Biology subjects

Derkarabetian, S.

Publications and source records attributed to Derkarabetian, S..

3 recordsLinked to original sources

bioRxiv2023

From the shallows to the depths: A new probe set to target ultraconserved elements for Malacostraca

Since its introduction about a decade ago, target enrichment sequencing of ultraconserved elements (UCEs) has proven to be an invaluable tool for studies across evolutionary scales, and thus employed from population genetics, to historical biogeography and deep phylogenetics. UCE probe sets are available for an increasing range of major taxonomic groups, including cnidarians, vertebrates, terrestrial arthropods, and mollusks. Here, we present the first probe set targeting UCEs in crustaceans, specifically designed for decapods and other malacostracan lineages. Probes were designed using published genomes of nine decapod and one peracarid species, as well as raw Nanopore long reads of one additional brachyuran species. The final probe set consists of about 20,000 probes, targeting 1,348 unique UCE loci. Preliminary analyses of UCE data obtained from an intertidal mangrove crab, and from deep-sea squat lobsters indicate high UCE recovery rates (about 1,000 loci per sample) in evolutionarily shallow datasets. To test the probe set at deeper phylogenetic levels, we compiled a dataset across Malacostraca (including representatives of Decapoda, Peracarida, Euphausiacea, Stomatopoda, and Phyllocarida), and were able to recover hundreds of UCEs for the non-decapod species, expanding the targeted use of this UCE probeset to all Malacostraca. Additionally, we recovered similar numbers of UCEs from historical museum specimens up to > 150 years old, that were included in all datasets, confirming that UCEs are a fruitful technique for leveraging museum specimens for genomic studies. Overall, our results highlight the versatility of this UCE probe set and its high potential for crustacean evolutionary studies.

evolutionary biology↗

bioRxiv2021

The challenge of delimiting cryptic species, and a supervised machine learning solution

AO_SCPLOWBSTRACTC_SCPLOWThe diversity of biological and ecological characteristics of organisms, and the underlying genetic patterns and processes of speciation, makes the development of universally applicable genetic species delimitation methods challenging. Many approaches, like those incorporating the multispecies coalescent, sometimes delimit populations and overestimate species numbers. This issue is exacerbated in taxa with inherently high population structure due to low dispersal ability, and in cryptic species resulting from nonecological speciation. These taxa present a conundrum when delimiting species: analyses rely heavily, if not entirely, on genetic data which over split species, while other lines of evidence lump. We showcase this conundrum in the harvester Theromaster brunneus, a low dispersal taxon with a wide geographic distribution and high potential for cryptic species. Integrating morphology, mitochondrial, and sub-genomic (double-digest RADSeq and ultraconserved elements) data, we find high discordance across analyses and data types in the number of inferred species, with further evidence that multispecies coalescent approaches over split. We demonstrate the power of a supervised machine learning approach in effectively delimiting cryptic species by creating a "custom" training dataset derived from a well-studied lineage with similar biological characteristics as Theromaster. This novel approach uses known taxa with particular biological characteristics to inform unknown taxa with similar characteristics, and uses modern computational tools ideally suited for species delimitation while also considering the biology and natural history of organisms to make more biologically informed species delimitation decisions. In principle, this approach is universally applicable for species delimitation of any taxon with genetic data, particularly for cryptic species.

evolutionary biology↗

bioRxiv2018

An Empirical Demonstration of Unsupervised Machine Learning in Species Delimitation

One major challenge to delimiting species with genetic data is successfully differentiating species divergences from population structure, with some current methods biased towards overestimating species numbers. Many fields of science are now utilizing machine learning (ML) approaches, and in systematics and evolutionary biology, supervised ML algorithms have recently been incorporated to infer species boundaries. However, these methods require the creation of training data with associated labels. Unsupervised ML, on the other hand, uses the inherent structure in data and hence does not require any user-specified training labels, thus providing a more objective approach to species delimitation. In the context of integrative taxonomy, we demonstrate the utility of three unsupervised ML approaches, specifically random forests, variational autoencoders, and t-distributed stochastic neighbor embedding, for species delimitation utilizing a short-range endemic harvestman taxon (Laniatores, Metanonychus). First, we combine mitochondrial data with examination of male genitalic morphology to identify a priori species hypotheses. Then we use single nucleotide polymorphism data derived from sequence capture of ultraconserved elements (UCEs) to test the efficacy of unsupervised ML algorithms in successfully identifying a priori species, comparing results to commonly used genetic approaches. Finally, we use two validation methods to assess a priori species hypotheses using UCE data. We find that unsupervised ML approaches successfully cluster samples according to species level divergences and not to high levels of population structure, while standard model-based validation methods over-split species, in some instances suggesting that all sampled individuals are distinct species. Moreover, unsupervised ML approaches offer the benefits of better data visualization in two-dimensional space and the ability to accommodate various data types. We argue that ML methods may be better suited for species delimitation relative to currently used model-based validation methods, and that species delimitation in a truly integrative framework provides more robust final species hypotheses relative to separating delimitation into distinct \"discovery\" and \"validation\" phases. Unsupervised ML is a powerful analytical approach that can be incorporated into many aspects of systematic biology, including species delimitation. Based on results of our empirical dataset, we make several taxonomic changes including description of a new species.

evolutionary biology↗