title: Dmel10_E10h

data_processing:
Sequencing reads alignment: The sequences corresponding to the library identification barcode (first 6 bases of read 1) and the reverse-transcription primer (first 15 bases of read 2) were trimmed prior to mapping. Trimmed reads were mapped with STAR, with parameters described in Tables S2-3 of the original publication. All uniquely mapping reads were kept. As a rescue strategy for multiply mapping reads, if all alignments for those reads started within an annotated transposon and overlapped the same gene annotation, the alignment starting in the closest transposon insertion was selected. All non-rescued multi-mappers were discarded.

Data analysis pipeline: PCR duplicates, defined as reads sharing the same alignment coordinates (start, end and splice sites), were removed from the individual datasets. To avoid over-collapsing, we took advantage of the fact that the long random sequence (15-mer) of our reverse-transcription primer often primes with mismatches. We used this sequence as a pseudo-random barcode allowing us to distinguish between true duplicates (same barcode) and independent identical inserts. All collapsed datasets were then combined prior to peak calling. The density of cDNA 5' ends across the genome was determined from this combined dataset, as well as the density of coverage by second (i.e., downstream) sequencing reads. Peaks were called by a sliding window algorithm that assesses the significance of local signal enrichment given a null distribution. Downstream read coverage in the same window was used to correct for local transcript abundance, by subtracting from the raw signal a pseudocount proportional to this coverage. After FDR correction, significant windows in close proximity to each other were merged into peaks, and those were trimmed at the edges down to the first base with signal. (Parameters: window width 15 bases, null distribution negative binomial with k=4, background weight 0.5, FDR 0.01, merging range 150 bases). These peaks were connected to annotated genes based on cDNA structure information. For each peak, if we could find at least 2 inserts having their 5' in the peak and overlapping an annotated exon of a gene, the peak was functionally linked to that gene. If a peak could potentially be linked to several genes, ties were broken by removing all links that were 5-fold weaker than the strongest one. For quantification, the signal for each peak and each timepoint was derived from the uncollapsed datasets, and normalized to dataset size (defined as the total number of reads attributed to any genic TSS). We built partial transcript models by running Cufflinks separately on the set of reads coming from each peak for each given dataset, and collapsing all transcripts for each peak using Cuffmerge. For a more detailed description of the analysis pipeline, please refer to the original publication.

*_+.bw file: bigWig coverage by cDNA 5' ends (+ strand).
*_-.bw file: bigWig coverage by cDNA 5' ends (- strand).
Genome Build:
Dmel10_E10h_-.bw: dm3
Dmel10_E10h_+.bw: dm3

value: y; cn bw sp

value: embryos 9-10h after egg laying


title: Dmel08_E08h

status: Public on Aug 29 2012

type: SRA

city: Woodbury

first: Philippe

last: Batut

state: NY

address: 500 Sunnyside Blvd.

country: USA

institute: CSHL

description:
RNA (5'-complete cDNAs selected by TEX digest, template-switching and cap-trapping).
RNA-Seq (RAMPAGE)

library_source: transcriptomic

data_processing:
Sequencing reads alignment: The sequences corresponding to the library identification barcode (first 6 bases of read 1) and the reverse-transcription primer (first 15 bases of read 2) were trimmed prior to mapping. Trimmed reads were mapped with STAR, with parameters described in Tables S2-3 of the original publication. All uniquely mapping reads were kept. As a rescue strategy for multiply mapping reads, if all alignments for those reads started within an annotated transposon and overlapped the same gene annotation, the alignment starting in the closest transposon insertion was selected. All non-rescued multi-mappers were discarded.

Data analysis pipeline: PCR duplicates, defined as reads sharing the same alignment coordinates (start, end and splice sites), were removed from the individual datasets. To avoid over-collapsing, we took advantage of the fact that the long random sequence (15-mer) of our reverse-transcription primer often primes with mismatches. We used this sequence as a pseudo-random barcode allowing us to distinguish between true duplicates (same barcode) and independent identical inserts. All collapsed datasets were then combined prior to peak calling. The density of cDNA 5' ends across the genome was determined from this combined dataset, as well as the density of coverage by second (i.e., downstream) sequencing reads. Peaks were called by a sliding window algorithm that assesses the significance of local signal enrichment given a null distribution. Downstream read coverage in the same window was used to correct for local transcript abundance, by subtracting from the raw signal a pseudocount proportional to this coverage. After FDR correction, significant windows in close proximity to each other were merged into peaks, and those were trimmed at the edges down to the first base with signal. (Parameters: window width 15 bases, null distribution negative binomial with k=4, background weight 0.5, FDR 0.01, merging range 150 bases). These peaks were connected to annotated genes based on cDNA structure information. For each peak, if we could find at least 2 inserts having their 5' in the peak and overlapping an annotated exon of a gene, the peak was functionally linked to that gene. If a peak could potentially be linked to several genes, ties were broken by removing all links that were 5-fold weaker than the strongest one. For quantification, the signal for each peak and each timepoint was derived from the uncollapsed datasets, and normalized to dataset size (defined as the total number of reads attributed to any genic TSS). We built partial transcript models by running Cufflinks separately on the set of reads coming from each peak for each given dataset, and collapsing all transcripts for each peak using Cuffmerge. For a more detailed description of the analysis pipeline, please refer to the original publication.

*_+.bw file: bigWig coverage by cDNA 5' ends (+ strand).
*_-.bw file: bigWig coverage by cDNA 5' ends (- strand).
Genome Build:
Dmel08_E08h_-.bw: dm3
Dmel08_E08h_+.bw: dm3

molecule: total RNA

organism: Drosophila melanogaster

source_name: Whole organism, embryos

growth_protocol: Stocks of the y; cn bw sp strain were maintained in standard cornmeal medium bottles in a 24ºC incubator. Embryo collections were performed in population cages (Flystuff, #59-116). 2- to 7-day-old flies were left to acclimatize to the cage for at least 48h and regularly fed with grape juice-agar plates (Flystuff, #47-102) generously loaded with yeast paste. After two 2-hour pre-lays, embryos were collected in 1-hour windows and aged appropriately (24 timepoints, 0-24h). Embryos were washed with deionized water, dechorionated for 90 sec with 50% bleach, rinsed abundantly with water, and snap-frozen in liquid nitrogen. Larvae and pupae were collected as described previously (Graveley, Brooks et al. 2010). For L1 and L2 stages, 2-hour embryo collections were aged for 42 or 66 hours, larvae were briefly rinsed with deionized water and snap-frozen. For L3 stages, embryos were transferred to bottles containing cornmeal medium supplemented with 0.05% bromophenol blue, and wandering L3 larvae were staged based on gut staining (dark, light or clear gut) and snap-frozen. For pupae, 2-hour embryo collections were transferred to standard cornmeal medium bottles, the positions of new white prepupae on the walls of the bottle were marked, and pupae were collected and snap-frozen at the desired age. For adults, 0- to 12-hour-old flies were sexed and kept in vials with cornmeal medium for 5 days, and then snap-frozen.

extract_protocol:
RNA extraction: Total RNA was extracted from adult flies using Trizol (Invitrogen) according to the manufacturer's instructions and treated with DNaseI (Roche). Extraction from embryos, larvae and pupae was performed using the RNAdvance Tissue kit (Agencourt #A32649) according to the manufacturer's instructions, including DNaseI treatment. We systematically checked on a Bioanalyzer (Agilent) that the RNA was of very high quality. 5'monophosphate species --mainly ribosomes -- were depleted by TEX digest.

Library preparation & sequencing: Three multiplexed libraries were prepared: one for embryos (24 barcoded samples), one for larvae and pupae (10 samples), and one for adults (2 samples). The reverse-transcription was run in parallel for all samples destined to the same library, and the samples were pooled right after reverse-transcription. Our 5'-complete cDNA selection strategy relies on the combination of two orthogonal enrichment methods: reverse-transcriptase template-switching, and cap-trapping. The template-switching approach is based on the ability of reverse-transcriptase to add linker sequences to the ends of 5'-complete cDNAs -- preferentially if they are made from capped transcripts. Cap-trapping relies on the biotinylation of capped RNA molecules and specific pulldown of their associated 5'-complete cDNAs. The libraries were run on a DNA HS Bioanalyzer chip for quality control, quantified by quantitative PCR, and sequenced on one lane each on an Illumina GAIIx (adults, 2x76bp) or HiSeq (embryos, larvae and pupae, 2x101bp). Please see Supplemetary Material of the original publication for a detailed protocol.

treatment_protocol: None.

tag: strain

tag: tissue

value: whole body

tag: developmental stage

value: embryos 7-8h after egg laying


title: mRRBS 29

city: Cambridge

first: Kendell

last: Clement

state: MA

address: 7 Divinity Ave.

institute: Harvard University

library_source: genomic

data_processing:
Alignment: bisulfite-converted reads were aligned to the hg19 human genome using MAQ in bisulfite alignment mode using the following parameters: -D -s 0 -M c -e 100
Genome_build: hg19
Supplementary_files_format_and_content: Bed files contain the number of methylated and unmethylated reads for covered CpGs

molecule: genomic DNA

organism: Homo sapiens

source_name: mRRBS library 29

extract_protocol: mRRBS was performed according to the mRRBS protocol (Boyle et al. 2012)

tag: mrrbs library


title: mRRBS 34

source_name: mRRBS library 34


title: Dmel13_E13h

data_processing:
Sequencing reads alignment: The sequences corresponding to the library identification barcode (first 6 bases of read 1) and the reverse-transcription primer (first 15 bases of read 2) were trimmed prior to mapping. Trimmed reads were mapped with STAR, with parameters described in Tables S2-3 of the original publication. All uniquely mapping reads were kept. As a rescue strategy for multiply mapping reads, if all alignments for those reads started within an annotated transposon and overlapped the same gene annotation, the alignment starting in the closest transposon insertion was selected. All non-rescued multi-mappers were discarded.

Data analysis pipeline: PCR duplicates, defined as reads sharing the same alignment coordinates (start, end and splice sites), were removed from the individual datasets. To avoid over-collapsing, we took advantage of the fact that the long random sequence (15-mer) of our reverse-transcription primer often primes with mismatches. We used this sequence as a pseudo-random barcode allowing us to distinguish between true duplicates (same barcode) and independent identical inserts. All collapsed datasets were then combined prior to peak calling. The density of cDNA 5' ends across the genome was determined from this combined dataset, as well as the density of coverage by second (i.e., downstream) sequencing reads. Peaks were called by a sliding window algorithm that assesses the significance of local signal enrichment given a null distribution. Downstream read coverage in the same window was used to correct for local transcript abundance, by subtracting from the raw signal a pseudocount proportional to this coverage. After FDR correction, significant windows in close proximity to each other were merged into peaks, and those were trimmed at the edges down to the first base with signal. (Parameters: window width 15 bases, null distribution negative binomial with k=4, background weight 0.5, FDR 0.01, merging range 150 bases). These peaks were connected to annotated genes based on cDNA structure information. For each peak, if we could find at least 2 inserts having their 5' in the peak and overlapping an annotated exon of a gene, the peak was functionally linked to that gene. If a peak could potentially be linked to several genes, ties were broken by removing all links that were 5-fold weaker than the strongest one. For quantification, the signal for each peak and each timepoint was derived from the uncollapsed datasets, and normalized to dataset size (defined as the total number of reads attributed to any genic TSS). We built partial transcript models by running Cufflinks separately on the set of reads coming from each peak for each given dataset, and collapsing all transcripts for each peak using Cuffmerge. For a more detailed description of the analysis pipeline, please refer to the original publication.

