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1,145 results for “somatic”
Conserved regulation of RNA processing in somatic cell reprogramming
<p><strong>Data set 1. Transcript expression across human RNA-Seq samples: estimated read counts. </strong>The file contains estimated read counts, generated by kallisto (<a href="https://pachterlab.github.io/kallisto/">https://pachterlab.github.io/kallisto/</a>), for human transcripts and RNA-Seq samples used in this study (see Additional file 2 of the accompanying publication). The format is a compressed (GZIP) tab-separated transcript-by-sample matrix. Ensembl transcript identifiers and a combined Sequence Read Archive study/sample name identifier serve as row and column names, respectively.</p> <p><strong>Data set 2. Transcript expression across murine RNA-Seq samples: estimated read counts. </strong>As in Data set 1, but for mouse transcripts.</p> <p><strong>Data set 3. Transcript expression across simian RNA-Seq samples: estimated read counts. </strong>As in Data set 1, but for chimpanzee transcripts.</p> <p><strong>Data set 4. Transcript expression across across human RNA-Seq samples: estimated transcript abundances. </strong>As in Data set 1, but instead of read counts, transcript abundances in transcripts per million (TPM), as estimated by kallisto (<a href="https://pachterlab.github.io/kallisto/">https://pachterlab.github.io/kallisto/</a>), are listed. Format, column and row names as in Data set 1.</p> <p><strong>Data set 5. Transcript expression across murine RNA-Seq samples: estimated transcript abundances. </strong>As in Data set 4, but for mouse transcripts.</p> <p><strong>Data set 6. Transcript expression across simian RNA-Seq samples: estimated transcript abundances. </strong>As in Data set 4, but for chimpanzee transcripts.</p> <p><strong>Data set 7. Differential expression analyses across human RNA-Seq sample groups: log fold changes. </strong>The file contains log fold changes, inferred by edgeR (<a href="http://bioconductor.org/packages/release/bioc/html/edgeR.html">http://bioconductor.org/packages/release/bioc/html/edgeR.html</a>), for human genes and the RNA-Seq sample group contrasts listed in Additional file 3 of the accompanying publication in a compressed (GZIP) TSV gene-by-comparison matrix. Ensembl gene identifiers and a descriptive contrast identifier serve as row and column names, respectively.</p> <p><strong>Data set 8. Differential expression analyses across murine RNA-Seq sample groups: log fold changes. </strong>As in Data set 7, but for mouse genes.</p> <p><strong>Data set 9. Differential expression analyses across simian RNA-Seq sample groups: log fold changes. </strong>As in Data set 7, but for chimpanzee genes.</p> <p><strong>Data set 10. Differential expression analyses across human RNA-Seq sample groups: false discovery rates. </strong>The file contains false discovery rates (FDR) for the differential expression analyses summarized in Data set 7. Format, column and row names as in Data set 7.</p> <p><strong>Data set 11. Differential expression analyses across murine RNA-Seq sample groups: false discovery rates. </strong>As in Data set 10, but for mouse genes.</p> <p><strong>Data set 12. Differential expression analyses across simian RNA-Seq sample groups: false discovery rates. </strong>As in Data set 10, but for chimpanzee genes.</p> <p><strong>Data set 13. Quantification of alternative splicing events across human RNA-Seq samples. </strong>The file contains ‘percent spliced in’ (PSI) values computed by SUPPA (<a href="https://github.com/comprna/SUPPA">https://github.com/comprna/SUPPA</a>) for annotated alternative splicing events (inferred from the transcript annotation of the human genome, Ensembl release 84; <a href="http://www.ensembl.org/">http://www.ensembl.org/</a>). The format is a compressed (GZIP) tab-separated transcript-by-sample matrix. SUPPA-provided event identifiers and a combined Sequence Read Archive study/sample name identifier serve as row and column names, respectively.</p> <p><strong>Data set 14. Quantification of alternative splicing events across murine RNA-Seq samples. </strong>As in Data set 13, but for mouse alternative splicing events.</p> <p><strong>Data set 15. Differential splicing analyses across human RNA-Seq sample groups: differences in ‘percent spliced in’ (ΔPSI). </strong>The file contains ΔPSI values for human alternative splicing events (as in Data set 13). The RNA-Seq sample group contrasts are listed in Additional file 3 of the accompanying publication. Values were inferred by SUPPA’s diffSplice functionality (<a href="https://github.com/comprna/SUPPA">https://github.com/comprna/SUPPA</a>). The format is a compressed (GZIP) tab-separated gene-by-comparison matrix. SUPPA event identifiers and a descriptive contrast identifier serve as row and column names, respectively.</p> <p><strong>Data set 16. Differential splicing analyses across murine RNA-Seq sample groups: differences in ‘percent spliced in’ (ΔPSI). </strong>As in Data set 15, but for mouse alternative splicing events.</p> <p><strong>Data set 17. Differential splicing analyses across human RNA-Seq sample groups: P values. </strong>The file contains P values for the differential splicing analysis of human alternative splicing events summarized in Data set 15. Format, column and row names as in Data set 15.</p> <p><strong>Data set 18. Differential splicing analyses across murine RNA-Seq sample groups: P values. </strong>The file contains P values for the differential splicing analysis of mouse alternative splicing events summarized in Data set 16. Format, column and row names as in Data set 15.</p> <p><strong>Data set 19. Transcript expression across murine RNA-Seq time course data: estimated read counts. </strong>As in Data set 2, but for the time course data generated for the accompanying publication.</p> <p><strong>Data set 20. Transcript expression across murine RNA-Seq time course data: estimated transcript abundances. </strong>As in Data set 5, but for the time course data generated for the accompanying publication.</p> <p><strong>Data set 21. Quantification of alternative splicing events across murine RNA-Seq time course data. </strong>As in Data set 14, but for the time course data generated for the accompanying publication.</p>
