0
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      The complete mitochondrial genome of the springtail Allacma fusca, the internal phylogenetic relationships and gene order of Symphypleona

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Symphypleona ( sensu stricto) are a group of Collembola (=springtails) that, despite displaying some variation in gene order, have been poorly investigated under the phylomitogenomic perspective. How families and subfamilies of this taxon are evolutionary related is still partially unknown. For this reason we sequenced, and herein described, the complete mitochondrial genome sequence of Allacma fusca (Sminthuridae). This sequence, alongside others from the literature, is here used to study the phylogenetic relationships among Symphypleona.

          Related collections

          Most cited references 12

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          MrBayes 3.2: Efficient Bayesian Phylogenetic Inference and Model Choice Across a Large Model Space

          Since its introduction in 2001, MrBayes has grown in popularity as a software package for Bayesian phylogenetic inference using Markov chain Monte Carlo (MCMC) methods. With this note, we announce the release of version 3.2, a major upgrade to the latest official release presented in 2003. The new version provides convergence diagnostics and allows multiple analyses to be run in parallel with convergence progress monitored on the fly. The introduction of new proposals and automatic optimization of tuning parameters has improved convergence for many problems. The new version also sports significantly faster likelihood calculations through streaming single-instruction-multiple-data extensions (SSE) and support of the BEAGLE library, allowing likelihood calculations to be delegated to graphics processing units (GPUs) on compatible hardware. Speedup factors range from around 2 with SSE code to more than 50 with BEAGLE for codon problems. Checkpointing across all models allows long runs to be completed even when an analysis is prematurely terminated. New models include relaxed clocks, dating, model averaging across time-reversible substitution models, and support for hard, negative, and partial (backbone) tree constraints. Inference of species trees from gene trees is supported by full incorporation of the Bayesian estimation of species trees (BEST) algorithms. Marginal model likelihoods for Bayes factor tests can be estimated accurately across the entire model space using the stepping stone method. The new version provides more output options than previously, including samples of ancestral states, site rates, site d N /d S rations, branch rates, and node dates. A wide range of statistics on tree parameters can also be output for visualization in FigTree and compatible software.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Integrative Genomics Viewer

