metadata: run_metadata: 8 rows where experiment.library_strategy = "OTHER", tissue_curation = "Multi-tissue" and tissue_curation

8 rows where experiment.library_strategy = "OTHER", tissue_curation = "Multi-tissue" and tissue_curation_coarse = "Multi-system"

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.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.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
43987	43987	SRR6811828	SRX3768868	SRS3023414	SRP121343	PRJNA415636	Simultaneous lineage tracing and cell type identification using CRISPR/Cas9 induced genetic scars	GSE106121	Other	A key goal of developmental biology is to understand how a single cell transforms into a full grown organism consisting of many different cell types. Single cell RNA sequencing scRNA seq has become a widely used method due to its ability to identify all cell types in a tissue or organ in a systematic manner. However a major challenge is to organize the resulting taxonomy of cell types into lineage trees revealing the developmental origin of cells. Here we present a strategy for simultaneous lineage tracing and transcriptome profiling in thousands of single cells. By combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes we reconstruct developmental lineage trees in zebrafish larvae and adult fish. In future analyses LINNAEUS LINeage tracing by Nuclease Activated Editing of Ubiquitous Sequences can be used as a systematic approach for identifying the lineage origin of novel cell types or of known cell types under different conditions. Overall design: Combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes.	pubmed:29644996	Pancreas 3 exo scar	GSM3032171	source name:Pancreas except primary islet liver\|strain/background:Zebrabow M\|tissue:Pancreas except primary islet liver\|developmental stage:Adult	Pancreas 3 exo scar	Library strategy: Targeted amplification Scar reads have the same structure as transcript reads: they consist of a barcode a UMI and a scar. The scar sequences were aligned using bwa mem6 to a reference of RFP. We defined a cell as a barcode with at least 500 reads. We removed reads that were unmapped had an incorrect barcode or did not start with the exact PCR primer we used. We truncated all scar sequences to 75 nucleotides and filtered out shorter sequences. To correct for sequencing errors we implemented several rounds of scar filtering Supplementary Fig. 2 in publication. We started by counting the number of times each molecule was sequenced. Sequencing errors will typically have fewer reads than the actual scars they originate from. As a first filtering step we therefore removed all molecules only seen once to reduce the complexity in the dataset for consecutive filtering steps. In the second filtering step we aimed to remove easily recognizable sequencing errors. To this end we consecutively considered scar sequences that have the same cellular barcode and UMI UMIs that have the same cellular barcode and scar sequence and cellular barcodes that have the same UMI and scar sequence. In each step we kept only the molecule with the highest number of reads. The rationale behind this is that it is very improbable to have two valid scar sequences in the same cell with the same UMI or to have a scar sequence with the same UMI appear in two different cells. The observation of two different UMIs for the same scar in the same cell is much more likely and corresponds to detection of multiple transcripts from the same locus but information about scar expression levels was not required in our downstream analysis. In the third filtering step we specifically targeted sequencing errors within each cell. We compared the scar sequences found within a cell to each other. We filtered out sequences that had a Hamming distance of 2 or less to another scar sequence in the same cell that occurred in at least eight tim…	Pancreas except primary islet liver	Single cell dissociation. 10X Genomics Chromium	strain/background:Zebrabow M\|tissue:Pancreas except primary islet liver\|developmental stage:Adult	GSM3032171	GSM3032171: Pancreas 3 exo scar; Danio rerio; OTHER	GSM3032171	1	Single cell dissociation. 10X Genomics Chromium	GEO Accession:GSM3032171	OTHER	TRANSCRIPTOMIC	other	PAIRED	ILLUMINA	NextSeq 500	SRP121343		P7exo_scar_R1.fastq.gz P7exo_scar_R2.fastq.gz	fastq fastq	4291410352.0	34608148.0	GSM3032171 r1	0:26 1:98	A:1266869162;C:1345773385;G:950601988;T:726078376;N:2087441	26	98	1266869162	1345773385	950601988	726078376	2087441	SRX3768868	SRS3023414	SRA623333	GEO	Max Delbrück Center	2	0.00014	0.00241	0.00012	0.00024	0.99995	0.99691	0.0	0.65306	26	98	T	T	mates < 9% mapping rate	illumina	nextseq	unknown	other	unknown	sc	single_cell_droplet	10x	Germany	2018-03-06	Adult	Adult	Multi-tissue	Multi-system
