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1,021 results for “RNA sequence analysis”
RNA sequencing dataset for prediction of liver hepatocellular carcinoma using SIMON analysis
<p>The LIHC dataset was used for data mining and for the generation of machine learning model for the detection of liver hepatocellular carcinoma cells (LIHC) using the SIMON platform as described in the "SIMON: open-source knowledge discovery platform" publication (<a href="https://doi.org/10.1101/2020.08.16.252767">https://doi.org/10.1101/2020.08.16.252767</a>). The LIHC dataset was obtained from the <em>GSEABenchmarkeR</em> package ( <a href="https://doi.org/10.1093/bib/bbz158">https://doi.org/10.1093/bib/bbz158</a>) and it contains RNA expression data from 374 liver hepatocellular carcinoma (LIHC) cells and 50 adjacent normal cells.</p>
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)
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>
Data for 'Comparative Analysis of Single-Cell RNA Sequencing Methods'
<p>Raw sequencing data to "Comparative Analysis of Single-Cell RNA Sequencing Methods". </p> <p>https://www.ncbi.nlm.nih.gov/pubmed/28212749</p> <p> </p> <p>In addition to the GEO submission https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE75790, you can find here raw bam files for UMI-methods tagged with cell barcode and UMI sequences.</p> <p>MD5 checksum: f10825509952fffd9c4dc0c1dcb9eb8e</p>
Sequencing summaries & Nanopolish eventalign outputs for downstream analysis of yeast RNA and synthetic oligonucleotides
<p>This dataset contains:</p> <ol> <li>A set of text files from running the tool Nanopolish eventalign on several nanopore direct RNA sequencing data sets produced by Jay Hesselberth's lab at the University of Colorado (BioProject accession number <a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA910992">PRJNA910992</a>), as well as external sequencing data sets from <a href="https://pubmed.ncbi.nlm.nih.gov/34893601/">PMID: 34893601</a> (synthetic oligonucleotides from Leger et al) and <a href="https://pubmed.ncbi.nlm.nih.gov/35252946/">PMID: 35252946</a> (yeast rRNA data from Stephenson et al).</li> <li>"Sequencing summary" files produced by MinKNOW, from nanopore sequencing of yeast mRNA and synthetic RNA oligos in the Hesselberth lab. We analyze these files in an associated manuscript to determine the "end status" of each read during the sequencing run.</li> </ol> <p>These files can be used as inputs to the R markdown documents at <a href="https://github.com/hesselberthlab/RNARePore">https://github.com/hesselberthlab/RNARePore</a> to reproduce the figures in the associated manuscript.</p>
Dataset for: mRNA vaccine quality analysis using RNA sequencing
<p>The success of mRNA vaccines has been realised, in part, by advances in manufacturing that enabled billions of doses to be produced at sufficient quality and safety. However, mRNA vaccines must be rigorously analysed to measure their integrity and detect contaminants that reduce their effectiveness and induce side-effects. Currently, mRNA vaccines and therapies are analysed using a range of time-consuming and costly methods. Here we describe a streamlined method to analyse mRNA vaccines and therapies using long-read nanopore sequencing. Compared to other industry-standard techniques, VAX-seq can comprehensively measure key mRNA vaccine quality attributes, including sequence, length, integrity, and purity. We also show how direct RNA sequencing can analyse mRNA chemistry, including the detection of nucleoside modifications. To support this approach, we provide supporting software to automatically report on mRNA and plasmid template quality and integrity. Given these advantages, we anticipate that RNA sequencing methods, such as VAX-seq, will become central to the development and manufacture of mRNA drugs.</p>
Dataset for: mRNA vaccine quality analysis using RNA sequencing
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Bulk RNA sequencing analysis of Lin- leukemia BCR-ABL and BCR-ABL/MSI2-HOXA9 cells (post-transplantation)
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RNA sequencing analysis of CAF treated by colon cancer cell-derived exosomes
<p>In this study, we analyzed differentially expressed genes by obtaining gene expression values through transcriptome sequencing of Homo sapiens, and performed functional classification and gene annotation for significant genes based on gene ontology and pathway information. After the pre-processed trimmed reads were mapped to a known reference genome using the HISAT2 program, transcript assembly was performed through the StringTie program. As a result, expression profile values were obtained for each sample for the known transcript, and read count, based on transcript/gene Fragment per Kilobase of transcript per Million mapped reads (FPKM), Transcripts per Kilobase (TPM) Million) values have been summarized. This value was subjected to DEG (Differentially Expressed Genes) analysis using edgeR for comparison combinations (HT-29_Exo-CAF vs. CTL_PBS-CAF, LoVo_Exo-CAF vs. CTL_PBS-CAF, and SW480_Exo-CAF vs. CTL_PBS-CAF), and genes that satisfies the condition |fc|>=2 & exactTest raw p-value<0.05 in at least one comparison combination Dogs were extracted. Transcriptome resequencing data was used to compare expression profiles between comparable samples. Gene Ontology Enrichment analysis was performed using the g:Profiler tool (https://biit.cs.ut.ee/gprofiler/) for a list of genes with significant expression level differences. GO_stat is the result of organizing the associated gene and test stat based on term_id. GO_genes is the result of arranging the associated term_id and DEG analysis result stat based on the gene.</p>
