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FIGURE 8 in The identity of the Javan Krait, Bungarus javanicus Kopstein, 1932 (Squamata: Elapidae): evidence from mitochondrial and nuclear DNA sequence analyses and morphology
FIGURE 8. Ventral view of the partially leucistic juvenile Bungarus candidus (UK 96-1) from Linggarjati. Photo by Ulrich Kuch.
FIGURE 7 in The identity of the Javan Krait, Bungarus javanicus Kopstein, 1932 (Squamata: Elapidae): evidence from mitochondrial and nuclear DNA sequence analyses and morphology
FIGURE 7. Juvenile Bungarus candidus (UK 96-1) from Linggarjati (Kabupaten Cirebon, West Java, Indonesia; 400– 500 m above sea level) with white snout, reduced black bands on anterior body, and white dorsals with dark tips on the rest of the body. Photo by Ulrich Kuch.
FIGURE 6 in The identity of the Javan Krait, Bungarus javanicus Kopstein, 1932 (Squamata: Elapidae): evidence from mitochondrial and nuclear DNA sequence analyses and morphology
FIGURE 6. Contour map of Java and Madura (Bali, adjacent to the east, not shown). Capital letters indicate collecting areas: A, Losarang (near Indramayu); B, area of the type locality of Bungarus javanicus (near Mt. Ciremai, e.g., Linggarjati); C, Purwokerto basin; D, coastal plain near Cilacap. Dots and circles, respectively, mark collecting localities of additional examined specimens and literature records of black-and-white banded Bungarus candidus.
FIGURE 5 in The identity of the Javan Krait, Bungarus javanicus Kopstein, 1932 (Squamata: Elapidae): evidence from mitochondrial and nuclear DNA sequence analyses and morphology
FIGURE 5. Black adult female Bungarus candidus (SMF 76271) from Linggarjati (Kabupaten Cirebon, West Java, Indonesia; 400–500 m above sea level). The colour pattern of this snake closely resembles that of the type specimen of Bungarus javanicus. Photo by Ulrich Kuch.
FIGURE 4 in The identity of the Javan Krait, Bungarus javanicus Kopstein, 1932 (Squamata: Elapidae): evidence from mitochondrial and nuclear DNA sequence analyses and morphology
FIGURE 4. Kopstein's (1936) third specimen of Bungarus javanicus, the 'intermediary' snake from Linggarjati (ZRC 2.4379). Photo by Ulrich Kuch.
FIGURE 1 in The identity of the Javan Krait, Bungarus javanicus Kopstein, 1932 (Squamata: Elapidae): evidence from mitochondrial and nuclear DNA sequence analyses and morphology
FIGURE 1. Line drawing of the type specimen of Bungarus javanicus. From the original description (Kopstein 1932).
Inlist and Source Code Files for "Fossil Signatures of Main-sequence Convective Core Overshoot Estimated through Asteroseismic Analyses"
<p>MESA (r12778) and GYRE (version 6.0) inlist files used in the work described in "Fossil Signatures of Main-sequence Convective Core Overshoot Estimated through Asteroseismic Analyses".</p><p>Two subdirectories are provided in the archive:</p><p>1) The directory called "inlists" contains different MESA inlist files for different evolutionary period (pms=pre main sequence, ms=main sequence, and rgb=red giant branch) of the stellar model. The file named "inlist_0all" is applied to all evolutionary periods. The different inlist files for the different evolutionary states are called by putting their names in the "inlist" file, for example the include file named "inlist" evolves a MESA model from the pre main sequence until ZAMS (using the stop_near_zams = .true. option in the "inlist_1pms" file). An example gyre (version 6.0) inlist is also included in the "inlists" subdirectory. The Python scripts used to evaluate the matrix elements (which are used to determine the dipolar mixed-mode frequencies) discussed in this work are available at https://gitlab.com/darthoctopus/mesatricks. </p><p>2) Custom stopping conditions (used for stopping a model before the red giant branch) as well as a custom diffusion cutoff (see Viani et al. 2018, ApJ, 858, 28) are included in the run_star_extras.f file in the "src" subdirectory. </p>
FIGURE 6. Bayesian tree inferred from LSU gene DNA sequences. Posterior probabilities exceeding 50 in A new species of the genus Tripylina Brzeski, 1963 (Nematoda: Enoplida: Trischistomatidae) from Zhejiang Province, eastern China
FIGURE 6. Bayesian tree inferred from LSU gene DNA sequences. Posterior probabilities exceeding 50% are given on appropriate clades. Nematode species and GenBank numbers are listed for each taxon.
