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Figure 3 in Molecular cloning and sequence analysis of the gene encoding interleukin-6 of the giant panda (Ailuropoda melanoleuca)
Figure 3. Phylogenetic relationships of IL-6 sequences from seven species in Carnivora. (A) Neighbour-joining tree of IL-6 nucleotide sequences based on Kimura's 2-parameter distances. (B) Maximum-parsimony tree of IL-6 mature protein sequences.
Figure 1 in Molecular cloning and sequence analysis of the gene encoding interleukin-6 of the giant panda (Ailuropoda melanoleuca)
Figure 1. RT-PCR of giant panda IL-6. The expected, 700bp fragment of giant panda IL-6 cDNA was amplified.
Figure 6 in Species boundaries in Philaethria butterflies: an integrative taxonomic analysis based on genitalia ultrastructure, wing geometric morphometrics, DNA sequences, and amplified fragment length polymorphisms
Figure 6. Evolutionary relationships of Philaethria based on DNA sequences from specimens of Philaethria wernickei (southern population; Atlantic Rain Forest) and individuals previously described as Philaethria pygmalion (northern population; Amazon Forest), depicted by the green shading (grey in print version). Philaethria diatonica and Philaethria dido were used to root the tree. Purple (grey) circles represent individuals from the Atlantic Rain Forest and black triangles indicate samples from the Amazon Basin. A, consensus Bayesian tree based on mitochondrial (cytochrome oxidase subunit I, Co-I) and nuclear [triose-phosphate isomerase (Tpi), wingless (Wg), and tyrosine hydroxylase (TH)] DNA sequences. Posterior probabilities are shown above branches. Bootstrap node support based on maximum likelihood analysis is indicated below branches. Asterisks indicate node support lower than 70%. B, Median-joining network based on mtDNA and nuclear loci sequence data describing the relationship between haplotypes (purple indicates southern population, and black, northern population). Nucleotide substitutions are shown on the branches as small transverse bars. Circle size is proportional to haplotype frequency.
Figure 2 in Species boundaries in Philaethria butterflies: an integrative taxonomic analysis based on genitalia ultrastructure, wing geometric morphometrics, DNA sequences, and amplified fragment length polymorphisms
Figure 2. Location of linear measurements (A) and schematic representation (B, C) of Philaethria wings showing veins and landmarks adopted in this study. A, hind wing dorsal and ventral (detail) views, showing measured vectors. B, fore wing. C, hind wing. See Appendix S2 for details on morphological definitions of landmarks.
Figure 4 in Species boundaries in Philaethria butterflies: an integrative taxonomic analysis based on genitalia ultrastructure, wing geometric morphometrics, DNA sequences, and amplified fragment length polymorphisms
Figure 4. Linear variation in hind wing size and medial postdiscal bands for Philaethria wernickei and Philaethria pygmalion (left column), and in relation to latitude when samples from the two species are combined (right column). A, D, hind wing length. B, E, hind wing length/postdiscal band ratio (AB/DE). C, F, inner and medial postdiscal band ratio (EF/DF). See Fig. 2A for details on wing position of corresponding measurements. Numbers above boxes indicate the number of specimens measured in each class.
Figure 1 in Species boundaries in Philaethria butterflies: an integrative taxonomic analysis based on genitalia ultrastructure, wing geometric morphometrics, DNA sequences, and amplified fragment length polymorphisms
Figure 1. Geographical distributions of Philaethria wernickei and Philaethria pygmalion, and corresponding variation in male genitalia ultrastructure and ventral hind wing colour. A, shaded areas show distribution ranges proposed by Constantino & Salazar (2010) for P. wernickei (green) and P. pygmalion (red); green circles and red triangles represent collection localities of the material analysed in this study. B, variation in valva's cucullus, external view. C, variation in the colour pattern of hind wing surface, ventral view.
Figure 3 in Species boundaries in Philaethria butterflies: an integrative taxonomic analysis based on genitalia ultrastructure, wing geometric morphometrics, DNA sequences, and amplified fragment length polymorphisms
Figure 3. Male genitalia of Philaethria wernickei and Philaethria pygmalion. A, P. wernickei, lateral view. B, P. pygmalion, lateral view. C, schematic representation of generalized genitalia for both, in lateral view. D, F, H, J, scanning electron micrographs of P. wernickei; E, G, I, K, scanning electron micrographs of P. pygmalion. D, E, ampulla external view. F, G, ampulla internal view. H, I, ampulla ornamentation in detail. J, K, fultura inferior distal end. Scale bars = 150, 30, and 100 μm, for D–G, H–I, and J–K, respectively.
Figure 8 in Species boundaries in Philaethria butterflies: an integrative taxonomic analysis based on genitalia ultrastructure, wing geometric morphometrics, DNA sequences, and amplified fragment length polymorphisms
Figure 8. STRUCTURE-based clustering of Philaethria wernickei individuals from low (0–10°S) to high (20–25°S) latitudes (north and south populations, respectively) based on amplified fragment length polymorphism loci. Each individual is represented by a vertical line divided into segments of different colour that represent genetic clusters (K) from 1–4.
Figure 7 in Species boundaries in Philaethria butterflies: an integrative taxonomic analysis based on genitalia ultrastructure, wing geometric morphometrics, DNA sequences, and amplified fragment length polymorphisms
Figure 7. Multilocus consensus Bayesian tree based on cytochrome oxidase subunit I (Co-I), triose-phosphate isomerase (Tpi), wingless (Wg), and tyrosine hydroxylase (TH) sequences from specimens of Philaethria wernickei (Atlantic Rain Forest, purple circles) and individuals previously described as Philaethria pygmalion (Amazon Forest, black triangles) depicted by the green shading (grey in print version). Philaethria pygmalion and Philaethria dido were used to root the tree. Posterior probabilities are shown above branches and bootstrap node support based on maximum likelihood analysis is indicated below branches. Asterisks indicate node support lower than 70%.
