{"database": "metadata", "table": "run_metadata", "rows": [[67829, "SRR17335725", "SRX13511109", "SRS11405350", "SRP352585", "PRJNA792582", "circRNA based biomarker candidates for acute cypermethrin and chlorpyrisfos toxication in the brain of Zebrafish Danio rerio", "GSE192669", "Transcriptome Analysis", "Cypermethrin CYP and chlorpyrifos CPF are pesticides which are frequently used in agricultural areas around the world. These chemicals have been shown to cause serious toxicological damage in the brain of fish  which is non target organisms. However  circRNAs associated with acute brain toxicity caused by cypermethrin and chlorpyrifos have not been studied yet. In this study  circRNAs were identified and characterized using RNA seq in Zebrafish brains exposed to acute cypermethrin and chlorpyrifos toxicity. A total of 10375 circRNAs were detected. It was determined that 6 circRNAs were up regulated  10 circRNAs were down regulated in CYP brain samples compared to controls . In addition  it was found that 57 circRNAs are up regulated and 3 circRNAs down regulated in CPF brain samples compared to controls. Moreover  62 circRNAs were down regulated in the CYP samples  when CYP and CPF samples were compared. However  up regulated circRNA could not be detected. It was revealed that the detected circRNAs specifically regulated the MAPK signaling pathway  endocytosis mechanism  apoptosis and p53 signaling pathway. This study  which was conducted for the first time in terms of the subject of the study  could bring a different perspective especially to pesticide toxicity studies. Overall design: Examination of  circRNA related to acute cypermethrin and chlopyrifos toxication in brain of zebrafish", null, "pubmed:35304207", null, "Cont3", "GSM5754464", null, "source name:Brain tissue|tissue:Brain|treatment:No toxication|geo loc name:missing|collection date:missing", "Cont3", "Raw data raw reads of FASTQ format were firstly processed through in house scripts. In this step  clean data clean reads were obtained by removing reads containing adapter and poly N sequences and reads with low quality from raw data. At the same time  Q20  Q30 and GC content of the clean data were calculated. All the downstream analyses were based on the clean data with high quality.An upgraded computational pipeline CIRCexplorer2 is applied to detect circRNAs and identify alternative back splicing in back spliced circular RNAs circRNAs The expression of circRNAs is usually represented by the fragments that are mapped to the back spliced exon\u2013exon junction sites. In addition to the raw fragment numbers  normalized RNA seq fragments that are mapped to a specific back spliced exon\u2013exon junction by total mapped fragments is used to quantitate circRNA expression. With FPM Fragments mapped to back  spliced junction Per Million mapped fragments  circRNAs from different samples with distinct sequencing depths can be directly compared. The input data for differential gene expression analysis is read counts from gene expression level analysis. For DESeq2 with biological replicates Differential expression analysis between two conditions/groups three biological replicates per condition was performed using DESeq2 R package. DESeq2 provides statistical routines for determining differential expression in digital gene expression data using a model based on the negative binomial distribution. The resulting P values were adjusted using the Benjamini and Hochberg\u2019s approach for controlling the False Discovery Rate FDR. Genes with an adjusted P value < 0.05 found by DESeq2 were assigned as differentially expressed. A common way for searching shared functions among genes is to incorporate the biological knowledge provided by biological ontologies. Gene Ontology GO annotates genes to  biological processes  molecular functions  and cellular components in a directed acyclic graph structure  and Kyoto Encyclopedia of Genes and Genomes KEGG annotates genes to  pathways. KEGG is a database resource for understanding high level functions and utilities of the biological system  such as the cell  the organism and the ecosystem  from molecular level  information  especially large  scale molecular datasets generated by genome sequencing and other high  throughput experimental technologies http://www.genome.jp/kegg/. We  used KOBAS software to test the statistical enrichment of differential expression genes or circRNA host genes in KEGG pathways. circRNAs can act as miRNA sponge to inhibit the functioning of miRNAs. To further study the functions of circRNAs  microRNA target site in exons of circRNA loci were identified using miRanda animal species or psRobot plant species. software could be used to construct the circRNA miRNA gene networks. Genome build: UMD3.1", "Brain tissue", null, "Total RNA was isolated with Trizol from brain tissues RNA libraries were prepared for sequencing using standard Illumina protocols", null, "tissue:Brain|treatment:No toxication", "GSM5754464", "GSM5754464: Cont3; Danio rerio; ncRNA Seq", "GSM5754464 r1", "GSM5754464", "1", "Total RNA was isolated with Trizol from brain tissues RNA libraries were prepared for sequencing using standard Illumina protocols", null, "ncRNA-Seq", "TRANSCRIPTOMIC", "size fractionation", "PAIRED", "ILLUMINA", "Illumina HiSeq 4000", null, "SRP352585", null, "loader:fastq load.py", "Cont3_1.fq.gz Cont3_2.fq.gz", "fastq fastq", 15202443900.0, 50674813.0, "GSM5754464 r1", "0:150 1:150", "A:4400470018;C:3173803928;G:3218623722;T:4409335756;N:210476", 150, 150, null, null, 4400470018, 3173803928, 3218623722, 4409335756, 210476, "SRX13511109", "SRS11405350", "SRA1349262", "Atat\u00fcrk University", "Atat\u00fcrk University", 2, 0.86129, 0.84677, 0.45657, 0.44799, 0.67884, 0.68083, 0.48291, 0.4763, 150, 150, "B", "B", "biological fallback assumption", "illumina", "hiseq_era", "unknown", "size_fractionation", "unknown", "bulk", "unknown", "unknown", null, "Turkey", "2021-12-27", "Undetermined", "Undetermined", "Brain", "Nervous 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": ["67829"], "units": {}, "query_ms": 10.538344999076799}