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1. Phylogenomic Data Reveal Widespread Introgression Across the Range of an Alpine and Arctic Specialist

   https://doi.org/10.1093/sysbio/syaa071  

   Funk, Erik R ; Spellman, Garth M ; Winker, Kevin ; Withrow, Jack J ; Ruegg, Kristen C ; Zavaleta, Erika ; Taylor, Scott A ( September 2020 , Systematic Biology)

   Smith, Stephen (Ed.)

   Abstract Understanding how gene flow affects population divergence and speciation remains challenging. Differentiating one evolutionary process from another can be difficult because multiple processes can produce similar patterns, and more than one process can occur simultaneously. Although simple population models produce predictable results, how these processes balance in taxa with patchy distributions and complicated natural histories is less certain. These types of populations might be highly connected through migration (gene flow), but can experience stronger effects of genetic drift and inbreeding, or localized selection. Although different signals can be difficult to separate, the application of high-throughput sequence data can provide the resolution necessary to distinguish many of these processes. We present whole-genome sequence data for an avian species group with an alpine and arctic tundra distribution to examine the role that different population genetic processes have played in their evolutionary history. Rosy-finches inhabit high elevation mountaintop sky islands and high-latitude island and continental tundra. They exhibit extensive plumage variation coupled with low levels of genetic variation. Additionally, the number of species within the complex is debated, making them excellent for studying the forces involved in the process of diversification, as well as an important species group in which to investigate species boundaries. Total genomic variation suggests a broadly continuous pattern of allele frequency changes across the mainland taxa of this group in North America. However, phylogenomic analyses recover multiple distinct, well supported, groups that coincide with previously described morphological variation and current species-level taxonomy. Tests of introgression using D-statistics and approximate Bayesian computation reveal significant levels of introgression between multiple North American taxa. These results provide insight into the balance between divergent and homogenizing population genetic processes and highlight remaining challenges in interpreting conflict between different types of analytical approaches with whole-genome sequence data. [ABBA-BABA; approximate Bayesian computation; gene flow; phylogenomics; speciation; whole-genome sequencing.] 

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2. Reticulate evolutionary history and extensive introgression in mosquito species revealed by phylogenetic network analysis

   https://doi.org/10.1111/mec.13544  

   Wen, Dingqiao ; Yu, Yun ; Hahn, Matthew W. ; Nakhleh, Luay ( March 2016 , Molecular Ecology)

   The role of hybridization and subsequent introgression has been demonstrated in an increasing number of species. Recently, Fontaineet al. (Science, 347, 2015, 1258524) conducted a phylogenomic analysis of six members of theAnopheles gambiaespecies complex. Their analysis revealed a reticulate evolutionary history and pointed to extensive introgression on all four autosomal arms. The study further highlighted the complex evolutionary signals that the co‐occurrence of incomplete lineage sorting (ILS) and introgression can give rise to in phylogenomic analyses. While tree‐based methodologies were used in the study, phylogenetic networks provide a more natural model to capture reticulate evolutionary histories. In this work, we reanalyse theAnophelesdata using a recently devised framework that combines the multispecies coalescent with phylogenetic networks. This framework allows us to captureILSand introgression simultaneously, and forms the basis for statistical methods for inferring reticulate evolutionary histories. The new analysis reveals a phylogenetic network with multiple hybridization events, some of which differ from those reported in the original study. To elucidate the extent and patterns of introgression across the genome, we devise a new method that quantifies the use of reticulation branches in the phylogenetic network by each genomic region. Applying the method to the mosquito data set reveals the evolutionary history of all the chromosomes. This study highlights the utility of ‘network thinking’ and the new insights it can uncover, in particular in phylogenomic analyses of large data sets with extensive gene tree incongruence.

