title: mRNA-seq for PP242 treated PC3 cells, replicate #1.

data_processing:
Each sequencing run resulted in approximately 20-25 million raw reads per sample, of which 5-12 million unique reads were used for subsequent analysis. Ribosome footprint and RNA-Seq sequencing reads were aligned against a library of transcripts from the UCSC Known Genes database GRCh37/hg19. The first 25 nucleotides of each read were aligned using Bowtie and this initial alignment was then extended to encompass the full fragment-derived portion of the sequencing read while excluding the linker sequence. Read density profiles were then constructed for the canonical transcript of each gene, using only reads with 0 or 1 total mismatches between the read sequence and the reference sequence, comprised of the transcript fragment followed by the linker sequence. Footprint reads were assigned to an A site nucleotide at position +15 to +17 of the alignment, based on the total fragment length; mRNA reads were assigned to the first nucleotide of the alignment. The average read density per codon was then computed for the coding sequence of each transcript, excluding the first 15 and last 5 codons, which can display atypical ribosome accumulation.
Average read density was used as a measure of mRNA abundance (RNA-Seq reads) and of protein synthesis (ribosome profiling reads). For most analyses, genes were filtered to require at least 256 reads in the relevant RNA-Seq samples. Translational efficiency was computed as the ratio of ribosome footprint read density to RNA-Seq read density, scaled to normalize the translational efficiency of the median gene to 1.0 after excluding regulated genes (log2 fold-change +/- 1.5 after normalizing for the all-gene median). Changes in protein synthesis, mRNA abundance, and translational efficiency were similarly computed as the ratio of read densities between different samples, normalized to give the median gene a ratio of 1.0. This normalization corrects for differences in the absolute number of sequencing reads obtained for different libraries. 5333 (replicate #1) and 3977 (replicate #2) unique mRNAs passed a preset read threshold of 256 reads for single-gene quantification for all treatment conditions.
processed data file contains per gene read count, columns: 1) UCSC ID, 2) Length of trimmed CDS, 3) Read count in trimmed CDS
Genome Build:
mrna_pp242_a_qexpr.txt: UCSC GRCh37/hg19


title: mRNA-seq for rapamycin treated PC3 cells, replicate #1.

status: Public on Feb 28 2012

type: SRA

city: San Francisco

first: Andrew

middle: Caleb

last: Hsieh

state: CA

address: 1450 3rd Street, HD Rm 319

department: Urology

country: USA

institute: UCSF

description: 3hr rapamycin treated human PC3 prostate cancer cells.

library_source: transcriptomic

data_processing:
Each sequencing run resulted in approximately 20-25 million raw reads per sample, of which 5-12 million unique reads were used for subsequent analysis. Ribosome footprint and RNA-Seq sequencing reads were aligned against a library of transcripts from the UCSC Known Genes database GRCh37/hg19. The first 25 nucleotides of each read were aligned using Bowtie and this initial alignment was then extended to encompass the full fragment-derived portion of the sequencing read while excluding the linker sequence. Read density profiles were then constructed for the canonical transcript of each gene, using only reads with 0 or 1 total mismatches between the read sequence and the reference sequence, comprised of the transcript fragment followed by the linker sequence. Footprint reads were assigned to an A site nucleotide at position +15 to +17 of the alignment, based on the total fragment length; mRNA reads were assigned to the first nucleotide of the alignment. The average read density per codon was then computed for the coding sequence of each transcript, excluding the first 15 and last 5 codons, which can display atypical ribosome accumulation.
Average read density was used as a measure of mRNA abundance (RNA-Seq reads) and of protein synthesis (ribosome profiling reads). For most analyses, genes were filtered to require at least 256 reads in the relevant RNA-Seq samples. Translational efficiency was computed as the ratio of ribosome footprint read density to RNA-Seq read density, scaled to normalize the translational efficiency of the median gene to 1.0 after excluding regulated genes (log2 fold-change +/- 1.5 after normalizing for the all-gene median). Changes in protein synthesis, mRNA abundance, and translational efficiency were similarly computed as the ratio of read densities between different samples, normalized to give the median gene a ratio of 1.0. This normalization corrects for differences in the absolute number of sequencing reads obtained for different libraries. 5333 (replicate #1) and 3977 (replicate #2) unique mRNAs passed a preset read threshold of 256 reads for single-gene quantification for all treatment conditions.
processed data file contains per gene read count, columns: 1) UCSC ID, 2) Length of trimmed CDS, 3) Read count in trimmed CDS
Genome Build:
mrna_rapa_a_qexpr.txt: UCSC GRCh37/hg19

