less than
three sequences per species were sampled. We further
explore the theoretical limitations of ABGD through
simulation of explicit speciation and population
genetics scenarios. Our results emphasize in particular
the sensitivity of the method to the presence of recent
speciation events, via (unrealistically) high rates of
speciation or large numbers of species. In conclusion,
ABGD is fast, simple method to split a sequence
alignment data set into candidate species that should
be complemented with other evidence in an integrative
taxonomic approach.},
doi = {10.1111/j.1365-294X.2011.05239.x},
issn = {1365-294X},
language = {eng},
month = apr,
pmid = {21883587},
year = 2012
}