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Multi-rate Poisson tree processes for single-locus species delimitation under maximum likelihood and Markov chain Monte Carlo
Author(s) -
Paschalia Kapli,
Sarah Lutteropp,
Jian Zhang,
Kassian Kobert,
Pavlos Pavlidis,
Alexandros Stamatakis,
Tomáš Flouri
Publication year - 2017
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btx025
Subject(s) - intraspecific competition , markov chain monte carlo , computer science , poisson distribution , dna barcoding , sampling (signal processing) , bayesian probability , biology , evolutionary biology , statistics , ecology , artificial intelligence , mathematics , filter (signal processing) , computer vision
In recent years, molecular species delimitation has become a routine approach for quantifying and classifying biodiversity. Barcoding methods are of particular importance in large-scale surveys as they promote fast species discovery and biodiversity estimates. Among those, distance-based methods are the most common choice as they scale well with large datasets; however, they are sensitive to similarity threshold parameters and they ignore evolutionary relationships. The recently introduced "Poisson Tree Processes" (PTP) method is a phylogeny-aware approach that does not rely on such thresholds. Yet, two weaknesses of PTP impact its accuracy and practicality when applied to large datasets; it does not account for divergent intraspecific variation and is slow for a large number of sequences.

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