43989	43989	SRR6811826	SRX3768866	SRS3023382	SRP121343	PRJNA415636	Simultaneous lineage tracing and cell type identification using CRISPR/Cas9 induced genetic scars	GSE106121	Other	A key goal of developmental biology is to understand how a single cell transforms into a full grown organism consisting of many different cell types. Single cell RNA sequencing scRNA seq has become a widely used method due to its ability to identify all cell types in a tissue or organ in a systematic manner. However a major challenge is to organize the resulting taxonomy of cell types into lineage trees revealing the developmental origin of cells. Here we present a strategy for simultaneous lineage tracing and transcriptome profiling in thousands of single cells. By combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes we reconstruct developmental lineage trees in zebrafish larvae and adult fish. In future analyses LINNAEUS LINeage tracing by Nuclease Activated Editing of Ubiquitous Sequences can be used as a systematic approach for identifying the lineage origin of novel cell types or of known cell types under different conditions. Overall design: Combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes.	pubmed:29644996	Heart 3 scar	GSM3032169	source name:Heart and blood\|strain/background:Zebrabow M\|tissue:Heart and blood\|developmental stage:Adult	Heart 3 scar	Library strategy: Targeted amplification Scar reads have the same structure as transcript reads: they consist of a barcode a UMI and a scar. The scar sequences were aligned using bwa mem6 to a reference of RFP. We defined a cell as a barcode with at least 500 reads. We removed reads that were unmapped had an incorrect barcode or did not start with the exact PCR primer we used. We truncated all scar sequences to 75 nucleotides and filtered out shorter sequences. To correct for sequencing errors we implemented several rounds of scar filtering Supplementary Fig. 2 in publication. We started by counting the number of times each molecule was sequenced. Sequencing errors will typically have fewer reads than the actual scars they originate from. As a first filtering step we therefore removed all molecules only seen once to reduce the complexity in the dataset for consecutive filtering steps. In the second filtering step we aimed to remove easily recognizable sequencing errors. To this end we consecutively considered scar sequences that have the same cellular barcode and UMI UMIs that have the same cellular barcode and scar sequence and cellular barcodes that have the same UMI and scar sequence. In each step we kept only the molecule with the highest number of reads. The rationale behind this is that it is very improbable to have two valid scar sequences in the same cell with the same UMI or to have a scar sequence with the same UMI appear in two different cells. The observation of two different UMIs for the same scar in the same cell is much more likely and corresponds to detection of multiple transcripts from the same locus but information about scar expression levels was not required in our downstream analysis. In the third filtering step we specifically targeted sequencing errors within each cell. We compared the scar sequences found within a cell to each other. We filtered out sequences that had a Hamming distance of 2 or less to another scar sequence in the same cell that occurred in at least eight tim…	Heart and blood	Single cell dissociation. 10X Genomics Chromium	strain/background:Zebrabow M\|tissue:Heart and blood\|developmental stage:Adult	GSM3032169	GSM3032169: Heart 3 scar; Danio rerio; OTHER	GSM3032169	1	Single cell dissociation. 10X Genomics Chromium	GEO Accession:GSM3032169	OTHER	TRANSCRIPTOMIC	other	PAIRED	ILLUMINA	NextSeq 500	SRP121343		H7_scar_R1.fastq.gz H7_scar_R2.fastq.gz	fastq fastq	3999397296.0	32253204.0	GSM3032169 r1	0:26 1:98	A:1170970784;C:1255843391;G:881154893;T:689473188;N:1955040	26	98	1170970784	1255843391	881154893	689473188	1955040	SRX3768866	SRS3023382	SRA623333	GEO	Max Delbrück Center	2	0.00018	0.00228	0.00015	0.00019	0.99991	0.99659	0.8	0.67235	26	98	T	T	mates < 9% mapping rate	illumina	nextseq	unknown	other	unknown	sc	single_cell_droplet	10x	Germany	2018-03-06	Adult	Adult	Multi-tissue	Multi-system
