Special Issue "Tools for Population and Evolutionary Genetics"

A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Population and Evolutionary Genetics and Genomics".

Deadline for manuscript submissions: closed (31 July 2019).

Special Issue Editors

Dr. David Alvarez-Ponce
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Guest Editor
Department of Biology, University of Nevada, Reno, NV, USA
Interests: Molecular Evolution; Comparative Genomics; Bioinformatics.
Special Issues and Collections in MDPI journals
Dr. Julie M. Allen
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Guest Editor
Department of Biology, University of Nevada, Reno, NV, USA
Interests: Co-evolution; Phylogenomics; Bioinformatics
Special Issues and Collections in MDPI journals
Dr. Won C. Yim
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Guest Editor
Department of Biochemistry and Molecular Biology, MS330, University of Nevada, Reno, NV, USA
Interests: Genomics; Comparative genomics; Crop genetics; Bioinformatics
Special Issues and Collections in MDPI journals
Dr. Marco Fondi
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Guest Editor
Department of Biology, University of Florence, 50019 Florence, Italy
Interests: systems biology; evolutionary genomics; metabolic modelling
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, the development of next generation sequencing techniques has fueled an explosion in the pace at which genomic data sets are generated, while dramatically decreasing the costs of genome sequencing. Comparison of these datasets can uncover remarkable information about the evolution of organisms. The availability of datasets of ever-increasing size and complexity has resulted in a growing need for computational tools that allow their effective and efficient analysis.

This special issue focuses on tools for population and evolutionary genetics, including, but not limited to, bioinformatics approaches, and computational tools, algorithms and resources. We welcome submissions of reviews, research articles, and short communications. We also encourage the submission of manuscripts describing new tools, in the form of “concept papers”.

Update: A new edition in form of Topical Collection has been started in Genes here: 

https://www.mdpi.com/journal/genes/special_issues/population_evo_tools

Dr. David Alvarez-Ponce
Dr. Julie M. Allen
Dr. Won C. Yim
Dr. Marco Fondi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Genes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Bioinformatics
  • Comparative Genomics
  • Population genomics
  • Phylogenomics
  • Evolution

Published Papers (9 papers)

