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Fig. 3 in Sequencing and analysis of the complete mitochondrial genome of the giant dobsonfly Acanthacorydalis orientalis (McLachlan) (Insecta: Megaloptera: Corydalidae)
Fig. 3. Predicted secondary structure of the rrnl in the Acanthacorydalis orientalis mt genome. Roman numerals denote the conserved Watson-Crick base pairing and dot (•) indicates G-U base pairing.
Fig. 4 in Sequencing and analysis of the complete mitochondrial genome of the giant dobsonfly Acanthacorydalis orientalis (McLachlan) (Insecta: Megaloptera: Corydalidae)
Fig. 4. Predicted secondary structure of the rrns in the A. orientalis mt genome. Roman numerals denote the conserved domain structure. Dash (-) indicates Watson-Crick base pairing and dot (•) indicates G-U base pairing.
Fig. 1 in Sequencing and analysis of the complete mitochondrial genome of the giant dobsonfly Acanthacorydalis orientalis (McLachlan) (Insecta: Megaloptera: Corydalidae)
Fig. 1. Mitochondrial genome map of Acanthacorydalis orientalis. The tRNAs are denoted by the color blocks and are labeled according to the IUPACIUB single-letter amino acid codes. Gene name without underline indicates the direction of transcription
Fig. 2 in Sequencing and analysis of the complete mitochondrial genome of the giant dobsonfly Acanthacorydalis orientalis (McLachlan) (Insecta: Megaloptera: Corydalidae)
Fig. 2. Inferred secondary structure of 22 tRNAs of the Acanthacorydalis orientalis mt genome. The tRNAs are labeled with the abbreviations of their corresponding amino acids. Dash (-) indicates Watson-Crick bonds and dot (·) indicates GU bonds.
Fig. 5 in Sequencing and analysis of the complete mitochondrial genome of the giant dobsonfly Acanthacorydalis orientalis (McLachlan) (Insecta: Megaloptera: Corydalidae)
Fig. 5. Phylogenetic relationships among the sequenced Megaloptera insects. Numbers at the nodes are Bayesian posterior probabilities (left) and ML bootstrap values (right).
Fig. 6 in Complete mitochondrial genome sequence of Bonasa sewerzowi (Galliformes: Phasianidae) and phylogenetic analysis
Fig. 6. The phylogenetic relationship of Bonasa among Galliformes based on the complete mitogenome. Branch lengths and topologies were obtained from Maximum Likelihood analyses. The numbers were the bootstrap values of MP/ML/BI trees in turn. * indicates that MP or BI tree was inconsistent with ML tree.
Fig. 4 in Complete mitochondrial genome sequence of Bonasa sewerzowi (Galliformes: Phasianidae) and phylogenetic analysis
Fig. 4. The structure of CR in Bonasa sewerzowi mitochondrial genome and comparasion with B. bonasia.
Fig. 5 in Complete mitochondrial genome sequence of Bonasa sewerzowi (Galliformes: Phasianidae) and phylogenetic analysis
Fig. 5. Nucleotide composition of different partitions from two Bonasa mitogenomes. AT-skew, (A-T)/(A+T); GC-skew, (G-C)/(G+C); PCG-1st, the first codon positions of PCGs; PCG-2nd, the second codon positions of PCGs; PCG-3rd, the third codon positions of PCGs.
Fig. 3 in Complete mitochondrial genome sequence of Bonasa sewerzowi (Galliformes: Phasianidae) and phylogenetic analysis
Fig. 3. The srRNA secondary structure of Bonasa sewerzowi mitogenome and comparasion with B. bonasia. The different nucleotides in B. bonasia was pointed out.
Fig. 1 in Complete mitochondrial genome sequence of Bonasa sewerzowi (Galliformes: Phasianidae) and phylogenetic analysis
Fig. 1. Gene map of the B. sewerzowi mitochondrial genome. Transfer RNA genes are designated by single-letter amino acid codes. L1, L2, S1, and S2 denote trnL (uur), trnL (cun), trnS (ucn) and trnS (agy), respectively.
Fig. 2 in Complete mitochondrial genome sequence of Bonasa sewerzowi (Galliformes: Phasianidae) and phylogenetic analysis
Fig. 2. The lrRNA secondary structure of Bonasa sewerzowi mitogenome and comparasion with B. bonasia. The different nucleotides in B.