Cardelino: Integrating whole exomes and single-cell transcriptomes to reveal phenotypic impact of somatic variants
<p>This dataset consists of the reference data files, metadata and processed results files for the paper "Cardelino: Integrating whole exomes and single-cell transcriptomes to reveal phenotypic impact of somatic variants," which investigates clonality in normal human dermal fibroblast cell populations in 32 cell lines from distinct donors, using bulk whole-exome sequencing and single-cell RNA-sequencing data.</p> <p>This dataset contains everything required to reproduce the results presented in the paper from processed data and results of our data processing workflows. Our analyses can be reproduced using the <a href="https://github.com/davismcc/fibroblast-clonality">source code</a> and instructions available at our <a href="https://davismcc.github.io/fibroblast-clonality/">project website</a>.</p> <p>The <em>entire</em> analysis workflow from raw data to final results is also reproducible but is substantially more complicated and computationally intensive. It also requires large datasets to be obtained from other repositories. Specifically, single-cell RNA-seq data have been deposited in the ArrayExpress database at EMBL-EBI under accession number E-MTAB-7167. Whole-exome sequencing data is available through the HipSci portal (www.hipsci.org). Combined with the dataset in this repository and following the instructions on the project website, it is possible to run our entire analysis pipeline.</p> <p> </p>
Somatal length and density in 2010 for the 1980-82 Eriophorum vaginatum reciprocal transplant experiment
These data were collected in July 2010 for tussocks transplanted in 1980-82 in a reciprocal transplant experiment and harvested in 2011. Important variables are garden name, source population, length and density of stomata, and the temperature of tussocks.
Low-frequency somatic mutations are heritable in tropical trees Dicorynia guianensis and Sextonia rubra
<p>Somatic mutations potentially play a role in plant evolution, but common expectations pertaining to plant somatic mutation remain insufficiently tested. Unlike in most animals, the plant germline is assumed to be set aside late in development, leading to the expectation that plants accumulate somatic mutations along growth. Therefore, several predictions were made on the fate of somatic mutations: mutations have generally low frequency in plant tissues; mutations at high frequency have a higher chance of intergenerational transmission; branching topology of the tree dictates mutation distribution; and, exposure to UV radiation increases mutagenesis. To provide new insights into mutation accumulation and transmission in plants, we produced two high-quality reference genomes and a unique dataset of 60 high-coverage whole-genome sequences of two tropical tree species, <i>Dicorynia guianensis</i> (Fabaceae) and <i>Sextonia rubra </i>(Lauraceae). We identified 15,066 <i>de novo</i> somatic mutations in <i>D. guianensis</i> and 3,208 in <i>S. rubra</i>, surprisingly almost all found at low frequency. We demonstrate that: 1) low-frequency mutations can be transmitted to the next generation; 2) mutation phylogenies deviate from the branching topology of the tree; and 3) mutation rates and mutation spectra are not demonstrably affected by differences in UV exposure. Altogether, our results suggest far more complex links between plant growth, ageing, UV exposure, and mutation rates than commonly thought.</p>
Fastq files for benchmarking somatic variant calling pipelines
<p>The <a href="https://download.imgag.de/public/validation_dataset_somatic/readme.html" target="_blank" rel="noopener">original .bam files</a> were provided by <a href="https://www.medizin.uni-tuebingen.de/de/das-klinikum/mitarbeiter/profil/3377" target="_blank" rel="noopener">Marc Sturm</a> from the <a href="https://www.medizin.uni-tuebingen.de/de/das-klinikum/einrichtungen/institute/medizinische-genetik-und-angewandte-genomik" target="_blank" rel="noopener">Institut für Medizinische Genetik und Angewandte Genomik at the University Clinic Tübingen.</a></p> <p>The files were transformed into .fq.gz files using the <a href="https://github.com/nf-core/bamtofastq" target="_blank" rel="noopener">nf-core/bamtofastq pipeline.</a> All information on the pipeline run can be found in the <a href="../api/records/10805134/draft/files/execution_report_2024-03-11_11-44-17.html/content" target="_blank" rel="noopener noreferrer">execution_report_2024-03-11_11-44-17.html</a>. The FASTQ files for the normal sample were directly uploaded to <a href="https://osf.io/cduyq/files/onedrive">the corresponding osf project</a>.</p> <p> </p> <p> </p>
Supporting data for the manuscript "Severus: accurate detection and characterization of somatic structural variation in tumor genomes using long reads"
<p>Supporting data for the manuscript "Severus: accurate detection and characterization of somatic structural variation in tumor genomes using long reads".</p> <p>The archive contains files that are necessary to reproduce the cell line benchmarks from the paper, including:</p> <ul> <li>Scripts and command lines</li> <li>Original VCF outpurs of all tools used in benchmarking</li> <li>Minda evaluations and truthset VCF files</li> <li>Full Severus outputs + visualizations</li> <li>truvari calls</li> </ul>
