{"database": "metadata", "table": "run_metadata", "rows": [[63260, "SRR13724987", "SRX10113011", "SRS8268907", "SRP306756", "PRJNA702191", "Phosphatidylinositol 3 kinase signaling controls survival and stemness of hematopoietic stem and progenitor cells", "GSE166900", "Transcriptome Analysis", "Loss of Pten and inhibition of PI3K induced apoptosis of hematopoietic stem/progenitor cells upon endothelial to hematopoietic transition and surviving hematopoietic stem/progenitor cells committed to all blood lineages but displayed reduced stemness Overall design: AGMs 36hpf or CHTs 5dpf were dissected and dissociated  using FACS sorting HSPCs were isolated and single cell RNA seq was performed.", null, "pubmed:33714985", null, "SBF 26", "GSM5087790", null, "tissue:Aorta Gonad Mes1phros|developmental stage:36hpf", "SBF 26", "Well numbers X001 X384 in *.TranscriptCounts.tsv refer to well numbers of the 384 well plate in the following order: A1 to A24   B1 to B24   until P24. The right mate of each read pair was mapped to the ensemble of all gene loci. Reads mapping to multiple loci were discarded. During sequencing  Read 1 was assigned 26/75* base pairs and was used for identification of the Illumina library barcode  cel barcode and UMI. R2 was assigned 60/75* base pairs and used to map to the reference transcriptome of Zv9 with BWA Anders and Huber  2010. Data was demultiplexed as described in Gr\u00fcn et al. 2014. Mapping and generation of count tables was automated using the MapAndGo script1. https://github.com/anna alemany/transcriptomics/tree/master/mapandgo Tabular separated file indicating number of transcripts per section obtained as previously described Gr\u00fcn  Dominic  Lennart Kester  and Alexander Van Oudenaarden. \"Validation of noise models for single cell transcriptomics.\" Nature methods 11.6 2014: 637. We refer to transcripts as unique molecules based on UMI correction. *coutc* files contain number of reads  *coutb* files contain number of observed UMI and *coutt* files contain number of unique UMI corrected transcripts per gene per cell The left read contains the barcode information: the first eight bases correspond to a cell specific barcode* followed by a 6bp unique molecular identifier UMI. The remainder of the left read contains a polyT stretch followed by a number <40 of transcript derived bases. Read 1 was not used for quantification. cell specific barcodes are specified in the file \u201ccel seq2 barcodes.csv\u201d Genome build: zV9 Danio Rerio", "Aorta Gonad Mesonephros", null, "Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols", null, "developmental stage:36hpf|sort day see plate layout:11|genotype or treatment see plate layout:LY294002 treated", "GSM5087790", "GSM5087790: SBF 26; Danio rerio; RNA Seq", "GSM5087790", null, "1", "Cells were sorted into 384 well plates. All wells underwent Cel Seq2 protocol RNA libraries were prepared for sequencing using standard Illumina protocols", "GEO Accession:GSM5087790", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "NextSeq 500", null, "SRP306756", null, null, "HUB-SF-026_HNN3JBGXC_S4_L001_R1_001.fastq.gz HUB-SF-026_HNN3JBGXC_S4_L001_R2_001.fastq.gz", "fastq fastq", 724015252.0, 8418782.0, "GSM5087790 r1", "0:26 1:60", "A:181448033;C:140181369;G:139987828;T:262155531;N:242491", 26, 60, null, null, 181448033, 140181369, 139987828, 262155531, 242491, "SRX10113011", "SRS8268907", "SRA1196923", "GEO", "Hubrecht Institute", 2, 0.10352, 0.78777, 0.09808, 0.22696, 0.99078, 0.81389, 0.60135, 0.52625, 26, 60, "T", "B", "sc-like readlen", "illumina", "nextseq", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_plate", "celseq", null, "Netherlands", "2021-02-16", "Pharyngula", "Embryo", "Multi-tissue", "Multi-system"]], "columns": ["rowid", "run.accession", "experiment.accession", "sample.accession", "study.accession", "bioproject", "study.title", "study.alias", "study.type", "study.abstract", "study.attributes", "study.PMIDs", "sample.description", "sample.title", "sample.alias", "sample.centername", "sample.attributes", "GEOsample.title", "GEOsample.dataprocessing", "GEOsample.source", "GEOsample.treatmentprotocol", "GEOsample.extractprotocol", "GEOsample.growthprotocol", "GEOsample.characteristics", "GEOsample.accession", "experiment.title", "experiment.alias", "experiment.library_name", "experiment.design_description", "experiment.library_construction_protocol", "experiment.attributes", "experiment.library_strategy", "experiment.library_source", "experiment.library_selection", "experiment.library_layout", "experiment.platform", "experiment.instrument_model", "experiment.spot_descriptor", "experiment.study_ref", "run.title", "run.attributes", "run.filename", "run.semantic_name", "run.total_bases", "run.total_spots", "run.alias", "run.read_lengths", "run.base_counts", "run.r1_length", "run.r2_length", "run.r3_length", "run.r4_length", "run.Acount", "run.Ccount", "run.Gcount", "run.Tcount", "run.Ncount", "run.experiment", "run.pool_member", "submission.accession", "submission.srasource", "submission.bioprojectsource", "seqdetective.n_mates", "seqdetective.mapping_rate.mate1", "seqdetective.mapping_rate.mate2", "seqdetective.nofeature_rate.mate1", "seqdetective.nofeature_rate.mate2", "seqdetective.sparsity.mate1", "seqdetective.sparsity.mate2", "seqdetective.pos_strand_rate.mate1", "seqdetective.pos_strand_rate.mate2", "seqdetective.readlen.mate1", "seqdetective.readlen.mate2", "seqdetective.judgement.mate1", "seqdetective.judgement.mate2", "seqdetective.judgement.reason", "platform_family", "instrument_generation", "read_bias", "selection_class", "prep_kit", "sc_or_bulk", "tech_class", "technology", "tech_variant", "submission.bioprojectsource.country", "earliest_date", "devstage_curation", "devstage_curation_coarse", "tissue_curation", "tissue_curation_coarse"], "primary_keys": ["rowid"], "primary_key_values": ["63260"], "units": {}, "query_ms": 9.677584001110517}