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6,609 results for “RNA sequencing”
Рис. 1. ФиΛогенетические Αеревья хантавируса AMRV и его прироΑного носитеΛя восточноазиатской мыши Apodemus peninsulae Thomas, 1906. А. ФиΛогенетическое Αерево восточноазиатской мыши Apodemus peninsulae, построенное метоΑом «максимаΛьного правΑопоΑобия» (ML) и поΛученное на основе анаΛиза участка гена цитохрома b мтΔНК (744 п.н.). В узΛах ветвΛения указаны бутстреп-поΑΑержки, рассчитанные ΑΛя 1000 повторов. Цветными Λиниями обозначены фиΛогенетические Λинии: Αве Китайские (зеΛеный), Корейская «Korea» (синий), Амурская «Amur» (красный). ПоΛужирным шрифтом выΑеΛены собственные образцы. Названия образцов из GenBank/NCBI быΛи сокращены; B. ФиΛогенетическое Αерево из работы Α. Н. Яшиной с ΑопоΛнениями, построенное метоΑом «бΛижайшего сосеΑа» (NJ) на основе посΛеΑоватеΛьностей фрагмента М-сегмента (2737–2980 н.п.) генома хантавирусов. В узΛах ветвΛения указаны бутстреппоΑΑержки, рассчитанные ΑΛя 1000 повторов. Жирным выΑеΛены иссΛеΑованные РНК изоΛяты (Яшина 2012; Яшина и Αр. 2019) Fig. 1. Phylogenetic trees of AMRV and its natural reservoir host — the Korean field mouse Apodemus peninsulae Thomas, 1906. A. Phylogenetic tree of the Korean field mouse Apodemus peninsulae constructed by the "maximum likelihood" method (ML). The data are obtained from the analysis of the cytochrome b mtDNA gene fragments (744 bp). Bootstrap supports calculated for 1,000 repeats are indicated in the branching nodes. Colored lines indicate phylogenetic lines: two Chinese (green), Korea (blue), and Amur (red). Own samples are highlighted in bold. The names of the samples from GenBank/NCBI have been shortened; B. Phylogenetic tree from L. N. Yashina's work with additions constructed by the neighbour joining method (NJ). It is based on the sequences of an M-segment fragment (2737–2980 bp) of the hantavirus genome. Bootstrap supports calculated for 1,000 repeats are indicated in the branching nodes. The researched RNA isolates are highlighted in bold (Yashina 2012; Yashina et al. 2019) in Variability of the gene cyt b in the Korean field mouse Apodemus peninsulae Thomas, 1906 - a reservoir host of AMRV in the Khasansky District of Primorsky Krai
Рис. 1. ФиΛогенетические Αеревья хантавируса AMRV и его прироΑного носитеΛя восточноазиатской мыши Apodemus peninsulae Thomas, 1906. А. ФиΛогенетическое Αерево восточноазиатской мыши Apodemus peninsulae, построенное метоΑом «максимаΛьного правΑопоΑобия» (ML) и поΛученное на основе анаΛиза участка гена цитохрома b мтΔНК (744 п.н.). В узΛах ветвΛения указаны бутстреп-поΑΑержки, рассчитанные ΑΛя 1000 повторов. Цветными Λиниями обозначены фиΛогенетические Λинии: Αве Китайские (зеΛеный), Корейская «Korea» (синий), Амурская «Amur» (красный). ПоΛужирным шрифтом выΑеΛены собственные образцы. Названия образцов из GenBank/NCBI быΛи сокращены; B. ФиΛогенетическое Αерево из работы Α. Н. Яшиной с ΑопоΛнениями, построенное метоΑом «бΛижайшего сосеΑа» (NJ) на основе посΛеΑоватеΛьностей фрагмента М-сегмента (2737–2980 н.п.) генома хантавирусов. В узΛах ветвΛения указаны бутстреппоΑΑержки, рассчитанные ΑΛя 1000 повторов. Жирным выΑеΛены иссΛеΑованные РНК изоΛяты (Яшина 2012; Яшина и Αр. 2019) Fig. 1. Phylogenetic trees of AMRV and its natural reservoir host — the Korean field mouse Apodemus peninsulae Thomas, 1906. A. Phylogenetic tree of the Korean field mouse Apodemus peninsulae constructed by the "maximum likelihood" method (ML). The data are obtained from the analysis of the cytochrome b mtDNA gene fragments (744 bp). Bootstrap supports calculated for 1,000 repeats are indicated in the branching nodes. Colored lines indicate phylogenetic lines: two Chinese (green), Korea (blue), and Amur (red). Own samples are highlighted in bold. The names of the samples from GenBank/NCBI have been shortened; B. Phylogenetic tree from L. N. Yashina's work with additions constructed by the neighbour joining method (NJ). It is based on the sequences of an M-segment fragment (2737–2980 bp) of the hantavirus genome. Bootstrap supports calculated for 1,000 repeats are indicated in the branching nodes. The researched RNA isolates are highlighted in bold (Yashina 2012; Yashina et al. 2019)