Genome annotations of Drosophila melanogaster and Drosophila simulans wild-type strains from long read sequencing assemblies
<p>Genome assemblies were performed for eight wild-type strains of Drosophila melanogaster and Drosophila simulans from Oxford Nanopore long read sequencing (please refer to Mohamed et al. Cells 2020 (doi:10.3390/cells9081776)). Assemblies were deposited in the European Nucleotide Archive (ENA) at EMBL-EBI under accession number PRJEB50024 (<a href="https://www.ebi.ac.uk/ena/browser/view/PRJEBxxxx">https://www.ebi.ac.uk/ena/browser/view/</a>PRJEB50024).</p> <p>Transposable Element annotations: we used RepeatMasker 4.1.0 (<a href="http://repeatmasker.org/">http://repeatmasker.org/</a>) -species Drosophila, followed by OneCodeToFindThemAll (Bailly-Bechet et al. 2014) with default parameters.</p> <p>Gene annotations: We retrieved gtf files from FlyBase : <a>ftp.flybase.net/genomes/D</a><a>rosophila_melanogaster/dmel_r6,46_FB2022_03/gft/dmel-all-r6.46.gtf.gz</a> and <a>ftp.flybase.net/genomes/Drosophila_simulans/dsim_r2,02_FB2017_04/gtf/dsim-all-</a><a>r2,02.gtf.gz</a>. The corresponding fasta files were also downloaded from FlyBase: <a>ftp.flybase.net/genomes/Drosophila_melanogaster/dmel_r6,46_FB2022_03/</a><a>fasta</a><a>/dmel-all-</a><a>chromosome-</a><a>r6.46.</a><a>fasta</a><a>.gz</a> and <a>ftp.flybase.net/genomes/Drosophila_simulans/dsim_r2,02_FB2017_04/</a><a>fasta</a><a>/dsim-all-</a><a>chromosome-</a><a>r2,02.</a><a>fasta</a><a>.gz</a>. We used Liftoff (Shumate and Salzberg, 2020) to lift over gene annotations from the references to our genome assemblies. We used -flank 0.2 and only kept the “gene” and “exon” terms.</p>
Figure 2. A in Mitochondrial Dna Sequence Data Indicate Evidence For Multiple Species Within Peromyscus Maniculatus
Figure 2. A) Phylogenetic tree generated using Bayesian (MrBayes; Huelsenbeck and Ronquist 2001), maximum likelihood (RAxML; Version 8.1.17, Stamatakis 2006), and parsimony methods (PAUP* v. 4.0a165, Swofford 2002) and DNA sequence data from the mitochondrial cytochrome-b gene. The topology depicted is from the Bayesian analysis. Clade probability values (≥ 0.95) for the Bayesian analysis are indicated by an asterisk (*) and are to the left of the first slash, bootstrap values for the maximum likelihood analysis are shown between the two slashes, and bootstrap values obtained from the parsimony analysis are to the right of the last slash. Line at bottom of figure depicts the nucleotide substitution rate per site per million years. B) Same phylogenetic tree as depicted in Figure 2A except unsupported nodes (C, G, and H) were collapsed.
Figure 4. Approximate distributions and associated divergence times for A in Mitochondrial Dna Sequence Data Indicate Evidence For Multiple Species Within Peromyscus Maniculatus
Figure 4. Approximate distributions and associated divergence times for A) Peromyscus maniculatus-like ancestor; B) P. melanotis-like ancestor; C) P. gambelii/keeni/sejugis/sp.-like ancestor; D) P. polionotus-like ancestor; E) P. sonoriensis-like ancestor; F) P. labecula and P. maniculatus - like ancestor; G) P. keeni/sp.-like ancestor; and H) P. keeni-like, P. gambelii-like, P. sejugis-like, and P. sp.-like ancestors. Divergence times were estimated from the BEAST analysis (Version 2.4, Bouckaert et al. 2014) of the mitochondrial cytochrome-b gene dataset (see Fig. 3). Shading schemes that correspond to species distributions are shown in the inset.
Figure 1 in Mitochondrial Dna Sequence Data Indicate Evidence For Multiple Species Within Peromyscus Maniculatus
Figure 1. Distribution of selected populations and species of the Peromyscus maniculatus species group from Canada, Mexico, and the United States. Shaded areas represent distributions of taxa (defined in figure insert) as originally defined by Hall (1981) and modified based on the results of this study. Closed circles represent collecting localities listed in the Appendix; note that multiple individuals may be represented by a single closed circle. White boxes with black stars indicate type localities for each taxon and triangles indicate localities where haplotypes representing P. sonoriensis were found to be in sympatry with samples of P. gambelii and P. labecula, respectively.