44008	44008	SRR6211484	SRX3320759	SRS2626332	SRP121343	PRJNA415636	Simultaneous lineage tracing and cell type identification using CRISPR/Cas9 induced genetic scars	GSE106121	Other	A key goal of developmental biology is to understand how a single cell transforms into a full grown organism consisting of many different cell types. Single cell RNA sequencing scRNA seq has become a widely used method due to its ability to identify all cell types in a tissue or organ in a systematic manner. However a major challenge is to organize the resulting taxonomy of cell types into lineage trees revealing the developmental origin of cells. Here we present a strategy for simultaneous lineage tracing and transcriptome profiling in thousands of single cells. By combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes we reconstruct developmental lineage trees in zebrafish larvae and adult fish. In future analyses LINNAEUS LINeage tracing by Nuclease Activated Editing of Ubiquitous Sequences can be used as a systematic approach for identifying the lineage origin of novel cell types or of known cell types under different conditions. Overall design: Combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes.	pubmed:29644996	Heart 2 scar	GSM2830055	source name:Heart and blood\|strain/background:Zebrabow M\|tissue:Heart and blood\|developmental stage:Adult	Heart 2 scar	Library strategy: Targeted amplification Scar reads have the same structure as transcript reads: they consist of a barcode a UMI and a scar. The scar sequences were aligned using bwa mem6 to a reference of RFP. We defined a cell as a barcode with at least 500 reads. We removed reads that were unmapped had an incorrect barcode or did not start with the exact PCR primer we used. We truncated all scar sequences to 75 nucleotides and filtered out shorter sequences. To correct for sequencing errors we implemented several rounds of scar filtering Supplementary Fig. 2 in publication. We started by counting the number of times each molecule was sequenced. Sequencing errors will typically have fewer reads than the actual scars they originate from. As a first filtering step we therefore removed all molecules only seen once to reduce the complexity in the dataset for consecutive filtering steps. In the second filtering step we aimed to remove easily recognizable sequencing errors. To this end we consecutively considered scar sequences that have the same cellular barcode and UMI UMIs that have the same cellular barcode and scar sequence and cellular barcodes that have the same UMI and scar sequence. In each step we kept only the molecule with the highest number of reads. The rationale behind this is that it is very improbable to have two valid scar sequences in the same cell with the same UMI or to have a scar sequence with the same UMI appear in two different cells. The observation of two different UMIs for the same scar in the same cell is much more likely and corresponds to detection of multiple transcripts from the same locus but information about scar expression levels was not required in our downstream analysis. In the third filtering step we specifically targeted sequencing errors within each cell. We compared the scar sequences found within a cell to each other. We filtered out sequences that had a Hamming distance of 2 or less to another scar sequence in the same cell that occurred in at least eight tim…	Heart and blood	Single cell dissociation. 10X Genomics Chromium	strain/background:Zebrabow M\|tissue:Heart and blood\|developmental stage:Adult	GSM2830055	GSM2830055: Heart 2 scar; Danio rerio; OTHER	GSM2830055	1	Single cell dissociation. 10X Genomics Chromium	GEO Accession:GSM2830055	OTHER	TRANSCRIPTOMIC	other	PAIRED	ILLUMINA	NextSeq 500	SRP121343		H6_scar_R1.fastq.gz H6_scar_R2.fastq.gz	fastq fastq	2079107172.0	15065994.0	GSM2830055 r1	0:28 1:110	A:580640871;C:641740953;G:501505035;T:355092832;N:127481	28	110	580640871	641740953	501505035	355092832	127481	SRX3320759	SRS2626332	SRA623333	GEO	Max Delbrück Center	2	5e-05	0.00253	4e-05	0.00023	1.0	0.99778		0.58419	28	110	T	T	mates < 9% mapping rate	illumina	nextseq	unknown	other	unknown	sc	single_cell_droplet	10x	Germany	2017-10-24	Adult	Adult	Multi-tissue	Multi-system
44009	44009	SRR6211483	SRX3320758	SRS2626348	SRP121343	PRJNA415636	Simultaneous lineage tracing and cell type identification using CRISPR/Cas9 induced genetic scars	GSE106121	Other	A key goal of developmental biology is to understand how a single cell transforms into a full grown organism consisting of many different cell types. Single cell RNA sequencing scRNA seq has become a widely used method due to its ability to identify all cell types in a tissue or organ in a systematic manner. However a major challenge is to organize the resulting taxonomy of cell types into lineage trees revealing the developmental origin of cells. Here we present a strategy for simultaneous lineage tracing and transcriptome profiling in thousands of single cells. By combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes we reconstruct developmental lineage trees in zebrafish larvae and adult fish. In future analyses LINNAEUS LINeage tracing by Nuclease Activated Editing of Ubiquitous Sequences can be