Research Dataset Catalog and ShareScore Ratings | ScienceDex

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Single-cell RNA sequencing of Lymph node-infiltrating HSC-derived phagocytes of Ms4a3Ai14 using 10X Genomics platform. IFN-γ and GM-CSF control complementary differentiation programs in the monocyte to phagocyte transition during neuroinflammation.

Single-cell RNA sequencing of Lymph node-infiltrating HSC-derived phagocytes of Ms4a3Ai14 at onset and peak EAE using 10X Genomics platform. The sorted cells were loaded into 10x Genomics Chromium in parallel. Libraries were prepared as per the manufacturer's protocol (Chromium Next GEM Single Cell 3ʹ Reagent Kits v3.1 protocol) and sequenced on an Illumina NovaSeq sequencer according to 10X Genomics recommendations (paired-end reads, R1=28, i7=8, R2=91) to a depth of around 50,000 reads per cell. Initial processing was done using Cell Ranger (v3.1.0) mkfastq and count (reads were aligned to GENCODE reference build GRCm38.p6 Release M23 with added tdTomato sequence for the dataset from Ms4a3Ai14 mouse and collapse UMIs). Starting from the filtered gene-cell count matrix produced by CellRranger's in-built cell calling algorithms, we proceeded with Seurat v4 workflow.

opencc-by-4.0Nov 2021View details →

zenodo32/100

Single-cell RNA sequencing of Bone Marrow-infiltrating HSC-derived phagocytes of Ms4a3Ai14 using 10X Genomics platform. IFN-γ and GM-CSF control complementary differentiation programs in the monocyte to phagocyte transition during neuroinflammation.

Single-cell RNA sequencing of Bone Marrow-infiltrating HSC-derived phagocytes of Ms4a3Ai14 at onset and peak EAE using 10X Genomics platform. The sorted cells were loaded into 10x Genomics Chromium in parallel. Libraries were prepared as per the manufacturer's protocol (Chromium Next GEM Single Cell 3ʹ Reagent Kits v3.1 protocol) and sequenced on an Illumina NovaSeq sequencer according to 10X Genomics recommendations (paired-end reads, R1=28, i7=8, R2=91) to a depth of around 50,000 reads per cell. Initial processing was done using Cell Ranger (v3.1.0) mkfastq and count (reads were aligned to GENCODE reference build GRCm38.p6 Release M23 with added tdTomato sequence for the dataset from Ms4a3Ai14 mouse and collapse UMIs). Starting from the filtered gene-cell count matrix produced by CellRranger's in-built cell calling algorithms, we proceeded with Seurat v4 workflow.

opencc-by-4.0Nov 2021View details →

zenodo32/100

Single-cell RNA sequencing of Blood-infiltrating HSC-derived phagocytes of Ms4a3Ai14 using 10X Genomics platform. IFN-γ and GM-CSF control complementary differentiation programs in the monocyte to phagocyte transition during neuroinflammation.

Single-cell RNA sequencing of Blood-infiltrating HSC-derived phagocytes of Ms4a3Ai14 at onset and peak EAE using 10X Genomics platform. The sorted cells were loaded into 10x Genomics Chromium in parallel. Libraries were prepared as per the manufacturer's protocol (Chromium Next GEM Single Cell 3ʹ Reagent Kits v3.1 protocol) and sequenced on an Illumina NovaSeq sequencer according to 10X Genomics recommendations (paired-end reads, R1=28, i7=8, R2=91) to a depth of around 50,000 reads per cell. Initial processing was done using Cell Ranger (v3.1.0) mkfastq and count (reads were aligned to GENCODE reference build GRCm38.p6 Release M23 with added tdTomato sequence for the dataset from Ms4a3Ai14 mouse and collapse UMIs). Starting from the filtered gene-cell count matrix produced by CellRranger's in-built cell calling algorithms, we proceeded with Seurat v4 workflow.

opencc-by-4.0Nov 2021View details →

dryad32/100

Deep sequencing data for document titled: Rolling circle RNA synthesis catalysed by RNA

RNA-catalysed RNA replication is widely considered a key step in the emergence of life's first genetic system. However, RNA replication can be impeded by the extraordinary stability of duplex RNA products, which must be dissociated for re-initiation of the next replication cycle. Here we have explored rolling circle synthesis (RCS) as a potential solution to this strand separation problem. RCS on small circular RNAs - as indicated by molecular dynamics simulations - induces a progressive build-up of conformational strain with destabilisation of nascent strand 5' and 3' ends. At the same time, we observe sustained RCS by a triplet polymerase ribozyme on small circular RNAs over multiple orbits with strand displacement yielding concatemeric RNA products. Furthermore, we show RCS of a circular Hammerhead ribozyme capable of self-cleavage and re-circularisation. Thus, all steps of a viroid-like RNA replication pathway can be catalysed by RNA alone. Our results have implications for the emergence of RNA replication and for understanding the potential of RNA to support complex genetic processes.