*_+.bw file: bigWig coverage by cDNA 5' ends (+ strand).
*_-.bw file: bigWig coverage by cDNA 5' ends (- strand).
Genome Build:
Dmel13_E13h_-.bw: dm3
Dmel13_E13h_+.bw: dm3

value: embryos 12-13h after egg laying


title: mRRBS 26

source_name: mRRBS library 26


title: mRRBS 28

source_name: mRRBS library 28


title: Dmel07_E07h

data_processing:
Sequencing reads alignment: The sequences corresponding to the library identification barcode (first 6 bases of read 1) and the reverse-transcription primer (first 15 bases of read 2) were trimmed prior to mapping. Trimmed reads were mapped with STAR, with parameters described in Tables S2-3 of the original publication. All uniquely mapping reads were kept. As a rescue strategy for multiply mapping reads, if all alignments for those reads started within an annotated transposon and overlapped the same gene annotation, the alignment starting in the closest transposon insertion was selected. All non-rescued multi-mappers were discarded.

Data analysis pipeline: PCR duplicates, defined as reads sharing the same alignment coordinates (start, end and splice sites), were removed from the individual datasets. To avoid over-collapsing, we took advantage of the fact that the long random sequence (15-mer) of our reverse-transcription primer often primes with mismatches. We used this sequence as a pseudo-random barcode allowing us to distinguish between true duplicates (same barcode) and independent identical inserts. All collapsed datasets were then combined prior to peak calling. The density of cDNA 5' ends across the genome was determined from this combined dataset, as well as the density of coverage by second (i.e., downstream) sequencing reads. Peaks were called by a sliding window algorithm that assesses the significance of local signal enrichment given a null distribution. Downstream read coverage in the same window was used to correct for local transcript abundance, by subtracting from the raw signal a pseudocount proportional to this coverage. After FDR correction, significant windows in close proximity to each other were merged into peaks, and those were trimmed at the edges down to the first base with signal. (Parameters: window width 15 bases, null distribution negative binomial with k=4, background weight 0.5, FDR 0.01, merging range 150 bases). These peaks were connected to annotated genes based on cDNA structure information. For each peak, if we could find at least 2 inserts having their 5' in the peak and overlapping an annotated exon of a gene, the peak was functionally linked to that gene. If a peak could potentially be linked to several genes, ties were broken by removing all links that were 5-fold weaker than the strongest one. For quantification, the signal for each peak and each timepoint was derived from the uncollapsed datasets, and normalized to dataset size (defined as the total number of reads attributed to any genic TSS). We built partial transcript models by running Cufflinks separately on the set of reads coming from each peak for each given dataset, and collapsing all transcripts for each peak using Cuffmerge. For a more detailed description of the analysis pipeline, please refer to the original publication.

*_+.bw file: bigWig coverage by cDNA 5' ends (+ strand).
*_-.bw file: bigWig coverage by cDNA 5' ends (- strand).
Genome Build:
Dmel07_E07h_-.bw: dm3
Dmel07_E07h_+.bw: dm3

value: embryos 6-7h after egg laying


title: Dmel06_E06h

data_processing:
Sequencing reads alignment: The sequences corresponding to the library identification barcode (first 6 bases of read 1) and the reverse-transcription primer (first 15 bases of read 2) were trimmed prior to mapping. Trimmed reads were mapped with STAR, with parameters described in Tables S2-3 of the original publication. All uniquely mapping reads were kept. As a rescue strategy for multiply mapping reads, if all alignments for those reads started within an annotated transposon and overlapped the same gene annotation, the alignment starting in the closest transposon insertion was selected. All non-rescued multi-mappers were discarded.

Data analysis pipeline: PCR duplicates, defined as reads sharing the same alignment coordinates (start, end and splice sites), were removed from the individual datasets. To avoid over-collapsing, we took advantage of the fact that the long random sequence (15-mer) of our reverse-transcription primer often primes with mismatches. We used this sequence as a pseudo-random barcode allowing us to distinguish between true duplicates (same barcode) and independent identical inserts. All collapsed datasets were then combined prior to peak calling. The density of cDNA 5' ends across the genome was determined from this combined dataset, as well as the density of coverage by second (i.e., downstream) sequencing reads. Peaks were called by a sliding window algorithm that assesses the significance of local signal enrichment given a null distribution. Downstream read coverage in the same window was used to correct for local transcript abundance, by subtracting from the raw signal a pseudocount proportional to this coverage. After FDR correction, significant windows in close proximity to each other were merged into peaks, and those were trimmed at the edges down to the first base with signal. (Parameters: window width 15 bases, null distribution negative binomial with k=4, background weight 0.5, FDR 0.01, merging range 150 bases). These peaks were connected to annotated genes based on cDNA structure information. For each peak, if we could find at least 2 inserts having their 5' in the peak and overlapping an annotated exon of a gene, the peak was functionally linked to that gene. If a peak could potentially be linked to several genes, ties were broken by removing all links that were 5-fold weaker than the strongest one. For quantification, the signal for each peak and each timepoint was derived from the uncollapsed datasets, and normalized to dataset size (defined as the total number of reads attributed to any genic TSS). We built partial transcript models by running Cufflinks separately on the set of reads coming from each peak for each given dataset, and collapsing all transcripts for each peak using Cuffmerge. For a more detailed description of the analysis pipeline, please refer to the original publication.

*_+.bw file: bigWig coverage by cDNA 5' ends (+ strand).
*_-.bw file: bigWig coverage by cDNA 5' ends (- strand).
Genome Build:
Dmel06_E06h_-.bw: dm3
Dmel06_E06h_+.bw: dm3

value: embryos 5-6h after egg laying


title: Dmel12_E12h

data_processing:
Sequencing reads alignment: The sequences corresponding to the library identification barcode (first 6 bases of read 1) and the reverse-transcription primer (first 15 bases of read 2) were trimmed prior to mapping. Trimmed reads were mapped with STAR, with parameters described in Tables S2-3 of the original publication. All uniquely mapping reads were kept. As a rescue strategy for multiply mapping reads, if all alignments for those reads started within an annotated transposon and overlapped the same gene annotation, the alignment starting in the closest transposon insertion was selected. All non-rescued multi-mappers were discarded.

Data analysis pipeline: PCR duplicates, defined as reads sharing the same alignment coordinates (start, end and splice sites), were removed from the individual datasets. To avoid over-collapsing, we took advantage of the fact that the long random sequence (15-mer) of our reverse-transcription primer often primes with mismatches. We used this sequence as a pseudo-random barcode allowing us to distinguish between true duplicates (same barcode) and independent identical inserts. All collapsed datasets were then combined prior to peak calling. The density of cDNA 5' ends across the genome was determined from this combined dataset, as well as the density of coverage by second (i.e., downstream) sequencing reads. Peaks were called by a sliding window algorithm that assesses the significance of local signal enrichment given a null distribution. Downstream read coverage in the same window was used to correct for local transcript abundance, by subtracting from the raw signal a pseudocount proportional to this coverage. After FDR correction, significant windows in close proximity to each other were merged into peaks, and those were trimmed at the edges down to the first base with signal. (Parameters: window width 15 bases, null distribution negative binomial with k=4, background weight 0.5, FDR 0.01, merging range 150 bases). These peaks were connected to annotated genes based on cDNA structure information. For each peak, if we could find at least 2 inserts having their 5' in the peak and overlapping an annotated exon of a gene, the peak was functionally linked to that gene. If a peak could potentially be linked to several genes, ties were broken by removing all links that were 5-fold weaker than the strongest one. For quantification, the signal for each peak and each timepoint was derived from the uncollapsed datasets, and normalized to dataset size (defined as the total number of reads attributed to any genic TSS). We built partial transcript models by running Cufflinks separately on the set of reads coming from each peak for each given dataset, and collapsing all transcripts for each peak using Cuffmerge. For a more detailed description of the analysis pipeline, please refer to the original publication.

*_+.bw file: bigWig coverage by cDNA 5' ends (+ strand).
*_-.bw file: bigWig coverage by cDNA 5' ends (- strand).
Genome Build:
Dmel12_E12h_-.bw: dm3
Dmel12_E12h_+.bw: dm3

value: embryos 11-12h after egg laying


title: mRRBS 31

source_name: mRRBS library 31


title: Dmel09_E09h

data_processing:
Sequencing reads alignment: The sequences corresponding to the library identification barcode (first 6 bases of read 1) and the reverse-transcription primer (first 15 bases of read 2) were trimmed prior to mapping. Trimmed reads were mapped with STAR, with parameters described in Tables S2-3 of the original publication. All uniquely mapping reads were kept. As a rescue strategy for multiply mapping reads, if all alignments for those reads started within an annotated transposon and overlapped the same gene annotation, the alignment starting in the closest transposon insertion was selected. All non-rescued multi-mappers were discarded.

Data analysis pipeline: PCR duplicates, defined as reads sharing the same alignment coordinates (start, end and splice sites), were removed from the individual datasets. To avoid over-collapsing, we took advantage of the fact that the long random sequence (15-mer) of our reverse-transcription primer often primes with mismatches. We used this sequence as a pseudo-random barcode allowing us to distinguish between true duplicates (same barcode) and independent identical inserts. All collapsed datasets were then combined prior to peak calling. The density of cDNA 5' ends across the genome was determined from this combined dataset, as well as the density of coverage by second (i.e., downstream) sequencing reads. Peaks were called by a sliding window algorithm that assesses the significance of local signal enrichment given a null distribution. Downstream read coverage in the same window was used to correct for local transcript abundance, by subtracting from the raw signal a pseudocount proportional to this coverage. After FDR correction, significant windows in close proximity to each other were merged into peaks, and those were trimmed at the edges down to the first base with signal. (Parameters: window width 15 bases, null distribution negative binomial with k=4, background weight 0.5, FDR 0.01, merging range 150 bases). These peaks were connected to annotated genes based on cDNA structure information. For each peak, if we could find at least 2 inserts having their 5' in the peak and overlapping an annotated exon of a gene, the peak was functionally linked to that gene. If a peak could potentially be linked to several genes, ties were broken by removing all links that were 5-fold weaker than the strongest one. For quantification, the signal for each peak and each timepoint was derived from the uncollapsed datasets, and normalized to dataset size (defined as the total number of reads attributed to any genic TSS). We built partial transcript models by running Cufflinks separately on the set of reads coming from each peak for each given dataset, and collapsing all transcripts for each peak using Cuffmerge. For a more detailed description of the analysis pipeline, please refer to the original publication.

*_+.bw file: bigWig coverage by cDNA 5' ends (+ strand).
*_-.bw file: bigWig coverage by cDNA 5' ends (- strand).
Genome Build:
Dmel09_E09h_-.bw: dm3
Dmel09_E09h_+.bw: dm3

value: embryos 8-9h after egg laying


title: mRRBS 30

source_name: mRRBS library 30


title: Dmel04_E04h

data_processing:
Sequencing reads alignment: The sequences corresponding to the library identification barcode (first 6 bases of read 1) and the reverse-transcription primer (first 15 bases of read 2) were trimmed prior to mapping. Trimmed reads were mapped with STAR, with parameters described in Tables S2-3 of the original publication. All uniquely mapping reads were kept. As a rescue strategy for multiply mapping reads, if all alignments for those reads started within an annotated transposon and overlapped the same gene annotation, the alignment starting in the closest transposon insertion was selected. All non-rescued multi-mappers were discarded.