Fig. 1 in Phylogenetic position of the freshwater fish trypanosome, Trypanosoma ophiocephali (Kinetoplastida) inferred from the complete small subunit ribosomal RNA gene sequence
Fig. 1 The neighbor-joining tree of aquatic trypanosomes constructed from complete small subunit ribosomal RNA (SSrRNA) sequences indicating the systematic position of T. ophiocephali and phylogenetic relationships among the aquatic trypanosomes whose sequences are available. T. lewisi, T. theileri, and T. avium are taken as the outgroup. Bootstrap values are shown for the maximum parsimony/neighborjoining/Bayes analyses
Supplementary data for: Detection of expressed mutations in acute myeloid leukemia cells using single cell RNA-sequencing
<p>Supplemental data for the publication:<br> Detection of expressed mutations in acute myeloid leukemia cells using single cell RNA-sequencing </p> <p>Contents: <br> - expression_matrices.tar - Gene/Barcode expression matrices from `cellranger count`<br> - *.seurat.rds - R object files with Seurat analyses and data structures for each sample<br> - scrna_mutations.tar.gz - copy of a git repository containing additional scripts and data - also hosted at <a href="https://github.com/genome/scrna_mutations">https://github.com/genome/scrna_mutations</a> (snapshot as of May 20, 2019)</p>
A comparison of automatic cell identification methods for single-cell RNA-sequencing data
<p>Benchmark datasets used to evaluate the performance of 22 classifiers for cell type classification for scRNA-seq data</p>
Bigwig files of ChIP and RNA sequencing experiments in Theileria annulata
<p>This repository provides bigwig files for ChIP and RNA sequencing experiments in Theileria annulata. DeepTools generated Coverage files, ReadCount normalised files and SES normalised files are provided. Also provided are genome and annotation files for visualisation with IGV software.</p>
MinION sequence data: MinION sequencing of colorectal cancer tumor microbiomes – a comparison with amplicon-based and RNA-Sequencing
<p>MinION sequencing data that was unmapped by minimap2 for the 11 samples using in the "MinION sequencing of colorectal cancer tumor microbiomes – a comparison with amplicon-based and RNA-Sequencing" paper.</p>
Deep learning model for characterizing protein-RNA interactions from sequence at single-base resolution
<p> </p> <p><a href="https://zenodo.org/api/records/14021440/draft/files/encode_eclip.h5/content" target="_blank" rel="noopener noreferrer">encode_eclip.h5</a> - This file contains the training, validation, and test data for the Reformer model.</p> <p><a href="https://zenodo.org/api/records/14021440/draft/files/encode_eclip_bc.h5/content" target="_blank" rel="noopener noreferrer">encode_eclip_bc.h5</a> - This file contains the training, validation, and test data for the Reformer-BC model.</p> <p><a href="https://zenodo.org/api/records/14027315/draft/files/Reformer-code.zip/content" target="_blank" rel="noopener">Reformer-code.zip</a> - This file contains the training code of Reformer.</p>
Fig. 2 in Some Unusual Small-Subunit Ribosomal RNA Sequences of Metazoans
Fig. 2. Phylogenetic tree of the centipedes based on the combined analysis of Edgecombe et al. (1999). The arrow indicates where the insertion of ca. 300 bp at region V7 occurred during the evolution of centipedes.