Lingering legacies: Past growth and parental experience influence somatic growth in a fish population
<p>Body size and growth rate can influence individual and population success by mediating fitness. Understanding the factors that influence growth can be difficult to disentangle, however, because growth can be shaped by environmental conditions recently experienced, as well as legacy effects from conditions experienced earlier in life and by parents (via parental effects). To improve understanding of growth among annual cohorts (1982-2015) of Lake Erie Walleye (<em>Sander vitreus</em>), a species with life history and growth characteristics similar to many other long-lived, iteroparous fishes, we determined the role of the following hypothesized factors: H1) recent environmental conditions; H2) traits and experiences of the cohort, including growth, in the previous year; H3) early-life cohort density; H4) early-life body size; and H5) parental composition and environment. We evaluated the relative importance of these hypothesized factors using piecewise structural equation modeling in an information-theoretic framework. Our results indicated that cohort-specific growth of Lake Erie Walleye was most strongly influenced by traits (growth) and experiences of the cohort during the previous year (H2) and parental composition and environment (H5). The observed negative relationship with growth during the previous year may indicate that Walleye exhibits compensatory growth. The relationships with parental sizes and environments may mean that parental contributions to offspring affect cohorts into adulthood, with serious implications for the effects of climate change. Warm winters appear to negatively influence offspring growth performance for many years. Legacy effects had a stronger influence on cohort growth than recent environmental conditions, providing a new understanding of how somatic growth is regulated in Lake Erie's Walleye population. Specifically, the parental composition and environment appear important via epigenetic and/or egg-provisioning legacies, with carryover effects modifying growth over the years. Ultimately, our findings demonstrate that understanding recent growth in animal populations similar to Lake Erie Walleye may require knowledge of past conditions, including those experienced by parents.</p>
SI Figure 4: SEM images of either unwashed (left) or washed (right) E. antarcticus nematodes. A. Unwashed head region with arrows pointing to attached material and possible fungal hyphae. B. Washed head region with arrows pointing to the remaining attached material. C. Unwashed annules with arrows pointing to commonly attached foreign material. D. Washed annules with arrows pointing to remaining attached material. E. Unwashed somatic pore with arrows pointing to the common organic material. F. Washed vulva with an arrow pointing to remaining attached organic material. G. Unwashed cuticle with arrows showing a possible biofilm. H. Washed cuticle showing single attached cells indicated with arrows. I. Unwashed cuticle showing an off-axis line of attached material. J. Washed cuticle showing a similar off-axis line of material (as indicated with arrow) but reduced in quantity compared to the unwashed. in External and internal microbiomes of Antarctic nematodes are distinct, but more similar to each other than the surrounding environment
SI Figure 4: SEM images of either unwashed (left) or washed (right) E. antarcticus nematodes. A. Unwashed head region with arrows pointing to attached material and possible fungal hyphae. B. Washed head region with arrows pointing to the remaining attached material. C. Unwashed annules with arrows pointing to commonly attached foreign material. D. Washed annules with arrows pointing to remaining attached material. E. Unwashed somatic pore with arrows pointing to the common organic material. F. Washed vulva with an arrow pointing to remaining attached organic material. G. Unwashed cuticle with arrows showing a possible biofilm. H. Washed cuticle showing single attached cells indicated with arrows. I. Unwashed cuticle showing an off-axis line of attached material. J. Washed cuticle showing a similar off-axis line of material (as indicated with arrow) but reduced in quantity compared to the unwashed.
Fig. 9 in Ultrastructural Studies on a Model Tintinnid - Schmidingerella meunieri (Kofoid and Campbell, 1929) Agatha and Strüder-Kypke, 2012 (Ciliophora). I. Somatic Kinetids with Unique Ultrastructure
Fig. 9. Tree displaying the phylogenetic relationships based on morphological and genetic data of euplotids, hypotrichs, oligotrichids, and choreotrichids. (A) Overall morphologies of the taxa [illustrations: euplotid after Ehrenberg (1830); hypotrich from Deitmer et al. (1984); oligotrichid from Montagnes (1996); choreotrichids from Petz et al. (1995), Petz and Foissner (1992), and Gruber et al. (2018)]. (B) Kinetid structures. For strobilidiids, inferred from micrographs and description in Grim (1987). (C) Molecular genealogy with apomorphies (black squares) in the kinetid ultrastructures mapped on the branches with the most parsimonious placement based on the current state of knowledge. Black circles represent basal bodies and red dots associated common cilia as inferred from protargol-stained material. Asterisks mark homoplasy. Dark blue, transverse ribbon; grey, desmose; light blue, kinetodesmal fibril; orange, extraordinary microtubular ribbons; purple, postciliary ribbon; BB, basal body; Kd, kinetodesmal fibril; Pc, postciliary ribbon; T, transverse ribbon.