FIGURE. Phylogram of Panus generated from Maximum likelihood analysis of ITS sequence data. Lentinus crinitus (MK408650) was selected as the outgroup taxon. Maximum likelihood bootstrap values greater than 60% are indicated above the nodes. The new record Panus similis (HKAS 121668) is in black bold. in Yunnan-Guizhou Plateau: a mycological hotspot
FIGURE. Phylogram of Panus generated from Maximum likelihood analysis of ITS sequence data. Lentinus crinitus (MK408650) was selected as the outgroup taxon. Maximum likelihood bootstrap values greater than 60% are indicated above the nodes. The new record Panus similis (HKAS 121668) is in black bold.
16s rRNA sequences, R code used for amplicon analysis and example code for NMGS analysis
<p>This submission contains the following data presented in: "Selection processes of Arctic seasonal glacier snowpack bacterial communities" by Keuschnig et al.</p> <p>the R code used to analyze the 16S rRNA amplicon data</p> <p>the script used for NMGS analysis</p> <p>the sequences obtained from snow samples</p>
Comprehensive benchmark and architectural analysis of deep learning models for Nanopore sequencing basecalling
<p>Placeholder data for the Lambda phage data used in: Comprehensive benchmark and architectural analysis of deep learning models for Nanopore sequencing basecalling.</p> <p>For the complete dataset see the Sequence Read Archive under the PRJNA926802 bioproject ID.</p>
R notebooks to reproduce all analyses from the manuscript "grandR: a comprehensive package for nucleotide conversion sequencing data analysis"
<p>This package contains all R notebooks to reproduce the analyses from our manuscript "grandR: a comprehensive package for nucleotide conversion sequencing data analysis".</p> <p>In the zip file you find</p> <ul> <li>several rds files in the data folder: They contain grandR objects of both simulated and real SLAM-seq data sets. You can delete them and create them again by either just "knitting" the notebooks (which will generate all data necessary for this notebook and save it into the data folder), or by executing the generateAllDataFiles.R script ("Rscript generateAllDataFiles.R"), which will generate all rds files that do not exist).</li> <li>several R notebooks (Rmd): "Knitting" them will generate all figures from the manuscript. Without the data files (rds), this will be slow!</li> <li>knit_all.bash: Execute to "knit" all notebooks</li> <li>clean.bash: Clear the output of "knitting" the notebooks</li> </ul> <p> </p>
Fig. 4 in An analysis of variations in morphological characteristics, essential oil content, and genetic sequencing among and within major Iranian Juniper (Juniperus spp.) populations
Fig. 4. (A) A map of Iran showing the relative geographic location of each habitat, (B) Representative of DNA fragments generated by the UBC807 primer in the nine juniper populations. The left-most (L) column corresponds to the biological ruler (Ladder) and the right-most column () is a negative control., (C) Dendrogram obtained from five ISSR primers using UPGMA method by Dice similarity coefficient for 27 juniper genotypes (D) Principal Component Analysis based on Dice matrix for 27 juniper genotypes.
Fig. 2 in An analysis of variations in morphological characteristics, essential oil content, and genetic sequencing among and within major Iranian Juniper (Juniperus spp.) populations
Fig. 2. (A): Cluster analysis using an average of 40 compounds identified in the 27 individuals from across the Juniperus genus. (B): Cluster analysis dendrogram of juniper populations evaluated based on 13 morphological characters using SPSS 0.16 and Average Linkage method (Within Group). The abbreviations of the labels are given in Table S6.
Fig. 1 in An analysis of variations in morphological characteristics, essential oil content, and genetic sequencing among and within major Iranian Juniper (Juniperus spp.) populations
Fig. 1. Typical representative GC-MS chromatograms of EOs collected from (A1): J. excelsa of Torbat-Heydaryeh (JET1), (A2): J. sabina of Ramsar (JSR) and (A3): J. communis of Tooskestan (JCT), (B1): Analysis of EO percentage among the studied populations (B2,3): Mean comparison of top 10 EO constituents.
Fig. 3 in An analysis of variations in morphological characteristics, essential oil content, and genetic sequencing among and within major Iranian Juniper (Juniperus spp.) populations
Fig. 3. Mean comparison of morphological characteristics in 9 juniper populations. (Cwe: Cone weight, CL: Cone length, CD: Cone length, SN: Seed number of cone, SL: Seed length, Swi: Seed width, Swe: Cone weight, NL: Needle length, Nwi: Needle width, NLP5: Number of leaves per 5-mm section of ultimate lateral branchlet, RCL/D: Ratio of cone length to diameter, RCD/SN: Ratio of cone diameter to seed number, RSL/Wi: Ratio of seed length to width.)
Testing of NBIA Genes: Analysis of Genetic Heterogeneity and Validation of Mitochondrial Markers for Assessing Causality of Sequence Variants.
ClinicalTrials.gov study NCT05615571. IPD Sharing: NO. Countries: 1. Publications: 1.
Using 16S rRNA Gene Sequencing Analysis Intestinal Microbiota in Constipation Patients
ClinicalTrials.gov study NCT02984969. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
ANDDI-PRENATOME - Feasibility Study for a " Fast " Pangenomic High Throughput Sequencing Analysis in Prenatal Diagnosis
ClinicalTrials.gov study NCT03964441. IPD Sharing: Not stated. Countries: 1. Publications: 2.
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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.