    

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3. Summary Tests of Introgression Are Highly Sensitive to Rate Variation Across Lineages

   https://doi.org/10.1093/sysbio/syad056  

   Frankel, Lauren E. ; Ané, Cécile ; Folk, ed., Ryan ( September 2023 , Systematic Biology)

   The evolutionary implications and frequency of hybridization and introgression are increasingly being recognized across the tree of life. To detect hybridization from multi-locus and genome-wide sequence data, a popular class of methods are based on summary statistics from subsets of 3 or 4 taxa. However, these methods often carry the assumption of a constant substitution rate across lineages and genes, which is commonly violated in many groups. In this work, we quantify the effects of rate variation on the D test (also known as ABBA–BABA test), the D3 test, and HyDe. All 3 tests are used widely across a range of taxonomic groups, in part because they are very fast to compute. We consider rate variation across species lineages, across genes, their lineage-by-gene interaction, and rate variation across gene-tree edges. We simulated species networks according to a birth–death-hybridization process, so as to capture a range of realistic species phylogenies. For all 3 methods tested, we found a marked increase in the false discovery of reticulation (type-1 error rate) when there is rate variation across species lineages. The D3 test was the most sensitive, with around 80% type-1 error, such that D3 appears to more sensitive to a departure from the clock than to the presence of reticulation. For all 3 tests, the power to detect hybridization events decreased as the number of hybridization events increased, indicating that multiple hybridization events can obscure one another if they occur within a small subset of taxa. Our study highlights the need to consider rate variation when using site-based summary statistics, and points to the advantages of methods that do not require assumptions on evolutionary rates across lineages or across genes.

    

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4. Chromosome‐scale inference of hybrid speciation and admixture with convolutional neural networks

   https://doi.org/10.1111/1755-0998.13355  

   Blischak, Paul D. ; Barker, Michael S. ; Gutenkunst, Ryan N. ( March 2021 , Molecular Ecology Resources)

   Inferring the frequency and mode of hybridization among closely related organisms is an important step for understanding the process of speciation and can help to uncover reticulated patterns of phylogeny more generally. Phylogenomic methods to test for the presence of hybridization come in many varieties and typically operate by leveraging expected patterns of genealogical discordance in the absence of hybridization. An important assumption made by these tests is that the data (genes or SNPs) are independent given the species tree. However, when the data are closely linked, it is especially important to consider their nonindependence. Recently, deep learning techniques such as convolutional neural networks (CNNs) have been used to perform population genetic inferences with linked SNPs coded as binary images. Here, we use CNNs for selecting among candidate hybridization scenarios using the tree topology (((P₁,P₂),P₃), Out) and a matrix of pairwise nucleotide divergence (d_XY) calculated in windows across the genome. Using coalescent simulations to train and independently test a neural network showed that our method, HyDe‐CNN, was able to accurately perform model selection for hybridization scenarios across a wide breath of parameter space. We then used HyDe‐CNN to test models of admixture inHeliconiusbutterflies, as well as comparing it to phylogeny‐based introgression statistics. Given the flexibility of our approach, the dropping cost of long‐read sequencing and the continued improvement of CNN architectures, we anticipate that inferences of hybridization using deep learning methods like ours will help researchers to better understand patterns of admixture in their study organisms.

    

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5. Sequoia: an interactive visual analytics platform for interpretation and feature extraction from nanopore sequencing datasets

   https://doi.org/10.1186/s12864-021-07791-z  

   Koonchanok, Ratanond ; Daulatabad, Swapna Vidhur ; Mir, Quoseena ; Reda, Khairi ; Janga, Sarath Chandra ( July 2021 , BMC Genomics)

   Direct-sequencing technologies, such as Oxford Nanopore’s, are delivering long RNA reads with great efficacy and convenience. These technologies afford an ability to detect post-transcriptional modifications at a single-molecule resolution, promising new insights into the functional roles of RNA. However, realizing this potential requires new tools to analyze and explore this type of data.

   Here, we present Sequoia, a visual analytics tool that allows users to interactively explore nanopore sequences. Sequoia combines a Python-based backend with a multi-view visualization interface, enabling users to import raw nanopore sequencing data in a Fast5 format, cluster sequences based on electric-current similarities, and drill-down onto signals to identify properties of interest. We demonstrate the application of Sequoia by generating and analyzing ~ 500k reads from direct RNA sequencing data of human HeLa cell line. We focus on comparing signal features from m6A and m5C RNA modifications as the first step towards building automated classifiers. We show how, through iterative visual exploration and tuning of dimensionality reduction parameters, we can separate modified RNA sequences from their unmodified counterparts. We also document new, qualitative signal signatures that characterize these modifications from otherwise normal RNA bases, which we were able to discover from the visualization.

   Sequoia’s interactive features complement existing computational approaches in nanopore-based RNA workflows. The insights gleaned through visual analysis should help users in developing rationales, hypotheses, and insights into the dynamic nature of RNA. Sequoia is available athttps://github.com/dnonatar/Sequoia.

    

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