Single-cell temporal transcriptomics of the human cell cycle
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE146773 | GEO | 2020/12/11
REPOSITORIES: GEO
Similar Datasets
Project description:This SuperSeries is composed of the SubSeries listed below. Useful links: Parsed data: (ftp://ftp.ebi.ac.uk/pub/databases/scnmt_gastrulation) Github repository: (https://github.com/rargelaguet/scnmt_gastrulation)
2019-09-30 | GSE121708 | GEO
Project description:We developed HyDrop, a new droplet microfluidic single-cell sequencing protocol for single-cell ATAC-seq and single-cell RNA-seq. We applied HyDrop-ATAC to flash-frozen mouse cortex and generated 7996 single-cell chromatin accessibility profiles. We applied HyDrop-RNA to flash-frozen mouse cortex and generated 9508 single-cell transcriptomes. We also applied HyDrop-RNA to a small population of FAC-sorted neurons from the Drosophila brain to demonstrate the protocol's applicability to low-input samples. The data available in this repository can be used to replicate all the figures in our manuscript using our data analysis tutorial available here: https://github.com/aertslab/hydrop_data_analysis
2021-06-04 | GSE175684 | GEO
Project description:Collection of HeLa maintenance and quality control runs for several years. The dataset was acquired to be used for exploration of deep learning models, initially in the scope of imputation. The repository for this archiv contains hints on how to use the three curated datasets and the single runs as filtered data. It provides links to the files on PRIDE.https://github.com/RasmussenLab/hela_qc_mnt_data The current pre-print of the data descriptor can be found here:https://doi.org/10.21203/rs.3.rs-3083547/v2
2023-12-12 | PXD042233 | Pride
Project description:We systematically benchmarked 8 single-cell ATAC sequencing technologies for their capacity to generate high-quality single-cell open chromatin profiles, and to elucidate the regulatory landscape of complex samples. Our study contains 47 individual human PBMC scATAC-seq experiments from a reference male and female donor. To streamline data analysis, we devised PUMATAC (https://github.com/aertslab/PUMATAC), a flexible and universal data analysis pipeline and best practices repository for scATAC-seq. Systematic technology-specific differences in sequencing library complexity and biases in tagmentation specificity were found to impact the accuracy of cell type annotation, genotype demultiplexing, peak calling, differential region accessibility, and motif enrichment. Together, our data forms a new scATAC-seq reference of more than 169, 000 PBMC cells with matched single-cell multiome and RNA-seq data.
2023-05-22 | GSE194028 | GEO
Project description:This model provides a molecularly detailed description of the biochemical reaction networks governing the mammalian cell cycle, including the restriction point, the G1S, G2/M and metaphase/anaphase transitions. Kinetic laws are described with mass action kinetics. The reaction network includes DNA, (phospho)proteins and complexes, but omits mRNAs for simplicity. To estimate the model parameters, time courses of eight cell cycle regulators in two compartments were reconstructed from single cell snapshot measurements. The resulting optimization problem was defined in PEtab (see https://github.com/paulflang/cell_cycle_petab) and solved with the parallel global optimisation metaheuristic saCeSS.
2024-01-15 | MODEL2401050001 | BioModels
Project description:Sequence analyses shown that most exons of VZV circVLTs were overlapped with ORFs, except for exon5, which was an ideal mutation position. Using a galK positive/counter selection VZV bacteria artificial chromosome (BAC) system, we replaced upstream (104,765-104,785 Mutation2, pOKA-M2) or downstream (104,785-104,805, Mutation1, pOKA-M1) of circVLTs BSJ exon5 with 20 bp random sequences in VZV genomic DNA level. The code used in this study and extended data are available from the GitHub repository (https://github.com/ShaominYang/VZV_circRNA)
2024-04-27 | GSE223957 | GEO
Project description:This repository contains all the FASTQ files for the five data modalities (scRNA-seq, scATAC-seq, Multiome, CITE-seq+scVDJ-seq, and spatial transcriptomics) used in the article \\"An Atlas of Cells in The Human Tonsil,\\" published in Immunity in 2024. Inspired by the TCGA barcodes, we have named each fastq file with the following convention: [TECHNOLOGY].[DONOR_ID].[SUBPROJECT].[GEM_ID].[LIBRARY_ID].[LIBRARY_TYPE].[LANE].