Fig. 4 in Ultrastructural Studies on a Model Tintinnid - Schmidingerella meunieri (Kofoid and Campbell, 1929) Agatha and Strüder-Kypke, 2012 (Ciliophora). I. Somatic Kinetids with Unique Ultrastructure
Fig. 4. Longitudinal sections of dorsal dikinetids in Schmidingerella meunieri in the transmission electron microscope. (A, D) Two dikinetids each. The posterior dikinetidal basal body (on the left) has associated a cilium, whereas the anterior basal body (on the right) displays a condylocilium. Arrows mark extraordinary ribbons I or II. (B, C) Longitudinal sections of condylocilia. III, extraordinary microtubular ribbon III; AP, axosomal plate; Axn, axoneme; Axs, axosome; CG, core granule; Co, condylocilium; Cw, cartwheel; M, cell membrane; P, perilemma. Scale bars: 250 nm.
Fig. 1 in Ultrastructural Studies on a Model Tintinnid - Schmidingerella meunieri (Kofoid and Campbell, 1929) Agatha and Strüder-Kypke, 2012 (Ciliophora). I. Somatic Kinetids with Unique Ultrastructure
Fig. 1. Schmidingerella meunieri from the Northeast Pacific in vivo (A), in the scanning electron microscope (B), and in the transmission electron microscope (C) and a kinetal map of a congener after protargol staining (D). (A) The living cell is attached by its peduncle to the bottom of the lorica. (B) Contracted, naked specimen. (C) Longitudinal ultrathin section. (D) Scheme of ciliary pattern in Schmidingerella arcuata (modified from Agatha and Strüder-Kypke 2012). AM, adoral membranelles; DK, dorsal kinety; L, lorica; LA, lateral ciliary field; LF, left ciliary field; Pe, peduncle; RF, right ciliary field; SC, somatic cilia; VK, ventral kinety. Scale bars: 50 µm (A, C), 20 µm (B).
Fig. 2 in Ultrastructural Studies on a Model Tintinnid - Schmidingerella meunieri (Kofoid and Campbell, 1929) Agatha and Strüder-Kypke, 2012 (Ciliophora). I. Somatic Kinetids with Unique Ultrastructure
Fig. 2. Cross sections of dorsal dikinetids at different levels (from proximal to distal) in Schmidingerella meunieri in the transmission electron microscope. (A) The two basal bodies are sectioned at different levels, indicating a more proximal position of the anterior one. Arrowheads mark the electron-dense cuffs. (B) The dikinetid shows the electron-dense cuff (arrowhead) around the posterior basal body and the postciliary ribbon extending parallel to that of the more anterior dikinetid (arrow). (C) Slightly oblique section showing the three extraordinary microtubular ribbons extending perpendicularly or obliquely to the kinety axis leftwards: ribbons I and II originate near the posterior basal body, ribbon III commences near the anterior one. The arrow marks the long postciliary ribbon from the previous dikinetid. (D) The extraordinary ribbons I, II, and III are shown in cross sections. Note the fibrillar structure of the kinetodesmal fibril. (E) The dikinetid is located in a ciliary pit. While the anterior basal body terminates with a condylocilium, the posterior cilium displays a typical axoneme and is attached to the pit's left wall by a cytoplasmic connection (arrowheads). (F) The short condylocilium at the anterior basal body is not visible any longer. The ribbons II and III and the transverse ribbon terminate close to the cell cortex. I–III, extraordinary microtubular ribbons I–III; Axn, axoneme; Axs, axosome; CG, core granule; Co, condylocilium; CP, ciliary pit; D, desmose; Kd, kinetodesmal fibril; M, cell membrane; Pc, postciliary ribbon; T, transverse ribbon. Scale bars: 250 nm.
Fig. 7 in Ultrastructural Studies on a Model Tintinnid - Schmidingerella meunieri (Kofoid and Campbell, 1929) Agatha and Strüder-Kypke, 2012 (Ciliophora). I. Somatic Kinetids with Unique Ultrastructure
Fig. 7. Schematic drawing of the anterior portion of two adjacent kineties in the left ciliary field of Schmidingerella meunieri compiling all observations from transmission electron micrographs. The 20°–30° angles of the kinetids are not considered. The kineties are monokinetidal, except for one anterior dikinetid. The microtubules of the connecting (ribbons I) and postciliary ribbons form a network. I–III, extraordinary microtubular ribbons I–III; AP, axosomal plate; Axn, axoneme; Axs, axosome; CC, cytoplasmic connection; CG, core granule; CM, cortical microtubules; CP, ciliary pit; CT, central microtubules; EC, electron-dense cuff; Kd, kinetodesmal fibril; P, perilemma; Pc, postciliary ribbon; T, transverse ribbon.
