metadata: run_metadata: 12 rows where devstage_curation = "Segmentation" and tissue

12 rows where devstage_curation = "Segmentation" and tissue_curation = "Undetermined"

descending

Link	rowid ▼	run.accession	experiment.accession	sample.accession	study.accession	bioproject	study.title	study.alias	study.type	study.abstract	study.PMIDs	sample.title	sample.alias	sample.attributes	GEOsample.title	GEOsample.dataprocessing	GEOsample.source	GEOsample.extractprotocol	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.study_ref	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	submission.bioprojectsource.country	earliest_date	devstage_curation	devstage_curation_coarse	tissue_curation	tissue_curation_coarse
33865	33865	SRR30779427	SRX26181410	SRS22725399	SRP534298	PRJNA1164307	Time resolved single cell Multiomic zebrafish atlas	PRJNA1164307	Other	During development dynamic interplay between transcription factors chromatin and genes termed gene regulatory network GRN shapes the cell fate determination along the developmental trajectory. Recent advances in joint measurement of chromatin accessibility and gene expression enabled the genome wide identification of regulatory relationships. Here we assess the dynamics of the gene regulatory network in zebrafish development using joint single cell ATAC and single cell RNA sequencing. We discovered some key regulatory modules that exhibit cell type and time dependent activity suggesting that the role of transcription factors vary over cell type and timepoints. With time resolved GRNs combined with linear modeling framework we performed a systematic in silico knock out simulation using CellOracle. This in silico knock out simulation revealed that the role of transcription factors is shared between mesodermal and neuro ectodermal lineages in the early timepoints but later commit significantly to either lineages. Together we provide a dataset and a framework to systematically dissect the role of transcription factors during the zebrafish embryonic development.		TDR125	TDR125 19hpf EKW NA none 10xmultiome	strain:EKW\|dev stage:20 somites 19 hpf\|collection date:2023 09 13\|geo loc name:USA: San Francisco\|sex:N/A\|tissue:organism\|BioSampleModel:Model organism or animal							TDR125 19hpf RNA	TDR125 19hpf RNA EKW NA none 10xmultiome	TDR125 19hpf RNA EKW NA none 10xmultiome	10x multiome nuc seq			RNA-Seq	TRANSCRIPTOMIC SINGLE CELL	cDNA	PAIRED	ILLUMINA	Illumina NovaSeq 6000	SRP534298		TDR125_19hpf_RNA_EKW_NA_none_10xmultiome_S3_L001_I1_001.fastq.gz TDR125_19hpf_RNA_EKW_NA_none_10xmultiome_S3_L001_I2_001.fastq.gz TDR125_19hpf_RNA_EKW_NA_none_10xmultiome_S3_L001_R1_001.fastq.gz TDR125_19hpf_RNA_EKW_NA_none_10xmultiome_S3_L001_R2_001.fastq.gz TDR125_19hpf_RNA_EKW_NA_none_10xmultiome_S3_L002_I1_001.fastq.gz TDR125_19hpf_RNA_EKW_NA_none_10xmultiome_S3_L002_I2_001.fastq.gz TDR125_19hpf_RNA_EKW_NA_none_10xmultiome_S3_L002_R1_001.fastq.gz TDR125_19hpf_RNA_EKW_NA_none_10xmultiome_S3_L002_R2_001.fastq.gz TDR125_19hpf_RNA_EKW_NA_none_10xmultiome_S3_L003_I1_001.fastq.gz TDR125_19hpf_RNA_EKW_NA_none_10xmultiome_S3_L003_I2_001.fastq.gz TDR125_19hpf_RNA_EKW_NA_none_10xmultiome_S3_L003_R1_001.fastq.gz TDR125_19hpf_RNA_EKW_NA_none_10xmultiome_S3_L003_R2_001.fastq.gz TDR125_19hpf_RNA_EKW_NA_none_10xmultiome_S3_L004_I1_001.fastq.gz TDR125_19hpf_RNA_EKW_NA_none_10xmultiome_S3_L004_I2_001.fastq.gz TDR125_19hpf_RNA_EKW_NA_none_10xmultiome_S3_L004_R1_001.fastq.gz TDR125_19hpf_RNA_EKW_NA_none_10xmultiome_S3_L004_R2_001.fastq.gz	fastq fastq fastq fastq fastq fastq fastq fastq fastq fastq fastq fastq fastq fastq fastq fastq			TDR125 19hpf RNA EKW NA none 10xmultiome S3 L001 I1 001.fastq.gz												SRX26181410		SRA1977819	Chan Zuckerberg Biohub San Francisco\|Computational Biology	Chan Zuckerberg Biohub San Francisco															illumina	novaseq_era	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	10x	United States	2024-09-23	Segmentation	Embryo	Undetermined	Embryo Imprecise
33867	33867	SRR30779430	SRX26181407	SRS22725398	SRP534298	PRJNA1164307	Time resolved single cell Multiomic zebrafish atlas	PRJNA1164307	Other	During development dynamic interplay between transcription factors chromatin and genes termed gene regulatory network GRN shapes the cell fate determination along the developmental trajectory. Recent advances in joint measurement of chromatin accessibility and gene expression enabled the genome wide identification of regulatory relationships. Here we assess the dynamics of the gene regulatory network in zebrafish development using joint single cell ATAC and single cell RNA sequencing. We discovered some key regulatory modules that exhibit cell type and time dependent activity suggesting that the role of transcription factors vary over cell type and timepoints. With time resolved GRNs combined with linear modeling framework we performed a systematic in silico knock out simulation using CellOracle. This in silico knock out simulation revealed that the role of transcription factors is shared between mesodermal and neuro ectodermal lineages in the early timepoints but later commit significantly to either lineages. Together we provide a dataset and a framework to systematically dissect the role of transcription factors during the zebrafish embryonic development.		