Data analysis pipeline: PCR duplicates, defined as reads sharing the same alignment coordinates (start, end and splice sites), were removed from the individual datasets. To avoid over-collapsing, we took advantage of the fact that the long random sequence (15-mer) of our reverse-transcription primer often primes with mismatches. We used this sequence as a pseudo-random barcode allowing us to distinguish between true duplicates (same barcode) and independent identical inserts. All collapsed datasets were then combined prior to peak calling. The density of cDNA 5' ends across the genome was determined from this combined dataset, as well as the density of coverage by second (i.e., downstream) sequencing reads. Peaks were called by a sliding window algorithm that assesses the significance of local signal enrichment given a null distribution. Downstream read coverage in the same window was used to correct for local transcript abundance, by subtracting from the raw signal a pseudocount proportional to this coverage. After FDR correction, significant windows in close proximity to each other were merged into peaks, and those were trimmed at the edges down to the first base with signal. (Parameters: window width 15 bases, null distribution negative binomial with k=4, background weight 0.5, FDR 0.01, merging range 150 bases). These peaks were connected to annotated genes based on cDNA structure information. For each peak, if we could find at least 2 inserts having their 5' in the peak and overlapping an annotated exon of a gene, the peak was functionally linked to that gene. If a peak could potentially be linked to several genes, ties were broken by removing all links that were 5-fold weaker than the strongest one. For quantification, the signal for each peak and each timepoint was derived from the uncollapsed datasets, and normalized to dataset size (defined as the total number of reads attributed to any genic TSS). We built partial transcript models by running Cufflinks separately on the set of reads coming from each peak for each given dataset, and collapsing all transcripts for each peak using Cuffmerge. For a more detailed description of the analysis pipeline, please refer to the original publication.

*_+.bw file: bigWig coverage by cDNA 5' ends (+ strand).
*_-.bw file: bigWig coverage by cDNA 5' ends (- strand).
Genome Build:
Dmel04_E04h_-.bw: dm3
Dmel04_E04h_+.bw: dm3

value: embryos 3-4h after egg laying


title: Dmel05_E05h

data_processing:
Sequencing reads alignment: The sequences corresponding to the library identification barcode (first 6 bases of read 1) and the reverse-transcription primer (first 15 bases of read 2) were trimmed prior to mapping. Trimmed reads were mapped with STAR, with parameters described in Tables S2-3 of the original publication. All uniquely mapping reads were kept. As a rescue strategy for multiply mapping reads, if all alignments for those reads started within an annotated transposon and overlapped the same gene annotation, the alignment starting in the closest transposon insertion was selected. All non-rescued multi-mappers were discarded.

Data analysis pipeline: PCR duplicates, defined as reads sharing the same alignment coordinates (start, end and splice sites), were removed from the individual datasets. To avoid over-collapsing, we took advantage of the fact that the long random sequence (15-mer) of our reverse-transcription primer often primes with mismatches. We used this sequence as a pseudo-random barcode allowing us to distinguish between true duplicates (same barcode) and independent identical inserts. All collapsed datasets were then combined prior to peak calling. The density of cDNA 5' ends across the genome was determined from this combined dataset, as well as the density of coverage by second (i.e., downstream) sequencing reads. Peaks were called by a sliding window algorithm that assesses the significance of local signal enrichment given a null distribution. Downstream read coverage in the same window was used to correct for local transcript abundance, by subtracting from the raw signal a pseudocount proportional to this coverage. After FDR correction, significant windows in close proximity to each other were merged into peaks, and those were trimmed at the edges down to the first base with signal. (Parameters: window width 15 bases, null distribution negative binomial with k=4, background weight 0.5, FDR 0.01, merging range 150 bases). These peaks were connected to annotated genes based on cDNA structure information. For each peak, if we could find at least 2 inserts having their 5' in the peak and overlapping an annotated exon of a gene, the peak was functionally linked to that gene. If a peak could potentially be linked to several genes, ties were broken by removing all links that were 5-fold weaker than the strongest one. For quantification, the signal for each peak and each timepoint was derived from the uncollapsed datasets, and normalized to dataset size (defined as the total number of reads attributed to any genic TSS). We built partial transcript models by running Cufflinks separately on the set of reads coming from each peak for each given dataset, and collapsing all transcripts for each peak using Cuffmerge. For a more detailed description of the analysis pipeline, please refer to the original publication.

*_+.bw file: bigWig coverage by cDNA 5' ends (+ strand).
*_-.bw file: bigWig coverage by cDNA 5' ends (- strand).
Genome Build:
Dmel05_E05h_-.bw: dm3
Dmel05_E05h_+.bw: dm3

value: embryos 4-5h after egg laying


title: mRRBS 27

source_name: mRRBS library 27


title: mRRBS 25

source_name: mRRBS library 25


title: H2A.Zac_SKNO1_ChIPseq

city: Nijmegen

last: Stunnenberg

address: Geert Grooteplein 28

department: Department of Molecular Biology (274)

institute: Radboud University

description:
SS20
Chromatin IP against H2A.Z.ac (Abcam).

data_processing: Reads (32 bp) were aligned to the human (Homo sapiens) reference genome (hg18, NCBI Build 36) using the GERALD (Eland) application from the Casava 1.7 tool provided by Illumina. Sequenced tags were extended back to reconstitute the restriction enzyme sites. Those reads not starting with the respective enzyme site were filtered out. The Fisher test was applied to call accessible regions. All of the bed files contain hg18 chromosomal coordinates of the tags (first three columns are chromosome, start, end). For NA-seq, wiggle files were generated using Fseq (Boyle et al. 2008). Wiggle files for ChIP-seq data were produced as before (Martens et al. 2010).

source_name: SKNO-1 cells (leukemic), untreated, H2A.Zac ChIP-Seq

growth_protocol: Leukemic cells NB4 and SKNO-1 were cultured in normal cell culture containing 10% serum.

extract_protocol: For NA-seq, isolated nuclei from different AML leukemic cells were treated for a very short time with Nlalll or Hpall restriction enzymes. Purified DNA was further shortened using Sau3A1 enzyme (Gargiulo et al. 2009 (PMID 19289091)). Size-selected library was sequenced from the respective restriction enzyme side. ChIP-seq was performed as before (Martens et al. 2010 (PMID 20159609)).

treatment_protocol: Leukemic cells (NB4) were treated with ATRA for 48 hours.

tag: cell line

tag: cell type

value: leukemic cells

tag: treatment

tag: chip antibody

tag: antibody vendor

value: Abcam

tag: antibody catalog #

tag: antibody lot


title: mRRBS 23

source_name: mRRBS library 23


title: HpaII_SKNO1_NAseq

description:
SS18
Restriction enzyme treatment of nuclei with HpaII.
NA-Seq

source_name: SKNO-1 cells (leukemic), untreated, HpaII NA-Seq

tag: restriction enzyme

value: HpaII (R0171M)


title: Dmel03_E03h

data_processing:
Sequencing reads alignment: The sequences corresponding to the library identification barcode (first 6 bases of read 1) and the reverse-transcription primer (first 15 bases of read 2) were trimmed prior to mapping. Trimmed reads were mapped with STAR, with parameters described in Tables S2-3 of the original publication. All uniquely mapping reads were kept. As a rescue strategy for multiply mapping reads, if all alignments for those reads started within an annotated transposon and overlapped the same gene annotation, the alignment starting in the closest transposon insertion was selected. All non-rescued multi-mappers were discarded.

Data analysis pipeline: PCR duplicates, defined as reads sharing the same alignment coordinates (start, end and splice sites), were removed from the individual datasets. To avoid over-collapsing, we took advantage of the fact that the long random sequence (15-mer) of our reverse-transcription primer often primes with mismatches. We used this sequence as a pseudo-random barcode allowing us to distinguish between true duplicates (same barcode) and independent identical inserts. All collapsed datasets were then combined prior to peak calling. The density of cDNA 5' ends across the genome was determined from this combined dataset, as well as the density of coverage by second (i.e., downstream) sequencing reads. Peaks were called by a sliding window algorithm that assesses the significance of local signal enrichment given a null distribution. Downstream read coverage in the same window was used to correct for local transcript abundance, by subtracting from the raw signal a pseudocount proportional to this coverage. After FDR correction, significant windows in close proximity to each other were merged into peaks, and those were trimmed at the edges down to the first base with signal. (Parameters: window width 15 bases, null distribution negative binomial with k=4, background weight 0.5, FDR 0.01, merging range 150 bases). These peaks were connected to annotated genes based on cDNA structure information. For each peak, if we could find at least 2 inserts having their 5' in the peak and overlapping an annotated exon of a gene, the peak was functionally linked to that gene. If a peak could potentially be linked to several genes, ties were broken by removing all links that were 5-fold weaker than the strongest one. For quantification, the signal for each peak and each timepoint was derived from the uncollapsed datasets, and normalized to dataset size (defined as the total number of reads attributed to any genic TSS). We built partial transcript models by running Cufflinks separately on the set of reads coming from each peak for each given dataset, and collapsing all transcripts for each peak using Cuffmerge. For a more detailed description of the analysis pipeline, please refer to the original publication.

*_+.bw file: bigWig coverage by cDNA 5' ends (+ strand).
*_-.bw file: bigWig coverage by cDNA 5' ends (- strand).
Genome Build:
Dmel03_E03h_-.bw: dm3
Dmel03_E03h_+.bw: dm3

value: embryos 2-3h after egg laying


title: p300_SKNO1_ChIPseq

description:
SS21
Chromatin IP against p300 (Santa Cruz Biotechnology).

source_name: SKNO-1 cells (leukemic), untreated, p300 ChIP-Seq

value: Santa Cruz Biotechnology


title: mRRBS 24

source_name: mRRBS library 24


title: mRRBS 22

source_name: mRRBS library 22


title: Dmel01_E01h

data_processing:
Sequencing reads alignment: The sequences corresponding to the library identification barcode (first 6 bases of read 1) and the reverse-transcription primer (first 15 bases of read 2) were trimmed prior to mapping. Trimmed reads were mapped with STAR, with parameters described in Tables S2-3 of the original publication. All uniquely mapping reads were kept. As a rescue strategy for multiply mapping reads, if all alignments for those reads started within an annotated transposon and overlapped the same gene annotation, the alignment starting in the closest transposon insertion was selected. All non-rescued multi-mappers were discarded.

Data analysis pipeline: PCR duplicates, defined as reads sharing the same alignment coordinates (start, end and splice sites), were removed from the individual datasets. To avoid over-collapsing, we took advantage of the fact that the long random sequence (15-mer) of our reverse-transcription primer often primes with mismatches. We used this sequence as a pseudo-random barcode allowing us to distinguish between true duplicates (same barcode) and independent identical inserts. All collapsed datasets were then combined prior to peak calling. The density of cDNA 5' ends across the genome was determined from this combined dataset, as well as the density of coverage by second (i.e., downstream) sequencing reads. Peaks were called by a sliding window algorithm that assesses the significance of local signal enrichment given a null distribution. Downstream read coverage in the same window was used to correct for local transcript abundance, by subtracting from the raw signal a pseudocount proportional to this coverage. After FDR correction, significant windows in close proximity to each other were merged into peaks, and those were trimmed at the edges down to the first base with signal. (Parameters: window width 15 bases, null distribution negative binomial with k=4, background weight 0.5, FDR 0.01, merging range 150 bases). These peaks were connected to annotated genes based on cDNA structure information. For each peak, if we could find at least 2 inserts having their 5' in the peak and overlapping an annotated exon of a gene, the peak was functionally linked to that gene. If a peak could potentially be linked to several genes, ties were broken by removing all links that were 5-fold weaker than the strongest one. For quantification, the signal for each peak and each timepoint was derived from the uncollapsed datasets, and normalized to dataset size (defined as the total number of reads attributed to any genic TSS). We built partial transcript models by running Cufflinks separately on the set of reads coming from each peak for each given dataset, and collapsing all transcripts for each peak using Cuffmerge. For a more detailed description of the analysis pipeline, please refer to the original publication.