Fig. 8 in Ultrastructural Studies on a Model Tintinnid - Schmidingerella meunieri (Kofoid and Campbell, 1929) Agatha and Strüder-Kypke, 2012 (Ciliophora). I. Somatic Kinetids with Unique Ultrastructure
Fig. 8. Schematics of somatic kinetids in Schmidingerella meunieri, namely of a dikinetid (A) and a monokinetid (B). The postciliary ribbons consist probably of four or five microtubules. The structure of ribbon III is somewhat variable, ranging from one layer containing four microtubules to two layers with six microtubules as in ribbon I. 1–9, triplets 1–9; I–III, extraordinary microtubular ribbons I–III; D, desmose; Kd, kinetodesmal fibril; Pc, postciliary ribbon; T, transverse ribbon.
Fig. 5 in Ultrastructural Studies on a Model Tintinnid - Schmidingerella meunieri (Kofoid and Campbell, 1929) Agatha and Strüder-Kypke, 2012 (Ciliophora). I. Somatic Kinetids with Unique Ultrastructure
Fig. 5. Schematic drawing of two consecutive dikinetids in an obliquely orientated dorsal kinety or posterior portion of the ventral kinety in Schmidingerella meunieri compiling all observations from transmission electron micrographs. The 20°–30° angles of the dikinetids are not considered. The posterior basal bodies insert somewhat more distally than the anterior ones and their cilia are connected with the left walls of the pits. The postciliary ribbons are shown in abridged form. I–III, extraordinary microtubular ribbons I–III; AP, axosomal plate; Axn, axoneme; Axs, axosome; CC, cytoplasmic connection; CG, core granule; CM, cortical microtubules; Co, condylocilium; CP, ciliary pit; CT, central tubules; EC, electron-dense cuff; Kd, kinetodesmal fibril; P, perilemma; Pc, postciliary ribbon; T, transverse ribbon.
Training data for 'Somatic variant calling' tutorial (Galaxy Training Material)
<p>The data provided here are part of a Galaxy Training Network tutorial that demonstrates identification of somatic and germline variants from tumor and normal sample pairs.</p>
Supporting data for RCANE: A Deep Learning Algorithm for Whole-genome Pan-Cancer Somatic Copy Number Aberration Prediction using RNA-seq Data.
<p>This is the data repository for <em>RCANE: A Deep Learning Algorithm for Whole-genome Pan-Cancer Somatic Copy Number Aberration Prediction using RNA-seq Data</em>. To use this dataset, please refer to <a href="https://github.com/HowardGech/RCANE" target="_blank" rel="noopener">https://github.com/HowardGech/RCANE</a>.</p>
Prediction of metabolites associated with somatic mutations in cancers
<ul> <li>GEMs_AML: 16 acute myeloid leukemia (AML) patient-specific genome-scale metabolic models (GEMs) reconstructed using their corresponding RNA-seq data and Recon 2M.2</li> <li>GEMs_PCAWG: 943 cancer patient-specific GEMs for 24 different cancer types reconstructed using the Pan-Cancer Analysis of Whole Genomes (PCAWG) RNA-seq data and generic human GEM 'Recon 2M.2'</li> <li>GEMs_RCC: 20 renal cell carcinoma (RCC) patient-specific GEMs reconstructed using their corresponding RNA-seq data and Recon 2M.2</li> <li>GEMs_TCGA_LAML: 113 AML patient-specific GEMs reconstructed using The Cancer Genome Atlas (TCGA) LAML RNA-seq data and Recon 2M.2</li> </ul>
Haplotype-aware reference genome reveals hidden somatic mutations of sweet orange
<p><strong>Filename: </strong>ASE_in_five_fruit_development.txt</p> <p><strong>Description: </strong>Based on our haplotype sequences, we confirmed biallelic genes showed significant expression difference between two alleles in at least one fruit developmental stage. We collected the RNA-seq data from fruit of Newhall navel orange at five developmental stages (90, 120, 150, 180 and 210 days after bloom). RNA-seq data from previous project GSE108930 in NCBI database.</p> <p> </p> <p><strong>Filename: </strong>Biallelic_genes_haplogenomes.tsv</p> <p><strong>Description: </strong>The biallelic genes were identified using the Genespace program.</p> <p> </p> <p><strong>Filename: </strong>Haplogenomes_CENH3_chip_peaks.bw</p> <p><strong>Description: </strong>The CENH3 sequences were collected from BankIt ID 2305947. These reads (including the input library as a control) were aligned to the two assembled haplotypes using Bowtie2 (v2.5.1) with default parameters. MACS2 (v2.2.7.1) with the additional parameters “-f BAM -ghs -B -q 0.01” was used to perform peak calling. The peaks generated from CENH3 chip-seq.</p> <p> </p> <p><strong>Filename: </strong>Haplogenomes_Control_chip_peaks.bw</p> <p><strong>Description:</strong> The peaks generated from Control chip-seq.</p> <p> </p> <p><strong>Filename: </strong>Haplotype_based_79accessions_somatic_variations.vcf</p> <p><strong>Description: </strong>The small somatic variations generated based on the haplotype-based method. The derived somatic mutations were identified based on nine samples from the outgroup (Earlier Clade I).</p> <p> </p> <p><strong>Filename: </strong>HaplotypeA_CuteSV.vcf</p> <p><strong>Description: </strong>The HiFi reads were mapped to haplotype A. We called SVs using the CuteSV program.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeA_gene_function_annotation.tsv</p> <p><strong>Description: </strong>The gene annotations of haplotype A.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeA_gene_model.gff3</p> <p><strong>Description:</strong> The gene structure model of haplotype A.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeA_genome.fa</p> <p><strong>Description:</strong> The genome sequences of haplotype A.