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Research

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Open AccessArticle
A Method for the Structure-Based, Genome-Wide Analysis of Bacterial Intergenic Sequences Identifies Shared Compositional and Functional Features
Genes 2019, 10(10), 834; https://doi.org/10.3390/genes10100834 - 22 Oct 2019
Abstract
In this paper, we propose a computational strategy for performing genome-wide analyses of intergenic sequences in bacterial genomes. Following similar directions of a previous paper, where a method for genome-wide analysis of eucaryotic Intergenic sequences was proposed, here we developed a tool for [...] Read more.
In this paper, we propose a computational strategy for performing genome-wide analyses of intergenic sequences in bacterial genomes. Following similar directions of a previous paper, where a method for genome-wide analysis of eucaryotic Intergenic sequences was proposed, here we developed a tool for implementing similar concepts in bacteria genomes. This allows us to (i) classify intergenic sequences into clusters, characterized by specific global structural features and (ii) draw possible relations with their functional features. Full article
(This article belongs to the Special Issue Tools for Population and Evolutionary Genetics)
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Open AccessArticle
Eukaryote Genes Are More Likely than Prokaryote Genes to Be Composites
Genes 2019, 10(9), 648; https://doi.org/10.3390/genes10090648 - 28 Aug 2019
Abstract
The formation of new genes by combining parts of existing genes is an important evolutionary process. Remodelled genes, which we call composites, have been investigated in many species, however, their distribution across all of life is still unknown. We set out to examine [...] Read more.
The formation of new genes by combining parts of existing genes is an important evolutionary process. Remodelled genes, which we call composites, have been investigated in many species, however, their distribution across all of life is still unknown. We set out to examine the extent to which genomes from cells and mobile genetic elements contain composite genes. We identify composite genes as those that show partial homology to at least two unrelated component genes. In order to identify composite and component genes, we constructed sequence similarity networks (SSNs) of more than one million genes from all three domains of life, as well as viruses and plasmids. We identified non-transitive triplets of nodes in this network and explored the homology relationships in these triplets to see if the middle nodes were indeed composite genes. In total, we identified 221,043 (18.57%) composites genes, which were distributed across all genomic and functional categories. In particular, the presence of composite genes is statistically more likely in eukaryotes than prokaryotes. Full article
(This article belongs to the Special Issue Tools for Population and Evolutionary Genetics)
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Open AccessArticle
GWEHS: A Genome-Wide Effect Sizes and Heritability Screener
Genes 2019, 10(8), 558; https://doi.org/10.3390/genes10080558 - 24 Jul 2019
Abstract
During the last decade, there has been a huge development of Genome-Wide Association Studies (GWAS), and thousands of loci associated to complex traits have been detected. These efforts have led to the creation of public databases of GWAS results, making a huge source [...] Read more.
During the last decade, there has been a huge development of Genome-Wide Association Studies (GWAS), and thousands of loci associated to complex traits have been detected. These efforts have led to the creation of public databases of GWAS results, making a huge source of information available on the genetic background of many diverse traits. Here we present GWEHS (Genome-Wide Effect size and Heritability Screener), an open-source online application to screen loci associated to human complex traits and diseases from the NHGRI-EBI GWAS Catalog. This application provides a way to explore the distribution of effect sizes of loci affecting these traits, as well as their contribution to heritability. Furthermore, it allows for making predictions on the change in the expected mean effect size, as well as in the heritability as new loci are found. The application enables inferences on whether the additive contribution of loci expected to be discovered in the future will be able to explain the estimates of familial heritability for the different traits. We illustrate the use of this tool, compare some of the results obtained with those from a previous meta-analysis, and discuss its uses and limitations. Full article
(This article belongs to the Special Issue Tools for Population and Evolutionary Genetics)
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Open AccessArticle
Pangloss: A Tool for Pan-Genome Analysis of Microbial Eukaryotes
Genes 2019, 10(7), 521; https://doi.org/10.3390/genes10070521 - 10 Jul 2019
Cited by 1
Abstract
Although the pan-genome concept originated in prokaryote genomics, an increasing number of eukaryote species pan-genomes have also been analysed. However, there is a relative lack of software intended for eukaryote pan-genome analysis compared to that available for prokaryotes. In a previous study, we [...] Read more.
Although the pan-genome concept originated in prokaryote genomics, an increasing number of eukaryote species pan-genomes have also been analysed. However, there is a relative lack of software intended for eukaryote pan-genome analysis compared to that available for prokaryotes. In a previous study, we analysed the pan-genomes of four model fungi with a computational pipeline that constructed pan-genomes using the synteny-dependent Pan-genome Ortholog Clustering Tool (PanOCT) approach. Here, we present a modified and improved version of that pipeline which we have called Pangloss. Pangloss can perform gene prediction for a set of genomes from a given species that the user provides, constructs and optionally refines a species pan-genome from that set using PanOCT, and can perform various functional characterisation and visualisation analyses of species pan-genome data. To demonstrate Pangloss’s capabilities, we constructed and analysed a species pan-genome for the oleaginous yeast Yarrowia lipolytica and also reconstructed a previously-published species pan-genome for the opportunistic respiratory pathogen Aspergillus fumigatus. Pangloss is implemented in Python, Perl and R and is freely available under an open source GPLv3 licence via GitHub. Full article
(This article belongs to the Special Issue Tools for Population and Evolutionary Genetics)
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Open AccessArticle
Going Deeper into High and Low Phylogenetic Relationships of Protura
Genes 2019, 10(4), 292; https://doi.org/10.3390/genes10040292 - 10 Apr 2019
Cited by 2
Abstract
Proturans are small, wingless, soil-dwelling arthropods, generally associated with the early diversification of Hexapoda. Their bizarre morphology, together with conflicting results of molecular studies, has nevertheless made their classification ambiguous. Furthermore, their limited dispersal capability (due to the primarily absence of wings) and [...] Read more.