TDR119	TDR119 16hpf EKW NA none 10xmultiome	strain:EKW\|dev stage:15 somites 16 hpf\|collection date:2023 06 30\|geo loc name:USA: San Francisco\|sex:N/A\|tissue:organism\|BioSampleModel:Model organism or animal							TDR119 16hpf RNA	TDR119 16hpf RNA EKW NA none 10xmultiome	TDR119 16hpf RNA EKW NA none 10xmultiome	10x multiome nuc seq			RNA-Seq	TRANSCRIPTOMIC SINGLE CELL	cDNA	PAIRED	ILLUMINA	Illumina NovaSeq 6000	SRP534298		TDR119_16hpf_RNA_EKW_NA_none_10xmultiome_S2_L001_I1_001.fastq.gz TDR119_16hpf_RNA_EKW_NA_none_10xmultiome_S2_L001_I2_001.fastq.gz TDR119_16hpf_RNA_EKW_NA_none_10xmultiome_S2_L001_R1_001.fastq.gz TDR119_16hpf_RNA_EKW_NA_none_10xmultiome_S2_L001_R2_001.fastq.gz TDR119_16hpf_RNA_EKW_NA_none_10xmultiome_S2_L002_I1_001.fastq.gz TDR119_16hpf_RNA_EKW_NA_none_10xmultiome_S2_L002_I2_001.fastq.gz TDR119_16hpf_RNA_EKW_NA_none_10xmultiome_S2_L002_R1_001.fastq.gz TDR119_16hpf_RNA_EKW_NA_none_10xmultiome_S2_L002_R2_001.fastq.gz	fastq fastq fastq fastq fastq fastq fastq fastq	137611060524.0	997181598.0	TDR119 16hpf RNA EKW NA none 10xmultiome S2 L001 I1 001.fastq.gz	0:10 1:10 2:28 3:90	A:26107486444;C:20041229894;G:22171622563;T:21408164549;N:17840370	10	10	28	90	26107486444	20041229894	22171622563	21408164549	17840370	SRX26181407	SRS22725398	SRA1977819	Chan Zuckerberg Biohub San Francisco\|Computational Biology	Chan Zuckerberg Biohub San Francisco												B		usable mapping rate	illumina	novaseq_era	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	10x	United States	2024-09-24	Segmentation	Embryo	Undetermined	Embryo Imprecise
33868	33868	SRR30779433	SRX26181405	SRS22725397	SRP534298	PRJNA1164307	Time resolved single cell Multiomic zebrafish atlas	PRJNA1164307	Other	During development dynamic interplay between transcription factors chromatin and genes termed gene regulatory network GRN shapes the cell fate determination along the developmental trajectory. Recent advances in joint measurement of chromatin accessibility and gene expression enabled the genome wide identification of regulatory relationships. Here we assess the dynamics of the gene regulatory network in zebrafish development using joint single cell ATAC and single cell RNA sequencing. We discovered some key regulatory modules that exhibit cell type and time dependent activity suggesting that the role of transcription factors vary over cell type and timepoints. With time resolved GRNs combined with linear modeling framework we performed a systematic in silico knock out simulation using CellOracle. This in silico knock out simulation revealed that the role of transcription factors is shared between mesodermal and neuro ectodermal lineages in the early timepoints but later commit significantly to either lineages. Together we provide a dataset and a framework to systematically dissect the role of transcription factors during the zebrafish embryonic development.		TDR128	TDR128 14hpf EKW NA none 10xmultiome	strain:EKW\|dev stage:10 somites 14 hpf\|collection date:2023 11 22\|geo loc name:USA: San Francisco\|sex:N/A\|tissue:organism\|BioSampleModel:Model organism or animal							TDR128 14hpf RNA	TDR128 14hpf RNA EKW NA none 10xmultiome	TDR128 14hpf RNA EKW NA none 10xmultiome	10x multiome nuc seq			RNA-Seq	TRANSCRIPTOMIC SINGLE CELL	cDNA	PAIRED	ILLUMINA	Illumina NovaSeq 6000	SRP534298		TDR128_14hpf_RNA_EKW_NA_none_10xmultiome_S6_L001_I1_001.fastq.gz TDR128_14hpf_RNA_EKW_NA_none_10xmultiome_S6_L001_I2_001.fastq.gz TDR128_14hpf_RNA_EKW_NA_none_10xmultiome_S6_L001_R1_001.fastq.gz TDR128_14hpf_RNA_EKW_NA_none_10xmultiome_S6_L001_R2_001.fastq.gz TDR128_14hpf_RNA_EKW_NA_none_10xmultiome_S6_L002_I1_001.fastq.gz TDR128_14hpf_RNA_EKW_NA_none_10xmultiome_S6_L002_I2_001.fastq.gz TDR128_14hpf_RNA_EKW_NA_none_10xmultiome_S6_L002_R1_001.fastq.gz TDR128_14hpf_RNA_EKW_NA_none_10xmultiome_S6_L002_R2_001.fastq.gz TDR128_14hpf_RNA_EKW_NA_none_10xmultiome_S6_L003_I1_001.fastq.gz TDR128_14hpf_RNA_EKW_NA_none_10xmultiome_S6_L003_I2_001.fastq.gz TDR128_14hpf_RNA_EKW_NA_none_10xmultiome_S6_L003_R1_001.fastq.gz TDR128_14hpf_RNA_EKW_NA_none_10xmultiome_S6_L003_R2_001.fastq.gz TDR128_14hpf_RNA_EKW_NA_none_10xmultiome_S6_L004_I1_001.fastq.gz TDR128_14hpf_RNA_EKW_NA_none_10xmultiome_S6_L004_I2_001.fastq.gz TDR128_14hpf_RNA_EKW_NA_none_10xmultiome_S6_L004_R1_001.fastq.gz TDR128_14hpf_RNA_EKW_NA_none_10xmultiome_S6_L004_R2_001.fastq.gz	fastq fastq fastq fastq fastq fastq fastq fastq fastq fastq fastq fastq fastq fastq fastq fastq			TDR128 14hpf RNA EKW NA none 10xmultiome S6 L001 I1 001.fastq.gz												SRX26181405		SRA1977819	Chan Zuckerberg Biohub San Francisco\|Computational Biology	Chan Zuckerberg Biohub San Francisco															illumina	novaseq_era	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	10x	United States	2024-09-23	Segmentation	Embryo	Undetermined	Embryo Imprecise
