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Fig. 4 in Phylogenetic analysis of the genus Argia Rambur, 1842 (Odonata: Coenagrionidae), based on morphological characters of larvae and mitochondrial DNA sequences
Fig. 4 Evolutionary scenario for the larval morphology inferred by direct optimization of characters on one of the two most parsimonious trees recovered in the phylogenetic analysis after applying implied weights. Black circles indicate transformation of unique characters; white circles indicate parallelism or reversion. The number above the circle indicates
Supplementary Table S2. DNA polymorphisms in gene sequences of 56 sea buckthorn accessions based on the analysis of whole-genome sequencing data.
Open the record for dataset details and reuse information.
Figure 2 in Molecular cloning and sequence analysis of the gene encoding interleukin-6 of the giant panda (Ailuropoda melanoleuca)
Figure 2. Alignment of deduced amino acid sequences of IL-6 mature proteins for seven species in Carnivora. Dots indicate amino acids identical to the top sequence and dashes denote the gaps.
FIGURE 1 in Association of larvae and adults of Mexican species of Macrelmis (Coleoptera: Elmidae): a preliminary analysis using DNA sequences
FIGURE 1. Strict consensus cladogram obtained. The numbers on the nodes indicate the results of symmetrical resampling.
FIGURE. Phylogram of Tolypocladium generated from Maximum likelihood analysis of ITS, SSU and LSU sequence data. Purpureocillium lilacinum (CBS 284.36) was selected as an outgroup taxon. The tree topology of the ML analysis was similar to the BI. Maximum likelihood bootstrap values greater than 75 and Bayesian posterior probabilities over 0.90 were indicated above the nodes. The scale bar indicates 0.006 changes. The new species was in blue. in Yunnan-Guizhou Plateau: a mycological hotspot
FIGURE. Phylogram of Tolypocladium generated from Maximum likelihood analysis of ITS, SSU and LSU sequence data. Purpureocillium lilacinum (CBS 284.36) was selected as an outgroup taxon. The tree topology of the ML analysis was similar to the BI. Maximum likelihood bootstrap values greater than 75 and Bayesian posterior probabilities over 0.90 were indicated above the nodes. The scale bar indicates 0.006 changes. The new species was in blue.
Repository for Single Cell RNA Sequencing Analysis of The EMT6 Dataset
<p><strong>Table of Contents</strong></p><ol><li>Main Description</li><li>File Descriptions</li><li>Linked Files</li><li>Installation and Instructions</li></ol><p> </p><p> </p><p> </p><p><strong>1. Main Description</strong></p><p>---------------------------</p><p>This is the Zenodo repository for the manuscript titled "A TCR β chain-directed antibody-fusion molecule that activates and expands subsets of T cells and promotes antitumor activity.". The code included in the file titled `marengo_code_for_paper_jan_2023.R` was used to generate the figures from the single-cell RNA sequencing data.</p><p>The following libraries are required for script execution:</p><ul><li>Seurat</li><li>scReportoire</li><li>ggplot2</li><li>stringr</li><li>dplyr</li><li>ggridges</li><li>ggrepel</li><li>ComplexHeatmap</li></ul><p> </p><p> </p><p><strong>File Descriptions</strong></p><p>---------------------------</p><ul><li>The code can be downloaded and opened in RStudios.</li><li>The "marengo_code_for_paper_jan_2023.R" contains all the code needed to reproduce the figues in the paper</li><li>The "Marengo_newID_March242023.rds" file is available at the following address: https://zenodo.org/badge/DOI/10.5281/zenodo.7566113.svg (Zenodo DOI: 10.5281/zenodo.7566113).</li><li>The "all_res_deg_for_heat_updated_march2023.txt" file contains the unfiltered results from DGE anlaysis, also used to create the heatmap with DGE and volcano plots.</li><li>The "genes_for_heatmap_fig5F.xlsx" contains the genes included in the heatmap in figure 5F.