Proturans are small, wingless, soil-dwelling arthropods, generally associated with the early diversification of Hexapoda. Their bizarre morphology, together with conflicting results of molecular studies, has nevertheless made their classification ambiguous. Furthermore, their limited dispersal capability (due to the primarily absence of wings) and their euedaphic lifestyle have greatly complicated species-level identification. Mitochondrial and nuclear markers have been applied herein to investigate and summarize proturan systematics at different hierarchical levels. Two new mitochondrial genomes are described and included in a phylum-level phylogenetic analysis, but the position of Protura could not be resolved with confidence due to an accelerated rate of substitution and extensive gene rearrangements. Mitochondrial and nuclear loci were also applied in order to revise the intra-class systematics, recovering three proturan orders and most of the families/subfamilies included as monophyletic, with the exception of the subfamily Acerentominae. At the species level, most morphologically described species were confirmed using molecular markers, with some exceptions, and the advantages of including nuclear, as well as mitochondrial, markers and morphology are discussed. At all levels, an enlarged taxon sampling and the integration of data from different sources may be of significant help in solving open questions that still persist on the evolutionary history of Protura. Full article
(This article belongs to the Special Issue Tools for Population and Evolutionary Genetics)
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Open AccessArticle
ProtParCon: A Framework for Processing Molecular Data and Identifying Parallel and Convergent Amino Acid Replacements
Genes 2019, 10(3), 181; https://doi.org/10.3390/genes10030181 - 26 Feb 2019
Cited by 1
Abstract
Studying parallel and convergent amino acid replacements in protein evolution is frequently used to assess adaptive evolution at the molecular level. Identifying parallel and convergent replacements involves multiple steps and computational routines, such as multiple sequence alignment, phylogenetic tree inference, ancestral state reconstruction, [...] Read more.
Studying parallel and convergent amino acid replacements in protein evolution is frequently used to assess adaptive evolution at the molecular level. Identifying parallel and convergent replacements involves multiple steps and computational routines, such as multiple sequence alignment, phylogenetic tree inference, ancestral state reconstruction, topology tests, and simulation of sequence evolution. Here, we present ProtParCon, a Python 3 package that provides a common interface for users to process molecular data and identify parallel and convergent amino acid replacements in orthologous protein sequences. By integrating several widely used programs for computational biology, ProtParCon implements general functions for handling multiple sequence alignment, ancestral-state reconstruction, maximum-likelihood phylogenetic tree inference, and sequence simulation. ProtParCon also contains a built-in pipeline that automates all these sequential steps, and enables quick identification of observed and expected parallel and convergent amino acid replacements under different evolutionary assumptions. The most up-to-date version of ProtParCon, including scripts containing user tutorials, the full API reference and documentation are publicly and freely available under an open source MIT License via GitHub. The latest stable release is also available on PyPI (the Python Package Index). Full article
(This article belongs to the Special Issue Tools for Population and Evolutionary Genetics)
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Open AccessArticle
Ancient Ancestry Informative Markers for Identifying Fine-Scale Ancient Population Structure in Eurasians
Genes 2018, 9(12), 625; https://doi.org/10.3390/genes9120625 - 12 Dec 2018
Cited by 3
Abstract
The rapid accumulation of ancient human genomes from various areas and time periods potentially enables the expansion of studies of biodiversity, biogeography, forensics, population history, and epidemiology into past populations. However, most ancient DNA (aDNA) data were generated through microarrays designed for modern-day [...] Read more.
The rapid accumulation of ancient human genomes from various areas and time periods potentially enables the expansion of studies of biodiversity, biogeography, forensics, population history, and epidemiology into past populations. However, most ancient DNA (aDNA) data were generated through microarrays designed for modern-day populations, which are known to misrepresent the population structure. Past studies addressed these problems by using ancestry informative markers (AIMs). It is, however, unclear whether AIMs derived from contemporary human genomes can capture ancient population structures, and whether AIM-finding methods are applicable to aDNA. Further the high missingness rates in ancient—and oftentimes haploid—DNA can also distort the population structure. Here, we define ancient AIMs (aAIMs) and develop a framework to evaluate established and novel AIM-finding methods in identifying the most informative markers. We show that aAIMs identified by a novel principal component analysis (PCA)-based method outperform all of the competing methods in classifying ancient individuals into populations and identifying admixed individuals. In some cases, predictions made using the aAIMs were more accurate than those made with a complete marker set. We discuss the features of the ancient Eurasian population structure and strategies to identify aAIMs. This work informs the design of single nucleotide polymorphism (SNP) microarrays and the interpretation of aDNA results, which enables a population-wide testing of primordialist theories. Full article
(This article belongs to the Special Issue Tools for Population and Evolutionary Genetics)
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Open AccessArticle
Cryptic Diversity Hidden within the Leafminer Genus Liriomyza (Diptera: Agromyzidae)
Genes 2018, 9(11), 554; https://doi.org/10.3390/genes9110554 - 15 Nov 2018
Abstract
Leafminer insects of the genus Liriomyza are small flies whose larvae feed on the internal tissue of some of the most important crop plants for the human diet. Several of these pest species are highly uniform from the morphological point of view, meaning [...] Read more.
Leafminer insects of the genus Liriomyza are small flies whose larvae feed on the internal tissue of some of the most important crop plants for the human diet. Several of these pest species are highly uniform from the morphological point of view, meaning molecular data represents the only reliable taxonomic tool useful to define cryptic boundaries. In this study, both mitochondrial and nuclear molecular markers have been applied to investigate the population genetics of some Tunisian populations of the polyphagous species Liriomyza cicerina, one of the most important pest of chickpea cultivars in the whole Mediterranean region. Molecular data have been collected on larvae isolated from chickpea, faba bean, and lentil leaves, and used for population genetics, phylogenetics, and species delimitation analyses. Results point toward high differentiation levels between specimens collected on the three different legume crops, which, according to the species delimitation methods, are also sufficient to define incipient species differentiation and cryptic species occurrence, apparently tied up with host choice. Genetic data have also been applied for a phylogenetic comparison among Liriomyza species, further confirming their decisive role in the systematic studies of the genus. Full article
(This article belongs to the Special Issue Tools for Population and Evolutionary Genetics)
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Review