33869	33869	SRR30779435	SRX26181403	SRS22725396	SRP534298	PRJNA1164307	Time resolved single cell Multiomic zebrafish atlas	PRJNA1164307	Other	During development dynamic interplay between transcription factors chromatin and genes termed gene regulatory network GRN shapes the cell fate determination along the developmental trajectory. Recent advances in joint measurement of chromatin accessibility and gene expression enabled the genome wide identification of regulatory relationships. Here we assess the dynamics of the gene regulatory network in zebrafish development using joint single cell ATAC and single cell RNA sequencing. We discovered some key regulatory modules that exhibit cell type and time dependent activity suggesting that the role of transcription factors vary over cell type and timepoints. With time resolved GRNs combined with linear modeling framework we performed a systematic in silico knock out simulation using CellOracle. This in silico knock out simulation revealed that the role of transcription factors is shared between mesodermal and neuro ectodermal lineages in the early timepoints but later commit significantly to either lineages. Together we provide a dataset and a framework to systematically dissect the role of transcription factors during the zebrafish embryonic development.		TDR127	TDR127 12hpf EKW NA none 10xmultiome	strain:EKW\|dev stage:5 somites 12 hpf\|collection date:2023 11 22\|geo loc name:USA: San Francisco\|sex:N/A\|tissue:organism\|BioSampleModel:Model organism or animal							TDR127 12hpf RNA	TDR127 12hpf RNA EKW NA none 10xmultiome	TDR127 12hpf RNA EKW NA none 10xmultiome	10x multiome nuc seq			RNA-Seq	TRANSCRIPTOMIC SINGLE CELL	cDNA	PAIRED	ILLUMINA	Illumina NovaSeq 6000	SRP534298		TDR127_12hpf_RNA_EKW_NA_none_10xmultiome_S5_L001_I1_001.fastq.gz TDR127_12hpf_RNA_EKW_NA_none_10xmultiome_S5_L001_I2_001.fastq.gz TDR127_12hpf_RNA_EKW_NA_none_10xmultiome_S5_L001_R1_001.fastq.gz TDR127_12hpf_RNA_EKW_NA_none_10xmultiome_S5_L001_R2_001.fastq.gz TDR127_12hpf_RNA_EKW_NA_none_10xmultiome_S5_L002_I1_001.fastq.gz TDR127_12hpf_RNA_EKW_NA_none_10xmultiome_S5_L002_I2_001.fastq.gz TDR127_12hpf_RNA_EKW_NA_none_10xmultiome_S5_L002_R1_001.fastq.gz TDR127_12hpf_RNA_EKW_NA_none_10xmultiome_S5_L002_R2_001.fastq.gz TDR127_12hpf_RNA_EKW_NA_none_10xmultiome_S5_L003_I1_001.fastq.gz TDR127_12hpf_RNA_EKW_NA_none_10xmultiome_S5_L003_I2_001.fastq.gz TDR127_12hpf_RNA_EKW_NA_none_10xmultiome_S5_L003_R1_001.fastq.gz TDR127_12hpf_RNA_EKW_NA_none_10xmultiome_S5_L003_R2_001.fastq.gz TDR127_12hpf_RNA_EKW_NA_none_10xmultiome_S5_L004_I1_001.fastq.gz TDR127_12hpf_RNA_EKW_NA_none_10xmultiome_S5_L004_I2_001.fastq.gz TDR127_12hpf_RNA_EKW_NA_none_10xmultiome_S5_L004_R1_001.fastq.gz TDR127_12hpf_RNA_EKW_NA_none_10xmultiome_S5_L004_R2_001.fastq.gz	fastq fastq fastq fastq fastq fastq fastq fastq fastq fastq fastq fastq fastq fastq fastq fastq			TDR127 12hpf RNA EKW NA none 10xmultiome S5 L001 I1 001.fastq.gz												SRX26181403		SRA1977819	Chan Zuckerberg Biohub San Francisco\|Computational Biology	Chan Zuckerberg Biohub San Francisco															illumina	novaseq_era	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	10x	United States	2024-09-23	Segmentation	Embryo	Undetermined	Embryo Imprecise
33870	33870	SRR30779437	SRX26181400	SRS22725394	SRP534298	PRJNA1164307	Time resolved single cell Multiomic zebrafish atlas	PRJNA1164307	Other	During development dynamic interplay between transcription factors chromatin and genes termed gene regulatory network GRN shapes the cell fate determination along the developmental trajectory. Recent advances in joint measurement of chromatin accessibility and gene expression enabled the genome wide identification of regulatory relationships. Here we assess the dynamics of the gene regulatory network in zebrafish development using joint single cell ATAC and single cell RNA sequencing. We discovered some key regulatory modules that exhibit cell type and time dependent activity suggesting that the role of transcription factors vary over cell type and timepoints. With time resolved GRNs combined with linear modeling framework we performed a systematic in silico knock out simulation using CellOracle. This in silico knock out simulation revealed that the role of transcription factors is shared between mesodermal and neuro ectodermal lineages in the early timepoints but later commit significantly to either lineages. Together we provide a dataset and a framework to systematically dissect the role of transcription factors during the zebrafish embryonic development.		TDR118	TDR118 16hpf EKW NA none 10xmultiome	strain:EKW\|dev stage:15 somites 16 hpf\|collection date:2023 06 23\|geo loc name:USA: San Francisco\|sex:N/A\|tissue:organism\|BioSampleModel:Model organism or animal							TDR118 16hpf RNA	TDR118 16hpf RNA EKW NA none 10xmultiome	TDR118 16hpf RNA EKW NA none 10xmultiome	10x multiome nuc seq			RNA-Seq	TRANSCRIPTOMIC SINGLE CELL	cDNA	PAIRED	ILLUMINA	Illumina NovaSeq 6000	SRP534298		TDR118_16hpf_RNA_EKW_NA_none_10xmultiome_S1_L001_I1_001.fastq.gz TDR118_16hpf_RNA_EKW_NA_none_10xmultiome_S1_L001_I2_001.fastq.gz TDR118_16hpf_RNA_EKW_NA_none_10xmultiome_S1_L001_R1_001.fastq.gz TDR118_16hpf_RNA_EKW_NA_none_10xmultiome_S1_L001_R2_001.fastq.gz TDR118_16hpf_RNA_EKW_NA_none_10xmultiome_S1_L002_I1_001.fastq.gz TDR118_16hpf_RNA_EKW_NA_none_10xmultiome_S1_L002_I2_001.fastq.gz TDR118_16hpf_RNA_EKW_NA_none_10xmultiome_S1_L002_R1_001.fastq.gz