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeA_PEPPER_OUTPUT.zip</p> <p><strong>Description: </strong>The small variations of sweet orange using the haplotype A as the reference genome.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeA_TEs_annotation.gff3</p> <p><strong>Description:</strong> The TE annotations of haplotype A.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeB_gene_function_annotation.tsv</p> <p><strong>Description:</strong> The gene annotations of haplotype B.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeB_gene_model.gff3</p> <p><strong>Description:</strong> The gene structure model of haplotype B.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeB_genome.fa</p> <p><strong>Filename: </strong>ASE_in_five_fruit_development.txt</p> <p><strong>Description: </strong>Based on our haplotype sequences, we confirmed biallelic genes showed significant expression difference between two alleles in at least one fruit developmental stage. We collected the RNA-seq data from fruit of Newhall navel orange at five developmental stages (90, 120, 150, 180 and 210 days after bloom). RNA-seq data from previous project GSE108930 in NCBI database.</p> <p> </p> <p><strong>Filename: </strong>Biallelic_genes_haplogenomes.tsv</p> <p><strong>Description: </strong>The biallelic genes were identified using the Genespace program.</p> <p> </p> <p><strong>Filename: </strong>Haplogenomes_CENH3_chip_peaks.bw</p> <p><strong>Description: </strong>The CENH3 sequences were collected from BankIt ID 2305947. These reads (including the input library as a control) were aligned to the two assembled haplotypes using Bowtie2 (v2.5.1) with default parameters. MACS2 (v2.2.7.1) with the additional parameters “-f BAM -ghs -B -q 0.01” was used to perform peak calling. The peaks generated from CENH3 chip-seq.</p> <p> </p> <p><strong>Filename: </strong>Haplogenomes_Control_chip_peaks.bw</p> <p><strong>Description:</strong> The peaks generated from Control chip-seq.</p> <p> </p> <p><strong>Filename: </strong>Haplotype_based_79accessions_somatic_variations.vcf</p> <p><strong>Description: </strong>The small somatic variations generated based on the haplotype-based method. The derived somatic mutations were identified based on nine samples from the outgroup (Earlier Clade I).</p> <p> </p> <p><strong>Filename: </strong>HaplotypeA_CuteSV.vcf</p> <p><strong>Description: </strong>The HiFi reads were mapped to haplotype A. We called SVs using the CuteSV program.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeA_gene_function_annotation.tsv</p> <p><strong>Description: </strong>The gene annotations of haplotype A.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeA_gene_model.gff3</p> <p><strong>Description:</strong> The gene structure model of haplotype A.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeA_genome.fa</p> <p><strong>Description:</strong> The genome sequences of haplotype A.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeA_PEPPER_OUTPUT.zip</p> <p><strong>Description: </strong>The small variations of sweet orange using the haplotype A as the reference genome.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeA_TEs_annotation.gff3</p> <p><strong>Description:</strong> The TE annotations of haplotype A.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeB_gene_function_annotation.tsv</p> <p><strong>Description:</strong> The gene annotations of haplotype B.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeB_gene_model.gff3</p> <p><strong>Description:</strong> The gene structure model of haplotype B.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeB_genome.fa</p> <p><strong>Description:</strong> The genome sequences of haplotype B.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeB_TEs_annotation.gff3</p> <p><strong>Description:</strong> The TE annotations of haplotype B.</p> <p> </p> <p><strong>Filename: </strong>Single_reference_87accessions_somatic_variations.vcf</p> <p><strong>Description:</strong> The small somatic variations generated based on the single reference genome (Haplotype A).</p> <p> </p> <p><strong>Filename: </strong>Somatic_material_RNA_seq_matrix.txt</p> <p><strong>Description: </strong>The expression matrix of BT_3 and BT_5 (a set of somatic mutation material).</p> <p> </p> <p><strong>Filename: </strong>ASE_in_five_fruit_development.txt</p> <p><strong>Description: </strong>Based on our haplotype sequences, we confirmed biallelic genes showed significant expression difference between two alleles in at least one fruit developmental stage. We collected the RNA-seq data from fruit of Newhall navel orange at five developmental stages (90, 120, 150, 180 and 210 days after bloom). RNA-seq data from previous project GSE108930 in NCBI database.</p> <p> </p> <p><strong>Filename: </strong>Biallelic_genes_haplogenomes.tsv</p> <p><strong>Description: </strong>The biallelic genes were identified using the Genespace program.</p> <p> </p> <p><strong>Filename: </strong>Haplogenomes_CENH3_chip_peaks.bw</p> <p><strong>Description: </strong>The CENH3 sequences were collected from BankIt ID 2305947. These reads (including the input library as a control) were aligned to the two assembled haplotypes using Bowtie2 (v2.5.1) with default parameters. MACS2 (v2.2.7.1) with the additional parameters “-f BAM -ghs -B -q 0.01” was used to perform peak calling. The peaks generated from CENH3 chip-seq.</p> <p> </p> <p><strong>Filename: </strong>Haplogenomes_Control_chip_peaks.bw</p> <p><strong>Description:</strong> The peaks generated from Control chip-seq.