scholarly journals VESPA: Very large-scale Evolutionary and Selective Pressure Analyses

2017 ◽  
Author(s):  
Andrew E. Webb ◽  
Thomas A. Walsh ◽  
Mary J O'Connell
Keyword(s):  
Phylogenetic Trees ◽  
Large Scale ◽  
Selective Pressure ◽  
Gene Families ◽  
Pressure Variation ◽  
Phylogeny Reconstruction ◽  
Entire Genome ◽  
Protein Coding ◽  
Large Sets ◽  
Pressure Analysis

Large-scale molecular evolutionary analyses of protein coding sequences requires a number of preparatory inter-related steps from finding gene families, to generating alignments and phylogenetic trees and assessing selective pressure variation. Each phase of these analyses can represent significant challenges particularly when working with the entire genome of large sets of species. We present VESPA, software capable of automating a selective pressure analysis using codeML in addition to the preparatory analyses and summary statistics. VESPA is written in python and is designed to run within a UNIX environment. Large-scale gene family identification, sequence alignment, and phylogeny reconstruction are all important aspects of large-scale molecular evolutionary analyses. VESPA provides flexible software for simplifying these processes along with downstream selective pressure variation analyses. The software automatically interprets results from codeML and produces simplified summary files to assist the user in better understanding the results. VESPA may be found at the following website: www.mol-evol.org/VESPA

scholarly journals VESPA: Very large-scale Evolutionary and Selective Pressure Analyses

2016 ◽  
Author(s):  
Andrew E. Webb ◽  
Thomas A. Walsh ◽  
Mary J O'Connell
Keyword(s):  
Phylogenetic Trees ◽  
Large Scale ◽  
Selective Pressure ◽  
Gene Families ◽  
Pressure Variation ◽  
Phylogeny Reconstruction ◽  
Entire Genome ◽  
Protein Coding ◽  
Large Sets ◽  
Pressure Analysis

Large-scale molecular evolutionary analyses of protein coding sequences requires a number of preparatory inter-related steps from finding gene families, to generating alignments and phylogenetic trees and assessing selective pressure variation. Each phase of these analyses can represent significant challenges particularly when working with the entire genome of large sets of species. We present VESPA, software capable of automating a selective pressure analysis using codeML in addition to the preparatory analyses and summary statistics. VESPA is written in python and is designed to run within a UNIX environment. Large-scale gene family identification, sequence alignment, and phylogeny reconstruction are all important aspects of large-scale molecular evolutionary analyses. VESPA provides flexible software for simplifying these processes along with downstream selective pressure variation analyses. The software automatically interprets results from codeML and produces simplified summary files to assist the user in better understanding the results. VESPA may be found at the following website: www.mol-evol.org/VESPA

scholarly journals VESPA: Very large-scale Evolutionary and Selective Pressure Analyses

2017 ◽  
Author(s):  
Andrew E. Webb ◽  
Thomas A. Walsh ◽  
Mary J O'Connell
Keyword(s):  
Phylogenetic Trees ◽  
Large Scale ◽  
Selective Pressure ◽  
Gene Families ◽  
Pressure Variation ◽  
Phylogeny Reconstruction ◽  
Entire Genome ◽  
Protein Coding ◽  
Large Sets ◽  
Pressure Analysis

Large-scale molecular evolutionary analyses of protein coding sequences requires a number of preparatory inter-related steps from finding gene families, to generating alignments and phylogenetic trees and assessing selective pressure variation. Each phase of these analyses can represent significant challenges particularly when working with the entire genome of large sets of species. We present VESPA, software capable of automating a selective pressure analysis using codeML in addition to the preparatory analyses and summary statistics. VESPA is written in python and is designed to run within a UNIX environment. Large-scale gene family identification, sequence alignment, and phylogeny reconstruction are all important aspects of large-scale molecular evolutionary analyses. VESPA provides flexible software for simplifying these processes along with downstream selective pressure variation analyses. The software automatically interprets results from codeML and produces simplified summary files to assist the user in better understanding the results. VESPA may be found at the following website: www.mol-evol.org/VESPA

scholarly journals VESPA: Very large-scale Evolutionary and Selective Pressure Analyses

2017 ◽  
Vol 3 ◽  
pp. e118 ◽  
Author(s):  
Andrew E. Webb ◽  
Thomas A. Walsh ◽  
Mary J. O’Connell
Keyword(s):  
Phylogenetic Trees ◽  
Large Scale ◽  
Selective Pressure ◽  
Gene Families ◽  
Pressure Variation ◽  
Phylogeny Reconstruction ◽  
Protein Coding ◽  
Coding Sequences ◽  
A Genome ◽  
Pressure Analysis

