Tree sequences as a general-purpose tool for population genetic inference.

As population genetics data increases in size new methods have been developed to store genetic information in efficient ways, such as tree sequences. These data structures are computationally and storage efficient, but are not interchangeable with existing data structures used for many population genetic inference methodologies such as the use of convolutional neural networks (CNNs) applied to population genetic alignments. To better utilize these new data structures we propose and implement a graph convolutional network (GCN) to directly learn from tree sequence topology and node data, allowing for the use of neural network applications without an intermediate step of converting tree sequences to population genetic alignment format. We then compare our approach to standard CNN approaches on a set of previously defined benchmarking tasks including recombination rate estimation, positive selection detection, introgression detection, and demographic model parameter inference. We show that tree sequences can be directly learned from using a GCN approach and can be used to perform well on these common population genetics inference tasks with accuracies roughly matching or even exceeding that of a CNN-based method. As tree sequences become more widely used in population genetics research we foresee developments and optimizations of this work to provide a foundation for population genetics inference moving forward.

PhyloMatcher: a tool for resolving conflicts in taxonomic nomenclature.

Summary: Large-scale comparative studies rely on the application of both phylogenetic trees and phenotypic data, both of which come from a variety of sources, but due to the changing nature of phylogenetic classification over time, many taxon names in comparative datasets do not match the nomenclature in phylogenetic trees. Manual curation of taxonomic synonyms in large comparative datasets can be daunting. To address this issue, we introduce PhyloMatcher, a tool which allows for programmatic querying of the National Center for Biotechnology Information Taxonomy and Global Biodiversity Information Facility databases to find associated synonyms with given target species names. Availability and implementation: PhyloMatcher is easily installed as a Python package with pip, or as a standalone GUI application. PhyloMatcher source code and documentation are freely available at https://github.com/Lswhiteh/PhyloMatcher, the GUI application can be downloaded from the Releases page.

PhyloMatcher: a tool for resolving conflicts in taxonomic nomenclature.

Summary: Large-scale comparative studies rely on the application of both phylogenetic trees and phenotypic data, both of which come from a variety of sources, but due to the changing nature of phylogenetic classification over time, many taxon names in comparative datasets do not match the nomenclature in phylogenetic trees. Manual curation of taxonomic synonyms in large comparative datasets can be daunting. To address this issue, we introduce PhyloMatcher, a tool which allows for programmatic querying of two commonly used taxonomic databases to find associated synonyms with given target species names. Availability and implementation: PhyloMatcher is easily installed as a Python package with pip, or as a standalone GUI application. PhyloMatcher source code and documentation are freely available at https://github.com/Lswhiteh/PhyloMatcher, the GUI application can be downloaded from the Releases page. Contact: Lswhiteh@unc.edu. Supplemental Information: We provide documentation for PhyloMatcher, including walkthrough instructions for the GUI application on the Releases page of https://github.com/Lswhiteh/PhyloMatcher.

Timesweeper: accurately identifying selective sweeps using population genomic time series.

Despite decades of research, identifying selective sweeps, the genomic footprints of positive selection, remains a core problem in population genetics. Of the myriad methods that have been developed to tackle this task, few are designed to leverage the potential of genomic time-series data. This is because in most population genetic studies of natural populations, only a single period of time can be sampled. Recent advancements in sequencing technology, including improvements in extracting and sequencing ancient DNA, have made repeated samplings of a population possible, allowing for more direct analysis of recent evolutionary dynamics. Serial sampling of organisms with shorter generation times has also become more feasible due to improvements in the cost and throughput of sequencing. With these advances in mind, here we present Timesweeper, a fast and accurate convolutional neural network-based tool for identifying selective sweeps in data consisting of multiple genomic samplings of a population over time. Timesweeper analyzes population genomic time-series data by first simulating training data under a demographic model appropriate for the data of interest, training a one-dimensional convolutional neural network on said simulations, and inferring which polymorphisms in this serialized data set were the direct target of a completed or ongoing selective sweep. We show that Timesweeper is accurate under multiple simulated demographic and sampling scenarios, identifies selected variants with high resolution, and estimates selection coefficients more accurately than existing methods. In sum, we show that more accurate inferences about natural selection are possible when genomic time-series data are available; such data will continue to proliferate in coming years due to both the sequencing of ancient samples and repeated samplings of extant populations with faster generation times, as well as experimentally evolved populations where time-series data are often generated. Methodological advances such as Timesweeper thus have the potential to help resolve the controversy over the role of positive selection in the genome. We provide Timesweeper as a Python package for use by the community.

Hedgehog Pathway Activation Alters Ciliary Signaling in Primary Hypothalamic Cultures.

Primary cilia dysfunction has been associated with hyperphagia and obesity in both ciliopathy patients and mouse models of cilia perturbation. Neurons throughout the brain possess these solitary cellular appendages, including in the feeding centers of the hypothalamus. Several cell biology questions associated with primary neuronal cilia signaling are challenging to address in vivo. Here we utilize primary hypothalamic neuronal cultures to study ciliary signaling in relevant cell types. Importantly, these cultures contain neuronal populations critical for appetite and satiety such as pro-opiomelanocortin (POMC) and agouti related peptide (AgRP) expressing neurons and are thus useful for studying signaling involved in feeding behavior. Correspondingly, these cultured neurons also display electrophysiological activity and respond to both local and peripheral signals that act on the hypothalamus to influence feeding behaviors, such as leptin and melanin concentrating hormone (MCH). Interestingly, we found that cilia mediated hedgehog signaling, generally associated with developmental processes, can influence ciliary GPCR signaling (Mchr1) in terminally differentiated neurons. Specifically, pharmacological activation of the hedgehog-signaling pathway using the smoothened agonist, SAG, attenuated the ability of neurons to respond to ligands (MCH) of ciliary GPCRs. Understanding how the hedgehog pathway influences cilia GPCR signaling in terminally differentiated neurons could reveal the molecular mechanisms associated with clinical features of ciliopathies, such as hyperphagia-associated obesity.

A CreER mouse to study melanin concentrating hormone signaling in the developing brain.

The neuropeptide, melanin concentrating hormone (MCH), and its G protein-coupled receptor, melanin concentrating hormone receptor 1 (Mchr1), are expressed centrally in adult rodents. MCH signaling has been implicated in diverse behaviors such as feeding, sleep, anxiety, as well as addiction and reward. While a model utilizing the Mchr1 promoter to drive constitutive expression of Cre recombinase (Mchr1-Cre) exists, there is a need for an inducible Mchr1-Cre to determine the roles for this signaling pathway in neural development and adult neuronal function. Here, we generated a BAC transgenic mouse where the Mchr1 promotor drives expression of tamoxifen inducible CreER recombinase. Many aspects of the Mchr1-Cre expression pattern are recapitulated by the Mchr1-CreER model, though there are also notable differences. Most strikingly, compared to the constitutive model, the new Mchr1-CreER model shows strong expression in adult animals in hypothalamic brain regions involved in feeding behavior but diminished expression in regions involved in reward, such as the nucleus accumbens. The inducible Mchr1-CreER allele will help reveal the potential for Mchr1 signaling to impact neural development and subsequent behavioral phenotypes, as well as contribute to the understanding of the MCH signaling pathway in terminally differentiated adult neurons and the diverse behaviors that it influences.