*_+.bw file: bigWig coverage by cDNA 5' ends (+ strand).
*_-.bw file: bigWig coverage by cDNA 5' ends (- strand).
Genome Build:
Dmel01_E01h_-.bw: dm3
Dmel01_E01h_+.bw: dm3

value: embryos 0-1h after egg laying


title: Dmel02_E02h

data_processing:
Sequencing reads alignment: The sequences corresponding to the library identification barcode (first 6 bases of read 1) and the reverse-transcription primer (first 15 bases of read 2) were trimmed prior to mapping. Trimmed reads were mapped with STAR, with parameters described in Tables S2-3 of the original publication. All uniquely mapping reads were kept. As a rescue strategy for multiply mapping reads, if all alignments for those reads started within an annotated transposon and overlapped the same gene annotation, the alignment starting in the closest transposon insertion was selected. All non-rescued multi-mappers were discarded.

Data analysis pipeline: PCR duplicates, defined as reads sharing the same alignment coordinates (start, end and splice sites), were removed from the individual datasets. To avoid over-collapsing, we took advantage of the fact that the long random sequence (15-mer) of our reverse-transcription primer often primes with mismatches. We used this sequence as a pseudo-random barcode allowing us to distinguish between true duplicates (same barcode) and independent identical inserts. All collapsed datasets were then combined prior to peak calling. The density of cDNA 5' ends across the genome was determined from this combined dataset, as well as the density of coverage by second (i.e., downstream) sequencing reads. Peaks were called by a sliding window algorithm that assesses the significance of local signal enrichment given a null distribution. Downstream read coverage in the same window was used to correct for local transcript abundance, by subtracting from the raw signal a pseudocount proportional to this coverage. After FDR correction, significant windows in close proximity to each other were merged into peaks, and those were trimmed at the edges down to the first base with signal. (Parameters: window width 15 bases, null distribution negative binomial with k=4, background weight 0.5, FDR 0.01, merging range 150 bases). These peaks were connected to annotated genes based on cDNA structure information. For each peak, if we could find at least 2 inserts having their 5' in the peak and overlapping an annotated exon of a gene, the peak was functionally linked to that gene. If a peak could potentially be linked to several genes, ties were broken by removing all links that were 5-fold weaker than the strongest one. For quantification, the signal for each peak and each timepoint was derived from the uncollapsed datasets, and normalized to dataset size (defined as the total number of reads attributed to any genic TSS). We built partial transcript models by running Cufflinks separately on the set of reads coming from each peak for each given dataset, and collapsing all transcripts for each peak using Cuffmerge. For a more detailed description of the analysis pipeline, please refer to the original publication.

*_+.bw file: bigWig coverage by cDNA 5' ends (+ strand).
*_-.bw file: bigWig coverage by cDNA 5' ends (- strand).
Genome Build:
Dmel02_E02h_-.bw: dm3
Dmel02_E02h_+.bw: dm3

value: y; cn bw sp

value: embryos 1-2h after egg laying


title: NlaIII_SKNO1_genomic_control_NAseq

description:
SS17
Restriction enzyme treatment of genomic DNA with NlaIII.
NA-Seq

source_name: SKNO-1 cells (leukemic), untreated, NlaIII NA-Seq

value: NlaIII (R0125L)


title: mRRBS 21

source_name: mRRBS library 21


title: mRRBS 19

source_name: mRRBS library 19


title: NlaIII_AML_patient1_NAseq

description:
SS15
Restriction enzyme treatment of nuclei with NlaIII.
NA-Seq

source_name: AML patient cells (leukemic), untreated, NlaIII NA-Seq

tag: disease state

value: acute myeloid leukemia (AML)


title: HpaII_SKNO1_genomic_control_NAseq

description:
SS19
Restriction enzyme treatment of genomic DNA with HpaII.
NA-Seq


title: mRRBS 18

source_name: mRRBS library 18


title: mRRBS 17

source_name: mRRBS library 17


title: H4K20me3_untreated_NB4_ChIPseq

description:
SS14
Chromatin IP against H4K20me3 (Upstate).

source_name: NB4 cells (leukemic), untreated, H4K20me3 ChIP-Seq

value: Upstate


title: H3K4me1_untreated_NB4_ChIPseq

description:
SS12
Chromatin IP against H3K4me1 (Diagenode).

source_name: NB4 cells (leukemic), untreated, H3K4me1 ChIP-Seq

value: Diagenode


title: H3K4me3_untreated_NB4_ChIPseq

description:
SS13
Chromatin IP against H3K4me3 (Diagenode).

source_name: NB4 cells (leukemic), untreated, H3K4me3 ChIP-Seq


title: degradome library from Solanum lycopersicum leaf

city: Norwich

first: Simon

last: Moxon

state: Norfolk

address: University of East Anglia

country: United Kingdom

institute: University of East Anglia

data_processing: Removed adaptors. Sequences without adapters were discarded. Mapped sequences to tomato Unigenes version Tomato_200607_build_2.seq from http://solgenomics.net.

organism: Solanum lycopersicum

source_name: leaf

growth_protocol: Plants were grown in pots with soil in a growth chamber having a photoperiod of 16/8h day/night, a thermoperiod 23/ 18ºC  day/night and humidity of 40%.

extract_protocol: Solanum lycopersicon total RNA from different tissues was extracted by phenol/chloroform. Briefly, the homogenized plant materials were resuspended in extraction buffer (0.1 M glycine-NaOH,pH 9.0, 100 mM NaCl, 10 mM EDTA, 2% SDS, and 1% sodiumlauroylsarcosine) and mixed with an equal volume of phenol. The aqueous phase was treated with equal volumes of phenol and chloroform, precipitated with ethanol, and resuspended in sterile water. Poly(A) RNA was isolated from total RNA by MicroPoly(A) Purist Kit (Ambion, Cat # AM1919) and were ligated to 5'-adapter that includes an MmeI recognition site.

value: leaf


title: H2A.Zac_untreated_NB4_ChIPseq

description:
SS10
Chromatin IP against H2A.Z.ac (Abcam).

source_name: NB4 cells (leukemic), untreated, H2A.Zac ChIP-Seq


title: mRRBS 15

source_name: mRRBS library 15


title: mRRBS 14

source_name: mRRBS library 14


title: p300_untreated_NB4_ChIPseq

description:
SS11
Chromatin IP against p300 (Santa Cruz Biotechnology).

source_name: NB4 cells (leukemic), untreated, p300 ChIP-Seq


title: mRRBS 13

source_name: mRRBS library 13


title: degradome library from Solanum lycopersicum flower

source_name: flower

value: flower


title: H2A.Z_untreated_NB4_ChIPseq

description:
SS9
Chromatin IP against H2A.Z (Abcam).

source_name: NB4 cells (leukemic), untreated, H2A.Z ChIP-Seq


title: mRRBS 11

source_name: mRRBS library 11


title: mRRBS 10

source_name: mRRBS library 10


title: mRRBS 12

source_name: mRRBS library 12


title: mRRBS 9

source_name: mRRBS library 9


title: NlaIII_NB4_genomic_control_NAseq

description:
SS5
Restriction enzyme treatment of genomic DNA with NlaIII.
NA-Seq

source_name: NB4 cells (leukemic), untreated, NlaIII NA-Seq


title: HpaII_NB4_genomic_control_NAseq

description:
SS6
Restriction enzyme treatment of genomic DNA with HpaII.
NA-Seq

source_name: NB4 cells (leukemic), untreated, HpaII NA-Seq


title: NlaIII_untreated_APL_NAseq

description:
SS7
Restriction enzyme treatment of nuclei with NlaIII.
NA-Seq

source_name: APL patient cells (leukemic), untreated, NlaIII NA-Seq

value: acute promyelocytic leukemia (APL)


title: HpaII_ATRA_NB4_duplo_NAseq

description:
SS4b
Restriction enzyme treatment of nuclei with HpaII.
NA-Seq

source_name: NB4 cells (leukemic), ATRA, HpaII NA-Seq

value: 48 hrs all-trans retinoic acid (ATRA)


description: degradome library from Solanum lycopersicum green fruit 10-15mm diameter

source_name: green fruit 10-15mm

value: green fruit


title: HpaII_ATRA_NB4_first_NAseq

description:
SS4a
Restriction enzyme treatment of nuclei with HpaII.
NA-Seq

value: leukemic cells


title: HpaII_untreated_NB4_duplo_NAseq

description:
SS3b
Restriction enzyme treatment of nuclei with HpaII.
NA-Seq


title: mRRBS 8

source_name: mRRBS library 8


title: mRRBS 6

source_name: mRRBS library 6


title: mRRBS 7

source_name: mRRBS library 7


title: NlaIII_ATRA_NB4_first_NAseq

description:
SS2a
Restriction enzyme treatment of nuclei with NlaIII.
NA-Seq

source_name: NB4 cells (leukemic), ATRA, NlaIII NA-Seq


title: mRRBS 5

source_name: mRRBS library 5


title: NlaIII_ATRA_NB4_duplo_NAseq

description:
SS2b
Restriction enzyme treatment of nuclei with NlaIII.
NA-Seq


title: mRRBS 20

source_name: mRRBS library 20


title: NlaIII_AML_patient2_NAseq

description:
SS8
Restriction enzyme treatment of nuclei with NlaIII.
NA-Seq

source_name: AML patient cells (leukemic), untreated, NlaIII NA-Seq


title: mRRBS 4

source_name: mRRBS library 4


title: HpaII_untreated_NB4_first_NAseq

description:
SS3a
Restriction enzyme treatment of nuclei with HpaII.
NA-Seq


title: mRRBS 3

source_name: mRRBS library 3


title: NlaIII_untreated_NB4_duplo_NAseq

description:
SS1b
Restriction enzyme treatment of nuclei with NlaIII.
NA-Seq


title: mRRBS 2

source_name: mRRBS library 2


title: NlaIII_untreated_NB4_first_NAseq

description:
SS1a
Restriction enzyme treatment of nuclei with NlaIII.
NA-Seq


title: mRRBS 16

source_name: mRRBS library 16


title: HUES9

status: Public on Aug 28 2012

city: La Jolla

first: Kun

last: Zhang

state: CA

address: 9500 Gilman Dr Mailcode: 0412

department: Bioengineering

institute: University of California, San Diego

data_processing:
Illumina OLB 1.9.3 (for HiSeq run) and OLB 1.8.0 (for GA run) were used for base-calling
Adaptor sequences (27bp from 5' end) were trimmed from bisulfite reads prior to mapping
Bisulfite reads were mapped using the protocol from Diep et al, Nature Methods 2012. Briefly, all 'C's were replaced by 'T's in reads and mapped to a reference with all 'C's converted to 'T's. After mapping, the modified reads were replaced by the original reads then pileup files were generated using SAMTOOLS
Methylation fractions were calculated as the number of cytosines over the total thymines and cytosines for CpGs where at least 90% of base called were thymines and cytosines
CpG sites with at least 10 depth of coverage were reported in final processed file (BED format)
Genome_build: hg18
Supplementary_files_format_and_content: BED formated (tab-delimited) text files include chromosome position and methylation fraction value for each CpG in each sample.

extract_protocol:
Bisulfite padlock probes capture and sequencing (Diep et al, Nature Methods 2012)
Genomic DNA was extracted using the ALLPrep DNA/RNA Mini kit (Qiagen) and QIAamp DNA Micro kit (Qiagen). The bisulfite conversion and capture reactions were carried out on 1-1.2  g of each sample using previously established protocols (13, 28, 29). Briefly, DNA was bisulfite converted using the EZ-96Methylation Gold Kit  (Zymo Research). About 200-300 ng of converted gDNA from each sample was captured using prepared padlock probe oligonucleotides, resulting in a circular DNA library of targeted CpG sites. Each DNA library was amplified using limited cycle PCR and size-selected on polyacrylamide gels before sequencing.