Background Large-scale molecular evolutionary analyses of protein coding sequences requires a number of preparatory inter-related steps from finding gene families, to generating alignments and phylogenetic trees and assessing selective pressure variation. Each phase of these analyses can represent significant challenges, particularly when working with entire proteomes (all protein coding sequences in a genome) from a large number of species. Methods We present VESPA, software capable of automating a selective pressure analysis using codeML in addition to the preparatory analyses and summary statistics. VESPA is written in python and Perl and is designed to run within a UNIX environment. Results We have benchmarked VESPA and our results show that the method is consistent, performs well on both large scale and smaller scale datasets, and produces results in line with previously published datasets. Discussion Large-scale gene family identification, sequence alignment, and phylogeny reconstruction are all important aspects of large-scale molecular evolutionary analyses. VESPA provides flexible software for simplifying these processes along with downstream selective pressure variation analyses. The software automatically interprets results from codeML and produces simplified summary files to assist the user in better understanding the results. VESPA may be found at the following website: http://www.mol-evol.org/VESPA.

scholarly journals GeneRax: A tool for species tree-aware maximum likelihood based gene family tree inference under gene duplication, transfer, and loss

2019 ◽  
Author(s):  
Benoit Morel ◽  
Alexey M. Kozlov ◽  
Alexandros Stamatakis ◽  
Gergely J. Szöllősi
Keyword(s):  
Maximum Likelihood ◽  
Phylogenetic Trees ◽  
Large Scale ◽  
Simulated Data ◽  
Gene Families ◽  
Species Tree ◽  
Homologous Gene ◽  
Sequence Alignments ◽  
Full Likelihood ◽  
True Tree

AbstractInferring phylogenetic trees for individual homologous gene families is difficult because alignments are often too short, and thus contain insufficient signal, while substitution models inevitably fail to capture the complexity of the evolutionary processes. To overcome these challenges species tree-aware methods also leverage information from a putative species tree. However, only few methods are available that implement a full likelihood framework or account for horizontal gene transfers. Furthermore, these methods often require expensive data pre-processing (e.g., computing bootstrap trees), and rely on approximations and heuristics that limit the degree of tree space exploration. Here we present GeneRax, the first maximum likelihood species tree-aware phylogenetic inference software. It simultaneously accounts for substitutions at the sequence level as well as gene level events, such as duplication, transfer, and loss relying on established maximum likelihood optimization algorithms. GeneRax can infer rooted phylogenetic trees for multiple gene families, directly from the per-gene sequence alignments and a rooted, yet undated, species tree. We show that compared to competing tools, on simulated data GeneRax infers trees that are the closest to the true tree in 90% of the simulations in terms of relative Robinson-Foulds distance. On empirical datasets, GeneRax is the fastest among all tested methods when starting from aligned sequences, and it infers trees with the highest likelihood score, based on our model. GeneRax completed tree inferences and reconciliations for 1099 Cyanobacteria families in eight minutes on 512 CPU cores. Thus, its parallelization scheme enables large-scale analyses. GeneRax is available under GNU GPL at https://github.com/BenoitMorel/GeneRax.

scholarly journals Exploration of the Germline Genome of the Ciliate Chilodonella uncinata through Single-Cell Omics (Transcriptomics and Genomics)

mBio ◽  
2018 ◽  
Vol 9 (1) ◽  
Author(s):  
Xyrus X. Maurer-Alcalá ◽  
Rob Knight ◽  
Laura A. Katz
Keyword(s):  
Single Cell ◽  
Large Scale ◽  
Gc Content ◽  
Gene Families ◽  
Genome Rearrangements ◽  
Paramecium Tetraurelia ◽  
Protein Coding ◽  
Genome Data ◽  
Large Gene ◽  
Disproportionate Number

