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      Phylogenomic analysis of protein‐coding genes resolves complex gall wasp relationships

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          Abstract

          Gall wasps (Hymenoptera: Cynipidae) comprise 13 distinct tribes whose interrelationships remain incompletely understood. Recent analyses of ultra‐conserved elements (UCEs) represent the first attempt at resolving these relationships using phylogenomics. Here, we present the first analysis based on protein‐coding sequences from genome and transcriptome assemblies. Unlike UCEs, these data allow more sophisticated substitution models, which can potentially resolve issues with long‐branch attraction. We include data for 37 cynipoid species, including two tribes missing in the UCE analysis: Aylacini (s. str.) and Qwaqwaiini. Our results confirm the UCE result that Cynipidae are not monophyletic. Specifically, the Paraulacini and Diplolepidini + Pediaspidini fall outside a core clade (Cynipidae s. str.), which is more closely related to the insect‐parasitic Figitidae, and this result is robust to the exclusion of long‐branch taxa that could mislead the analysis. Given this, we here divide the Cynipidae into three families: the Paraulacidae stat. prom., Diplolepididae stat. prom. and Cynipidae (s. str.). Our results suggest that the Eschatocerini are the sister group of the remaining Cynipidae (s. str.). Within the Cynipidae (s. str.), the Aylacini (s. str.) are more closely related to oak gall wasps (Cynipini) and some of their inquilines (Ceroptresini) than to other herb gallers (Aulacideini and Phanacidini), and the Qwaqwaiini likely form a clade together with Synergini (s. str.) and Rhoophilini. Several alternative scenarios for the evolution of cynipid life histories are compatible with the relationships suggested by our analysis, but all are complex and require multiple shifts among parasitoids, inquilines and gall inducers.

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          IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies

          Large phylogenomics data sets require fast tree inference methods, especially for maximum-likelihood (ML) phylogenies. Fast programs exist, but due to inherent heuristics to find optimal trees, it is not clear whether the best tree is found. Thus, there is need for additional approaches that employ different search strategies to find ML trees and that are at the same time as fast as currently available ML programs. We show that a combination of hill-climbing approaches and a stochastic perturbation method can be time-efficiently implemented. If we allow the same CPU time as RAxML and PhyML, then our software IQ-TREE found higher likelihoods between 62.2% and 87.1% of the studied alignments, thus efficiently exploring the tree-space. If we use the IQ-TREE stopping rule, RAxML and PhyML are faster in 75.7% and 47.1% of the DNA alignments and 42.2% and 100% of the protein alignments, respectively. However, the range of obtaining higher likelihoods with IQ-TREE improves to 73.3-97.1%.
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            SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing.

            The lion's share of bacteria in various environments cannot be cloned in the laboratory and thus cannot be sequenced using existing technologies. A major goal of single-cell genomics is to complement gene-centric metagenomic data with whole-genome assemblies of uncultivated organisms. Assembly of single-cell data is challenging because of highly non-uniform read coverage as well as elevated levels of sequencing errors and chimeric reads. We describe SPAdes, a new assembler for both single-cell and standard (multicell) assembly, and demonstrate that it improves on the recently released E+V-SC assembler (specialized for single-cell data) and on popular assemblers Velvet and SoapDeNovo (for multicell data). SPAdes generates single-cell assemblies, providing information about genomes of uncultivatable bacteria that vastly exceeds what may be obtained via traditional metagenomics studies. SPAdes is available online ( http://bioinf.spbau.ru/spades ). It is distributed as open source software.
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              fastp: an ultra-fast all-in-one FASTQ preprocessor

              Abstract Motivation Quality control and preprocessing of FASTQ files are essential to providing clean data for downstream analysis. Traditionally, a different tool is used for each operation, such as quality control, adapter trimming and quality filtering. These tools are often insufficiently fast as most are developed using high-level programming languages (e.g. Python and Java) and provide limited multi-threading support. Reading and loading data multiple times also renders preprocessing slow and I/O inefficient. Results We developed fastp as an ultra-fast FASTQ preprocessor with useful quality control and data-filtering features. It can perform quality control, adapter trimming, quality filtering, per-read quality pruning and many other operations with a single scan of the FASTQ data. This tool is developed in C++ and has multi-threading support. Based on our evaluation, fastp is 2–5 times faster than other FASTQ preprocessing tools such as Trimmomatic or Cutadapt despite performing far more operations than similar tools. Availability and implementation The open-source code and corresponding instructions are available at https://github.com/OpenGene/fastp.
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                Journal
                Systematic Entomology
                Systematic Entomology
                Wiley
                0307-6970
                1365-3113
                January 2024
                October 03 2023
                January 2024
                : 49
                : 1
                : 110-137
                Affiliations
                [1 ] Centre for Epidemiology and Planetary Health Scotland's Rural College Inverness UK
                [2 ] Department of Bioinformatics and Genetics Swedish Museum of Natural History Stockholm Sweden
                [3 ] Department of Zoology Stockholm University Stockholm Sweden
                [4 ] Departamento de Biodiversidad y Biología Evolutiva Museo Nacional de Ciencias Naturales (CSIC) Madrid Spain
                [5 ] UMR Évolution, Génomes, Comportement et Écologie, IRD, CNRS Université Paris‐Saclay Gif‐sur‐Yvette France
                [6 ] Australian National Insect Collection, CSIRO Canberra Australia
                [7 ] Institute of Ecology and Evolution University of Edinburgh Edinburgh UK
                [8 ] Laboratoire de Biométrie et Biologie Evolutive Université Claude Bernard Lyon 1 Lyon France
                Article
                10.1111/syen.12611
                6e27e568-2452-4e86-bce6-6e97b58d2b0b
                © 2024

                http://creativecommons.org/licenses/by/4.0/

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