</p> <p> </p> <p><strong>Filename: </strong>Haplotype_based_79accessions_somatic_variations.vcf</p> <p><strong>Description: </strong>The small somatic variations generated based on the haplotype-based method. The derived somatic mutations were identified based on nine samples from the outgroup (Earlier Clade I).</p> <p> </p> <p><strong>Filename: </strong>HaplotypeA_CuteSV.vcf</p> <p><strong>Description: </strong>The HiFi reads were mapped to haplotype A. We called SVs using the CuteSV program.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeA_gene_function_annotation.tsv</p> <p><strong>Description: </strong>The gene annotations of haplotype A.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeA_gene_model.gff3</p> <p><strong>Description:</strong> The gene structure model of haplotype A.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeA_genome.fa</p> <p><strong>Description:</strong> The genome sequences of haplotype A.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeA_PEPPER_OUTPUT.zip</p> <p><strong>Description: </strong>The small variations of sweet orange using the haplotype A as the reference genome.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeA_TEs_annotation.gff3</p> <p><strong>Description:</strong> The TE annotations of haplotype A.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeB_gene_function_annotation.tsv</p> <p><strong>Description:</strong> The gene annotations of haplotype B.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeB_gene_model.gff3</p> <p><strong>Description:</strong> The gene structure model of haplotype B.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeB_genome.fa</p> <p><strong>Description:</strong> The genome sequences of haplotype B.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeB_TEs_annotation.gff3</p> <p><strong>Description:</strong> The TE annotations of haplotype B.</p> <p> </p> <p><strong>Filename: </strong>Single_reference_87accessions_somatic_variations.vcf</p> <p><strong>Description:</strong> The small somatic variations generated based on the single reference genome (Haplotype A).</p> <p> </p> <p><strong>Filename: </strong>Somatic_material_RNA_seq_matrix.txt</p> <p><strong>Description: </strong>The expression matrix of BT_3 and BT_5 (a set of somatic mutation material).</p> <p> </p> <p><strong>Filename: </strong>ASE_in_five_fruit_development.txt</p> <p><strong>Description: </strong>Based on our haplotype sequences, we confirmed biallelic genes showed significant expression difference between two alleles in at least one fruit developmental stage. We collected the RNA-seq data from fruit of Newhall navel orange at five developmental stages (90, 120, 150, 180 and 210 days after bloom). RNA-seq data from previous project GSE108930 in NCBI database.</p> <p> </p> <p><strong>Filename: </strong>Biallelic_genes_haplogenomes.tsv</p> <p><strong>Description: </strong>The biallelic genes were identified using the Genespace program.</p> <p> </p> <p><strong>Filename: </strong>Haplogenomes_CENH3_chip_peaks.bw</p> <p><strong>Description: </strong>The CENH3 sequences were collected from BankIt ID 2305947. These reads (including the input library as a control) were aligned to the two assembled haplotypes using Bowtie2 (v2.5.1) with default parameters. MACS2 (v2.2.7.1) with the additional parameters “-f BAM -ghs -B -q 0.01” was used to perform peak calling. The peaks generated from CENH3 chip-seq.</p> <p> </p> <p><strong>Filename: </strong>Haplogenomes_Control_chip_peaks.bw</p> <p><strong>Description:</strong> The peaks generated from Control chip-seq.</p> <p> </p> <p><strong>Filename: </strong>Haplotype_based_79accessions_somatic_variations.vcf</p> <p><strong>Description: </strong>The small somatic variations generated based on the haplotype-based method. The derived somatic mutations were identified based on nine samples from the outgroup (Earlier Clade I).</p> <p> </p> <p><strong>Filename: </strong>HaplotypeA_CuteSV.vcf</p> <p><strong>Description: </strong>The HiFi reads were mapped to haplotype A. We called SVs using the CuteSV program.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeA_gene_function_annotation.tsv</p> <p><strong>Description: </strong>The gene annotations of haplotype A.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeA_gene_model.gff3</p> <p><strong>Description:</strong> The gene structure model of haplotype A.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeA_genome.fa</p> <p><strong>Description:</strong> The genome sequences of haplotype A.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeA_PEPPER_OUTPUT.zip</p> <p><strong>Description: </strong>The small variations of sweet orange using the haplotype A as the reference genome.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeA_TEs_annotation.gff3</p> <p><strong>Description:</strong> The TE annotations of haplotype A.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeB_gene_function_annotation.tsv</p> <p><strong>Description:</strong> The gene annotations of haplotype B.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeB_gene_model.gff3</p> <p><strong>Description:</strong> The gene structure model of haplotype B.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeB_genome.fa</p> <p><strong>Description:</strong> The genome sequences of haplotype B.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeB_TEs_annotation.gff3</p> <p><strong>Description:</strong> The TE annotations of haplotype B.