Fig. 5 in Some Unusual Small-Subunit Ribosomal RNA Sequences of Metazoans
Fig. 5. Phylogenetic analysis of the data from fig. 4 using the ''fixed character states'' method of Wheeler (1999) implemented in the computer program POY (Gladstein and Wheeler, 1997). Commands: poy fixedstates noleading norandomizeoutgroup gap 1 maxtrees 20 multibuild 10 seed‾1 slop 2 checkslop 5. The two circles illustrate the insertions of the Geophilomorpha (ca. 300 bp), and the Scolopendridae (ca. 25 bp).
Fig. 1 in Some Unusual Small-Subunit Ribosomal RNA Sequences of Metazoans
Fig. 1. Schematic representation of the 18S rRNA locus. The gray squares represent the variable regions V2, V4, V7, and V9 with insertions (V2: Onychophora, Geophilomorpha, Cephalopoda, Archaeogastropoda; V4: Hexapoda, Crustacea, Pauropoda, Holothuroidea, Chaetognatha, Platyhelminthes, Cephalopoda; V7: Onychophora, Hexapoda, Crustacea, Pauropoda, Chilopoda, Platyhelminthes, Hirudinea, Cephalopoda, Gastropoda; V9: Onychophora, Crustacea, Cephalopoda). The black arrowheads represent particular insertions (10: Pauropoda; 11: Onychophora; E23–7: Onychophora and Pauropoda; E23–8: Pauropoda; 29: Pauropoda; 46: Protura). The black bar represents the 500 bp deletion of the Symphyla.
Fig. 3 in Some Unusual Small-Subunit Ribosomal RNA Sequences of Metazoans
Fig. 3. Phylogenetic tree based on 18S rRNA sequence data indicating the position of two symphylans (box) with respect to other myriapods (underlined taxa) in a phylogenetic analysis of arthropods (from Giribet, 1997). The two symphylans appear related to other myriapods.
Data related to research article: Towards mouse genetic-specific RNA-sequencing read mapping
<p>This dataset contains data related to the research article: "Towards mouse genetic-specific RNA-sequencing read mapping".</p>
Additional files of scTensor paper "scTensor detects many-to-many cell-cell interactions from single cell RNA-sequencing data"
<p>Complex biological systems are described as a multitude of cell-cell interactions (CCIs). Recent single-cell RNA-sequencing studies focus on CCIs based on ligand-receptor (L-R) gene co-expression. However, the analytical methods are still not mature; such methods cannot detect CCIs and the related L-R pairs simultaneously or also are not appropriate to detect many-to-many CCIs.</p> <p>In this work, we propose scTensor, a novel method for extracting representative triadic relationships (or hypergraphs), which include ligand-expression, receptor-expression, and related L-R pairs. Through extensive studies with simulated and empirical datasets, we have shown that scTensor could detect some hypergraphs, which cannot be detected by conventional methods, especially when those CCIs are many-to-many relationships.</p>
Raw Counts: A protocol for low-input RNA-sequencing of patients with febrile neutropenia captures relevant immunological information
<p>Raw counts for scientific article: </p> <p><em>"A protocol for low-input RNA-sequencing of patients with febrile neutropenia captures relevant immunological information"</em></p> <p>Victoria Probst*<sup>1</sup>, Lotte Møller Smedegaard*<sup>2</sup>, Arman Simonyan<sup>1</sup>, Yuliu Guo<sup>1</sup>, Olga Østrup<sup>1</sup>, Kia Hee Schultz Dungu<sup>2</sup><sub>, </sub>Nadja Hawwa Vissing<sup>2</sup><sub>, </sub>Ulrikka Nygaard<sup>2</sup><sub> </sub>and<sub> </sub>Frederik Otzen Bagger<sup>1</sup></p> <p><sup>*Shared first authorship</sup></p> <p>Data description: </p> <p>The raw counts are from 88 samples of 22 patients with leukaemia and suspected infection sequenced by a low-input protocol (Takara SMART-seq HT) (96% succeeded) and 15 of these were also processed by the standard protocol (Truseq).</p> <p> </p> <p><sup>CLI.CSV: Raw counts of control samples processed using a low input RNA sequencing protocol. 15 samples processed by the low-input protocol. </sup></p> <p><sup>CRNA.CSV: Raw counts of control samples processed using a standard RNA sequencing protocol. 15 samples processed by the standard protocol. </sup></p> <p><sup>FEB.CSV: Raw gene counts from patients. 88 samples processed by the low input protocol. 4 samples failed sequencing.</sup></p> <p> </p> <p> </p> <p> </p>