Figure 3 in Mitochondrial Dna Sequence Data Indicate Evidence For Multiple Species Within Peromyscus Maniculatus
Figure 3. Time-calibrated ultrametric tree obtained from the BEAST analysis (Version 2.4, Bouckaert et al. 2014) of the mitochondrial cytochrome-b gene dataset. Scale bars at nodes represent the 95% highest posterior densities and numbers associated to each node are the estimated divergence times in million years ago.
Phertilizer: growing a clonal tree from single-cell DNA sequencing data of tumors
<p>The is the supplementary data repository for the simulation input data for Phertilizer: growing a clonal tree from single-cell DNA sequencing data of tumors.</p>
Supplementary data for: Comparison of optical flow derivation techniques for retrieving tropospheric winds from satellite image sequences
<p>This study introduces a validation technique for quantitative comparison of algorithms which retrieve winds from passive detection of cloud- and water vapor-drift motions, also known as Atmospheric Motion Vectors (AMVs). The technique leverages airborne wind-profiling lidar data collected in tandem with 1-min refresh rate geostationary satellite imagery. AMVs derived with different approaches are used with accompanying numerical weather prediction model data to estimate the full profiles of lidar-sampled winds which enables ranking of feature tracking, quality control, and height-assignment accuracy and encourages meso-scale, multi-layer, multi-band wind retrieval solutions. The technique is used to compare the performance of two brightness motion, or "optical flow," retrieval algorithms used within AMVs, 1) Patch Matching (PM; used within operational AMVs) and 2) an advanced Variational Optical Flow (VOF) method enabled for most atmospheric motions by new-generation imagers. The VOF AMVs produce more accurate wind retrievals than the PM method within the benchmark in all imager bands explored. It is further shown that image regions with low texture and multi-layer-cloud scenes in visible and infrared bands are tracked significantly better with the VOF approach, implying VOF produces representative AMVs where PM typically breaks down. It is also demonstrated that VOF AMVs have reduced accuracy where the brightness texture does not advect with the mean wind (e.g. gravity waves), where the image temporal noise exceeds the natural variability, and when the height-assignment is poor. Finally, it is found that VOF AMVs have improved performance when using fine-temporal refresh rate imagery, such as 1-min versus 10-min data.</p>
Data from: Bratzel et al. (2022) Target-enrichment sequencing reveals for the first time a well-resolved phylogeny of the core Bromelioideae (Bromeliaceae). Taxon
<p>DNA sequence alignments used for phylogenetic analyses in Bratzel et al. (2022) Target-enrichment sequencing reveals for the first time a well-resolved phylogeny of the core Bromelioideae (Bromeliaceae). Taxon.</p>
Single molecule, full-length transcript sequencing provides insight into the extreme metabolism of ruby-throated hummingbird Archilochus colubris
<p>Hummingbirds can support their high metabolic rates exclusively by oxidizing ingested sugars, which is unsurprising given their sugar-rich nectar diet and use of energetically expensive hovering flight. However, they cannot rely on dietary sugars as a fuel during fasting periods, such as during the night, at first light, or when undertaking long-distance migratory flights, and must instead rely exclusively on onboard lipids. This metabolic flexibility is remarkable both in that the birds can switch between exclusive use of each fuel type within minutes and in that de novo lipogenesis from dietary sugar precursors is the principle way in which fat stores are built, sometimes at exceptionally high rates, such as during the few days prior to a migratory flight. The hummingbird hepatopancreas is the principle location of de novo lipogenesis and likely plays a key role in fuel selection, fuel switching, and glucose homeostasis. Yet understanding how this tissue, and the whole organism, achieves and moderates high rates of energy turnover is hampered by a fundamental lack of information regarding how genes coding for relevant enzymes differ in their sequence, expression, and regulation in these unique animals. To address this knowledge gap, we generated a de novo transcriptome of the hummingbird liver using PacBio full-length cDNA sequencing (Iso-Seq), yielding a total of 8.6Gb of sequencing data, or 2.6M reads from 4 different size fractions. We analyzed data using the SMRTAnalysis v3.1 Iso-Seq pipeline, including classification of reads and clustering of isoforms (ICE) followed by error-correction (Arrow). We performed orthology analysis to identify closely related sequences between our transcriptome and other avian and human gene sets. We also aligned our transcriptome against the Calypte anna genome where possible. Finally, we closely examined homology of critical lipid metabolic genes between our transcriptome data and avian and human genomes. We confirmed high levels of sequence divergence within hummingbird lipogenic enzymes, suggesting a high probability of adaptive divergent function in the lipogenic liver pathways. Our results have leveraged cutting-edge technology and a novel bioinformatics pipeline to provide a compelling first direct look at the transcriptome of this incredible organism.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.