FIGURE 5. Bayesian tree inferred from SSU gene DNA sequences. Posterior probabilities exceeding 50 in A new species of the genus Tripylina Brzeski, 1963 (Nematoda: Enoplida: Trischistomatidae) from Zhejiang Province, eastern China
FIGURE 5. Bayesian tree inferred from SSU gene DNA sequences. Posterior probabilities exceeding 50% are given on appropriate clades. Nematode species and GenBank numbers are listed for each taxon.
FIGURE 2 in DNA sequences and morphological variation in Lophiodes iwamotoi Ho, Serét & Shao, 2011 based on new material from New Caledonia
FIGURE 2. Polymorphic nucleotide sites at the cytochrome c oxidase subunit-I locus (A) and Rhodopsin locus (B) in two Lophiodes species collected from seamounts off New Caledonia. Diagnostic nucleotides of L. iwamotoi to L. mutilus are highlighted. Numeration of nucleotide sites starts from first nucleotide of the corresponding genes. The sequences used for the examination were deposited in NCBI Genbank. Their accession numbers are given.
FIGURE 1 in DNA sequences and morphological variation in Lophiodes iwamotoi Ho, Serét & Shao, 2011 based on new material from New Caledonia
FIGURE 1. Freshly caught specimens of Lophiodes iwamotoi. A. ASIZP 73486, 248 mm SL. B. ASIZP 73489, 247 mm SL. C. ASIZP 73488, 154 mm SL. Not to scale.
FIGURE 2. Phylogenetic results. A, Maximum likelihood tree from COI dataset rooted with Ophelia limacina. B, Maximum likelihood tree from ITS1 in Validation of three sympatric Thoracophelia species (Annelida: Opheliidae) from Dillon Beach, California using mitochondrial and nuclear DNA sequence data
FIGURE 2. Phylogenetic results. A, Maximum likelihood tree from COI dataset rooted with Ophelia limacina. B, Maximum likelihood tree from ITS1 dataset rooted according to the result for the COI dataset. Support values are shown as jackknife from parsimony analysis and bootstrap from maximum likelihood respectively separated by /. * indicates 100% values for each support measure.
FIGURE 1. The three sympatric Thoracophelia spp. from Dillon Beach. A, Thoracophelia dillonensis. B in Validation of three sympatric Thoracophelia species (Annelida: Opheliidae) from Dillon Beach, California using mitochondrial and nuclear DNA sequence data
FIGURE 1. The three sympatric Thoracophelia spp. from Dillon Beach. A, Thoracophelia dillonensis. B, Pectinate branchiae of T. dillonensis. C, Thoracophelia williamsi. D, Bifurcated branchiae with pinnules of T. williamsi. E, Thoracophelia mucronata. F, Bifurcated branchiae of T. mucronata. Scale bars all 1 mm.
Hunting Down White Dwarf--Main Sequence Binaries Using Multi-Wavelength Observations
<p>Identification of white dwarfs (WD) with main-sequence (MS) companions and characterization of their properties can put important constraints on our understanding of binary stellar evolution and guide the theoretical predictions for a wide range of interesting transient events relevant for, e.g., Rubin Observatory (LSST), ZTF, and LISA. In this study, we combine ultraviolet (UV) and optical color-magnitude diagrams (CMDs) to identify unresolved WD--MS binaries. In particular, we combine high-precision astrometric and photometric data in the optical from \gaia\ -DR3 and UV data from GALEX GR6/7 to identify 93 WD--MS candidates within 100 pc. Of these, 80 are newly identified. Using the Virtual Observatory SED Analyzer (VOSA) we fit the spectral energy distributions (SEDs) of all our candidates and derive stellar parameters, such as effective temperature, bolometric luminosity, and radius for both companions. We find that our identification method helps identify hotter and smaller WD companions (majority with ≥ 10,000 K and ≤ 0.02 R_Sun) relative to the WDs identified by past surveys. We infer that these WDs are relatively more massive (median ~0.76 M_Sun). We find that most of the MS companions in our binaries are of the K and M spectral types. </p>
Integrated single-cell RNA-sequencing data of unwounded and wounded mouse skin and fibroblasts.