TDR118_16hpf_RNA_EKW_NA_none_10xmultiome_S1_L002_R2_001.fastq.gz TDR118_16hpf_RNA_EKW_NA_none_10xmultiome_S1_L003_I1_001.fastq.gz TDR118_16hpf_RNA_EKW_NA_none_10xmultiome_S1_L003_I2_001.fastq.gz TDR118_16hpf_RNA_EKW_NA_none_10xmultiome_S1_L003_R1_001.fastq.gz TDR118_16hpf_RNA_EKW_NA_none_10xmultiome_S1_L003_R2_001.fastq.gz TDR118_16hpf_RNA_EKW_NA_none_10xmultiome_S1_L004_I1_001.fastq.gz TDR118_16hpf_RNA_EKW_NA_none_10xmultiome_S1_L004_I2_001.fastq.gz TDR118_16hpf_RNA_EKW_NA_none_10xmultiome_S1_L004_R1_001.fastq.gz TDR118_16hpf_RNA_EKW_NA_none_10xmultiome_S1_L004_R2_001.fastq.gz	fastq fastq fastq fastq fastq fastq fastq fastq fastq fastq fastq fastq fastq fastq fastq fastq			TDR118 16hpf RNA EKW NA none 10xmultiome S1 L001 I1 001.fastq.gz												SRX26181400		SRA1977819	Chan Zuckerberg Biohub San Francisco\|Computational Biology	Chan Zuckerberg Biohub San Francisco															illumina	novaseq_era	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	10x	United States	2024-09-23	Segmentation	Embryo	Undetermined	Embryo Imprecise
41888	41888	SRR5320521	SRX2619926	SRS2029889	SRP101558	PRJNA378498	Functional Analysis of Regional Gene Expression in Zebrafish Craniofacial Development	GSE95812	Transcriptome Analysis	Patterning of the facial skeleton involves the precise deployment of thousands of genes in distinct regions of the pharyngeal arches. Despite their significance for craniofacial development how genetic programs drive this regionalization remains poorly understood. Here we use combinatorial labeling of zebrafish cranial neural crest derived cells CNCCs to define global gene expression along the dorsoventral axis of the developing arches. Intersection of region specific transcriptomes with expression changes in response to signaling perturbations demonstrates complex roles for Endothelin1 Edn1 signaling in the intermediate joint forming region yet a surprisingly minor role in ventral most regions. Analysis of co variance across multiple sequencing experiments further reveals clusters of co regulated genes with in situ hybridization confirming the expression of novel genes with domain specific expression. We then performed mutational analysis of a number of these genes which uncovered antagonistic functions of two Edn1 targets follistatin a fsta and emx2 in regulating cartilaginous joints in the hyoid arch. Our unbiased discovery and functional analysis of genes with regional expression in arch CNCCs reveals complex regulation by Edn1 and points to novel candidates for craniofacial disorders. Overall design: mRNA profiles of 3 distinct populations of zebrafish pharyngeal arch cells isolated using FACS from 36 hpf wild type WT zebrafish. fli1a:GFP+;sox10:DsRed and fli1a:GFP ;sox10:DsRed+ populations were also isolated at the same time and sequenced as controls 3 replicate each. Additionally there are mRNA profiles of zebrafish pharyngeal arches all domains combined isolated by FACS from 36 hpf zebrafish with the following genotypes: edn1 / 2 replicates jag1b / jag1b+/+ control hsp70I:Gal4; UAS:Edn1 hsp70I:Gal4; UAS:Nicd and hsp70I:Gal4 control.	pubmed:28705894	WT sox10 1	GSM2526400	tissue:Mixed/Ear\|developmental stage:20 hpf\|genotype:WT\|treatment:N1	WT sox10 1	Raw sequencing data in Fastq format was imported into the Partek Flow® interface for alignment and quantification. Pre alignment QC showed that the reads from all samples had generally high quality with the average Phred quality score for each sample being above 30. Reads were then trimmed from both ends based on Phred quality score with a minimum end quality level of 20 and a minimum acceptable read length of 25. The TopHat 2 algorithm was used to align the trimmed reads to the zebrafish GRCz10 genome assembly Ensembl v80. Aligned reads were then quantified using the Partek E/M algorithm with default parameters to yield the TPM values. Genome build: GRCz10 Supplementary files format and content: csv text files include TPM values for each gene in each sample	Mixed/Ear	GFP/DsRed double positive double negative and single positive populations were collected directly into RLT lysis buffer Qiagen. Total RNA was immediately extracted using the RNeasy Micro kit Qiagen following the manufacturer’s protocol. The quality and quantity of extracted RNA were assessed on an Bioanalyzer Pico RNA chip Agilent Santa Clara CA. cDNA was then made from the extracted RNA using the SMARTer kit Clontech Mountain View CA according to the manufacturer’s instructions. The number of amplification cycles for cDNA synthesis was estimated based on input amounts of RNA. The size and the amount of the resulting cDNA were then confirmed by Bioanalyzer. Sonication was performed on a S2 ultrasonicator Covaris Woburn MA according to Clontech’s recommended conditions. DNA libraries were constructed using the Kapa Hyper prep kit Kapa Biosystems Wilmington MA and NextFlex adapters Bioo Scientific Austin TX. Libraries were visualized by Bioanalyzer analysis and quantified by qPCR Kapa library quantification kit. Sequencing was performed on Illumina HiSeq 2000 50 bp paired end reads and NextSeq 500 75 bp paired end reads machines