scholarly journals A framework for estimating the effects of sequential reproductive barriers: implementation using Bayesian models with field data from cryptic species

2018 ◽  
Author(s):  
Jean Peccoud ◽  
David R. J. Pleydell ◽  
Nicolas Sauvion
Keyword(s):  
Gene Flow ◽  
Sequence Data ◽  
Biological Data ◽  
Data Availability ◽  
Reproductive Barriers ◽  
Major Step ◽  
Cacopsylla Pruni ◽  
Bayesian Implementation ◽  
Study Sites ◽  
Species Frequencies

AbstractDetermining how reproductive barriers modulate gene flow between populations represents a major step towards understanding the factors shaping the course of speciation. Although many indices quantifying reproductive isolation (RI) have been proposed, they do not permit the quantification of cross direction-specific RI under varying species frequencies and over arbitrary sequences of barriers. Furthermore, techniques quantifying associated uncertainties are lacking, and statistical methods unrelated to biological process are still preferred for obtaining confidence intervals and p-values. To address these shortcomings, we provide new RI indices that model changes in gene flow for both directions of hybridization, and we implement them in a Bayesian model. We use this model to quantify RI between two species of the psyllid Cacopsylla pruni based on field genotypic data for mating individuals, inseminated spermatophores and progeny. The results showed that pre-insemination isolation was strong, mildly asymmetric and undistinguishably different between study sites despite large differences in species frequencies; that post-insemination isolation strongly affected the more common hybrid type; and that cumulative isolation was close to complete. In the light of these results, we discuss how these developments can strengthen comparative RI studies.Author contributionsJP and NS initiated the study and obtained biological data. JP and DRJP developed the porosity-based approach. DRJP conceived the Bayesian implementation and code. JP, DRJP and NS wrote the manuscript.Data availabilityMitochondrial sequence data will be available at Genbank, source code is available at xxx.

Differentiation and Interrelations of Two Representatives of Laudakia stellio Complex (Reptilia: Agamidae) in Israel

2011 ◽  
Vol 4 (2) ◽  
pp. 102-114 ◽  
Author(s):  
Evgenyi N. Panov ◽  
Larissa Yu. Zykova
Keyword(s):  
Population Structure ◽  
Gene Flow ◽  
Habitat Preference ◽  
Additional Data ◽  
Near East ◽  
Field Studies ◽  
Subspecies Level ◽  
Limited Gene Flow ◽  
Study Sites ◽  
And Behavior

Field studies were conducted in Central Negev within the breeding range of Laudakia stellio brachydactyla and in NE Israel (Qyriat Shemona) in the range of an unnamed form (tentatively “Near-East Rock Agama”), during March – May 1996. Additional data have been collected in Jerusalem at a distance of ca. 110 km from the first and about 170 km from the second study sites. A total of 63 individuals were caught and examined. The animals were marked and their subsequent movements were followed. Social and signal behavior of both forms were described and compared. Lizards from Negev and Qyriat Shemona differ from each other sharply in external morphology, habitat preference, population structure, and behavior. The differences obviously exceed the subspecies level. At the same time, the lizards from Jerusalem tend to be intermediate morphologically between those from both above-named localities, which permits admitting the existence of a limited gene flow between lizard populations of Negev and northern Israel. The lizards from NE Israel apparently do not belong to the nominate subspecies of L. stellio and should be regarded as one more subspecies within the species.

scholarly journals Significant cross-species gene flow detected in the Tamias quadrivittatus group of North American chipmunks

2021 ◽  
Author(s):  
Jiayi Ji ◽  
Donavan J. Jackson ◽  
Adam D. Leaché ◽  
Ziheng Yang
Keyword(s):  
Gene Flow ◽  
Sequence Data ◽  
Nuclear Genome ◽  
Likelihood Method ◽  
Recent Analysis ◽  
The Past ◽  
Rapid Speciation ◽  
Full Likelihood ◽  
Nuclear Loci ◽  
Robust Evidence

In the past two decades genomic data have been widely used to detect historical gene flow between species in a variety of plants and animals. The Tamias quadrivittatus group of North America chipmunks, which originated through a series of rapid speciation events, are known to undergo massive amounts of mitochondrial introgression. Yet in a recent analysis of targeted nuclear loci from the group, no evidence for cross-species introgression was detected, indicating widespread cytonuclear discordance. The study used heuristic methods that analyze summaries of the multilocus sequence data to detect gene flow, which may suffer from low power. Here we use the full likelihood method implemented in the Bayesian program BPP to reanalyze these data. We take a stepwise approach to constructing an introgression model by adding introgression events onto a well-supported binary species tree. The analysis detected robust evidence for multiple ancient introgression events affecting the nuclear genome, with introgression probabilities reaching 65%. We estimate population parameters and highlight the fact that species divergence times may be seriously underestimated if ancient cross-species gene flow is ignored in the analysis. Our analyses highlight the importance of using adequate statistical methods to reach reliable biological conclusions concerning cross-species gene flow.