            To the Editor Rapid improvements in sequencing and array-based platforms are resulting in a flood of diverse genome-wide data, including data from exome and whole genome sequencing, epigenetic surveys, expression profiling of coding and non-coding RNAs, SNP and copy number profiling, and functional assays. Analysis of these large, diverse datasets holds the promise of a more comprehensive understanding of the genome and its relation to human disease. Experienced and knowledgeable human review is an essential component of this process, complementing computational approaches. This calls for efficient and intuitive visualization tools able to scale to very large datasets and to flexibly integrate multiple data types, including clinical data. However, the sheer volume and scope of data poses a significant challenge to the development of such tools. To address this challenge we developed the Integrative Genomics Viewer (IGV), a lightweight visualization tool that enables intuitive real-time exploration of diverse, large-scale genomic datasets on standard desktop computers. It supports flexible integration of a wide range of genomic data types including aligned sequence reads, mutations, copy number, RNAi screens, gene expression, methylation, and genomic annotations (Figure S1). The IGV makes use of efficient, multi-resolution file formats to enable real-time exploration of arbitrarily large datasets over all resolution scales, while consuming minimal resources on the client computer (see Supplementary Text). Navigation through a dataset is similar to Google Maps, allowing the user to zoom and pan seamlessly across the genome at any level of detail from whole-genome to base pair (Figure S2). Datasets can be loaded from local or remote sources, including cloud-based resources, enabling investigators to view their own genomic datasets alongside publicly available data from, for example, The Cancer Genome Atlas (TCGA) 1 , 1000 Genomes (www.1000genomes.org/), and ENCODE 2 (www.genome.gov/10005107) projects. In addition, IGV allows collaborators to load and share data locally or remotely over the Web. IGV supports concurrent visualization of diverse data types across hundreds, and up to thousands of samples, and correlation of these integrated datasets with clinical and phenotypic variables. A researcher can define arbitrary sample annotations and associate them with data tracks using a simple tab-delimited file format (see Supplementary Text). These might include, for example, sample identifier (used to link different types of data for the same patient or tissue sample), phenotype, outcome, cluster membership, or any other clinical or experimental label. Annotations are displayed as a heatmap but more importantly are used for grouping, sorting, filtering, and overlaying diverse data types to yield a comprehensive picture of the integrated dataset. This is illustrated in Figure 1, a view of copy number, expression, mutation, and clinical data from 202 glioblastoma samples from the TCGA project in a 3 kb region around the EGFR locus 1, 3 . The investigator first grouped samples by tumor subtype, then by data type (copy number and expression), and finally sorted them by median copy number over the EGFR locus. A shared sample identifier links the copy number and expression tracks, maintaining their relative sort order within the subtypes. Mutation data is overlaid on corresponding copy number and expression tracks, based on shared participant identifier annotations. Several trends in the data stand out, such as a strong correlation between copy number and expression and an overrepresentation of EGFR amplified samples in the Classical subtype. IGV’s scalable architecture makes it well suited for genome-wide exploration of next-generation sequencing (NGS) datasets, including both basic aligned read data as well as derived results, such as read coverage. NGS datasets can approach terabytes in size, so careful management of data is necessary to conserve compute resources and to prevent information overload. IGV varies the displayed level of detail according to resolution scale. At very wide views, such as the whole genome, IGV represents NGS data by a simple coverage plot. Coverage data is often useful for assessing overall quality and diagnosing technical issues in sequencing runs (Figure S3), as well as analysis of ChIP-Seq 4 and RNA-Seq 5 experiments (Figures S4 and S5). As the user zooms below the ~50 kb range, individual aligned reads become visible (Figure 2) and putative SNPs are highlighted as allele counts in the coverage plot. Alignment details for each read are available in popup windows (Figures S6 and S7). Zooming further, individual base mismatches become visible, highlighted by color and intensity according to base call and quality. At this level, the investigator may sort reads by base, quality, strand, sample and other attributes to assess the evidence of a variant. This type of visual inspection can be an efficient and powerful tool for variant call validation, eliminating many false positives and aiding in confirmation of true findings (Figures S6 and S7). Many sequencing protocols produce reads from both ends (“paired ends”) of genomic fragments of known size distribution. IGV uses this information to color-code paired ends if their insert sizes are larger than expected, fall on different chromosomes, or have unexpected pair orientations. Such pairs, when consistent across multiple reads, can be indicative of a genomic rearrangement. When coloring aberrant paired ends, each chromosome is assigned a unique color, so that intra- (same color) and inter- (different color) chromosomal events are readily distinguished (Figures 2 and S8). We note that misalignments, particularly in repeat regions, can also yield unexpected insert sizes, and can be diagnosed with the IGV (Figure S9). There are a number of stand-alone, desktop genome browsers available today 6 including Artemis 7 , EagleView 8 , MapView 9 , Tablet 10 , Savant 11 , Apollo 12 , and the Integrated Genome Browser 13 . Many of them have features that overlap with IGV, particularly for NGS sequence alignment and genome annotation viewing. The Integrated Genome Browser also supports viewing array-based data. See Supplementary Table 1 and Supplementary Text for more detail. IGV focuses on the emerging integrative nature of genomic studies, placing equal emphasis on array-based platforms, such as expression and copy-number arrays, next-generation sequencing, as well as clinical and other sample metadata. Indeed, an important and unique feature of IGV is the ability to view all these different data types together and to use the sample metadata to dynamically group, sort, and filter datasets (Figure 1 above). Another important characteristic of IGV is fast data loading and real-time pan and zoom – at all scales of genome resolution and all dataset sizes, including datasets comprising hundreds of samples. Finally, we have placed great emphasis on the ease of installation and use of IGV, with the goal of making both the viewing and sharing of their data accessible to non-informatics end users. IGV is open source software and freely available at http://www.broadinstitute.org/igv/, including full documentation on use of the software. Supplementary Material 1
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              NOVOPlasty: de novo assembly of organelle genomes from whole genome data

              Abstract The evolution in next-generation sequencing (NGS) technology has led to the development of many different assembly algorithms, but few of them focus on assembling the organelle genomes. These genomes are used in phylogenetic studies, food identification and are the most deposited eukaryotic genomes in GenBank. Producing organelle genome assembly from whole genome sequencing (WGS) data would be the most accurate and least laborious approach, but a tool specifically designed for this task is lacking. We developed a seed-and-extend algorithm that assembles organelle genomes from whole genome sequencing (WGS) data, starting from a related or distant single seed sequence. The algorithm has been tested on several new (Gonioctena intermedia and Avicennia marina) and public (Arabidopsis thaliana and Oryza sativa) whole genome Illumina data sets where it outperforms known assemblers in assembly accuracy and coverage. In our benchmark, NOVOPlasty assembled all tested circular genomes in less than 30 min with a maximum memory requirement of 16 GB and an accuracy over 99.99%. In conclusion, NOVOPlasty is the sole de novo assembler that provides a fast and straightforward extraction of the extranuclear genomes from WGS data in one circular high quality contig. The software is open source and can be downloaded at https://github.com/ndierckx/NOVOPlasty.
                Bookmark

                Author and article information

                Journal
                Mitochondrial DNA B Resour
                Mitochondrial DNA B Resour
                Mitochondrial DNA. Part B, Resources
                Taylor & Francis
                2380-2359
                30 July 2020
                2020
                : 5
                : 3
                : 3103-3105
                Affiliations
                Department of Life Sciences, University of Siena , Siena, Italy
                Author notes
                CONTACT Claudio Cucini claudio.cucini@ 123456student.unisi.it Department of Life Sciences, University of Siena , Siena, Italy
                Article
                1800425
                10.1080/23802359.2020.1800425
                7782307
                © 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                Page count
                Figures: 1, Tables: 0, Pages: 3, Words: 1452
                Product
                Categories
                Research Article
                Mitogenome Announcement

                Comments

                Comment on this article