Illumina San Diego CA. DNA libraries were constructed and sequencing was performed at the Norris Cancer Center Molecular Genomics Next Gen Sequencing Core at USC.	developmental stage:20 hpf\|genotype:WT\|treatment:N1	GSM2526400	GSM2526400: WT sox10 1; Danio rerio; RNA Seq	GSM2526400		1	GFP/DsRed double positive double negative and single positive populations were collected directly into RLT lysis buffer Qiagen. Total RNA was immediately extracted using the RNeasy Micro kit Qiagen following the manufacturer’s protocol. The quality and quantity of extracted RNA were assessed on an Bioanalyzer Pico RNA chip Agilent Santa Clara CA. cDNA was then made from the extracted RNA using the SMARTer kit Clontech Mountain View CA according to the manufacturer’s instructions. The number of amplification cycles for cDNA synthesis was estimated based on input amounts of RNA. The size and the amount of the resulting cDNA were then confirmed by Bioanalyzer. Sonication was performed on a S2 ultrasonicator Covaris Woburn MA according to Clontech’s recommended conditions. DNA libraries were constructed using the Kapa Hyper prep kit Kapa Biosystems Wilmington MA and NextFlex adapters Bioo Scientific Austin TX. Libraries were visualized by Bioanalyzer analysis and quantified by qPCR Kapa library quantification kit. Sequencing was performed on Illumina HiSeq 2000 50 bp paired end reads and NextSeq 500 75 bp paired end reads machines Illumina San Diego CA. DNA libraries were constructed and sequencing was performed at the Norris Cancer Center Molecular Genomics Next Gen Sequencing Core at USC.	GEO Accession:GSM2526400	RNA-Seq	TRANSCRIPTOMIC	cDNA	PAIRED	ILLUMINA	Illumina HiSeq 2000	SRP101558				5233486400.0	52334864.0	GSM2526400 r1	0:50 1:50	A:1446820250;C:1072511594;G:1151585599;T:1561438904;N:1130053	50	50			1446820250	1072511594	1151585599	1561438904	1130053	SRX2619926	SRS2029889	SRA543392	GEO	Crump Lab, Broad CIRM Center for Regenerative Medicine and Stem Cell Research, University of Southern California	2	0.92493	0.72674	0.15162	0.12408	0.73551	0.76349	0.5106	0.51362	50	50	B	B	biological fallback assumption	illumina	hiseq_era	full_length	cdna_unspecified	smarter	bulk	unknown	unknown	United States	2017-03-08	Segmentation	Embryo	Undetermined	Embryo Imprecise
50538	50538	SRR8134461	SRX4955491	SRS3996628	SRP167225	PRJNA501843	Characterization of Transcriptomic Profile in Early Zebrafish PGCs by Single Cell Sequencing	PRJNA501843	Other	Single cell RNA seq was applied for studying the transcriptomic profile in early zebrafish PGCsprimordial germ cells by choosing three time points during zebrafish embryonic development. The three time points were 6hpfhpf also called shield stage 11hpfalso called 3 somite stage and 24hpfalso called prim 5 stage.			H11 2	strain:AB\|isolate:missing\|breed:missing\|cultivar:missing\|ecotype:missing\|dev stage:11hpf biological replicate 2\|sex:missing\|tissue:PGC\|BioSampleModel:Model organism or animal							RNA Seq of Danio rerio: PGC	H11 2	H11 2	smart2			RNA-Seq	TRANSCRIPTOMIC	other	PAIRED	ILLUMINA	Illumina HiSeq 2500	SRP167225		H11_2_R1.fastq H11_2_R2.fastq	fastq fastq	1034277500.0	4137110.0	H11 2 R1.fastq	0:125 1:125	A:286346530;C:232910512;G:234755237;T:280262966;N:2255	125	125			286346530	232910512	234755237	280262966	2255	SRX4955491	SRS3996628	SRA800727	Shanghai Institute of Biochemistry and Cell Biology, CAS\|State Key Laboratory of cell Biology	Shanghai Institute of Biochemistry and Cell Biology, CAS	2	0.94693	0.94598	0.03067	0.03082	0.8562	0.85774	0.51961	0.51451	125	125	B	B	biological fallback assumption	illumina	hiseq_era	unknown	other	unknown	sc_generic	single_cell_generic	generic-scrnaseq-only	China	2019-02-01	Segmentation	Embryo	Undetermined	Embryo Imprecise
50539	50539	SRR8134462	SRX4955490	SRS3996627	SRP167225	PRJNA501843	Characterization of Transcriptomic Profile in Early Zebrafish PGCs by Single Cell Sequencing	PRJNA501843	Other	Single cell RNA seq was applied for studying the transcriptomic profile in early zebrafish PGCsprimordial germ cells by choosing three time points during zebrafish embryonic development. The three time points were 6hpfhpf also called shield stage 11hpfalso called 3 somite stage and 24hpfalso called prim 5 stage.			H11 3	strain:AB\|isolate:missing\|breed:missing\|cultivar:missing\|ecotype:missing\|dev stage:11hpf biological replicate 3\|sex:missing\|tissue:PGC\|BioSampleModel:Model organism or animal							RNA Seq of Danio rerio: PGC	H11 3	H11 3	smart2			RNA-Seq	TRANSCRIPTOMIC	other	PAIRED	ILLUMINA	Illumina HiSeq 2500	SRP167225		H11_3_R1.fastq H11_3_R2.fastq	fastq fastq	1084328250.0	4337313.0	H11 3 R1.fastq	0:125 1:125	A:296459005;C:246835042;G:248291057;T:292740098;N:3048	125	125			296459005	246835042	248291057	292740098	3048	SRX4955490	SRS3996627	SRA800727	Shanghai Institute of Biochemistry and Cell Biology, CAS\|State Key Laboratory of cell Biology	Shanghai Institute of Biochemistry and Cell Biology, CAS	2	0.95199	0.95021	0.02101	0.02158	0.85914	0.86058	0.51593	0.41299	125	125	B	B	biological fallback assumption	illumina	hiseq_era	unknown	other	unknown	sc_generic	single_cell_generic	generic-scrnaseq-only	China	2019-02-01	Segmentation	Embryo	Undetermined	Embryo Imprecise