used as a systematic approach for identifying the lineage origin of novel cell types or of known cell types under different conditions. Overall design: Combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes.	pubmed:29644996	Pancreas 2 scar	GSM2830054	source name:Pancreas and liver\|strain/background:Zebrabow M\|tissue:Pancreas and liver\|developmental stage:Adult	Pancreas 2 scar	Library strategy: Targeted amplification Scar reads have the same structure as transcript reads: they consist of a barcode a UMI and a scar. The scar sequences were aligned using bwa mem6 to a reference of RFP. We defined a cell as a barcode with at least 500 reads. We removed reads that were unmapped had an incorrect barcode or did not start with the exact PCR primer we used. We truncated all scar sequences to 75 nucleotides and filtered out shorter sequences. To correct for sequencing errors we implemented several rounds of scar filtering Supplementary Fig. 2 in publication. We started by counting the number of times each molecule was sequenced. Sequencing errors will typically have fewer reads than the actual scars they originate from. As a first filtering step we therefore removed all molecules only seen once to reduce the complexity in the dataset for consecutive filtering steps. In the second filtering step we aimed to remove easily recognizable sequencing errors. To this end we consecutively considered scar sequences that have the same cellular barcode and UMI UMIs that have the same cellular barcode and scar sequence and cellular barcodes that have the same UMI and scar sequence. In each step we kept only the molecule with the highest number of reads. The rationale behind this is that it is very improbable to have two valid scar sequences in the same cell with the same UMI or to have a scar sequence with the same UMI appear in two different cells. The observation of two different UMIs for the same scar in the same cell is much more likely and corresponds to detection of multiple transcripts from the same locus but information about scar expression levels was not required in our downstream analysis. In the third filtering step we specifically targeted sequencing errors within each cell. We compared the scar sequences found within a cell to each other. We filtered out sequences that had a Hamming distance of 2 or less to another scar sequence in the same cell that occurred in at least eight tim…	Pancreas and liver	Single cell dissociation. 10X Genomics Chromium	strain/background:Zebrabow M\|tissue:Pancreas and liver\|developmental stage:Adult	GSM2830054	GSM2830054: Pancreas 2 scar; Danio rerio; OTHER	GSM2830054	1	Single cell dissociation. 10X Genomics Chromium	GEO Accession:GSM2830054	OTHER	TRANSCRIPTOMIC	other	PAIRED	ILLUMINA	NextSeq 500	SRP121343		P6_scar_R1.fastq.gz P6_scar_R2.fastq.gz	fastq fastq	1026715860.0	7439970.0	GSM2830054 r1	0:28 1:110	A:287816843;C:320879355;G:243598630;T:174359452;N:61580	28	110	287816843	320879355	243598630	174359452	61580	SRX3320758	SRS2626348	SRA623333	GEO	Max Delbrück Center	2	0.00029	0.00123	0.00028	6e-05	1.0	0.99914		0.25714	28	110	T	T	mates < 9% mapping rate	illumina	nextseq	unknown	other	unknown	sc	single_cell_droplet	10x	Germany	2017-10-24	Adult	Adult	Multi-tissue	Multi-system
44010	44010	SRR6211482	SRX3320757	SRS2626331	SRP121343	PRJNA415636	Simultaneous lineage tracing and cell type identification using CRISPR/Cas9 induced genetic scars	GSE106121	Other	A key goal of developmental biology is to understand how a single cell transforms into a full grown organism consisting of many different cell types. Single cell RNA sequencing scRNA seq has become a widely used method due to its ability to identify all cell types in a tissue or organ in a systematic manner. However a major challenge is to organize the resulting taxonomy of cell types into lineage trees revealing the developmental origin of cells. Here we present a strategy for simultaneous lineage tracing and transcriptome profiling in thousands of single cells. By combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes we reconstruct developmental lineage trees in zebrafish larvae and adult fish. In future analyses LINNAEUS LINeage tracing by Nuclease Activated Editing of Ubiquitous Sequences can be used as a systematic approach for identifying the lineage origin of novel cell types or of known cell types under different conditions. Overall design: Combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes.	pubmed:29644996	Heart 1 scar	GSM2830053	source name:Heart and blood\|strain/background:Zebrabow M\|tissue:Heart and blood\|developmental stage:Adult	Heart 1 scar	Library strategy: Targeted amplification Scar reads have the same structure as transcript reads: they consist of a barcode a