treatment_protocol: Cells were treated with mTeSR1 and Activin-A or BMP4 or media only for 5 days then collected with TrypLE (Invitrogen).

value: Human embryonic stem cells

tag: laboratory of generation

tag: reprogramming experiment

tag: reprogramming factors

tag: passage number


title: hESC-H1


title: hESC-H9


title: HUES2


title: MSChiPS8

value: Human mesenchymal stem cells

value: JCBI/CMRB

value: Experiment 1

value: Oct4, Sox2, Klf4 and cMyc


title: HUES3


title: HUES8


title: MSC_mesenchymal_stem_cells


title: MSChiPS4


title: IMR90_human_fibroblasts

value: Human fibroblasts


title: HFF.XF_human_fibroblasts


title: hNSC_human_neural_stem_cells

value: Human neural stem cells


title: FhiPS4F7


title: mRRBS 1

source_name: mRRBS library 1


title: HUVhiPS4F1

value: Human umbilical vein endothelial cells

value: JCBI/SALK


title: ASThiPS4F4

value: Human astrocytes


title: NSChiPS2F

value: HRS;MPIMB

value: Oct4 and Klf4


title: FhiPS4F2


title: ASThiPS4F5


title: HUVhiPS4F3

value: Experiment 2


title: ASThiPS4F3


title: HUVEC_human_umbilical_vein_endothelial_cells


title: ASThiPS4F1


title: ASThiPS4F2


title: HUVhiPS4F6


title: HUES6


title: K.MMTA_human_foreskin_keratinocytes

value: Human keratinocytes


title: FhiPS4F5


title: KhiPS4FA


title: KhiPS4F8


title: Astrocytes_human_astrocytes


title: H1-No_treatment-p54

data_processing:
Illumina OLB 1.9.3 (for HiSeq run)  was used for base-calling
Adaptor sequences (27bp from 5' end) were trimmed from bisulfite reads prior to mapping
Bisulfite reads were mapped using the protocol from Diep et al, Nature Methods 2012. Briefly, all 'C's were replaced by 'T's in reads and mapped to a reference with all 'C's converted to 'T's. After mapping, the modified reads were replaced by the original reads then pileup files were generated using SAMTOOLS
Methylation fractions were calculated as the number of cytosines over the total thymines and cytosines for CpGs where at least 90% of base called were thymines and cytosines
CpG sites with at least 10 depth of coverage were reported in final processed file (BED format)
Genome_build: hg18
Supplementary_files_format_and_content: BED formated (tab-delimited) text files include chromosome position and methylation fraction value for each CpG in each sample.


title: H9-No_treatment-p43


title: H1-No_treatment-p47


title: mRRBS 90

source_name: mRRBS library 90


title: Keratinocytes

source_name: Keratinocytes


title: HUES3-No_treatment-p36


title: HUES9-No_treatment-p39


title: HUES6-No_treatment-p39


title: H9-BMP4-p43


title: H1-BMP4-p47


title: HUES9-BMP4-p39


title: HUES3-BMP4-p36


title: HUES6-BMP4-p39


title: HUES9-ACTIVIN-p39

value: ACTIVIN


title: HUES6-ACTIVIN-p39


title: HUES3-ACTIVIN-p36


title: H1-ACTIVIN-p54


title: H9-ACTIVIN-p43


title: HUVhiPS4F3-No_treatment-p19


title: NlaIII_SKNO1_NAseq

description:
SS16
Restriction enzyme treatment of nuclei with NlaIII.
NA-Seq

source_name: SKNO-1 cells (leukemic), untreated, NlaIII NA-Seq

value: NlaIII (R0125L)


title: HUVhiPS4F6-No_treatment-p23


title: ASThiPS4F4-No_treatment-p36


title: ASThiPS4F5-No_treatment-p12


title: HUVhiPS4F3-BMP4-p20


title: HUVhiPS4F1-BMP4-p20


title: ASThiPS4F5-No_treatment-p16


title: HUVhiPS4F1-No_treatment-p19


title: HUVhiPS4F6-BMP4-p23


title: ASThiPS4F4-BMP4-p36


title: ASThiPS4F5-BMP4-p12


title: HUVhiPS4F3-ACTIVIN-p20


title: HUVhiPS4F1-ACTIVIN-p20


title: ASThiPS4F4-ACTIVIN-p36


title: ASThiPS4F5-ACTIVIN-p16


title: PGP1F_human_adult_fibroblasts

value: GC/HMS


title: Ribo-depleted_RNA

first: Richard

middle: A

last: Young

address: 9 Cambridge Center

institute: Whitehead Institute for Biomedical Research

data_processing:
Basecalls and quality scores were generated using Standard Illumina HiSeq Data Processing Pipeline.
All sequenced reads were aligned to the mouse genome (NCBI 37, mm9) using TopHat version V1.2.0 (Trapnell et al., 2009).
Final expression counts were calculated as Reads Per KB per Million mapped read (RPKM) (Mortazavi et al., 2008)
Genome_build: mm9
Supplementary_files_format_and_content: bedgraph

organism: Mus musculus

source_name: mESC_Ribo-depleted RNA

growth_protocol: V6.5 murine embryonic stem cells (mESCs) were grown on irradiated murine embryonic fibroblasts (MEFs) unless otherwise stated. Cells were grown under standard mESC conditions as described previously (Boyer et al., 2005).

extract_protocol: Total RNA was purified using mirVana miRNA isolation kit (Life technologies) following the manufacturer instructions. After DNAse I digestion, polyadenylated RNA and total RNA depleted of ribosomal content was used for preparation of two independent RNA-seq libraries. Polyadenylated and total RNA depleted of rRNA was fragmented with divalent cations under elevated temperature. First strand cDNA synthesis was performed with random hexamers and Superscript III reverse transcriptase. Second strand cDNA synthesis was performed using RNAseH and DNA Polymerase I. In the second-strand synthesis reaction dTTP was replaced with dUTP. Following cDNA synthesis, the double stranded products were end repaired, a single "A" base was added, and Illumina PE adaptors were ligated onto the cDNA products. The ligation products with an average size of 300 bp were purified using agarose gel electrophoresis. Following gel purification, one strand of cDNA was selectively destroyed during incubation of purified double-stranded DNA products with HK-UNG (Epicentre). The adapter ligated single-stranded cDNA was then amplified with 15 cycles of PCR and PCR products were purified using gel electrophoresis.

value: embryonic stem cells

tag: molecule type

value: total RNA depleted of ribosomal content


title: MutuI Input Control (Matched to MNase-seq) Rep1

city: New York

first: Aaron

last: Arvey

institute: Memorial Sloan-Kettering Cancer Center

description: MutuI IgG Control (Matched to MNase-seq)

data_processing:
Base calling was performed using Illumina software pipeline (Genome Analyzer)
Align to the human genome (hg19 UCSC) using bowtie (version 0.12.7) allowing 1 mismatch and reporting only uniquely aligning hits.  Unmapped hits were saved into a separate file and reads with more than one alignment in the human genome were discarded (--max /dev/null)
Align to the EBV genome (NC_007605) was performed on the unmapped reads that had no hits in the human genome.  Bowtie version and parameters as human genome.
Peak calling was perfomed on the viral genome using a cutoff of 30 reads per million
Genome_build: Viral genome: March 2010 NC_007605
Supplementary_files_format_and_content: Bowtie-generated WIG files

organism: human gammaherpesvirus 4

source_name: MutuI

extract_protocol:
Micrococcal nuclease I (MNase I) resistant DNA was extracted by phenol-chloroform and ethanol precipitation. The 150 bp mononucleosomal DNA was isolated from 1.5% agarose gel and purified by the QIAquick Gel extraction kit (QIAGEN) according to the manufacturer's protocol.
ChIP-Seq experiments were performed with 2 × 106 Raji cells per IP with either EBNA1 monoclonal antibody or control mouse IgG. Quantification of precipitated DNA was determined using real-time PCR and the standard curve method for absolute quantitation (ABI 7000 Real-Time PCR System).
For both experiments, purified DNA was then subject to sequencing according to the manufacturer's recommendations (Illumina).

treatment_protocol: Nuclei were isolated from 50M Mutu and Mutu-LCL cells with a Dounce homogenizer and treated with MNase (500 U/ml).

tag: assay reagents

tag: ebv-positive, human cell line

value: Mutu


title: MutuI Input Control (Matched to MNase-seq) Rep2


title: Raji IgG Control (Matched to EBNA1 ChIP)

source_name: Raji

value: IgG antibody [Santa Cruz, lot# I2310, catalog# sc-2027]

value: Raji


title: Raji EBNA1 ChIP

description: Raji EBNA1 ChIP

value: EBNA1 antibody [a custom polyclonal antibody made for us by the Pocono Rabbit Farm and Laboratory Inc, clone# 25139]


title: MutuI MNase-seq Rep1

description: MutuI MNase-seq

value: MNase [New England Biolabs, lot#0451204, catalog#R0525M]


title: PGP1-hiPS

value: Human fibroblasts

value: Experiment 1

value: Oct4, Sox2, Klf4 and cMyc


title: GM17253_R1

status: Public on Aug 27 2012

city: Berkeley

first: Oh Kyu

last: Yoon

address: Stanley Hall rm 378

department: Molecular & Cell Biology

institute: UC Berkeley

description: Polyadenylated transcript 3' ends

library_source: non-genomic

data_processing: Consecutive T's from beginning of reads were removed and then aligned to human genome (hg18) and splicing junctions using Mosaik.

source_name: Coriell Institute

growth_protocol: Cultured in T25 flasks at 37 degrees under 5% carbon dioxide using RPMI 1640 medium supplemented with 2mM L-glutamate and 15% fetal bovine serum

extract_protocol: 3'-end RNA-seq described in Yoon and Brem, RNA 16:1256-67, 2010. Polyadenylated RNA was selected using oligo(dT) magnetic beads, and then fragmented using zinc-catalyzed hydrolysis. Polyadenylated RNA fragments were selected using fresh oligo(dT) magnetic beads. First-strand synthesis was done using anchored oligo(dT) primer, and then double-stranded cDNA was made using RNase H and DNA Pol I. Subsequent steps were the same as Illumina's mRNA-seq protocol. Paired-end average insert size 200 bp.

treatment_protocol: ~1x10^7 cells were pelleted at 1000xg for 3 min, flash frozen in liquid nitrogen. Then, total RNA was isolated using Trizol.