molecule: polyA RNA

organism: Homo sapiens

source_name: PC3 human prostate cancer cells

extract_protocol: PC3 cells were treated with rapamycin (50 nM - Calbiochem) or PP242 (2.5 µM - Intellikine) for 3 hours. Cells were subsequently treated with cycloheximide (100 µg/mL - Sigma) and detergent lysis was performed in the dish. The lysate was treated with DNase and clarified, and a sample was taken for RNA-Seq analysis. Lysates were subjected to ribosome footprinting by nuclease treatment. Ribosome-protected fragments were purified, and deep sequencing libraries were generated from these fragments, as well as from poly(A) mRNA purified from non-nuclease treated lysates. These libraries were analyzed by sequencing on the Illumina GAII.

tag: cell line

tag: sample type

value: polyA RNA

tag: treatment

value: rapamycin


title: mRNA-seq for vehicle treated PC3 cells, replicate #1.

description: 3hr vehicle treated human PC3 prostate cancer cells.

data_processing:
Each sequencing run resulted in approximately 20-25 million raw reads per sample, of which 5-12 million unique reads were used for subsequent analysis. Ribosome footprint and RNA-Seq sequencing reads were aligned against a library of transcripts from the UCSC Known Genes database GRCh37/hg19. The first 25 nucleotides of each read were aligned using Bowtie and this initial alignment was then extended to encompass the full fragment-derived portion of the sequencing read while excluding the linker sequence. Read density profiles were then constructed for the canonical transcript of each gene, using only reads with 0 or 1 total mismatches between the read sequence and the reference sequence, comprised of the transcript fragment followed by the linker sequence. Footprint reads were assigned to an A site nucleotide at position +15 to +17 of the alignment, based on the total fragment length; mRNA reads were assigned to the first nucleotide of the alignment. The average read density per codon was then computed for the coding sequence of each transcript, excluding the first 15 and last 5 codons, which can display atypical ribosome accumulation.
Average read density was used as a measure of mRNA abundance (RNA-Seq reads) and of protein synthesis (ribosome profiling reads). For most analyses, genes were filtered to require at least 256 reads in the relevant RNA-Seq samples. Translational efficiency was computed as the ratio of ribosome footprint read density to RNA-Seq read density, scaled to normalize the translational efficiency of the median gene to 1.0 after excluding regulated genes (log2 fold-change +/- 1.5 after normalizing for the all-gene median). Changes in protein synthesis, mRNA abundance, and translational efficiency were similarly computed as the ratio of read densities between different samples, normalized to give the median gene a ratio of 1.0. This normalization corrects for differences in the absolute number of sequencing reads obtained for different libraries. 5333 (replicate #1) and 3977 (replicate #2) unique mRNAs passed a preset read threshold of 256 reads for single-gene quantification for all treatment conditions.
processed data file contains per gene read count, columns: 1) UCSC ID, 2) Length of trimmed CDS, 3) Read count in trimmed CDS
Genome Build:
mrna_dmso_a_qexpr.txt: UCSC GRCh37/hg19

value: vehicle


title: Col-0 RNA-seq

status: Public on Mar 05 2012

city: Dallas

first: Hume

last: Stroud

state: TX

address: 5323 Harry Hines Blvd

institute: UT Southwestern

description: mRNA-seq on poly-A purified RNA

data_processing: Alignment: Sequence reads were obtained and mapped to the TAIR8 Arabidospis genome using Bowtie (Langmead et al., 2009 Genome Biol). Uniquely mapping with two or fewer mismatches were retained. Only the uniquely mapping sequences are contained in the processed file, which were counted for the read occurances.

molecule: total RNA

organism: Arabidopsis thaliana

source_name: Col-0 RNA-seq

extract_protocol: mRNA-seq libraries were generated and sequenced on HiSeq 2000 as per the manufacturer instructions (Illumina).

