{"database": "metadata", "table": "run_metadata", "rows": [[38266, "SRR1609749", "SRX730402", "SRS719623", "SRP048807", "PRJNA263496", "Global identification of the gene networks and cis regulatory elements of the cold response in zebrafish", "GSE62221", "Transcriptome Analysis", "The transcriptional programs of ectothermic teleosts are directly influenced by water temperature. Although various cold responsive transcriptional patterns have been determined in fishes  the systematic molecular networks governing the temperature responses are still unknown. We profiled the transcriptional responses in eight tissues of zebrafish exposed to graded cold temperatures  ranging from normal 28\u00b0C to mild 18\u00b0C and severe 10\u00b0C cold  using RNA seq. The tissues varied in the number of cold responsive genes  of which the kidney appeared to be most sensitive  whereas the brain was the least. Fuzzy k means clustering revealed 34 gene clusters of distinct expression patterns  demonstrating diverse tissue specific responses in conjunction with multiple aspects of ubiquitous cross tissue responses to cold. Thirty one GO terms were over represented upon cold treatment. These terms are involved in basic cellular processes  such as RNA splicing and proton transport  as well tissue specific processes  such as \u2018negative regulation of endopeptidase activity\u2019 in the kidney. To identify the cis regulatory elements governing the concerted cold responses  the promoters of the genes that demonstrated strong co regulation were analyzed using an enriched motif discovery program  DREME. Eleven motifs  6 known and 5 novel  were identified. These motifs belong to the genes corresponding to the 16 over represented GO terms identified above. Some motifs  such as the AP 1 and STAT1 binding sites  are known to be stress responsive. By integrating comprehensive cold induced transcriptional changes with a cis motif identification tool  we identified genome wide regulatory networks for the cold response in zebrafish. The identified networks provided new insights into molecular mechanisms of thermal responses in teleosts. Overall design: Examination of gene expression of 24 samples eight tissues at three temperatures", null, "pubmed:26227973", null, "kidney10", "GSM1523044", null, "source name:kidney|tissue:kidney|temperature:10\u00b0C|strain:Tubingen|age:6 mpf", "kidney10", "Illumina Casava1.7 software used for basecalling. The raw reads were assessed for their quality using FASTX toolkit http://hannonlab.cshl.edu/fastx toolkit. Reads with a Phred quality score less than 5 over the 95% nt would be removed. TopHat was used to map the reads to the reference genome. Then HTSeq count http://www huber.embl.de/users/anders/HTSeq/doc/overview.html  which is a python based script  was then applied to count the number of reads mapped to the genes. Reads Per Kilobase of exon per Megabase of library size RPKM were calculated using a protocol from Chepelev et al.  Nucleic Acids Research  2009. In short  exons from all isoforms of a gene were merged to create one meta transcript. The number of reads falling in the exons of this meta transcript were counted and normalized by the size of the meta transcript and by the size of the library. Genome build: zebrafish genome sequence and gtf files were downloaded from the Ensembl release 72 Supplementary files format and content: tab delimited text files include RPKM values for each Sample ...", "kidney", "fish were maintained 12h to adapt low temperatures and then killed by pithing", "Tissues were removed  flash frozen on dry ice  and RNA was harvested using Trizol reagent. Illumina TruSeq RNA Sample Prep Kit was used with 1 ug of total RNA for the construction of sequencing libraries. RNA libraries were prepared for sequencing using standard Illumina protocols", null, "tissue:kidney|temperature:10\u00b0C|strain:Tubingen|age:6 mpf", "GSM1523044", "GSM1523044: kidney10; Danio rerio; RNA Seq", "GSM1523044", null, "1", "Tissues were removed  flash frozen on dry ice  and RNA was harvested using Trizol reagent. Illumina TruSeq RNA Sample Prep Kit was used with 1 ug of total RNA for the construction of sequencing libraries. RNA libraries were prepared for sequencing using standard Illumina protocols", "GEO Accession:GSM1523044", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "PAIRED", "ILLUMINA", "Illumina HiSeq 2000", null, "SRP048807", null, null, "kidney10_1.fq.gz kidney10_2.fq.gz", "fastq fastq", 4315071200.0, 21575356.0, "GSM1523044 r1", "0:100 1:100", "A:1152823680;C:1012018705;G:1003451232;T:1146696176;N:81407", 100, 100, null, null, 1152823680, 1012018705, 1003451232, 1146696176, 81407, "SRX730402", "SRS719623", "SRA189240", "GEO", "Shanghai Ocean University", 2, 0.95345, 0.94751, 0.05494, 0.05445, 0.7455, 0.74856, 0.53555, 0.52518, 100, 100, "B", "B", "biological fallback assumption", "illumina", "hiseq_era", "unknown", "cdna_unspecified", "trueseq", "bulk", "unknown", "unknown", null, "China", "2014-10-09", "Adult", "Adult", "Kidney", "Renal 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": ["38266"], "units": {}, "query_ms": 11.405842000385746}