UMI and a scar. The scar sequences were aligned using bwa mem6 to a reference of RFP. We defined a cell as a barcode with at least 500 reads. We removed reads that were unmapped had an incorrect barcode or did not start with the exact PCR primer we used. We truncated all scar sequences to 75 nucleotides and filtered out shorter sequences. To correct for sequencing errors we implemented several rounds of scar filtering Supplementary Fig. 2 in publication. We started by counting the number of times each molecule was sequenced. Sequencing errors will typically have fewer reads than the actual scars they originate from. As a first filtering step we therefore removed all molecules only seen once to reduce the complexity in the dataset for consecutive filtering steps. In the second filtering step we aimed to remove easily recognizable sequencing errors. To this end we consecutively considered scar sequences that have the same cellular barcode and UMI UMIs that have the same cellular barcode and scar sequence and cellular barcodes that have the same UMI and scar sequence. In each step we kept only the molecule with the highest number of reads. The rationale behind this is that it is very improbable to have two valid scar sequences in the same cell with the same UMI or to have a scar sequence with the same UMI appear in two different cells. The observation of two different UMIs for the same scar in the same cell is much more likely and corresponds to detection of multiple transcripts from the same locus but information about scar expression levels was not required in our downstream analysis. In the third filtering step we specifically targeted sequencing errors within each cell. We compared the scar sequences found within a cell to each other. We filtered out sequences that had a Hamming distance of 2 or less to another scar sequence in the same cell that occurred in at least eight tim…	Heart and blood	Single cell dissociation. 10X Genomics Chromium	strain/background:Zebrabow M\|tissue:Heart and blood\|developmental stage:Adult	GSM2830053	GSM2830053: Heart 1 scar; Danio rerio; OTHER	GSM2830053	1	Single cell dissociation. 10X Genomics Chromium	GEO Accession:GSM2830053	OTHER	TRANSCRIPTOMIC	other	PAIRED	ILLUMINA	NextSeq 500	SRP121343		H5_scar_R1.fastq.gz H5_scar_R2.fastq.gz	fastq fastq	1287546624.0	10218624.0	GSM2830053 r1	0:26 1:100	A:365272675;C:413037677;G:296495693;T:212570153;N:170426	26	100	365272675	413037677	296495693	212570153	170426	SRX3320757	SRS2626331	SRA623333	GEO	Max Delbrück Center	2	0.00023	0.00044	0.00022	3e-05	1.0	0.99955		0.32653	26	100	T	T	mates < 9% mapping rate	illumina	nextseq	unknown	other	unknown	sc	single_cell_droplet	10x	Germany	2017-10-24	Adult	Adult	Multi-tissue	Multi-system
44012	44012	SRR6211480	SRX3320755	SRS2626329	SRP121343	PRJNA415636	Simultaneous lineage tracing and cell type identification using CRISPR/Cas9 induced genetic scars	GSE106121	Other	A key goal of developmental biology is to understand how a single cell transforms into a full grown organism consisting of many different cell types. Single cell RNA sequencing scRNA seq has become a widely used method due to its ability to identify all cell types in a tissue or organ in a systematic manner. However a major challenge is to organize the resulting taxonomy of cell types into lineage trees revealing the developmental origin of cells. Here we present a strategy for simultaneous lineage tracing and transcriptome profiling in thousands of single cells. By combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes we reconstruct developmental lineage trees in zebrafish larvae and adult fish. In future analyses LINNAEUS LINeage tracing by Nuclease Activated Editing of Ubiquitous Sequences can be used as a systematic approach for identifying the lineage origin of novel cell types or of known cell types under different conditions. Overall design: Combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes.	pubmed:29644996	Pancreas 1 scar	GSM2830051	source name:Pancreas and liver\|strain/background:Zebrabow M\|tissue:Pancreas and liver\|developmental stage:Adult	Pancreas 1 scar	Library strategy: Targeted amplification Scar reads have the same structure as transcript reads: they consist of a barcode a UMI and a scar. The scar sequences were aligned using bwa mem6 to a reference of RFP. We defined a cell as a barcode with at least 500 reads. We removed reads that were unmapped had an incorrect barcode or did not start with the exact PCR primer we used. We truncated all scar sequences to 75 nucleotides and filtered out shorter sequences. To correct for sequencing errors we implemented several rounds of scar filtering Supplementary Fig. 2 in publication. We started by counting the number of times each