value: B-lymphocyte cell line

tag: average insert size


title: GM17189_R1

description: Polyadenylated transcript 3' ends

tag: cell line


title: GM17189_R2

description: Polyadenylated transcript 3' ends

tag: cell type


title: GM17106_R1

description: Polyadenylated transcript 3' ends


title: GM10860_R2

description: Polyadenylated transcript 3' ends


title: GM17253_R2

description: Polyadenylated transcript 3' ends


title: GM17207_R2

description: Polyadenylated transcript 3' ends


title: HUVhiPS4F6-ACTIVIN-p23

value: Human umbilical vein endothelial cells

tag: treatment


title: GM17220_R2

description: Polyadenylated transcript 3' ends


title: GM17220_R1

description: Polyadenylated transcript 3' ends


title: input_ChIPSeq

city: daejeon

first: Tae-Wook

last: Kang

address: 111 Gwahangno, Yuseong-gu

country: South Korea

institute: KRIBB

description: ChIP with anti-Flag(Oct4) and then reChIP with IgG

data_processing:
BaseCalls and mapping: Sequence reads were obtained and mapped using the Illumina Genome Analyzer Pipeline.
Peaks: Peak detection was performed with the Model-based Analysis of ChIP-Seq (MACS) algorithm (http://liulab.dfci.harvard.edu/MACS/)
Genome Build:
alpha_Oct4_SWGA_negative_peaks.txt: mm9
s1_control_merged_alpha_Oct4_SWGA_control_afterfiting.wig: mm9

source_name: ZHBTc4 F-Oct4_input_IgG_control

extract_protocol: Standard chromatin immunoprecipitation (ChIP) was performed. Briefly, formaldehyde cross-linked chromatin was fragmented to 100–500 bp by sonication and then subjected to ChIP with anti-Flag antibody. Flag-Oct4 enriched chromatin was eluted by competition with Flag peptide. The eluates were re-ChIPed with sWGA beads (or agarose beads as a control) and eluted by competition with 0.5M GlcNAc. After reverse the cross-linking, DNA was purified by phenol/chloroform and then precipitated using isopropanol. The purified DNAs were ligated to a pair of adaptors for sequencing on the Illumina Genome Analyzer. The ligation products were size fractioned to obtain 200-300 bp fragments on a 2% agarose gel and subjected to 18 cycles of PCR ampliﬁcation. Each library was diluted to 8 pM for 76 cycles of single-read sequencing on the Illumina Genome Analyzer II following the manufacturer’s recommended protocol.

value: ZHBTc4 F-Oct4

value: embryonic stem cell

tag: chip-seq antibody

value: anti-Flag antibody

tag: chip-seq antibody vendor

value: Sigma

tag: chip-seq antibody cat.#

tag: chip-seq antibody lot#


title: Zebrafish retina with Id2aMM morpholino treatment, replicate 2

city: Ulsan

first: Taejoon

last: Kwon

address: 50 Unist-gil

department: Department of Biomedical Engineering

institute: Ulsan National Institute of Science and Technology

data_processing:
Filter out all reads with no-call (‘N’) and low complexity (reads without all 4 bases).
Take reads that both paired-end reads are passed the above condition.
Map to EnsEMBL cDNA sequence, then calculate RPK100M (read counts per kb per 100 million of reads)
Summarize mapped reads per each cDNA.
Determine differentially expressed genes with DESeq
Genome_build: EnsEMBL version 66 cDNA sequences
Supplementary_files_format_and_content: tab-delimited text files include RPKM values for each Sample, DESeq output for differentially expressed genes.

organism: Danio rerio

extract_protocol: Total RNA was isolated from dissected retinae at 48 hpf using Trizol reagent. Illumina TruSeq RNA Sample Prep Kit was used with 1 microgram of total retinal RNA per condition to generate cDNA libraries by poly-A selection.

tag: tissue

value: retina

tag: genotype/variation

value: control


title: Zebrafish retina with id2a morpholino treatment, replicate 2

value: id2a knockdown


title: PolyA_RNA

source_name: mESC_polyA RNA

value: embryonic stem cells

value: Polyadenylated RNA


title: Zebrafish retina with Id2aMM morpholino treatment, replicate 1


title: Glucose + Straw 5 h C

city: Nottingham

first: David

last: Archer

address: University Park

department: School of Biology

institute: University of Nottingham

data_processing: SOLiD4 RNA-seq reads from each experimental sample were mapped to the published genome sequence of Aspergillus niger (JGI A. niger version 3.0 unmasked assembly scaffolds and gene annotation) using the BioScope 1.3.1 Whole Transcriptome Pipeline (LifeTechnologies). This included the initial filtering of reads against a collection of published A. niger rRNA sequences prior to mapping. BioScope provided the primary read alignment position of each read mapped against the complete genome sequence and exon spanning junctions using available gene  coordinate information. Read alignment results were recorded in BAM format for further downstream analysis.  Read counts per gene were calculated for each sample with Htseq-count (http://www-huber.embl.de/users/anders/HTSeq) using the BAM file and genome annotation as the input. Strand-specific RNA-seq reads, as generated by SOLiD, can be specified for when executing Htseq-count in determining accurate read counts per gene. These counts were then used to calculate normalized expression values (RPKM) for each gene.

organism: Aspergillus niger

source_name: Wild-type A. niger mycelia

growth_protocol: Slopes were incubated at 28 °C until the cultures had conidiated and spores were resuspended in 0.1% (v/v) Tween 20 (Sigma). Liquid batch cultures were inoculated with 10^6 spores/ml, and grown in 100 ml of minimal media [ all l–1: NaNO3, 6 g; KCl, 0.52 g; MgSO4 . 7H2O, 0.52 g; KH2PO4, 1.52 g; Na2B4O7.10H2O, 0.08 mg; CuSO4 . 5H2O, 1.6 mg; FePO4.H2O, 1.6 mg; MnSO4.4H2O, 1.6 mg; NaMoO4.2H2O, 1.6 mg] with the appropriate carbon source added to a final concentration of 1 % (w/v) in 250 ml conical flasks at 28 °C, shaken at 150 r.p.m.. The standard time-course consisted of growth for 48 h in 1 % (w/v) glucose media, after which mycelia were removed by filtration, washed thoroughly with water and transferred to fresh media containing 1 % (w/v) ground wheat straw as sole carbon source. Incubation was continued for 24 h. Glucose was then added exogenously to a final concentration of 1 % (w/v) and incubation continued for 6 hours.

extract_protocol:
Mycelia from each condition were frozen and ground under liquid nitrogen using a mortar and pestle. Total RNA was extracted from the ground material using the TRIzol reagent protocol (Invitrogen). An additional clean-up was performed using the RNEasy Mini Kit (Qiagen), following the manufacturer's RNA Clean-up protocol, including the additional on-column DNAse digest. 10µg of Total RNA was depleted of ribosomal RNA using the Ribominus Eukaryotic kit (Invitrogen, Cat. No. A10837-08). Solid whole transcriptome libraries were made as outlined in the Solid Whole transcriptome kit protocol (Applied Biosystems, Cat. No. 4425680). Quant-it HS dsDNA assay kit (Invitrogen, Cat. No. Q32851) was used to measure the concentration of libraries and pool equimolar amounts. Pooled libraries were gel purified using 2% size-select E-gels to 200bp-300bp (Invitrogen). Emulsion PCR (0.5 pM final concentration of pooled libraries) and beads enrichment was carried out according to Solid 3+ Templated bead preparation guide containing library. Sequencing was performed on a SOLiD 3+ ABi sequencer according to the manufacturer instructions to generate 50 bp reads in colour space.
Instrument model: SOLiD 3+ ABi sequencer

tag: strain

tag: growth condition

value: Straw + Glucose 5

value: mycelia


title: GM10860_R1

description: Polyadenylated transcript 3' ends


title: Glucose + Straw 5 h B


title: Glucose + Straw 5 h A


title: GM17106_R2

description: Polyadenylated transcript 3' ends


title: GM17207_R1

description: Polyadenylated transcript 3' ends


title: Straw 24 h C

value: Straw 24 h


title: Straw 24 h B


title: MutuI MNase-seq Rep2

description: MutuI MNase-seq

value: MNase [New England Biolabs, lot#0451204, catalog#R0525M]

value: Mutu


title: Glucose 48 h C

value: Glucose 48 h


title: Glucose 48 h B


title: Zebrafish retina with id2a morpholino treatment, replicate 1


title: Straw 24 h A


title: alpha-Oct4-SWGA_ChIPSeq

description: ChIP with anti-Flag(Oct4) and then reChIP with sWGA (Vector laboratories, AL1023S, V0604)

data_processing:
BaseCalls and mapping: Sequence reads were obtained and mapped using the Illumina Genome Analyzer Pipeline.
Peaks: Peak detection was performed with the Model-based Analysis of ChIP-Seq (MACS) algorithm (http://liulab.dfci.harvard.edu/MACS/)
Genome Build:
alpha_Oct4_SWGA_peaks.txt: mm9
s2_treat_merged_alpha_Oct4_SWGA_treat_afterfiting.wig: mm9

source_name: ZHBTc4 F-Oct4_alpha-Oct4-SWGA


title: genomic DNA from sample F1 follow-up (Bisulfite-Seq)

first: Thomas

last: Down

address: Tennis Court Road

department: WT/CRUK Gurdon Institute

institute: University of Cambridge

description: normal blood sample

data_processing:
Standard Illumina software was used for processing and basecalling
Read pairs were aligned to the GRCh37 genome using BWA 0.5.9
Read pairs were filtered (quality score >= 10) and length-normalized within each group (DBS and followup)
Read pairs were counted in 500bp windows centered on the probes from the Illumina HumanMethylation450K array
Genome_build: GRCh37/hg19
Supplementary_files_format_and_content: Tab-delimited files countaining read counts
Genome Build:
Followup-F1.counts.txt: GRCh37/hg19

extract_protocol:
genomic DNA was extracted from dried blood spot samples using the GenSolve kit as per manufacturers instructures.  DNA was extracted from other samples using the Qiagen DNeasy kit.
Bisulfite-seq libraries were prepared

tag: sample id

value: whole blood


title: genomic DNA from sample F2 dried blood spot (Bisulfite-Seq)

description: normal dried blood spot sample

data_processing:
Standard Illumina software was used for processing and basecalling
Read pairs were aligned to the GRCh37 genome using BWA 0.5.9
Read pairs were filtered (quality score >= 10) and length-normalized within each group (DBS and followup)
Read pairs were counted in 500bp windows centered on the probes from the Illumina HumanMethylation450K array
Genome_build: GRCh37/hg19
Supplementary_files_format_and_content: Tab-delimited files countaining read counts
Genome Build:
DBS-F2.counts.txt: GRCh37/hg19

tag: sample type

value: dried blood spot


title: genomic DNA from sample F1 dried blood spot (Bisulfite-Seq)

description: normal dried blood spot sample

data_processing:
Standard Illumina software was used for processing and basecalling
Read pairs were aligned to the GRCh37 genome using BWA 0.5.9
Read pairs were filtered (quality score >= 10) and length-normalized within each group (DBS and followup)
Read pairs were counted in 500bp windows centered on the probes from the Illumina HumanMethylation450K array
Genome_build: GRCh37/hg19
Supplementary_files_format_and_content: Tab-delimited files countaining read counts


title: genomic DNA from sample M dried blood spot (Bisulfite-Seq)

description: normal dried blood spot sample

data_processing:
Standard Illumina software was used for processing and basecalling
Read pairs were aligned to the GRCh37 genome using BWA 0.5.9
Read pairs were filtered (quality score >= 10) and length-normalized within each group (DBS and followup)
Read pairs were counted in 500bp windows centered on the probes from the Illumina HumanMethylation450K array
Genome_build: GRCh37/hg19
Supplementary_files_format_and_content: Tab-delimited files countaining read counts
Genome Build:
DBS-M.counts.txt: GRCh37/hg19

source_name: M

value: M


title: IgG control

status: Public on Aug 25 2012

first: Ozlem

last: Aksoy

address: 415 East 68th Street

department: Watson School of Biological Sciences

description: IgG ChIP-seq

data_processing:
The immunoprecipitated DNA was prepared for Illumina sequencing as described (Chicas et al. 2010).
The Illumina  GA sequencing tags were mapped to the unmasked human  reference genome (NCBI version 36, hg18) using the program  Bowtie with the parameter setting ‘‘-t -q -a –best –strata -m 1.’’
ChIP-seq peak regions were determined using MACS (Zhang  et al. 2008) to find ChIP-enriched regions compared with the  input DNA control with default parameter settings and a significance  threshold (P<1e-5).
We associated ChIP-enriched  regions to a target gene if it localized within the region from 3 kb  upstream of to 1 kb downstream from the transcription start site  of the gene. The gene coordinates were extracted according to  RefSeq gene annotation (hg18) downloaded from the University  of California at Santa Cruz Genome Browser.
Genome_build: hg18

source_name: IMR90 Human Fetal Lung Fibroblast cell line

extract_protocol: update

treatment_protocol: imr90 cells were infected with ER-Ras vector and indicated hairpins. The cells were collected 4 days after tamoxifen treatment. The ChIP experiments were done as previously described (Chicas  et al. 2010).