tag: ecotype

tag: tissue

value: 10 day seedlings


title: E14_Day6_RNA-seq

city: La Jolla

first: Pengfei

last: Yu

address: 9500 Gilman Drive MC 0419

department: Bioengineering

institute: University of California San Diego

data_processing:
Sequence reads were obtained and mapped to reference genomes (mm9, hg19, susScr2) using Bowtie. Only reads with one or fewer mismatches and uniquely mapped onto the genome were retained.
Genome Build:
E14_RNA_d6.bam.gz: mm9
E14_RNA_d6_genes.expr.gz: mm9
E14_RNA_d6_transcripts.expr.gz: mm9
E14_RNA_d6_transcripts.gtf.gz: mm9

organism: Mus musculus

source_name: mESC E14 under Activin A differentiation, day 6

growth_protocol: E14(mESC) were plated onto gelatin-coated dishes in Dulbecco's modified Eagle medium (DMEM; GIBCO), supplemented with 15% heat-inactivated fetal bovine serum (FBS; GIBCO), 0.055 mM -mercaptoethanol (GIBCO), 2 mM L-glutamine, 0.1 mM MEM nonessential amino acid, 5,000 units/ml penicillin/streptomycin and 1,000 units/ml of LIF (Millipore ESG1107) in incubator set at 37 ºC in 5% CO2.

extract_protocol:
The RNA extraction method is Trizol reagent (15596-026, Invitrogen).  The antibodies used in the experiment are listed below:  Histone H2A.Z antibody - ChIP Grade (ab4174), ab4174 Histone H3 (mono methyl K4) antibody - ChIP Grade , ab8895 Histone H3 (di methyl K4) antibody [Y47] - ChIP Grade (ab32356), ab32356 Histone H3 (tri methyl K4) antibody [mAbcam1012] - ChIP Grade (ab1012), ab1012 Histone H3 (tri methyl K4) antibody - ChIP Grade, ab8580 Histone H3 (tri methyl K9) antibody - ChIP Grade, ab8898 Histone H3 (tri methyl K36) antibody - ChIP Grade , ab9050 Histone H3 (acetyl K27) antibody - ChIP Grade, ab4729 antibody for TAF II p250 (6B3), sc735 Nanog antibody-ChIP Grade, ab21624 Oct4 antibody-ChIP Grade, ab19857 antibody for p300 (C-20), sc-585 KAT3B / p300 antibody [NM11] , ab3164 monoclonal antibody against 5-methylcytindine (catalog number: bi-mecy-0100)
DNA products for ChIP-seq are immuno-precipitated by antibodies of H3K27me3(Millipore, 07-449),H2AZ(Abcam, ab4174). MRE-seq was performed as described (NATURE| Vol 466|8 July 2010). DNA product for 5-hmC_ChIP-seq is generated by a selctive chemical labeling method (Nat. Methods 2011, 9, 75-77). Four parallel digests were performed (HpaII, Hin6I and SsiI are from Fermentas; HpyCH4IV is from NEB), each with 1 ug of DNA. Five units of enzyme per microgram DNA were added and incubated at 37˚C for 3 hrs. A second dose of five units-enzyme was added and the DNA was incubated for an additional 3 hrs. Digested DNA was precipitated with sodium acetate and ethanol, and 500 ng of each digest were combined into one tube.  Combined DNA was size-selected by electrophoresis on a 1% agarose gel. A 100-500 bp gel slice was excised and gel-purified using Qiagen Qiaquick columns, eluting in 30 ul of Qiagen EB buffer. Library construction was performed using the Illumina Genomic DNA Sample kit with paired-end adapters, following the manufacturer’s instructions with the following changes. For the end repair reaction, T4 DNA polymerase and T4 polynucleotide kinase were excluded and the klenow DNA polymerase was diluted 1:5 in water and 1 ul used per reaction. For paired-end oligo adapter ligation, adapters were diluted 1:10 in water and 1 ul used per reaction. After the second size selection, DNA was eluted in 36 ul EB buffer using Qiagen Qiaquick columns, and 12 ul used as template for PCR, using Illumina reagents and cycling conditions with 15 cycles. After cleanup with Qiagen MinElute columns, each library is examined by Quant-iT™ dsDNA HS Assay Kit (Qubit, Invitrogen, USA) and Agilent DNA Biaanalyzer (Agilent, USA). The libraries were sequenced with an Illunima Hi-seq Sequencing Systems. MeDIP-seq was performed as described (NATURE| Vol 466|8 July 2010). 3 ug DNA isolated as described above was sonicated to ,100–500 bp with a Bioruptor sonicator (Diagenode). Sonicated DNA was end-repaired, A-tailed, and ligated to paired-end adapters following the standard Illumina protocol. After agarose size-selection to remove unligated adapters, 1 ug of adaptor-ligated DNA was used for each immunoprecipitation using a mouse monoclonal anti-methylcytidine antibody (1 mg/ml, Eurogentec). For this, DNA was heat-denatured at 95 ˚C for 10 min, rapidly cooled on ice, and immunoprecipitated with 1 ul primary antibody per microgram of DNA overnight at 4 ˚C with rocking agitation in 500 ml immunoprecipitation buffer (10mMsodium phosphate buffer, pH 7.0, 140mMNaCl, 0.05% Triton X-100). To recover the immunoabsorbed DNA fragments, 2ul of rabbit anti-mouse IgG secondary antibody (2.5 mg/ml, Jackson Immunoresearch) and 100 ml Protein A/G beads (Pierce Biotechnology) were added and incubated for an additional 2 h at 4 ˚C with agitation. After immunoprecipitation a total of six to ten immunoprecipitation washes were performed with ice-cold immunoprecipitation buffer. A nonspecific mouse IgG immunoprecipitation (Jackson Immunoresearch) was performed in parallel to methyl DNA immunoprecipitation as a negative control. Washed beads were resuspended in TE buffer with 0.25% SDS and 0.25 mg/ml proteinase K for 2 h at 55 ˚C and then allowed to cool to room temperature. MeDIP and supernatant DNA were purified using Qiagen Qiaquic columns and eluted in 30 ul EB (Qiagen). Twelve cycles of PCR were performed on 10 ul of the immunoprecipitated DNA using the paired-end Illumina PCR primers. The resulting reactions were purified with Qiagen MinElute columns, after which a final size selection (192–592 bp) was performed by electrophoresis in 2% agarose. Libraries were quality controlled by Qubit and Agilent DNA Bioanalyzer analysis. An aliquot of each library was diluted in EB to 5 ng/ul and 1 ul used as template in four independent PCR reactions to confirm enrichment for methylated and de-enrichment for unmethylated sequences, compared to 5 ng of input (sonicated DNA). Two positive controls (SNRPN and MAGEA1 promoters) and two negative controls (a CpG-less sequence on chromosome 15 and GAPDH promoter) were amplified (Supplementary). Cycling was 95 ˚C for 30 s, 58 ˚C for 30 s, 72 ˚C for 30 s with 30 cycles. PCR products were visualized by 1.5% agarose gel electrophoresis.