Analysis of RNA-seq, DNA target enrichment, and Sanger nucleotide sequence data resolves deep splits in the phylogeny of cuckoo wasps (Hymenoptera: Chrysididae)
<p>The wasp family Chrysididae (cuckoo wasps, gold wasps) comprises exclusively parasitoid and kleptoparasitic species, many of which feature a stunning iridescent coloration and phenotypic adaptations to their parasitic life style. Previous attempts to infer phylogenetic relationships among the family's major lineages (subfamilies, tribes, genera) based on Sanger sequence data were insufficient to statistically resolve the monophyly and the phylogenetic position of the subfamily Amiseginae and the phylogenetic relationships among the tribes Allocoeliini, Chrysidini, Elampini, and Parnopini (Chrysidinae). Here, we present a phylogeny inferred from nucleotide sequence data of 492 nuclear single-copy genes (230,915 aligned amino acid sites) from 94 species of Chrysidoidea (representing Bethylidae, Chrysididae, Dryinidae, Plumariidae) and 45 outgroup species by combining RNA-seq and DNA target enrichment data. We find support for Amiseginae being more closely related to Cleptinae than to Chrysidinae. Furthermore, we find strong support for Allocoeliini being the sister lineage of all remaining Chrysidinae, while Elampini represent the sister lineage of Chrysidini and Parnopini. Our study corroborates results from a recent phylogenomic investigation which revealed Chrysidoidea as likely paraphyletic</p>
Analysis of RNA-seq, DNA target enrichment, and Sanger nucleotide sequence data resolves deep splits in the phylogeny of cuckoo wasps (Hymenoptera: Chrysididae)
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Differential analysis of binarized single-cell RNA sequencing data captures biological variation
<p>Processed datasets used for binary differential analysis experiments.</p>
Healthy woodchuck genome with viral sequences appended used for single-cell RNA-seq analysis
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Curated RNA Sequencing Data for Zebrafish (Danio rerio) snoRNA Expression Analysis
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Analysis of Public Short-Read RNA-Sequencing Data: PRJNA543316
<p><strong><span>PRJNA543316</span></strong></p> <p><span>Metformin is a front-line drug in the treatment of type-2 diabetes mellitus (T2DM). In addition to its antigluconeogenic and insulin-sensitizing properties, it has emerged as a potent inhibitor of the inflammatory response of macrophages. Specifically, metformin has been shown to reduce transcript levels of Il1b, the gene encoding the pro-inflammatory cytokine interleukin (IL)-1b, during long-term exposure of macrophages to the bacterial cell-wall component lipopolysaccharide (LPS). However, the extent to which metformin affects the early transcriptional response to LPS has never been investigated. Here, we show that metformin affects transcript levels of a large yet selective subset of LPS-responsive genes after only two hours of LPS exposure, mostly counteracting the effect of LPS rather than enhancing it. The affected genes are implicated in a variety of biological functions, in particular cellular movement and trafficking. Intriguingly, metformin affects transcript levels of Il1b at this early time point as well, but through a molecular mechanism fundamentally different from the regulation observed after longer exposure. While down-regulation of Il1b by metformin during the late stages of the LPS response has been shown to rely on stabilization of hypoxia-inducible factor (HIF)-1α and production of IL-10, Il1b inhibition at the early stage requires AMP-activated protein kinase (AMPK) activation but is independent of HIF-1α and IL-10. These results reveal an unexpected complexity in the anti-inflammatory properties of metformin and demonstrate that Il1b is down-regulated by distinct mechanisms in the early and late stages of the LPS response. Overall design: Bone-marrow-derived macrophages from WT C57Bl/6 mice were either left untreated, stimulated with 100 ng/ml LPS for 2 h or pretreated with 5 mM metformin for 6 h and then stimulated with 100 ng/ml LPS for 2 h.</span></p> <p><span>Pipeline: FastQ -> FastQC -> fastp -> STAR -> samtools -> multiQC</span></p>
['"Next-generation sequencing analysis of circulating micro-RNA expression in response to parabolic flight as a spaceflight analogue"']
['"Understanding physiologic reactions to weightlessness is an indispensable requirement for safe human space missions. While adaptations of human organ systems in response to weightlessness have been described in former studies, their molecular background needs further elucidation. The study aims to analyse changes in the expression of circulating miRNAs in serum in response to gravitational changes induced by parabolic flight as a spaceflight analogue."']
RNA Sequencing Facilitates Quantitative Analysis of differentially expressed genes during human erythroipoiesis
GEO Series GSE119315. Homo sapiens. 8 samples. Type: Expression profiling by high throughput sequencing.
RNA-sequencing analysis to investigate genes regulated by CASZ1 in SK-N-AS (AS) cells.
GEO Series GSE182872. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
Single-cell RNA-sequenceing analysis of mouse intestinal organoids cultured in Matriel or type I collagen
GEO Series GSE300601. Mus musculus. 2 samples. Type: Expression profiling by high throughput sequencing.
Single cell RNA sequencing analysis of the center, edge and non-wounded regions of healing skin on day 11 post-wounding in wild type mice
GEO Series GSE273111. Mus musculus. 3 samples. Type: 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.