scholarly journals Machine learning can differentiate venom toxins from other proteins having non-toxic physiological functions

2016 ◽  
Vol 2 ◽  
pp. e90 ◽  
Author(s):  
Ranko Gacesa ◽  
David J. Barlow ◽  
Paul F. Long
Keyword(s):  
Machine Learning ◽  
Sequence Data ◽  
Biological Data ◽  
Biological Databases ◽  
Web Based ◽  
Physiological Functions ◽  
Link Type ◽  
Venom Toxins ◽  
Venomous Animals ◽  
Toxin Protein

Ascribing function to sequence in the absence of biological data is an ongoing challenge in bioinformatics. Differentiating the toxins of venomous animals from homologues having other physiological functions is particularly problematic as there are no universally accepted methods by which to attribute toxin function using sequence data alone. Bioinformatics tools that do exist are difficult to implement for researchers with little bioinformatics training. Here we announce a machine learning tool called ‘ToxClassifier’ that enables simple and consistent discrimination of toxins from non-toxin sequences with >99% accuracy and compare it to commonly used toxin annotation methods. ‘ToxClassifer’ also reports the best-hit annotation allowing placement of a toxin into the most appropriate toxin protein family, or relates it to a non-toxic protein having the closest homology, giving enhanced curation of existing biological databases and new venomics projects. ‘ToxClassifier’ is available for free, either to download (https://github.com/rgacesa/ToxClassifier) or to use on a web-based server (http://bioserv7.bioinfo.pbf.hr/ToxClassifier/).

Comparative host–parasite population genetic structures: obligate fly ectoparasites on Galapagos seabirds

Parasitology ◽  
2013 ◽  
Vol 140 (9) ◽  
pp. 1061-1069 ◽  
Author(s):  
IRIS I. LEVIN ◽  
PATRICIA G. PARKER
Keyword(s):  
Gene Flow ◽  
Genetic Differentiation ◽  
Population Genetic ◽  
Sequence Data ◽  
Population Genetic Study ◽  
Haemosporidian Parasite ◽  
Dna Sequence Data ◽  
Sula Granti ◽  
Breeding Attempt ◽  
Genetic Structures

SUMMARYParasites often have shorter generation times and, in some cases, faster mutation rates than their hosts, which can lead to greater population differentiation in the parasite relative to the host. Here we present a population genetic study of two ectoparasitic flies, Olfersia spinifera and Olfersia aenescens compared with their respective bird hosts, great frigatebirds (Fregata minor) and Nazca boobies (Sula granti). Olfersia spinifera is the vector of a haemosporidian parasite, Haemoproteus iwa, which infects frigatebirds throughout their range. Interestingly, there is no genetic differentiation in the haemosporidian parasite across this range despite strong genetic differentiation between Galapagos frigatebirds and their non-Galapagos conspecifics. It is possible that the broad distribution of this one H. iwa lineage could be facilitated by movement of infected O. spinifera. Therefore, we predicted more gene flow in both fly species compared with the bird hosts. Mitochondrial DNA sequence data from three genes per species indicated that despite marked differences in the genetic structure of the bird hosts, gene flow was very high in both fly species. A likely explanation involves non-breeding movements of hosts, including movement of juveniles, and movement by adult birds whose breeding attempt has failed, although we cannot rule out the possibility that closely related host species may be involved.

Prediction of Compound-Protein Interactions with Machine Learning Methods

2011 ◽  
pp. 304-317
Author(s):  
Yoshihiro Yamanishi ◽  
Hisashi Kashima
Keyword(s):  
Machine Learning ◽  
Protein Interactions ◽  
Chemical Structure ◽  
Genomic Sequence ◽  
Sequence Data ◽  
Binary Classification ◽  
Biological Data ◽  
Supervised Machine Learning ◽  
Learning Methods ◽  
Machine Learning Methods

In silico prediction of compound-protein interactions from heterogeneous biological data is critical in the process of drug development. In this chapter the authors review several supervised machine learning methods to predict unknown compound-protein interactions from chemical structure and genomic sequence information simultaneously. The authors review several kernel-based algorithms from two different viewpoints: binary classification and dimension reduction. In the results, they demonstrate the usefulness of the methods on the prediction of drug-target interactions and ligand-protein interactions from chemical structure data and genomic sequence data.