Model-based analysis of sample index hopping reveals its widespread artifacts in multiplexed single-cell RNA-sequencing
<p>Supplementary data that are needed to rerun the reproducible notebooks from the first steps using Alevin output and configuration files.</p> <p>Intermediate R data object that can be used to rerun the reproducible notebooks after the filtering steps.</p> <p>Validation data for inferring the sample index hopping rate. The <em>hiseq4000_joined_datatable_plexed_nonplexed.zip file contains read counts for four samples (two non-multiplexed and two multiplexed) joined by a cell-barcode, UMI, and gene-ID (CUG) key combination. The hiseq4000_inner_joined_with_labels.zip file contains only those CUGs that are observed in both the non-multiplexed and multiplexed samples.</em><em> </em></p>
De novo nanopore sequencing overrepresents RNA modification landscape
<p>RNA modifications are critical to the functional diversity and regulatory complexity of the transcriptome. With increasing frequency, direct nanopore RNA sequencing is applied to identify RNA modifications de novo. Here, we directly compare the MS2 phage genome RNA modification profiles determined using nanopore to orthogonal LC-MS/MS assays. The results reveal very different views of the modification landscape, suggesting caution when calling new RNA modifications using nanopore alone.</p>
Single-cell RNA sequencing of sclerotome-derived fibroblasts in zebrafish
<p>Despite their importance in tissue maintenance and repair, fibroblast diversity and plasticity remain poorly understood. Using single-cell RNA sequencing, we uncover distinct sclerotome-derived fibroblast populations in zebrafish, including progenitor-like perivascular/interstitial fibroblasts, and specialized fibroblasts such as tenocytes. To determine fibroblast plasticity <em>in vivo</em>, we develop a laser-induced tendon ablation and regeneration model. Lineage tracing reveals that laser-ablated tenocytes are quickly regenerated by preexisting fibroblasts. By combining single-cell clonal analysis and live imaging, we demonstrate that perivascular/interstitial fibroblasts actively migrate to the injury site, where they proliferate and give rise to new tenocytes. By contrast, perivascular fibroblast-derived pericytes or specialized fibroblasts, including tenocytes, exhibit no regenerative plasticity. Interestingly, active Hedgehog (Hh) signaling is required for the proliferation of activated fibroblasts to ensure efficient tenocyte regeneration. Together, our work highlights the functional diversity of fibroblasts and establishes perivascular/interstitial fibroblasts as tenocyte progenitors that promote tendon regeneration in a Hh signaling-dependent manner.</p>
mTAGs: taxonomic profiling using degenerate consensus reference sequences of ribosomal RNA gene
<p>mTAGs is a tool for the taxonomic profiling of metagenomes. It detects sequencing reads belonging to the small subunit of the ribosomal RNA (SSU-rRNA) gene and annotates them through the alignment to full-length degenerate consensus SSU-rRNA reference sequences. The tool is capable of processing single-end and pair-end metagenomic reads, takes advantage of the information contained in any region of the SSU-rRNA gene and provides relative abundance profiles at multiple taxonomic ranks (Domain, Phylum, Class, Order, Family, Genus and OTUs defined at a 97% sequence identity cutoff).</p>
Single-cell RNA sequencing of human salivary gland derived mesenchymal stromal cells under cytokine treatment conditions
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Bulk RNA sequencing of human mesenchymal stromal cells derived from labial salivary glands, bone marrow, and adipose
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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.