tag: chip antibody

value: Anti-rabbit IgG antibody

tag: chip vendor


title: Input

description: Input from senescent cells


title: genomic DNA from sample M follow-up (Bisulfite-Seq)

description: normal blood sample

data_processing:
Standard Illumina software was used for processing and basecalling
Read pairs were aligned to the GRCh37 genome using BWA 0.5.9
Read pairs were filtered (quality score >= 10) and length-normalized within each group (DBS and followup)
Read pairs were counted in 500bp windows centered on the probes from the Illumina HumanMethylation450K array
Genome_build: GRCh37/hg19
Supplementary_files_format_and_content: Tab-delimited files countaining read counts
Genome Build:
Followup-M.counts.txt: GRCh37/hg19


title: Glucose 48 h A


title: genomic DNA from sample F2 follow-up (Bisulfite-Seq)

description: normal blood sample

data_processing:
Standard Illumina software was used for processing and basecalling
Read pairs were aligned to the GRCh37 genome using BWA 0.5.9
Read pairs were filtered (quality score >= 10) and length-normalized within each group (DBS and followup)
Read pairs were counted in 500bp windows centered on the probes from the Illumina HumanMethylation450K array
Genome_build: GRCh37/hg19
Supplementary_files_format_and_content: Tab-delimited files countaining read counts
Genome Build:
Followup-F2.counts.txt: GRCh37/hg19


title: Senescent/shE2F7

description: E2F7 ChIP-seq in Ras-induced senescent cells with E2F7 knockdown

value: E2F7 antibody

value: Kristian Helin's group (10 ug)

tag: cell state

value: senescent

value: infected with Ras and E2F7 hairpin


title: Senescent/shRB

description: E2F7 ChIP-seq in Ras-induced senescent cells with RB knockdown

value: infected with Ras and RB hairpin


title: Growing

description: E2F7 ChIP-seq in growing cells

value: growing


title: Senescent/shRen

description: E2F7 ChIP-seq in Ras-induced senescent cells

value: infected with Ras


title: endometrium 15 days postpartum 2

city: Dunsany

first: Cathriona

last: Foley

address: Grange

country: Ireland

institute: Teagasc

data_processing:
Base-calling done with CASAVA 1.4
mRNA-Seq reads were aligned to the Btau4.0 genome using TopHat (Settings: -r 200 --solexa1.3-quals --library-type fr-unstranded --segment-length 20 --segment-mismatches 1)
HtSeq-Count summarised the number of genes per gene (settings: mode = union)
Genome_build: Btau_4.0.62
Supplementary_files_format_and_content: Tab delimited txt files. Row 1 has ENSEMBL gene IDs and row 2 has the raw number of reads mapping to each gene.

organism: Bos taurus

source_name: Endometrial Biopsy

extract_protocol: Frozen endometrial tissue was homogenised and total RNA was extracted using Trizol. The Illumina mRNA Sequencing Sample Preparation Kit [2009] was used to construct libraries. Poly-T oligo-attached magnetic beads allowed the isolation and pruification of poly-A tailed mRNA which was fragmented, converted to double stranded cDNA, sequencing primers and adapters were attached. 300bp fragments were then size selected for sequencing.

tag: time

value: 15 days postpartum

value: endometrium

tag: breed

value: mixed

tag: histological grade of inflammation


title: endometrium 15 days postpartum 4


title: Quiescent

description: E2F7 ChIP-seq in quiescent cells

tag: cell line

value: quiescent

tag: treatment


title: endometrium 15 days postpartum 9


title: endometrium 30 days postpartum 1

value: 30 days postpartum


title: endometrium 30 days postpartum 3


title: endometrium 30 days postpartum 6


title: SUM 44PE

status: Public on Aug 24 2012

city: Stanford

first: Robert

middle: B.

last: West

address: 300 Pasteur Drive

department: Pathology

institute: Stanford School of Medicine

description:
Experiment type: RNA-seq
polyA RNA

data_processing:
Paired-end RNA-seq Illumina GAIIx sequence reads were mapped to hg18 using TopHat. Reads on chr10 were assembled into predicted transcripts using Scripture (Guttman et al., Nat Biotechnol 2010, 28(5):503-510).
Genome_build: hg18
Supplementary_files_format_and_content: Scripture bed files: hg18 bed files of the Scripture-predicted transcripts on chr10.

source_name: Breast cancer cell line

growth_protocol: Breast cancer cell lines were grown using ATCC protocols.

extract_protocol: RNA-seq: Total RNA from breast cancer cell lines was isolated using the QIAgen RNeasy Mini Kit (QIAgen). RNA-seq libraries were prepared using the Illumina mRNA-Seq Sample Prep Kit (Illumina, Inc.).

value: SUM 44PE

tag: cell type

value: breast cancer cells


title: UACC-812

description:
Experiment type: RNA-seq
polyA RNA


title: ZR-75-30

description:
Experiment type: RNA-seq
polyA RNA


title: HCC1500

description:
Experiment type: RNA-seq
polyA RNA


title: CAMA-1

description:
Experiment type: RNA-seq
polyA RNA


title: HCC1419

description:
Experiment type: RNA-seq
polyA RNA


title: STT5684_Fetal_normal_kidney

description:
Experiment type: 3SEQ
polyA RNA

data_processing:
3SEQ Illumina GAIIx sequence reads were mapped to hg18 with a two mismatch allowance using ELAND (Illumina Inc.). Sequence reads were further filtered to remove mapping artifacts caused by ambiguous mapping by requiring a posterior probability of at least 0.8 for the best alignment, where posterior probability was calculated as the ratio of the likelihood of the best alignment to the sum of the likelihoods of all alignments. Likelihood was taken from a binomial model of sequencing errors with constant substitution rate 0.01.
Detection of genomic regions (hg18) enriched for sequencing reads (peak-calling) was performed once, by combining filtered sequence reads from all 66 cancer libraries and using a custom Perl script to call genomic regions with greater than 150 reads across all samples. During this process, clusters of sequence reads, each within 20 bp of their nearest same-strand neighbor, were identified, and the coordinates for these genomic regions were defined using the start coordinates for the left-most and right-most sequence reads within each cluster. The list of peaks was then filtered to exclude any region that contained fewer than 25 different starting positions for the reads within the region, and in this way required each peak to possess a minimum of 25 unique reads. Once the list of peaks was filtered to 54,511, the number of sequence reads located within each peak was determined for each of the cancer samples as well as 27 additional normal tissue samples.
Increasing the threshold to 200 total reads as well as removing peaks antisense to known genes (library artifact) yielded 36,048 filtered peaks.
3SEQ expression data was normalized using the sequencing depth of each sample by scaling the data using the mean value of each sample. Data was further compressed to reduce outliers by taking the square root of each value.
Genome_build: hg18
Supplementary_files_format_and_content: 3SEQ bed files: hg18 bed files of the filtered 3SEQ sequence reads include the first base of each read on either the plus strand (blue) or the minus strand (red).
Supplementary_files_format_and_content: Raw_counts: Total 3SEQ reads in each peak for each sample. File includes read counts for 54,511 peaks for the 66 cancer libraries and the 27 normal libraries. The 'GSE28866_raw_counts_54511_peaks_cancer_and_normal.txt' file is linked to the Series record as a supplementary file.
Supplementary_files_format_and_content: Normalized_peaks: Normalized 3SEQ expression data for the 36,048 filtered peaks for the 66 cancer libraries and the 27 normal libraries. Peaks were classified as coding, lncRNA (Rinn, Bartel, Hughes, Gencode_lnc), other known transcripts (ref_known_Gencode_nc, all_mrna, other known), downstream, intron, promoter, or novel_intergenic. Peaks determined to be differentially expressed in one of the 17 cancer subtypes using a series of 2-Class SAM analyses are noted along with the type of cancer showing upregulation. Expression data was normalized using the sequencing depth of each sample by scaling the data using the mean value of each sample. Data was further compressed to reduce outliers by taking the square root of each value. The 'GSE28866_36048_normalized_peaks_cancer_and_normal.txt' file is linked to the Series record as a supplementary file.

source_name: Fetal normal kidney 15wks

extract_protocol: 3SEQ: Multiple 2mm-diameter cores were taken from each FFPE block, re-embedded in paraffin, and sliced into ~100 20 um sections for nucleic acid extraction. RNA was purified from FFPE slices following deparaffination with a xylene incubation, ethanol wash, and a protease digestion, using the RecoverAll Total Nucleic Acid Isolation Kit (Ambion). Isolation of the 3' ends of mRNA was achieved with an oligodT selection performed on at least 5 ug total RNA using the Oligotex mRNA mini Kit (QIAgen). RNA that was not sufficiently fragmented was heat sheared to a size of approximately 100-200 bp. The poly(A)-selected RNA was then subjected to first and second strand cDNA synthesis and Illumina library synthesis. (Duplicate 3SEQ libraries were created for two cancer samples: ESS STT5520 and LMS STT516.)

treatment_protocol: Clinical tissue samples were formalin-fixed and paraffin-embedded (FFPE).