scholarly journals A Bayesian Implementation of the Multispecies Coalescent Model with Introgression for Phylogenomic Analysis

2019 ◽  
Vol 37 (4) ◽  
pp. 1211-1223 ◽  
Author(s):  
Tomáš Flouri ◽  
Xiyun Jiao ◽  
Bruce Rannala ◽  
Ziheng Yang
Keyword(s):  
Gene Flow ◽  
Genomic Sequence ◽  
Sequence Data ◽  
Incomplete Lineage Sorting ◽  
Mosquito Species ◽  
Phylogenomic Analysis ◽  
Data Sets ◽  
Lineage Sorting ◽  
Coalescent Model ◽  
Multispecies Coalescent

Abstract Recent analyses suggest that cross-species gene flow or introgression is common in nature, especially during species divergences. Genomic sequence data can be used to infer introgression events and to estimate the timing and intensity of introgression, providing an important means to advance our understanding of the role of gene flow in speciation. Here, we implement the multispecies-coalescent-with-introgression model, an extension of the multispecies-coalescent model to incorporate introgression, in our Bayesian Markov chain Monte Carlo program Bpp. The multispecies-coalescent-with-introgression model accommodates deep coalescence (or incomplete lineage sorting) and introgression and provides a natural framework for inference using genomic sequence data. Computer simulation confirms the good statistical properties of the method, although hundreds or thousands of loci are typically needed to estimate introgression probabilities reliably. Reanalysis of data sets from the purple cone spruce confirms the hypothesis of homoploid hybrid speciation. We estimated the introgression probability using the genomic sequence data from six mosquito species in the Anopheles gambiae species complex, which varies considerably across the genome, likely driven by differential selection against introgressed alleles.

scholarly journals Sequencing era methods for identifying signatures of selection in the genome

2018 ◽  
Vol 20 (6) ◽  
pp. 1997-2008 ◽  
Author(s):  
Clare Horscroft ◽  
Sarah Ennis ◽  
Reuben J Pengelly ◽  
Timothy J Sluckin ◽  
Andrew Collins
Keyword(s):  
Genome Sequence ◽  
Sequence Data ◽  
Large Data ◽  
Substantial Improvement ◽  
Data Availability ◽  
Whole Genome Sequence ◽  
Whole Genome ◽  
Genome Sequence Data ◽  
Signatures Of Selection ◽  
The Impact

Abstract Insights into genetic loci which are under selection and their functional roles contribute to increased understanding of the patterns of phenotypic variation we observe today. The availability of whole-genome sequence data, for humans and other species, provides opportunities to investigate adaptation and evolution at unprecedented resolution. Many analytical methods have been developed to interrogate these large data sets and characterize signatures of selection in the genome. We review here recently developed methods and consider the impact of increased computing power and data availability on the detection of selection signatures. Consideration of demography, recombination and other confounding factors is important, and use of a range of methods in combination is a powerful route to resolving different forms of selection in genome sequence data. Overall, a substantial improvement in methods for application to whole-genome sequencing is evident, although further work is required to develop robust and computationally efficient approaches which may increase reproducibility across studies.

scholarly journals A Bayesian implementation of the multispecies coalescent model with introgression for comparative genomic analysis

2019 ◽  
Author(s):  
Thomas Flouris ◽  
Xiyun Jiao ◽  
Bruce Rannala ◽  
Ziheng Yang
Keyword(s):  
Gene Flow ◽  
Genomic Sequence ◽  
Sequence Data ◽  
Incomplete Lineage Sorting ◽  
Mosquito Species ◽  
Genomic Analysis ◽  
Comparative Genomic ◽  
Lineage Sorting ◽  
Multispecies Coalescent ◽  
Important Means

AbstractRecent analyses suggest that cross-species gene flow or introgression is common in nature, especially during species divergences. Genomic sequence data can be used to infer introgression events and to estimate the timing and intensity of introgression, providing an important means to advance our understanding of the role of gene flow in speciation. Here we implement the multispecies-coalescent-with-introgression (MSci) model, an extension of the multispecies-coalescent (MSC) model to incorporate introgression, in our Bayesian Markov chain Monte Carlo (MCMC) program BPP. The MSci model accommodates deep coalescence (or incomplete lineage sorting) and introgression and provides a natural framework for inference using genomic sequence data. Computer simulation confirms the good statistical properties of the method, although hundreds or thousands of loci are typically needed to estimate introgression probabilities reliably. Re-analysis of datasets from the purple cone spruce confirms the hypothesis of homoploid hybrid speciation. We estimated the introgression probability using the genomic sequence data from six mosquito species in the Anopheles gambiae species complex, which varies considerably across the genome, likely driven by differential selection against introgressed alleles.

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