ABSTRACTSeparate germline and somatic genomes are found in numerous lineages across the eukaryotic tree of life, often separated into distinct tissues (e.g., in plants, animals, and fungi) or distinct nuclei sharing a common cytoplasm (e.g., in ciliates and some foraminifera). In ciliates, germline-limited (i.e., micronuclear-specific) DNA is eliminated during the development of a new somatic (i.e., macronuclear) genome in a process that is tightly linked to large-scale genome rearrangements, such as deletions and reordering of protein-coding sequences. Most studies of germline genome architecture in ciliates have focused on the model ciliatesOxytricha trifallax,Paramecium tetraurelia, andTetrahymena thermophila, for which the complete germline genome sequences are known. Outside of these model taxa, only a few dozen germline loci have been characterized from a limited number of cultivable species, which is likely due to difficulties in obtaining sufficient quantities of “purified” germline DNA in these taxa. Combining single-cell transcriptomics and genomics, we have overcome these limitations and provide the first insights into the structure of the germline genome of the ciliateChilodonella uncinata, a member of the understudied classPhyllopharyngea. Our analyses reveal the following: (i) large gene families contain a disproportionate number of genes from scrambled germline loci; (ii) germline-soma boundaries in the germline genome are demarcated by substantial shifts in GC content; (iii) single-cell omics techniques provide large-scale quality germline genome data with limited effort, at least for ciliates with extensively fragmented somatic genomes. Our approach provides an efficient means to understand better the evolution of genome rearrangements between germline and soma in ciliates.IMPORTANCEOur understanding of the distinctions between germline and somatic genomes in ciliates has largely relied on studies of a few model genera (e.g.,Oxytricha,Paramecium,Tetrahymena). We have used single-cell omics to explore germline-soma distinctions in the ciliateChilodonella uncinata, which likely diverged from the better-studied ciliates ~700 million years ago. The analyses presented here indicate that developmentally regulated genome rearrangements between germline and soma are demarcated by rapid transitions in local GC composition and lead to diversification of protein families. The approaches used here provide the basis for future work aimed at discerning the evolutionary impacts of germline-soma distinctions among diverse ciliates.

scholarly journals GeneRax: A Tool for Species-Tree-Aware Maximum Likelihood-Based Gene  Family Tree Inference under Gene Duplication, Transfer, and Loss

2020 ◽  
Vol 37 (9) ◽  
pp. 2763-2774 ◽  
Author(s):  
Benoit Morel ◽  
Alexey M Kozlov ◽  
Alexandros Stamatakis ◽  
Gergely J Szöllősi
Keyword(s):  
Maximum Likelihood ◽  
Phylogenetic Trees ◽  
Large Scale ◽  
Simulated Data ◽  
Gene Families ◽  
Species Tree ◽  
Homologous Gene ◽  
Sequence Alignments ◽  
Full Likelihood ◽  
True Tree

Abstract Inferring phylogenetic trees for individual homologous gene families is difficult because alignments are often too short, and thus contain insufficient signal, while substitution models inevitably fail to capture the complexity of the evolutionary processes. To overcome these challenges, species-tree-aware methods also leverage information from a putative species tree. However, only few methods are available that implement a full likelihood framework or account for horizontal gene transfers. Furthermore, these methods often require expensive data preprocessing (e.g., computing bootstrap trees) and rely on approximations and heuristics that limit the degree of tree space exploration. Here, we present GeneRax, the first maximum likelihood species-tree-aware phylogenetic inference software. It simultaneously accounts for substitutions at the sequence level as well as gene level events, such as duplication, transfer, and loss relying on established maximum likelihood optimization algorithms. GeneRax can infer rooted phylogenetic trees for multiple gene families, directly from the per-gene sequence alignments and a rooted, yet undated, species tree. We show that compared with competing tools, on simulated data GeneRax infers trees that are the closest to the true tree in 90% of the simulations in terms of relative Robinson–Foulds distance. On empirical data sets, GeneRax is the fastest among all tested methods when starting from aligned sequences, and it infers trees with the highest likelihood score, based on our model. GeneRax completed tree inferences and reconciliations for 1,099 Cyanobacteria families in 8 min on 512 CPU cores. Thus, its parallelization scheme enables large-scale analyses. GeneRax is available under GNU GPL at https://github.com/BenoitMorel/GeneRax (last accessed June 17, 2020).  

scholarly journals High‐quality genomes reveal significant genetic divergence and cryptic speciation in the model organism Folsomia candida (Collembola)

2021 ◽  
Author(s):  
Yun-Xia Luan ◽  
Yingying Cui ◽  
Wan-Jun Chen ◽  
Jianfeng Jin ◽  
Ai-Min Liu ◽  
...  
Keyword(s):  
Large Scale ◽  
Test Organism ◽  
Gene Families ◽  
Species Differentiation ◽  
Folsomia Candida ◽  
Cryptic Speciation ◽  
High Quality ◽  
Protein Coding ◽  
Protein Coding Genes ◽  
Soil Arthropod