value: kidney

tag: developmental stage

value: fetal, 15wks

tag: disease status

value: normal

tag: tissue archive method

value: FFPE


title: STT5680_Fetal_normal_bowel

description:
Experiment type: 3SEQ
polyA RNA

source_name: Fetal normal bowel 14wks

tag: tissue

value: bowel

value: fetal, 14wks


title: STT5685_Fetal_normal_kidney

description:
Experiment type: 3SEQ
polyA RNA

source_name: Fetal normal kidney 14wks


title: STT5683_Fetal_normal_kidney

description:
Experiment type: 3SEQ
polyA RNA


title: STT5682_Fetal_normal_bowel

description:
Experiment type: 3SEQ
polyA RNA

source_name: Fetal normal bowel 13wks

value: fetal, 13wks


title: STT5687_Fetal_normal_lung

description:
Experiment type: 3SEQ
polyA RNA

source_name: Fetal normal lung 15wks

value: lung


title: STT5609_Adult_normal_uterus

description:
Experiment type: 3SEQ
polyA RNA

source_name: Adult normal uterus

value: uterus

value: adult


title: STT5681_Fetal_normal_bowel

description:
Experiment type: 3SEQ
polyA RNA

source_name: Fetal normal bowel 15wks


title: STT5688_Fetal_normal_lung

description:
Experiment type: 3SEQ
polyA RNA

source_name: Fetal normal lung 13wks


title: STT5686_Fetal_normal_lung

description:
Experiment type: 3SEQ
polyA RNA

source_name: Fetal normal lung 14wks


title: STT5696_Adult_normal_kidney

description:
Experiment type: 3SEQ
polyA RNA

source_name: Adult normal kidney


title: STT5695_Adult_normal_kidney

description:
Experiment type: 3SEQ
polyA RNA


title: STT5610_Adult_normal_colon

description:
Experiment type: 3SEQ
polyA RNA

source_name: Adult normal colon

value: colon


title: STT5588_Fetal_normal_lung

description:
Experiment type: 3SEQ
polyA RNA

source_name: Fetal normal lung

value: fetal


title: STT5692_Adult_normal_colon

description:
Experiment type: 3SEQ
polyA RNA


title: STT5693_Adult_normal_colon

description:
Experiment type: 3SEQ
polyA RNA


title: STT5603_Adult_normal_lung

description:
Experiment type: 3SEQ
polyA RNA

source_name: Adult normal lung


title: STT5698_Adult_normal_lung

description:
Experiment type: 3SEQ
polyA RNA


title: STT5761_Adult_normal_breast

description:
Experiment type: 3SEQ
polyA RNA

source_name: Adult normal breast

value: breast


title: STT5510_Adult_normal_breast

description:
Experiment type: 3SEQ
polyA RNA


title: STT5451_Adult_normal_breast

description:
Experiment type: 3SEQ
polyA RNA


title: STT5425_Adult_normal_breast

description:
Experiment type: 3SEQ
polyA RNA


title: STT5694_Adult_normal_kidney

description:
Experiment type: 3SEQ
polyA RNA


title: STT5518_DDLPS

description:
Experiment type: 3SEQ
polyA RNA

source_name: Dedifferentiated liposarcoma

value: dedifferentiated liposarcoma


title: STT5517_DDLPS

description:
Experiment type: 3SEQ
polyA RNA


title: STT5534_DDLPS

description:
Experiment type: 3SEQ
polyA RNA


title: STT5697_Adult_normal_lung

description:
Experiment type: 3SEQ
polyA RNA


title: STT5699_Adult_normal_lung

description:
Experiment type: 3SEQ
polyA RNA


title: STT516_LMS_rep1

description:
Experiment type: 3SEQ
polyA RNA

source_name: Leiomyosarcoma

value: leiomyosarcoma


title: STT516_LMS_rep2

description:
Experiment type: 3SEQ
polyA RNA


title: STT5591_MLS

description:
Experiment type: 3SEQ
polyA RNA

source_name: Myxoid liposarcoma

value: myxoid liposarcoma


title: STT5528_LMS

description:
Experiment type: 3SEQ
polyA RNA


title: STT1220_LMS

description:
Experiment type: 3SEQ
polyA RNA


title: STT5620_Adult_normal_breast

description:
Experiment type: 3SEQ
polyA RNA


title: STT5593_GIST

description:
Experiment type: 3SEQ
polyA RNA

source_name: Gastrointestinal stromal tumor

value: gastrointestinal stromal tumor


title: STT5594_GIST

description:
Experiment type: 3SEQ
polyA RNA


title: STT5599_GIST

description:
Experiment type: 3SEQ
polyA RNA


title: STT5597_GIST

description:
Experiment type: 3SEQ
polyA RNA


title: STT5533_SS

description:
Experiment type: 3SEQ
polyA RNA

source_name: Synovial sarcoma

value: synovial sarcoma


title: STT111_GIST

description:
Experiment type: 3SEQ
polyA RNA


title: STT5596_MLS

description:
Experiment type: 3SEQ
polyA RNA


title: STT2145_GIST

description:
Experiment type: 3SEQ
polyA RNA


title: STT5595_MLS

description:
Experiment type: 3SEQ
polyA RNA


title: STT1823_GIST

description:
Experiment type: 3SEQ
polyA RNA


title: STT5601_Adult_normal_lung

description:
Experiment type: 3SEQ
polyA RNA


title: STT5592_EMC

description:
Experiment type: 3SEQ
polyA RNA

source_name: Extraskeletal myxoid chondrosarcoma

value: extraskeletal myxoid chondrosarcoma


title: STT3705_SS

description:
Experiment type: 3SEQ
polyA RNA


title: STT5527_EMC

description:
Experiment type: 3SEQ
polyA RNA


title: STT5525_EMC

description:
Experiment type: 3SEQ
polyA RNA


title: STT5598_EWS

description:
Experiment type: 3SEQ
polyA RNA

source_name: Ewing's sarcoma

value: Ewing's sarcoma


title: STT5526_EMC

description:
Experiment type: 3SEQ
polyA RNA

value: FFPE


title: STT5600_EWS

description:
Experiment type: 3SEQ
polyA RNA


title: STT5590_MLS

description:
Experiment type: 3SEQ
polyA RNA


title: STT5522_EWS

description:
Experiment type: 3SEQ
polyA RNA


title: STT5523_EWS

description:
Experiment type: 3SEQ
polyA RNA


title: STT5530_MLS

description:
Experiment type: 3SEQ
polyA RNA


title: STT5531_MLS

description:
Experiment type: 3SEQ
polyA RNA


title: STT5520_ESS_rep2

description:
Experiment type: 3SEQ
polyA RNA

source_name: Endometrial stromal sarcoma

value: endometrial stromal sarcoma


title: STT5553_NPC

description:
Experiment type: 3SEQ
polyA RNA

source_name: Nasopharyngeal carcinoma

value: nasopharyngeal carcinoma


title: STT5532_SS

description:
Experiment type: 3SEQ
polyA RNA


title: STT5564_Skin_SCC

description:
Experiment type: 3SEQ
polyA RNA

source_name: Skin squamous cell carcinoma

value: skin squamous cell carcinoma


title: STT5519_ESS

description:
Experiment type: 3SEQ
polyA RNA


title: STT5520_ESS_rep1

description:
Experiment type: 3SEQ
polyA RNA


title: STT5548_Lung_SCC

description:
Experiment type: 3SEQ
polyA RNA

source_name: Lung squamous cell carcinoma

value: lung squamous cell carcinoma


title: STT5563_Skin_SCC

description:
Experiment type: 3SEQ
polyA RNA


title: STT5545_HN_SCC

description:
Experiment type: 3SEQ
polyA RNA

source_name: Head and neck squamous cell carcinoma

value: head and neck squamous cell carcinoma


title: STT5551_NPC

description:
Experiment type: 3SEQ
polyA RNA


title: STT5521_ESS

description:
Experiment type: 3SEQ
polyA RNA


title: STT5549_PUC

description:
Experiment type: 3SEQ
polyA RNA

source_name: Papillary urothelial carcinoma

value: papillary urothelial carcinoma


title: STT5546_HN_SCC

description:
Experiment type: 3SEQ
polyA RNA


title: STT5550_PUC

description:
Experiment type: 3SEQ
polyA RNA


title: STT5552_NPC

description:
Experiment type: 3SEQ
polyA RNA


title: STT5565_Skin_SCC

description:
Experiment type: 3SEQ
polyA RNA


title: STT5544_HN_SCC

description:
Experiment type: 3SEQ
polyA RNA


title: STT5542_HN_SCC

description:
Experiment type: 3SEQ
polyA RNA


title: STT5535_HN_SCC

description:
Experiment type: 3SEQ
polyA RNA


title: STT5543_ESS

description:
Experiment type: 3SEQ
polyA RNA


title: STT5559_Colon

description:
Experiment type: 3SEQ
polyA RNA

source_name: Colon adenocarcinoma

value: colon adenocarcinoma


title: STT5558_Colon

description:
Experiment type: 3SEQ
polyA RNA


title: STT5560_Prostate

description:
Experiment type: 3SEQ
polyA RNA

source_name: Prostate adenocarcinoma

value: prostate adenocarcinoma


title: STT5556_Lung

description:
Experiment type: 3SEQ
polyA RNA

source_name: Lung adenocarcinoma

value: lung adenocarcinoma


title: STT5547_Lung_SCC

description:
Experiment type: 3SEQ
polyA RNA


title: STT5557_Colon

description:
Experiment type: 3SEQ
polyA RNA


title: STT5555_Lung

description:
Experiment type: 3SEQ
polyA RNA


title: STT5554_Lung

description:
Experiment type: 3SEQ
polyA RNA


title: STT5581_Breast

description:
Experiment type: 3SEQ
polyA RNA

source_name: Breast invasive ductal carcinoma

value: breast invasive ductal carcinoma


title: STT5575_Breast

description:
Experiment type: 3SEQ
polyA RNA


title: STT5572_Breast

description:
Experiment type: 3SEQ
polyA RNA


title: STT5474_Breast

description:
Experiment type: 3SEQ
polyA RNA


title: STT5477_Breast

description:
Experiment type: 3SEQ
polyA RNA


title: U10_34_female

city: Zürich

first: Niklaus

last: Zemp

address: Universitätsstrasse 16

department: IBZ

country: Switzerland

institute: ETH Zurich

organism: Silene latifolia

source_name: female_small flower buds

extract_protocol: DNAse treatment; Transcripts purification;Transcripts breaking in presence of Zn; Double-stranded cDNA synthesis using random primers and RNase H; Ends repair;  3' A addition; Ligation of adapters; Gel purification to isolate fragments of specified inserts size; PCR amplification to generate the DNA Colonies Template Library, library purification;Quality control of the DNA Colony Template Library by cloning an aliquot into a TOPO plasmid and capillary sequencing of 4-8 clones;Library quantification and dilution at 10 nM; Flow-cell preparation on the cBot; High-throughput DNA sequencing on the illumina HiSeq 2000; Real-time base-calling on the HiSeq computer.

tag: population

value: 10 generation inbred line

value: small flower buds

tag: gender

value: female


title: STT5562_Prostate

description:
Experiment type: 3SEQ
polyA RNA


title: STT5463_Breast

description:
Experiment type: 3SEQ
polyA RNA


title: U10_37_female


title: U10_39_female


title: U10_09_male

source_name: male_small flower buds

value: male


title: SiCirbp Total RNA

city: Geneva

first: Guillaume

last: Rey

address: Campus Biotech, Chemin des Mines 9

institute: University of Geneva

data_processing:
Reads were filtered for rRNA sequences by mapping on 45S rRNA sequence with Bowtie software (version 0.12.7)
Read mapping with Tophat software (version 1.2.0) using the UCSC genes as a guide for splice junction mapping.
A single hit at the same genomic position was allowed for CLIP reads.
Bedgraph counts expressed in log2(1+counts per 10^6 sequenced reads)
Genome_build: mm9
Supplementary_files_format_and_content: bedgraph files

source_name: NIH3T3_siCIRP RNA-seq

growth_protocol: NIH3T3 cells were grown according to standard protocols.

extract_protocol: For CLIP-seq, in vivo biotinylated and UV-crosslinked CIRP-RNA complexes were purified on streptavidin beads and CLIP-seq libraries were constructed according to (Yeo et al., 2009) except that linker ligations were directly carried out on streptavidin beads. Linker-equipped CIRP-RNA complexes were eluted by TEV protease cleavage, RNA was liberated by proteinase K digestion, reverse transcribed and PCR-amplified. Libraries were subjected to standard Illumina sequencing protocols. For RNA-seq, total RNA was extracted using TRI-reagent according to manufacturer’s instructions (Sigma). Ribosomal RNA was selectively removed from total RNA through hybridization using Ribominus reagents according to the supplier’s instructions (Invitrogen). Directional, strand-specific cDNA libraries were prepared  and subjected to standard Illumina sequencing protocols.

treatment_protocol: NIH3T3 cells were kept at at 33°C for 8 hours before harvesting.

tag: treated with

value: CIRP siRNA


title: U10_49_male


title: STT5561_Prostate

description:
Experiment type: 3SEQ
polyA RNA


title: C-Term-CIRBP CLIP RNA

description:
Clipped RNA, CLIP-seq
Several different lots/batches of streptavidin beads were used for CLIP
processed data file: clip.plus.bedgraph, clip.minus.bedgraph (available as Series supplementary files)

source_name: NIH3T3_C-Term-CIRBP CLIP-seq

tag: clip beads

value: Dynabeads M-280 Streptavidin

tag: beads vendor

value: Invitrogen

tag: beads cat. #


title: SiCtrl Total RNA

source_name: NIH3T3_siCtrl RNA-seq

value: Control siRNA