tag: cell type


title: E14_Day6_H3K4me2_ChIP-seq

library_source: genomic

data_processing:
Sequence reads were obtained and mapped to reference genomes (mm9, hg19, susScr2) using Bowtie. Only reads with one or fewer mismatches and uniquely mapped onto the genome were retained.
Genome Build:
E14_H3K4me2_d6.bed.gz: mm9

molecule: genomic DNA

value: mESC E14 under Activin A differentiation, day 6

tag: ChIP


title: Ago 2 immunoprecipitated RNA extracted from mock infected NIH-3T3 cells

status: Public on Mar 01 2012

city: Strasbourg

first: Sébastien

last: Pfeffer

address: 2 allée Konrad Roentgen

department: UPR 9002 - Architecture et Réactivité de l'ARN

country: France

institute: CNRS - Institut de Biologie Moléculaire et Cellulaire (IBMC)

data_processing: Counts: Raw reads were first processed using the Dustmasker program (Morgulis A et al., 2006. A fast and symmetric DUST implementation to mask low-complexity DNA sequences. J Comput Biol 13:1028-1040) to filter out low complexity reads, and then trimmed using the FASTX-Toolkit (http://hannonlab.cshl.edu/fastx_toolkit) to remove the 3' adaptor. Only reads from 15 to 32 nt were kept for further analysis. Remaining sequences with counts included.