opencc-zeroFeb 2022View details →

zenodo32/100

Integration of single-cell RNA-sequencing data across tissues and cancer types towards immune cell characterization

To better understand dendritic cell states and subtypes, we collected individual single-cell RNAseq datasets from various studies and further integrated, batch corrected, and reprocessed the data using Besca (https://github.com/bedapub/besca). The following files are included: 1) study_table_integrated_DCs.xlsx - contains a list of studies from where the datasets were gathered. 2)  int_dcs.raw.h5ad - An anndata object file containing the combined raw single-cell counts for DCs from individual studies. The datasets were joined based on the union of variables. 3) intersection_genes_integrated_dcs.tsv - List of genes if the datasets were joined based on the intersection of variables. These variables were used in the subsequent analyses. 4) int_dcs.annotated.h5ad - An anndata object file containing single-cell logarithmized counts for DCs data that have been integrated and reprocessed. The rows of the file contain cells, and the columns contain highly variable genes. A sparse matrix containing the logarithmized counts from all the genes (from the intersection genes integrated dcs.tsv file) can also be found (adata.raw.X) in the object. In the observations, cell-type annotation is available at three different hierarchal levels. This data was further used to produce results for the publication (https://jitc.bmj.com/content/10/6/e004268) on the effects of Toll-like receptor 8 agonists on conventional DCs.

opencc-by-4.0Jun 2022View details →

zenodo32/100

RNA sequencing analysis of CAF treated by colon cancer cell-derived exosomes

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.

opencc-by-4.0Jul 2022View details →

zenodo32/100

An anti-influenza combined therapy assessed by single cell RNA-sequencing

Figures source data for "An anti-influenza combined therapy assessed by single cell RNA-sequencing"

opencc-by-4.0Sep 2022View details →

zenodo32/100

The single cell RNA sequencing of Bullous Pemphigoid

The Sparse count tables in 10XGenomics format (barcodes.tsv.gz features.tsv.gz matrix.mtx.gz) of 33 samples as detailed  in Nat Commun 15, 5949 (2024).   Including single-cell RNA sequencing (scRNA-seq) from five lesions of  Bullous Pemphigoid (BP) patients and eight normal skin of healthy donors, eight PBMC of BP patients and eight PBMC of healthy donors, four blister of BP patients. If you utilize this dataset in your research, kindly cite our article:  Liu, T., Wang, Z., Xue, X. et al. Single-cell transcriptomics analysis of bullous pemphigoid unveils immune-stromal crosstalk in type 2 inflammatory disease. Nat Commun 15, 5949 (2024). https://doi.org/10.1038/s41467-024-50283-3.

opencc-by-4.0Apr 2024View details →

zenodo32/100

Supplementary Figure S2 Pipeline for identification of lncRNA by RNA-sequencing.

Supplementary Figure S2 Pipeline for identification of lncRNA by RNA-sequencing. A total of 127,671 unique assembled transcripts were produced after mapping to the reference genome. Then, we filtered out the transcripts that possessed only a single exon, as well as shorter than 200 nt, retaining 102,134 transcripts. The transcripts that overlapped with coding gene exons in the sense orientation were filtered out using Cuffcompare software. In addition, the retaining transcripts with FPKM < 0.5 were discarded. Then, the protein coding potential of each transcript was accessed using the four most widely used tools (CPC, PFAM, phyloCSF and CNCI), of which transcripts accessed by total four tools were retained.

opencc-by-4.0Aug 2019View details →

zenodo32/100

ONT direct RNA sequencing Fast5 files from mouse embryonic stem cell (C57BL/6J x CAST/EiJ) - Rep1 (run3)

We present a novel approach that leverages Oxford Nanopore direct RNA sequencing technology to detect allele-biased patterns of N6-methyladenosine (m6A) modifications in native mRNAs. Our approach utilized human and mouse cells with known genetic variants to ascertain allelic origin of each mRNA molecule. We employed a supervised machine learning model to detect read-level modification ratios, providing a comprehensive understanding of allele-specific m6A modification (ASM) patterns. To analyze the effects of allele specific m6A modification in mouse, we used hybrid mosue embryonic stem cells (C57BL/6J x CAST/EiJ). This page contains replicate 1 (run 3)