</li></ul><p> </p><p> </p><p><strong>Linked Files</strong></p><p>---------------------</p><p> </p><p>This repository contains code for the analysis of single cell RNA-seq dataset. The dataset contains raw FASTQ files, as well as, the aligned files that were deposited in GEO. The "Rdata" or "Rds" file was deposited in Zenodo. Provided below are descriptions of the linked datasets:</p><p> </p><p>Gene Expression Omnibus (GEO) ID: GSE223311(https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE223311)</p><ul><li><strong>Title</strong>: Gene expression profile at single cell level of CD4+ and CD8+ tumor infiltrating lymphocytes (TIL) originating from the EMT6 tumor model from mSTAR1302 treatment.</li><li><strong>Description</strong>: This submission contains the "matrix.mtx", "barcodes.tsv", and "genes.tsv" files for each replicate and condition, corresponding to the aligned files for single cell sequencing data.</li><li><strong> Submission type</strong>: Private. In order to gain access to the repository, you must use a reviewer token (https://www.ncbi.nlm.nih.gov/geo/info/reviewer.html).</li></ul><p> </p><p>Sequence read archive (SRA) repository ID: SRX19088718 and SRX19088719</p><ul><li><strong>Title</strong>: Gene expression profile at single cell level of CD4+ and CD8+ tumor infiltrating lymphocytes (TIL) originating from the EMT6 tumor model from mSTAR1302 treatment.</li><li><strong>Description</strong>: This submission contains the **raw sequencing** or `.fastq.gz` files, which are tab delimited text files.</li><li><strong>Submission type</strong>: Private. In order to gain access to the repository, you must use a reviewer token (https://www.ncbi.nlm.nih.gov/geo/info/reviewer.html).</li></ul><p> </p><p>Zenodo DOI: 10.5281/zenodo.7566113(https://zenodo.org/record/7566113#.ZCcmvC2cbrJ)</p><ul><li><strong> Title</strong>: A TCR β chain-directed antibody-fusion molecule that activates and expands subsets of T cells and promotes antitumor activity.</li><li><strong>Description</strong>: This submission contains the "Rdata" or ".Rds" file, which is an R object file. This is a necessary file to use the code.</li><li><strong>Submission type</strong>: Restricted Acess. In order to gain access to the repository, you must contact the author.</li></ul><p> </p><p> </p><p> </p><p><strong>Installation and Instructions</strong></p><p>--------------------------------------</p><p>The code included in this submission requires several essential packages, as listed above. Please follow these instructions for installation:</p><p> </p><p>> Ensure you have R version 4.1.2 or higher for compatibility.</p><p> </p><p>> Although it is not essential, you can use R-Studios (Version 2022.12.0+353 (2022.12.0+353)) for accessing and executing the code.</p><p> </p><p>1. Download the *"Rdata" or ".Rds" file from Zenodo (https://zenodo.org/record/7566113#.ZCcmvC2cbrJ) (Zenodo DOI: 10.5281/zenodo.7566113).</p><p>2. Open R-Studios (https://www.rstudio.com/tags/rstudio-ide/) or a similar integrated development environment (IDE) for R.</p><p>3. Set your working directory to where the following files are located:</p><ul><li>marengo_code_for_paper_jan_2023.R</li><li>Install_Packages.R</li><li>Marengo_newID_March242023.rds</li><li>genes_for_heatmap_fig5F.xlsx</li><li>all_res_deg_for_heat_updated_march2023.txt</li></ul><p> </p><p>You can use the following code to set the working directory in R:</p><p>> setwd(directory)</p><p> </p><p>4. Open the file titled "Install_Packages.R" and execute it in R IDE. This script will attempt to install all the necessary pacakges, and its dependencies in order to set up an environment where the code in "marengo_code_for_paper_jan_2023.R" can be executed.</p><p>5. Once the "Install_Packages.R" script has been successfully executed, re-start R-Studios or your IDE of choice.</p><p>6. Open the file "marengo_code_for_paper_jan_2023.R" file in R-studios or your IDE of choice.</p><p>7. Execute commands in the file titled "marengo_code_for_paper_jan_2023.R" in R-Studios or your IDE of choice to generate the plots.</p>
Comprehensive Analysis of Gene Mutation Profile in Chinese NSCLC Patients by Next-generation Sequencing
ClinicalTrials.gov study NCT03609918. IPD Sharing: UNDECIDED. Countries: 0. Publications: 0.