<p>This repository contains the .h5ad files that store the integrated scRNA-seq data we generated for the work, Almet et al. (2023), "Fibroblasts evolve in single-cell state to drive extracellular matrix and signaling changes across wound healing", to be published in the Journal of Investigative Dermatology.</p><p>The integrated* files contain both raw counts, normalized counts, as well as unspliced and spliced count estimates that were obtained using kallisto|bustools and velocyto. We integrated the data from the following published datasets:</p><ol><li><a href=" https://doi.org/10.7554/eLife.60066">Phan et al. (2021)</a>: Unwounded P21 mice and small wound P21 + 7 mice</li><li><a href="https://doi.org/10.1016/j.celrep.2020.02.091">Haensel et al. (2020)</a>: Unwounded P49 mice and small wound P49 + 4 mice</li><li><a href="https://doi.org/10.1038/s41467-018-08247-x)">Guerrero-Juarez et al. (2019)</a>: Large wound day 12 mice</li><li><a href="https://doi.org/10.1016/j.stem.2020.07.008">Abbasi et al. (2020)</a>: Large wound day 14 mice</li><li><a href="https://doi.org/10.1126/sciadv.aay3704">Gay et al. (2020)</a>: Large wound fibrotic (hairless) and regenerative (hair follicle neogenesis) day 18 mice</li></ol><p>The unwounded_* files were used to briefly integrated unwounded skin scRNA-seq from mouse models of different ages that have been used to analyze wound healing in <a href="https://doi.org/10.1016/j.celrep.2020.02.091">Haensel et al. (2020),</a> <a href=" https://doi.org/10.7554/eLife.60066">Phan et al. (2021)</a>, and <a href="https://doi.org/10.1016/j.celrep.2022.111155">Vu et al. (2022)</a>, which generated scRNA-seq for unwounded skin from mice aged P21, P49, and P616, respectively. </p><p>The data can be loaded using the Python package Scanpy or AnnData, but you can also load it in R if you use zellkonverter. </p>
Sequence/simulation data for Direct Prediction of Intrinsically Disordered Protein Conformational Properties From Sequence
<p>This is a DOI-linked deposition of sequence/biophysical properties pairs used in the associated paper by Lotthammer et al:</p><p>Lotthammer, J. M.<strong>*</strong>, Ginell, G. M.<strong>*</strong>, Griffith, D.<strong>*</strong>, Emenecker, R. J. & Holehouse, A. S. <br>Direct Prediction of Intrinsically Disordered Protein Conformational Properties From Sequence.<br><i><strong>Nature Methods</strong></i> (<i>in press</i>), (2023).</p><p> </p>
Novaseq sequences
<p>16s R1 and R2 already assembled</p><p>ITS R1 and R2 not</p><p>primers and tags removed</p><p>NC = negative controls</p><p>Taer = aerosols</p><p>Tex = blanc extraction</p><p>Tpcr = PCR control</p><p>PC = positive DNA controls</p>
Evaluation of recombination detection methods for viral sequencing
<p>Recombination is a key evolutionary driver in shaping novel viral populations and lineages. When unaccounted for, recombination can impact evolutionary estimations, or complicate their interpretation. Therefore, identifying signals for recombination in sequencing data is a key prerequisite to further analyses. A repertoire of recombination detection methods have been developed over the past two decades, however, the prevalence of pandemic-scale viral sequencing data poses a computational challenge for existing methods. Here, we assessed five recombination detection methods (PhiPack (Profile), 3SEQ, GENECONV, VSEARCH (UCHIME), and gmos) to determine if any are suitable for the analysis of bulk sequencing data. To test the performance and scalability of these methods, we analysed simulated viral sequencing data across a range of sequence diversities, recombination frequencies, and sample sizes. Further, we provide a practical example for