</p> <p> </p> <p><strong>Filename: </strong>Single_reference_87accessions_somatic_variations.vcf</p> <p><strong>Description:</strong> The small somatic variations generated based on the single reference genome (Haplotype A).</p> <p> </p> <p><strong>Filename: </strong>Somatic_material_RNA_seq_matrix.txt</p> <p><strong>Description: </strong>The expression matrix of BT_3 and BT_5 (a set of somatic mutation material).</p> <p><strong>Filename: </strong>ASE_in_five_fruit_development.txt</p> <p><strong>Description: </strong>Based on our haplotype sequences, we confirmed biallelic genes showed significant expression difference between two alleles in at least one fruit developmental stage. We collected the RNA-seq data from fruit of Newhall navel orange at five developmental stages (90, 120, 150, 180 and 210 days after bloom). RNA-seq data from previous project GSE108930 in NCBI database.</p> <p> </p> <p><strong>Filename: </strong>Biallelic_genes_haplogenomes.tsv</p> <p><strong>Description: </strong>The biallelic genes were identified using the Genespace program.</p> <p> </p> <p><strong>Filename: </strong>Haplogenomes_CENH3_chip_peaks.bw</p> <p><strong>Description: </strong>The CENH3 sequences were collected from BankIt ID 2305947. These reads (including the input library as a control) were aligned to the two assembled haplotypes using Bowtie2 (v2.5.1) with default parameters. MACS2 (v2.2.7.1) with the additional parameters “-f BAM -ghs -B -q 0.01” was used to perform peak calling. The peaks generated from CENH3 chip-seq.</p> <p> </p> <p><strong>Filename: </strong>Haplogenomes_Control_chip_peaks.bw</p> <p><strong>Description:</strong> The peaks generated from Control chip-seq.</p> <p> </p> <p><strong>Filename: </strong>Haplotype_based_79accessions_somatic_variations.vcf</p> <p><strong>Description: </strong>The small somatic variations generated based on the haplotype-based method. The derived somatic mutations were identified based on nine samples from the outgroup (Earlier Clade I).</p> <p> </p> <p><strong>Filename: </strong>HaplotypeA_CuteSV.vcf</p> <p><strong>Description: </strong>The HiFi reads were mapped to haplotype A. We called SVs using the CuteSV program.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeA_gene_function_annotation.tsv</p> <p><strong>Description: </strong>The gene annotations of haplotype A.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeA_gene_model.gff3</p> <p><strong>Description:</strong> The gene structure model of haplotype A.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeA_genome.fa</p> <p><strong>Description:</strong> The genome sequences of haplotype A.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeA_PEPPER_OUTPUT.zip</p> <p><strong>Description: </strong>The small variations of sweet orange using the haplotype A as the reference genome.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeA_TEs_annotation.gff3</p> <p><strong>Description:</strong> The TE annotations of haplotype A.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeB_gene_function_annotation.tsv</p> <p><strong>Description:</strong> The gene annotations of haplotype B.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeB_gene_model.gff3</p> <p><strong>Description:</strong> The gene structure model of haplotype B.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeB_genome.fa</p> <p><strong>Description:</strong> The genome sequences of haplotype B.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeB_TEs_annotation.gff3</p> <p><strong>Description:</strong> The TE annotations of haplotype B.</p> <p> </p> <p><strong>Filename: </strong>Single_reference_87accessions_somatic_variations.vcf</p> <p><strong>Description:</strong> The small somatic variations generated based on the single reference genome (Haplotype A).</p> <p> </p> <p><strong>Filename: </strong>Somatic_material_RNA_seq_matrix.txt</p> <p><strong>Description: </strong>The expression matrix of BT_3 and BT_5 (a set of somatic mutation material).</p> <p><strong>Description:</strong> The genome sequences of haplotype B.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeB_TEs_annotation.gff3</p> <p><strong>Description:</strong> The TE annotations of haplotype B.</p> <p> </p> <p><strong>Filename: </strong>Single_reference_87accessions_somatic_variations.vcf</p> <p><strong>Description:</strong> The small somatic variations generated based on the single reference genome (Haplotype A).</p> <p> </p> <p><strong>Filename: </strong>Somatic_material_RNA_seq_matrix.txt</p> <p><strong>Description: </strong>The expression matrix of BT_3 and BT_5 (a set of somatic mutation material).</p>
Fig. 3. Dibolostethus Hoffman, 2009 somatic characters continued. A, E. Dibolostethus sicarius Hoffman, 2009 in A review of the previously monotypic tribe Dibolostethini (Chelodesmidae: Chelodesminae) with description of two new species and a summary of the Chelodesmidae of the Tropical Andes Biodiversity Hotspot
Fig. 3. Dibolostethus Hoffman, 2009 somatic characters continued. A, E. Dibolostethus sicarius Hoffman, 2009, holotype, ♂ (VMNH110810). B–D. D. kattani Means, Bouzan, Martínez-Torres & Ivanov sp. nov., holotype, ♂ (ICN-MD-1317-1). A. Coxa 2, mesal view, red arrow = gonopore. B. Left leg 4, posterior view, red arrow = tibial ventro-apical projection. C. Left leg of 5th leg pair showing incrassate femur, red arrow = tibial ventro-apical projection. D. Body ring 5, lateral view, red circle = anterior pair of sternal projections. E. Prefemur 5, red arrow = basal pore. Abbreviations: Cx = coxa; Pf = prefemur.
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.