50541	50541	SRR8134464	SRX4955488	SRS3996623	SRP167225	PRJNA501843	Characterization of Transcriptomic Profile in Early Zebrafish PGCs by Single Cell Sequencing	PRJNA501843	Other	Single cell RNA seq was applied for studying the transcriptomic profile in early zebrafish PGCsprimordial germ cells by choosing three time points during zebrafish embryonic development. The three time points were 6hpfhpf also called shield stage 11hpfalso called 3 somite stage and 24hpfalso called prim 5 stage.			H11 1	strain:AB\|isolate:missing\|breed:missing\|cultivar:missing\|ecotype:missing\|dev stage:11hpf biological replicate 1\|sex:missing\|tissue:PGC\|BioSampleModel:Model organism or animal							RNA Seq of Danio rerio: PGC	H11 1	H11 1	smart2			RNA-Seq	TRANSCRIPTOMIC	other	PAIRED	ILLUMINA	Illumina HiSeq 2500	SRP167225		H11_1_R1.fastq H11_1_R2.fastq	fastq fastq	707998500.0	2831994.0	H11 1 R1.fastq	0:125 1:125	A:194434087;C:160870744;G:162119289;T:190572787;N:1593	125	125			194434087	160870744	162119289	190572787	1593	SRX4955488	SRS3996623	SRA800727	Shanghai Institute of Biochemistry and Cell Biology, CAS\|State Key Laboratory of cell Biology	Shanghai Institute of Biochemistry and Cell Biology, CAS	2	0.94561	0.94128	0.03328	0.03394	0.8589	0.86062	0.48582	0.48548	125	125	B	B	biological fallback assumption	illumina	hiseq_era	unknown	other	unknown	sc_generic	single_cell_generic	generic-scrnaseq-only	China	2019-02-01	Segmentation	Embryo	Undetermined	Embryo Imprecise
59506	59506	SRR11924315	SRX8469989	SRS6770640	SRP265951	PRJNA637293	The shift from early to late types of ribosomes in zebrafish development involves changes at a subset of rRNA 2' O Me sites	GSE151797	Other	A sequencing based profiling method RiboMeth seq for ribose methylations was used to study methylation patterns during Zebrafish Danio rerio development Overall design: All samples were analyzed in biological triplicates except for adult tail trunk that was in duplicate.	pubmed:32912962	12 somite 3	GSM4591057	source name:12 somite stage\|tissue:12 somite stage\|rna fraction:size fractionated 20 40 nt whole cell RNA	12 somite 3	Library strategy: RiboMeth seq Barcode separation using python script Adaptor trimming using Cutadapt v. 2.0 Mapping to rRNA reference sequence using Bowtie2 v. 2.3.4.1 Counting read ends and calculating RiboMeth seq scores using python scripts The output FASTA files from small RNA seq were merged and used as the basis of the SNORD search and rRNA interaction prediction. Initially SNORDs were identified by running the merged FASTA file through snoScan Schattner et al. 2005 against zebrafish early and late rRNA reference sequences Locati et al. 2017. Genome build: early and late zebrafish rRNA locati et al. The reference sequences are available in the FASTA file on the series record. Supplementary files format and content: MS Excel file contains five prime and three prime read count and calculated RiboMeth seq score at all positions in the rRNA sequence.	12 somite stage	Tissues were homogenized and whole cell RNA was extracted using Qiazol Qiagen according to the manufacturer. RiboMeth seq: 5 10 ug of RNA was partially degraded by alkaline at denaturing temperatures. The size fraction 20 40 nt was purified on gels and linkers added using a system relying on a modified Arabidopsis tRNA ligase joining 2' three prime cyclic phosphate and five prime phosphate ends. The library fragments were then sequenced on the Ion Proton platform. See Birkedal U Christensen Dalsgaard M Krogh N Sabarinathan R Gorodkin J Nielsen H. Profiling of ribose methylations in RNA by high throughput sequencing. Angewandte Chemie. 2015;542:451 5 for detailed description	tissue:12 somite stage\|rna fraction:size fractionated 20 40 nt whole cell RNA	GSM4591057	GSM4591057: 12 somite 3; Danio rerio; OTHER	GSM4591057		1	Tissues were homogenized and whole cell RNA was extracted using Qiazol Qiagen according to the manufacturer. RiboMeth seq: 5 10 ug of RNA was partially degraded by alkaline at denaturing temperatures. The size fraction 20 40 nt was purified on gels and linkers added using a system relying on a modified Arabidopsis tRNA ligase joining 2' three prime cyclic phosphate and five prime phosphate ends. The library fragments were then sequenced on the Ion Proton platform. See Birkedal U Christensen Dalsgaard M Krogh N Sabarinathan R Gorodkin J Nielsen H. Profiling of ribose methylations in RNA by high throughput sequencing. Angewandte Chemie. 2015;542:451 5 for detailed description	GEO Accession:GSM4591057	OTHER	TRANSCRIPTOMIC	other	SINGLE	ION_TORRENT	Ion Torrent Proton	SRP265951	intentional duplicate	12_somite_3.bam GSE151797_Reference_sequence.fa	bam bam	105146264.0	3189309.0	GSM4591057 r1	0:32.97	A:24684898;C:33906144;G:26247070;T:20308152;N:0	32				24684898	33906144	26247070	20308152	0	SRX8469989	SRS6770640	SRA1083099	GEO	RNA Group - Prof. Henrik Nielsen, Department of Cellular and Molecular Medicine, University of Copenhagen	1	0.83304		0.22816		0.88325		0.69597		43		B		usable mapping rate	ion_torrent	ion_torrent	5prime	small_rna	unknown	bulk	unknown	unknown	Denmark	2020-06-04	Segmentation	Embryo	Undetermined	Embryo Imprecise