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Open AccessReview
Selecting among Alternative Scenarios of Human Evolution by Simulated Genetic Gradients
Genes 2018, 9(10), 506; https://doi.org/10.3390/genes9100506 - 18 Oct 2018
Cited by 3
Abstract
Selecting among alternative scenarios of human evolution is nowadays a common methodology to investigate the history of our species. This strategy is usually based on computer simulations of genetic data under different evolutionary scenarios, followed by a fitting of the simulated data with [...] Read more.
Selecting among alternative scenarios of human evolution is nowadays a common methodology to investigate the history of our species. This strategy is usually based on computer simulations of genetic data under different evolutionary scenarios, followed by a fitting of the simulated data with the real data. A recent trend in the investigation of ancestral evolutionary processes of modern humans is the application of genetic gradients as a measure of fitting, since evolutionary processes such as range expansions, range contractions, and population admixture (among others) can lead to different genetic gradients. In addition, this strategy allows the analysis of the genetic causes of the observed genetic gradients. Here, we review recent findings on the selection among alternative scenarios of human evolution based on simulated genetic gradients, including pros and cons. First, we describe common methodologies to simulate genetic gradients and apply them to select among alternative scenarios of human evolution. Next, we review previous studies on the influence of range expansions, population admixture, last glacial period, and migration with long-distance dispersal on genetic gradients for some regions of the world. Finally, we discuss this analytical approach, including technical limitations, required improvements, and advice. Although here we focus on human evolution, this approach could be extended to study other species. Full article
(This article belongs to the Special Issue Tools for Population and Evolutionary Genetics)
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