The collembolan Folsomia candida Willem, 1902, is an important representative soil arthropod that is widely distributed throughout the world and has been frequently used as a test organism in soil ecology and ecotoxicology studies. However, it is questioned as an ideal “standard” because of differences in reproductive modes and cryptic genetic diversity between strains from various geographical origins. In this study, we present two high-quality chromosome-level genomes of F. candida, for the parthenogenetic Danish strain (FCDK, 219.08 Mb, N50 of 38.47 Mb, 25,139 protein-coding genes) and the sexual Shanghai strain (FCSH, 153.09 Mb, N50 of 25.75 Mb, 21,609 protein-coding genes). The seven chromosomes of FCDK are each 25–54% larger than the corresponding chromosomes of FCSH, showing obvious repetitive element expansions and large-scale inversions and translocations but no whole-genome duplication. The strain-specific genes, expanded gene families and genes in nonsyntenic chromosomal regions identified in FCDK are highly related to its broader environmental adaptation. In addition, the overall sequence identity of the two mitogenomes is only 78.2%, and FCDK has fewer strain-specific microRNAs than FCSH. In conclusion, FCDK and FCSH have accumulated independent genetic changes and evolved into distinct species since diverging 10 Mya. Our work shows that F. candida represents a good model of rapidly cryptic speciation. Moreover, it provides important genomic resources for studying the mechanisms of species differentiation, soil arthropod adaptation to soil ecosystems, and Wolbachia-induced parthenogenesis as well as the evolution of Collembola, a pivotal phylogenetic clade between Crustacea and Insecta.

scholarly journals Comparative transcriptomic analysis reveals conserved transcriptional programs underpinning organogenesis and reproduction in land plants

2020 ◽  
Author(s):  
Irene Julca ◽  
Camilla Ferrari ◽  
María Flores-Tornero ◽  
Sebastian Proost ◽  
Ann-Cathrin Lindner ◽  
...  
Keyword(s):  
Phylogenetic Trees ◽  
Large Scale ◽  
Expression Profiles ◽  
Gene Families ◽  
Male Reproduction ◽  
Land Plants ◽  
Easy Access ◽  
Specific Gene ◽  
Male Gametes ◽  
Organ Specific

AbstractThe evolution of plant organs, including leaves, stems, roots, and flowers, mediated the explosive radiation of land plants, which shaped the biosphere and allowed the establishment of terrestrial animal life. Furthermore, the fertilization products of angiosperms, seeds serve as the basis for most of our food. The evolution of organs and immobile gametes required the coordinated acquisition of novel gene functions, the co-option of existing genes, and the development of novel regulatory programs. However, our knowledge of these events is limited, as no large-scale analyses of genomic and transcriptomic data have been performed for land plants. To remedy this, we have generated gene expression atlases for various organs and gametes of 10 plant species comprising bryophytes, vascular plants, gymnosperms, and flowering plants. Comparative analysis of the atlases identified hundreds of organ- and gamete-specific gene families and revealed that most of the specific transcriptomes are significantly conserved. Interestingly, the appearance of organ-specific gene families does not coincide with the corresponding organ’s appearance, suggesting that co-option of existing genes is the main mechanism for evolving new organs. In contrast to female gametes, male gametes showed a high number and conservation of specific genes, suggesting that male reproduction is highly specialized. The expression atlas capturing pollen development revealed numerous transcription factors and kinases essential for pollen biogenesis and function. To provide easy access to the expression atlases and these comparative analyses, we provide an online database, www.evorepro.plant.tools, that allows the exploration of expression profiles, organ-specific genes, phylogenetic trees, co-expression networks, and others.

scholarly journals Mutational patterns and clonal evolution from diagnosis to relapse in pediatric acute lymphoblastic leukemia

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shumaila Sayyab ◽  
Anders Lundmark ◽  
Malin Larsson ◽  
Markus Ringnér ◽  
Sara Nystedt ◽  
...  
Keyword(s):  
Acute Lymphoblastic Leukemia ◽  
Large Scale ◽  
Somatic Mutations ◽  
Lymphoblastic Leukemia ◽  
Clonal Evolution ◽  
Point Mutations ◽  
Driver Genes ◽  
Protein Coding ◽  
Pediatric Acute Lymphoblastic Leukemia ◽  
Evolutionary Trajectories

AbstractThe mechanisms driving clonal heterogeneity and evolution in relapsed pediatric acute lymphoblastic leukemia (ALL) are not fully understood. We performed whole genome sequencing of samples collected at diagnosis, relapse(s) and remission from 29 Nordic patients. Somatic point mutations and large-scale structural variants were called using individually matched remission samples as controls, and allelic expression of the mutations was assessed in ALL cells using RNA-sequencing. We observed an increased burden of somatic mutations at relapse, compared to diagnosis, and at second relapse compared to first relapse. In addition to 29 known ALL driver genes, of which nine genes carried recurrent protein-coding mutations in our sample set, we identified putative non-protein coding mutations in regulatory regions of seven additional genes that have not previously been described in ALL. Cluster analysis of hundreds of somatic mutations per sample revealed three distinct evolutionary trajectories during ALL progression from diagnosis to relapse. The evolutionary trajectories provide insight into the mutational mechanisms leading relapse in ALL and could offer biomarkers for improved risk prediction in individual patients.

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