59507	59507	SRR11924314	SRX8469988	SRS6770639	SRP265951	PRJNA637293	The shift from early to late types of ribosomes in zebrafish development involves changes at a subset of rRNA 2' O Me sites	GSE151797	Other	A sequencing based profiling method RiboMeth seq for ribose methylations was used to study methylation patterns during Zebrafish Danio rerio development Overall design: All samples were analyzed in biological triplicates except for adult tail trunk that was in duplicate.	pubmed:32912962	12 somite 2	GSM4591056	source name:12 somite stage\|tissue:12 somite stage\|rna fraction:size fractionated 20 40 nt whole cell RNA	12 somite 2	Library strategy: RiboMeth seq Barcode separation using python script Adaptor trimming using Cutadapt v. 2.0 Mapping to rRNA reference sequence using Bowtie2 v. 2.3.4.1 Counting read ends and calculating RiboMeth seq scores using python scripts The output FASTA files from small RNA seq were merged and used as the basis of the SNORD search and rRNA interaction prediction. Initially SNORDs were identified by running the merged FASTA file through snoScan Schattner et al. 2005 against zebrafish early and late rRNA reference sequences Locati et al. 2017. Genome build: early and late zebrafish rRNA locati et al. The reference sequences are available in the FASTA file on the series record. Supplementary files format and content: MS Excel file contains five prime and three prime read count and calculated RiboMeth seq score at all positions in the rRNA sequence.	12 somite stage	Tissues were homogenized and whole cell RNA was extracted using Qiazol Qiagen according to the manufacturer. RiboMeth seq: 5 10 ug of RNA was partially degraded by alkaline at denaturing temperatures. The size fraction 20 40 nt was purified on gels and linkers added using a system relying on a modified Arabidopsis tRNA ligase joining 2' three prime cyclic phosphate and five prime phosphate ends. The library fragments were then sequenced on the Ion Proton platform. See Birkedal U Christensen Dalsgaard M Krogh N Sabarinathan R Gorodkin J Nielsen H. Profiling of ribose methylations in RNA by high throughput sequencing. Angewandte Chemie. 2015;542:451 5 for detailed description	tissue:12 somite stage\|rna fraction:size fractionated 20 40 nt whole cell RNA	GSM4591056	GSM4591056: 12 somite 2; Danio rerio; OTHER	GSM4591056		1	Tissues were homogenized and whole cell RNA was extracted using Qiazol Qiagen according to the manufacturer. RiboMeth seq: 5 10 ug of RNA was partially degraded by alkaline at denaturing temperatures. The size fraction 20 40 nt was purified on gels and linkers added using a system relying on a modified Arabidopsis tRNA ligase joining 2' three prime cyclic phosphate and five prime phosphate ends. The library fragments were then sequenced on the Ion Proton platform. See Birkedal U Christensen Dalsgaard M Krogh N Sabarinathan R Gorodkin J Nielsen H. Profiling of ribose methylations in RNA by high throughput sequencing. Angewandte Chemie. 2015;542:451 5 for detailed description	GEO Accession:GSM4591056	OTHER	TRANSCRIPTOMIC	other	SINGLE	ION_TORRENT	Ion Torrent Proton	SRP265951	intentional duplicate	12_somite_2.bam GSE151797_Reference_sequence.fa	bam bam	255844263.0	7971494.0	GSM4591056 r1	0:32.09	A:53703561;C:85057062;G:64342817;T:52740823;N:0	32				53703561	85057062	64342817	52740823	0	SRX8469988	SRS6770639	SRA1083099	GEO	RNA Group - Prof. Henrik Nielsen, Department of Cellular and Molecular Medicine, University of Copenhagen	1	0.79601		0.23342		0.90281		0.75464		40		B		usable mapping rate	ion_torrent	ion_torrent	5prime	small_rna	unknown	bulk	unknown	unknown	Denmark	2020-06-04	Segmentation	Embryo	Undetermined	Embryo Imprecise
59508	59508	SRR11924312	SRX8469987	SRS6770638	SRP265951	PRJNA637293	The shift from early to late types of ribosomes in zebrafish development involves changes at a subset of rRNA 2' O Me sites	GSE151797	Other	A sequencing based profiling method RiboMeth seq for ribose methylations was used to study methylation patterns during Zebrafish Danio rerio development Overall design: All samples were analyzed in biological triplicates except for adult tail trunk that was in duplicate.	pubmed:32912962	12 somite 1	GSM4591055	source name:12 somite stage\|tissue:12 somite stage\|rna fraction:size fractionated 20 40 nt whole cell RNA	12 somite 1	Library strategy: RiboMeth seq Barcode separation using python script Adaptor trimming using Cutadapt v. 2.0 Mapping to rRNA reference sequence using Bowtie2 v. 2.3.4.1 Counting read ends and calculating RiboMeth seq scores using python scripts The output FASTA files from small RNA seq were merged and used as the basis of the SNORD search and rRNA interaction prediction. Initially SNORDs were identified by running the merged FASTA file through snoScan Schattner et al. 2005 against zebrafish early and late rRNA reference sequences Locati et al. 2017. Genome build: early and late zebrafish rRNA locati et al. The reference sequences are available in the FASTA file on the series record. Supplementary files format and content: MS Excel file contains five prime and three prime read count and calculated RiboMeth seq score at all positions in the rRNA sequence.	