[READ].fastq.gz which allows to retrieve all metadata from the name itself. Here is a full description of each field: - TECHNOLOGY: scRNA-seq, scATAC-seq, Multiome, CITE-seq+scVDJ-seq, and spatial transcriptomics (Visium). We also include the fastq files associated with the multiome experiments performed on two mantle cell lymphoma patients (MCL). - DONOR_ID: identifier for each of the 17 patients included in the cohort. We provide the donor-level metadata in the file \\"tonsil_atlas_donor_metadata.csv\\", including the hospital, sex, age, age group, cause for tonsillectomy and cohort type for every donor. - SUBPROJECT: each subproject corresponds to one run of the 10x Genomics Chromium™ Chip. - GEM_ID: each run of the 10x Genomics Chromium™ Chip consists of up to 8 \\"GEM wells\\" (see https://www.10xgenomics.com/support/software/cell-ranger/getting-started/cr-glossary): a set of partitioned cells (Gel Beads-in-emulsion) from a single 10x Genomics Chromium™ Chip channel. We give a unique identifier to each of these channels. - LIBRARY_ID: one or more sequencing libraries can be derived from a GEM well. For instance, multiome yields two libraries (ATAC and RNA) and CITE-seq+scVDJ yields 4 libraries (RNA, ADT, BCR, TCR). - LIBRARY_TYPE: the type of library for each library_id. Note that we used cell hashing () for a subset of the scRNA-seq libraries, and thus the library_type can be \\"not_hashed\\", \\"hashed_cdna\\" (RNA expression) or \\"hashed_hto\\" (the hashtag oligonucleotides). - LANE: to increase sequencing depth, each library was sequenced in more than one lane. Important: all lanes corresponding to the same sequencing library need to be inputed together to cellranger, because they come from the same set of cells. - READ: for scATAC-seq we have three reads (R1, R2 or R3), see cellranger-atac's documentation. While we find these names to be the most useful, they need to be changed to follow cellranger's conventions. We provide a code snippet in the README file of the GitHub repository associated with the tonsil atlas to convert between both formats (https://github.com/Single-Cell-Genomics-Group-CNAG-CRG/TonsilAtlas/). Besides the fastq files, cellranger (and other mappers) require additional files, which we also provide in this repository: - cell_hashing_metadata.csv: as mentioned above, we ran cell hashing (10.1186/s13059-018-1603-1) to detect doublets and reduce cost per cell. This file provides the sequence of the hashtag oligonucleotides in cellranger convention to allow demultiplexing. - cite_seq_feature_reference.csv: similar to the previous file, this one links each protein surface marker to the hashtag oligonucleotide that identified it in the CITE-seq experiment. - V10M16-059.gpr and V19S23-039.gpr: these correspond to the two slides of the two Visium experiments performed in the tonsil atlas. They are needed to run spaceranger. - [GEM_ID]_[SLIDE]_[CAPTURE_AREA].jpg: 8 images associated with the Visium experiments. Here, GEM_ID refers to each of the 4 capture areas in each slide. - [TECHNOLOGY]_sequencing_metadata.csv: the GEM-level metadata for each technology. It includes the relationship between subproject, gem_id, library_id, library_type and donor_id. These are the other repositories associated with the tonsil atlas: - Expression and accessibility matrices: https://zenodo.org/records/10373041 - Seurat objects: https://zenodo.org/records/8373756 - HCATonsilData package: https://bioconductor.org/packages/release/data/experiment/html/HCATonsilData.html - Azimuth: https://azimuth.hubmapconsortium.org/ - Github: https://github.com/Single-Cell-Genomics-Group-CNAG-CRG/TonsilAtlas
2024-05-02 | E-MTAB-13687 | biostudies-arrayexpress
Project description:Varicella-zoster virus (VZV), an alphaherpesvirus, causes chickenpox (varicella) in young children with an annual minimum of 140 million new cases and herpes zoster in senior, a painful and debilitating disease with 3-5‰ incidence. A complex structural transcriptome of VZV, which numerous novel transcripts, transcript isoforms, and unknown splice events are found during cell infection. Circular RNA (circRNA), a newly important component of the transcriptome, is increasing discoveries of circRNA function in mammalian cells. However, VZV encoded circRNA remains unexplored. The code used in this study and extended data are available from the GitHub repository (https://github.com/ShaominYang/VZV_circRNA)
2024-04-27 | GSE252124 | GEO
Project description:Several ovarian cancer tumors from different anatomical sites and therapy statuses. A GUI to explore the data can be found at: https://visium-shiny-9hzfl.ondigitalocean.app/ Code to run the shiny app (same output as the GUI): https://github.com/kwells4/visium_public_shiny Code to replicate the analysis can be found: https://github.com/kwells4/visium_ovarian_cancer
2022-10-10 | GSE213699 | GEO
Project description:We present a machine-learning method to prioritize the cell types most responsive to biological perturbations within high-dimensional single-cell data. We validate our method, Augur (https://github.com/neurorestore/Augur), on a compendium of single-cell RNA-seq, chromatin accessibility, and imaging transcriptomics datasets. We apply Augur to expose the neural circuits that enable walking after paralysis in response to spinal cord neurostimulation.
2020-06-05 | GSE142245 | GEO