Next Generation Sequencing Facilitates Quantitative Analysis of Wild Type, Mst1/2 dKO and Mst1/2; Yap tKO Initial segments of Epididymis Transcriptomes
GEO Series GSE138519. Mus musculus. 9 samples. Type: Expression profiling by high throughput sequencing.
Next Generation Sequencing Facilitates Quantitative Analysis of HIV Elite controller, HIV ART, HIV ART-naive and healthy control Transcriptomes
GEO Series GSE157198. Homo sapiens. 44 samples. Type: Expression profiling by high throughput sequencing.
Next generation sequencing analysis of transcriptomes at different timepoints after chlorprothixene treatment in NB4 and Kasumi-1 cells
GEO Series GSE124316. Homo sapiens. 8 samples. Type: Expression profiling by high throughput sequencing.
Next Generation Sequencing Facilitates Quantitative Analysis of WT and cry1 in dark and Blue light Transcriptomes
GEO Series GSE232026. Arabidopsis thaliana. 12 samples. Type: Expression profiling by high throughput sequencing.
Next Generation Sequencing - An Analysis of the Role of Human-Specific Isoform transcription factor Oct-1 in Nerve Cell Differentiation
GEO Series GSE153980. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
Next Generation Sequencing Facilitates Quantitative Analysis of Wild Type and dek33 mutant Transcriptomes
GEO Series GSE80250. Zea mays. 2 samples. Type: Expression profiling by high throughput sequencing.
Next Generation Sequencing Facilitates Quantitative Analysis of NP1 and NP1/CRTCHA Gut Transcriptomes
GEO Series GSE185159. Drosophila melanogaster. 6 samples. Type: Expression profiling by high throughput sequencing.
RNA sequencing analysis of gene expresssion profiles in Cic-null MZB cells
GEO Series GSE264654. Mus musculus. 4 samples. Type: Expression profiling by high throughput sequencing.
Next Generation Sequencing Facilitates Quantitative Analysis of antidepressants stressed E. coli K-12 MG1655 Transcriptomes
GEO Series GSE201666. Escherichia coli str. K-12 substr. MG1655. 48 samples. Type: Expression profiling by high throughput sequencing.
Next generation sequencing analysis of mouse adrenal glands after a one-hour dexamethasone treatment
GEO Series GSE189182. Mus musculus. 4 samples. Type: Expression profiling by high throughput sequencing.
RNA-sequencing analysis of the OsIMA overexpression and knockdown rice lines and Fe deficiency responsiveness of non-transformant (NT) rice
GEO Series GSE151941. Oryza sativa. 19 samples. Type: Expression profiling by high throughput sequencing.
Next Generation Sequencing Facilitates Quantitative Analysis of PIGS-KO, PIGS-HRD1-DKO, PIGS-HRD1-CD55-TKO, and PIGS-HRD1-CD55-TKO+HA-CD55 Transcriptomes
GEO Series GSE184822. Homo sapiens. 4 samples. Type: Expression profiling by high throughput sequencing.
smRNA sequencing analysis to identify p53-dependent non-coding RNA networks in Chronic Lymphocytic Leukemia
GEO Series GSE66186. Homo sapiens. 70 samples. Type: Non-coding RNA profiling by high throughput sequencing; Expression profiling by high throughput sequencing.
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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.