12 somite stage	Tissues were homogenized and whole cell RNA was extracted using Qiazol Qiagen according to the manufacturer. RiboMeth seq: 5 10 ug of RNA was partially degraded by alkaline at denaturing temperatures. The size fraction 20 40 nt was purified on gels and linkers added using a system relying on a modified Arabidopsis tRNA ligase joining 2' three prime cyclic phosphate and five prime phosphate ends. The library fragments were then sequenced on the Ion Proton platform. See Birkedal U Christensen Dalsgaard M Krogh N Sabarinathan R Gorodkin J Nielsen H. Profiling of ribose methylations in RNA by high throughput sequencing. Angewandte Chemie. 2015;542:451 5 for detailed description	tissue:12 somite stage\|rna fraction:size fractionated 20 40 nt whole cell RNA	GSM4591055	GSM4591055: 12 somite 1; Danio rerio; OTHER	GSM4591055		1	Tissues were homogenized and whole cell RNA was extracted using Qiazol Qiagen according to the manufacturer. RiboMeth seq: 5 10 ug of RNA was partially degraded by alkaline at denaturing temperatures. The size fraction 20 40 nt was purified on gels and linkers added using a system relying on a modified Arabidopsis tRNA ligase joining 2' three prime cyclic phosphate and five prime phosphate ends. The library fragments were then sequenced on the Ion Proton platform. See Birkedal U Christensen Dalsgaard M Krogh N Sabarinathan R Gorodkin J Nielsen H. Profiling of ribose methylations in RNA by high throughput sequencing. Angewandte Chemie. 2015;542:451 5 for detailed description	GEO Accession:GSM4591055	OTHER	TRANSCRIPTOMIC	other	SINGLE	ION_TORRENT	Ion Torrent Proton	SRP265951	intentional duplicate	12_somite_1.bam GSE151797_Reference_sequence.fa	bam bam	254000103.0	7990851.0	GSM4591055 r1	0:31.79	A:52359510;C:82090735;G:62850013;T:56699845;N:0	31				52359510	82090735	62850013	56699845	0	SRX8469987	SRS6770638	SRA1083099	GEO	RNA Group - Prof. Henrik Nielsen, Department of Cellular and Molecular Medicine, University of Copenhagen	1	0.69554		0.21063		0.88284		0.72326		43		B		usable mapping rate	ion_torrent	ion_torrent	5prime	small_rna	unknown	bulk	unknown	unknown	Denmark	2020-06-04	Segmentation	Embryo	Undetermined	Embryo Imprecise

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CREATE TABLE run_metadata("run.accession" VARCHAR, "experiment.accession" VARCHAR, "sample.accession" VARCHAR, "study.accession" VARCHAR, bioproject VARCHAR, "study.title" VARCHAR, "study.alias" VARCHAR, "study.type" VARCHAR, "study.abstract" VARCHAR, "study.attributes" VARCHAR, "study.PMIDs" VARCHAR, "sample.description" VARCHAR, "sample.title" VARCHAR, "sample.alias" VARCHAR, "sample.centername" VARCHAR, "sample.attributes" VARCHAR, "GEOsample.title" VARCHAR, "GEOsample.dataprocessing" VARCHAR, "GEOsample.source" VARCHAR, "GEOsample.treatmentprotocol" VARCHAR, "GEOsample.extractprotocol" VARCHAR, "GEOsample.growthprotocol" VARCHAR, "GEOsample.characteristics" VARCHAR, "GEOsample.accession" VARCHAR, "experiment.title" VARCHAR, "experiment.alias" VARCHAR, "experiment.library_name" VARCHAR, "experiment.design_description" VARCHAR, "experiment.library_construction_protocol" VARCHAR, "experiment.attributes" VARCHAR, "experiment.library_strategy" VARCHAR, "experiment.library_source" VARCHAR, "experiment.library_selection" VARCHAR, "experiment.library_layout" VARCHAR, "experiment.platform" VARCHAR, "experiment.instrument_model" VARCHAR, "experiment.spot_descriptor" VARCHAR, "experiment.study_ref" VARCHAR, "run.title" VARCHAR, "run.attributes" VARCHAR, "run.filename" VARCHAR, "run.semantic_name" VARCHAR, "run.total_bases" DOUBLE, "run.total_spots" DOUBLE, "run.alias" VARCHAR, "run.read_lengths" VARCHAR, "run.base_counts" VARCHAR, "run.r1_length" BIGINT, "run.r2_length" BIGINT, "run.r3_length" BIGINT, "run.r4_length" BIGINT, "run.Acount" BIGINT, "run.Ccount" BIGINT, "run.Gcount" BIGINT, "run.Tcount" BIGINT, "run.Ncount" BIGINT, "run.experiment" VARCHAR, "run.pool_member" VARCHAR, "submission.accession" VARCHAR, "submission.srasource" VARCHAR, "submission.bioprojectsource" VARCHAR, "seqdetective.n_mates" BIGINT, "seqdetective.mapping_rate.mate1" DOUBLE, "seqdetective.mapping_rate.mate2" DOUBLE, "seqdetective.nofeature_rate.mate1" DOUBLE, "seqdetective.nofeature_rate.mate2" DOUBLE, "seqdetective.sparsity.mate1" DOUBLE, "seqdetective.sparsity.mate2" DOUBLE, "seqdetective.pos_strand_rate.mate1" DOUBLE, "seqdetective.pos_strand_rate.mate2" DOUBLE, "seqdetective.readlen.mate1" BIGINT, "seqdetective.readlen.mate2" BIGINT, "seqdetective.judgement.mate1" VARCHAR, "seqdetective.judgement.mate2" VARCHAR, "seqdetective.judgement.reason" VARCHAR, platform_family VARCHAR, instrument_generation VARCHAR, read_bias VARCHAR, selection_class VARCHAR, prep_kit VARCHAR, sc_or_bulk VARCHAR, tech_class VARCHAR, technology VARCHAR, tech_variant VARCHAR, "submission.bioprojectsource.country" VARCHAR, earliest_date DATE, devstage_curation VARCHAR, devstage_curation_coarse VARCHAR, tissue_curation VARCHAR, tissue_curation_coarse VARCHAR);;
CREATE INDEX idx_run_bioproject ON run_metadata(bioproject);;
CREATE INDEX idx_run_run_accession ON run_metadata("run.accession");;