{"database": "metadata", "table": "run_metadata", "rows": [[59290, "SRR11806490", "SRX8357868", "SRS6673977", "SRP262133", "PRJNA633496", "Single cell sequencing reveals heterogeneity effects of arsenic or/and 2 2 dichloroacetamide on zebrafish liver", "GSE150751", "Transcriptome Analysis", "Arsenic and DBPs has been found to be one of the major risk in many regions of the world. However  current understanding of thier combined toxicities are unclear. Here we used single cell RNA sequencing to provide the transcriptome heterogeneity of 13563 liver cells obtained from zebrafishes exposed to 100\u00b5g/L arsenic  300\u00b5g/L dichloroacetanilide DCAcAm and co exposure for 23 days. Five liver cell populations were identified. We found that the hepatocytes and macrophages were the main target of arsenic and DCAcAm exposure. And the hepatocytes of male and female showed a huge difference  when expsoure to arsenic and DCAcAm. Overall design: Single cell RNA sequencing were adopted to learn the transcriptional variation in zebrafish liver exposure to arsenic and DCAcAm.", null, null, null, "As100", "GSM4558081", null, "source name:liver cells|tissue:liver single cell suspension|age:16 wpf|exposed pollutants:100ug/L arsenic", "As100", "We use FastQC to perform basic statistics on the quality of the raw reads. Then  those read sequences produced by the Illumina pipeline in FASTQ format were pre processed through Trimmomatic software which can be summarized as below:1 Remove low quality reads: scan the read with a 4 base wide sliding window  cutting when the average quality per base drops below 10 SLIDINGWINDOW:  4:10 2 Remove trailing low quality or N bases below quality 3 TRAILING:3 3 Remove adapters : there are two modes to remove the adapter sequence: a.  alignment with the adapter sequence  the number of matching bases were greater than 7 and mismatch=2; b.when read1 and read2 overlapping base scoring  greater than 30  removed non overlapping portions ILLUMINACLIP: adapter.fa:  2: 30: 7 4 Drop reads below the 26 bases long 5 Discard those reads that can not form paired The remaining reads that passed all the filtering steps was counted as clean reads and all subsequent analyses were based on this. At last  we use FastQC to perform basic statistics on the quality of the clean reads. Cell Ranger uses an aligner called STAR  which peforms splicing aware alignment of reads to the genome. Cell Ranger then uses the transcript annotation GTF to bucket the reads into exonic  intronic  and intergenic  and by whether the reads align confidently to the genome. A read is exonic if at least 50% of it intersects an exon  intronic if it is non exonic and intersects an intron  and intergenic otherwise. For reads that align to a single exonic locus but also align to 1 or more non exonic loci  the exonic locus is prioritized and the read is considered to be confidently mapped to the exonic locus with MAPQ 255. Cell Ranger further aligns exonic reads to annotated transcripts  looking for compatibility. A read that is compatible with the exons of an annotated transcript  and aligned to the same strand  is considered mapped to the transcriptome. If the read is compatible with a single gene annotation  it is considered uniquely confidently mapped to the transcriptome. Only reads that  are confidently mapped to the transcriptome are used for UMI counting. Cell Ranger takes as input the expected number of recovered cells  N see    expect cells. Let m be a robust estimate of the maximum total UMI counts  taken as the 99th percentile of the top N barcodes by total UMI counts. All barcodes whose total UMI counts exceed m/10 are called as cells. This is performed separately for each GEM group library and  if the reference contains multiple genomes  for each genome. Genome build: Danio rerio Ensemble 91 Supplementary files format and content: gene barcode expression matrix", "liver cells", "Exposure solutions were prepared by adding 100\u03bcg/L arsenic  300\u03bcg/L DCAcAm or both of them to culture water. The exposure solution were was replaced every 2 days.  post 23 d exposure  zebrafish were collected and liver were rapidly extracted on ice.", "The isolated liver tissue was digested into  cell suspension with dispase. Cells were loaded on a GemCode Single Cell Instrument10x Genomics  USA to generate single cell Gel bead in Emulsion GEMs. ScRNA seq libraries were prepared using the GemCode Single Cell 3\u2019 Gel Bead  Chip and Library Kits 10x Genomics  USA as per the manufacturer\u2019s protocol. Libraries were sequenced on an Illumina Hiseq PE150.", null, "tissue:liver single cell suspension|age:16 wpf|exposed pollutants:100ug/L arsenic", "GSM4558081", "GSM4558081: As100; Danio rerio; RNA Seq", "GSM4558081", null, "1", "The isolated liver tissue was digested into  cell suspension with dispase. Cells were loaded on a GemCode Single Cell Instrument10x Genomics  USA to generate single cell Gel bead in Emulsion GEMs. ScRNA seq libraries were prepared using the GemCode Single Cell three prime Gel Bead  Chip and Library Kits 10x Genomics  USA as per the manufacturer's protocol. Libraries were sequenced on an Illumina Hiseq PE150.", "GEO Accession:GSM4558081", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP262133", null, null, "As100_2.fq.gz As100_1.fq.gz", "fastq fastq", 102837948900.0, 342793163.0, "GSM4558081 r1", "0:150 1:150", "A:24894384372;C:19593836618;G:31314101265;T:27014778377;N:20848268", 150, 150, null, null, 24894384372, 19593836618, 31314101265, 27014778377, 20848268, "SRX8357868", "SRS6673977", "SRA1076689", "GEO", "Nanjing University", 2, 0.0, 0.91738, 0.0, 0.02836, 1.0, 0.89536, null, 0.46528, 150, 150, "T", "B", "mate1 technical by mapping diff", "illumina", "novaseq_era", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "10x", null, "China", "2020-05-18", "Adult", "Adult", "Liver", "Liver and Biliary 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": ["59290"], "units": {}, "query_ms": 6.932889009476639}