molecule was sequenced. Sequencing errors will typically have fewer reads than the actual scars they originate from. As a first filtering step we therefore removed all molecules only seen once to reduce the complexity in the dataset for consecutive filtering steps. In the second filtering step we aimed to remove easily recognizable sequencing errors. To this end we consecutively considered scar sequences that have the same cellular barcode and UMI UMIs that have the same cellular barcode and scar sequence and cellular barcodes that have the same UMI and scar sequence. In each step we kept only the molecule with the highest number of reads. The rationale behind this is that it is very improbable to have two valid scar sequences in the same cell with the same UMI or to have a scar sequence with the same UMI appear in two different cells. The observation of two different UMIs for the same scar in the same cell is much more likely and corresponds to detection of multiple transcripts from the same locus but information about scar expression levels was not required in our downstream analysis. In the third filtering step we specifically targeted sequencing errors within each cell. We compared the scar sequences found within a cell to each other. We filtered out sequences that had a Hamming distance of 2 or less to another scar sequence in the same cell that occurred in at least eight tim…	Pancreas and liver	Single cell dissociation. 10X Genomics Chromium	strain/background:Zebrabow M\|tissue:Pancreas and liver\|developmental stage:Adult	GSM2830051	GSM2830051: Pancreas 1 scar; Danio rerio; OTHER	GSM2830051	1	Single cell dissociation. 10X Genomics Chromium	GEO Accession:GSM2830051	OTHER	TRANSCRIPTOMIC	other	PAIRED	ILLUMINA	NextSeq 500	SRP121343		P5_scar_R1.fastq.gz P5_scar_R2.fastq.gz	fastq fastq	1192602600.0	9465100.0	GSM2830051 r1	0:26 1:100	A:346841760;C:383266222;G:268916933;T:193424756;N:152929	26	100	346841760	383266222	268916933	193424756	152929	SRX3320755	SRS2626329	SRA623333	GEO	Max Delbrück Center	2	0.00021	0.00091	0.00014	2e-05	0.99997	0.99892	0.0	0.23931	26	100	T	T	mates < 9% mapping rate	illumina	nextseq	unknown	other	unknown	sc	single_cell_droplet	10x	Germany	2017-10-24	Adult	Adult	Multi-tissue	Multi-system
59501	59501	SRR11924323	SRX8469995	SRS6770646	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	Adult tail 2	GSM4591062	source name:adult tail trunk\|tissue:adult tail trunk\|rna fraction:size fractionated 20 40 nt whole cell RNA	Adult tail 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.	adult tail trunk	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:adult tail trunk\|rna fraction:size fractionated 20 40 nt whole cell RNA	GSM4591062	GSM4591062: Adult tail 2; Danio rerio; OTHER	GSM4591062	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:GSM4591062	OTHER	TRANSCRIPTOMIC	other	SINGLE	ION_TORRENT	Ion Torrent Proton	SRP265951	intentional duplicate	Adult_tail_2.bam GSE151797_Reference_sequence.fa	bam bam	156647953.0	5517527.0	GSM4591062 r1	0:28.39	A:32903673;C:49609293;G:39096775;T:35038212;N:0	28		32903673	49609293	39096775	35038212	0	SRX8469995	SRS6770646	SRA1083099	GEO	RNA Group - Prof. Henrik Nielsen, Department of Cellular and Molecular Medicine, University of Copenhagen	1	0.80662		0.20653		0.88722		0.58074		37		B		usable mapping rate	ion_torrent	ion_torrent	5prime	small_rna	unknown	bulk	unknown	unknown	Denmark	2020-06-04	Adult	Adult	Multi-tissue	Multi-system
59502	59502	SRR11924321	SRX8469994	SRS6770645	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	Adult tail 1	GSM4591061	source name:adult tail trunk\|tissue:adult tail trunk\|rna fraction:size fractionated 20 40 nt whole cell RNA	Adult tail 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.	adult tail trunk	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:adult tail trunk\|rna fraction:size fractionated 20 40 nt whole cell RNA	GSM4591061	GSM4591061: Adult tail 1; Danio rerio; OTHER	GSM4591061	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:GSM4591061	OTHER	TRANSCRIPTOMIC	other	SINGLE	ION_TORRENT	Ion Torrent Proton	SRP265951	intentional duplicate	Adult_tail_1.bam GSE151797_Reference_sequence.fa	bam bam	50029636.0	1968329.0	GSM4591061 r1	0:25.42	A:9571393;C:14825016;G:13276309;T:12356918;N:0	25		9571393	14825016	13276309	12356918	0	SRX8469994	SRS6770645	SRA1083099	GEO	RNA Group - Prof. Henrik Nielsen, Department of Cellular and Molecular Medicine, University of Copenhagen	1	0.46312		0.12127		0.91806		0.67424		44		B		usable mapping rate	ion_torrent	ion_torrent	5prime	small_rna	unknown	bulk	unknown	unknown	Denmark	2020-06-04	Adult	Adult	Multi-tissue	Multi-system

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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");;