the analysis and validation of empirical data. We find that recombination detection methods need to be scalable, use an analytical approach and resolution that is suitable for the intended research application, and are accurate for the properties of a given dataset (e.g. sequence diversity and estimated recombination frequency). Analysis of simulated and empirical data revealed that the assessed methods exhibited considerable trade-offs between these criteria. Overall, we provide general guidelines for the validation of recombination detection results, the benefits and shortcomings of each assessed method, and future considerations for recombination detection methods for the assessment of large-scale viral sequencing data.</p>
Clone sequences
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Phased VCF outputs for "HiPhase: Jointly phasing small, structural, and tandem repeat variants from HiFi sequencing"
<p>The collection of raw and processed VCF files for the paper titled: "HiPhase: Jointly phasing small, structural, and tandem repeat variants from HiFi sequencing".</p><p>Raw VCF files from DeepVariant, pbsv, and TRGT are included in the unphased subfolder. Outputs from WhatsHap and HiPhase are in their own subfolders. Three replicates of HG002 are included in all subfolders. Layout is as follows:</p><blockquote><p>hiphase_vcfs</p><p>├── all_vcfs.md5</p><p>├── hiphase</p><p>│ ├── HG002-rep1.GRCh38.deepvariant.hiphase.vcf.gz</p><p>│ ├── HG002-rep1.GRCh38.deepvariant.hiphase.vcf.gz.tbi</p><p>│ ├── HG002-rep1.GRCh38.pbsv.hiphase.vcf.gz</p><p>│ ├── HG002-rep1.GRCh38.pbsv.hiphase.vcf.gz.tbi</p><p>│ ├── HG002-rep1.GRCh38.trgt.hiphase.vcf.gz</p><p>│ ├── HG002-rep1.GRCh38.trgt.hiphase.vcf.gz.tbi</p><p>│ ├── HG002-rep2.GRCh38.deepvariant.hiphase.vcf.gz</p><p>│ ├── HG002-rep2.GRCh38.deepvariant.hiphase.vcf.gz.tbi</p><p>│ ├── HG002-rep2.GRCh38.pbsv.hiphase.vcf.gz</p><p>│ ├── HG002-rep2.GRCh38.pbsv.hiphase.vcf.gz.tbi</p><p>│ ├── HG002-rep2.GRCh38.trgt.hiphase.vcf.gz</p><p>│ ├── HG002-rep2.GRCh38.trgt.hiphase.vcf.gz.tbi</p><p>│ ├── HG002-rep3.GRCh38.deepvariant.hiphase.vcf.gz</p><p>│ ├── HG002-rep3.GRCh38.deepvariant.hiphase.vcf.gz.tbi</p><p>│ ├── HG002-rep3.GRCh38.pbsv.hiphase.vcf.gz</p><p>│ ├── HG002-rep3.GRCh38.pbsv.hiphase.vcf.gz.tbi</p><p>│ ├── HG002-rep3.GRCh38.trgt.hiphase.vcf.gz</p><p>│ └── HG002-rep3.GRCh38.trgt.hiphase.vcf.gz.tbi</p><p>├── unphased</p><p>│ ├── HG002-rep1.GRCh38.deepvariant.vcf.gz</p><p>│ ├── HG002-rep1.GRCh38.deepvariant.vcf.gz.tbi</p><p>│ ├── HG002-rep1.GRCh38.pbsv.vcf.gz</p><p>│ ├── HG002-rep1.GRCh38.pbsv.vcf.gz.tbi</p><p>│ ├── HG002-rep1.GRCh38.trgt.vcf.gz</p><p>│ ├── HG002-rep1.GRCh38.trgt.vcf.gz.tbi</p><p>│ ├── HG002-rep2.GRCh38.deepvariant.vcf.gz</p><p>│ ├── HG002-rep2.GRCh38.deepvariant.vcf.gz.tbi</p><p>│ ├── HG002-rep2.GRCh38.pbsv.vcf.gz</p><p>│ ├── HG002-rep2.GRCh38.pbsv.vcf.gz.tbi</p><p>│ ├── HG002-rep2.GRCh38.trgt.vcf.gz</p><p>│ ├── HG002-rep2.GRCh38.trgt.vcf.gz.tbi</p><p>│ ├── HG002-rep3.GRCh38.deepvariant.vcf.gz</p><p>│ ├── HG002-rep3.GRCh38.deepvariant.vcf.gz.tbi</p><p>│ ├── HG002-rep3.GRCh38.pbsv.vcf.gz</p><p>│ ├── HG002-rep3.GRCh38.pbsv.vcf.gz.tbi</p><p>│ ├── HG002-rep3.GRCh38.trgt.vcf.gz</p><p>│ └── HG002-rep3.GRCh38.trgt.vcf.gz.tbi</p><p>└── whatshap</p><p> ├── HG002-rep1.GRCh38.deepvariant.whatshap.vcf.gz</p><p> ├── HG002-rep1.GRCh38.deepvariant.whatshap.vcf.gz.tbi</p><p> ├── HG002-rep2.GRCh38.deepvariant.whatshap.vcf.gz</p><p> ├── HG002-rep2.GRCh38.deepvariant.whatshap.vcf.gz.tbi</p><p> ├── HG002-rep3.GRCh38.deepvariant.whatshap.vcf.gz</p><p> └── HG002-rep3.GRCh38.deepvariant.whatshap.vcf.gz.tbi</p><p>3 directories, 43 files</p></blockquote>
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.