growth_protocol: NIH-3T3 fibroblasts (ATCC: CRL-1658) were cultured in DMEM medium containing 5% fetal calf serum and penicillin/streptomycin. For viral infection, NIH-3T3 cells were infected with wild-type MCMV at an MOI of 10.

extract_protocol: Libraries were prepared using either total RNA extracted with Trizol from mock-infected NIH-3T3 cells, or from cells infected with MCMV for 6 hours as well as from RNA extracted after Ago2 IP from the same samples. For libraries generated from total RNA, 10 μg of total RNA was used in the initial step. For libraries generated from small RNAs incorporated into Ago2 containing RISC complexes, a fraction of the mAgo2 IPs from either MCMV infected (6hpi), or control cells were used. Samples were size fractionated on a 17.5% PAGE gel, and small RNAs between 19 and 33 nt were excised and cloned as previously described (Pfeffer S, 2007. Identification of virally encoded microRNAs. Methods Enzymol. 427:51-63), except that small RNA PCR products were not concatamerized and instead sent directly for sequencing.

treatment_protocol: Ago2-immunoprecipitation was performed as recently described [Dolken et al. Cell Host Microbe 7: 324-334]. In short, 6 µg of purified monoclonal mAgo2-antibody (2D4, Wako) was added to 5 mL of RPMI-medium and incubated with 30 µL of Protein-G-Sepharose beads (GE Healthcare) in Pierce centrifuge columns (Thermo Scientific) under constant rotation at 4 °C over night. Columns were drained by gravity flow and washed once with the lysis buffer. Beads were subsequently incubated with 5 mL of cell lysates for 2.5 h under constant rotation at 4 °C. After incubation, the beads were washed four times with IP wash buffer (300 mM NaCl, 50 mM Tris HCl pH 7.5, 5 mM MgCl2, 0.1% NP-40, 1 mM NaF) and once with PBS to remove residual detergents. RNA was recovered from the beads by adding 700 µl of Qiazol to the columns. After 5 min the Qiazol lysates were collected from the columns. This step was repeated once and the Qiazol lysates were combined. RNA was prepared using the miRNeasy kit (Qiagen) according to the manufacturer's instructions. RNA samples were eluted in 30 µL of H2O.

value: fibroblast

tag: ip antibody

tag: ip antibody vendor

value: Wako

tag: ip antibody cat #


title: E14_Day6_H3K36me3_ChIP-seq

data_processing:
Sequence reads were obtained and mapped to reference genomes (mm9, hg19, susScr2) using Bowtie. Only reads with one or fewer mismatches and uniquely mapped onto the genome were retained.
Genome Build:
E14_H3K36me3_d6.bed.gz: mm9


title: E14_Day6_H3K27me3_ChIP-seq

data_processing:
Sequence reads were obtained and mapped to reference genomes (mm9, hg19, susScr2) using Bowtie. Only reads with one or fewer mismatches and uniquely mapped onto the genome were retained.
Genome Build:
E14_H3K27me3_d6.bed.gz: mm9


title: Total RNA extracted from MCMV infected NIH-3T3 cells

treatment_protocol: Ago2-immunoprecipitation was performed as recently described [Dolken et al. Cell Host Microbe 7: 324-334]. In short, 6 µg of purified monoclonal mAgo2-antibody (2D4, Wako) or monoclonal BrdU-antibody (abcam; used as control) was added to 5 mL of RPMI-medium and incubated with 30 µL of Protein-G-Sepharose beads (GE Healthcare) in Pierce centrifuge columns (Thermo Scientific) under constant rotation at 4 °C over night. Columns were drained by gravity flow and washed once with the lysis buffer. Beads were subsequently incubated with 5 mL of cell lysates for 2.5 h under constant rotation at 4 °C. After incubation, the beads were washed four times with IP wash buffer (300 mM NaCl, 50 mM Tris HCl pH 7.5, 5 mM MgCl2, 0.1% NP-40, 1 mM NaF) and once with PBS to remove residual detergents. RNA was recovered from the beads by adding 700 µl of Qiazol to the columns. After 5 min the Qiazol lysates were collected from the columns. This step was repeated once and the Qiazol lysates were combined. RNA was prepared using the miRNeasy kit (Qiagen) according to the manufacturer's instructions. RNA samples were eluted in 30 µL of H2O.

tag: infected with

value: murine cytomegalovirus (MCMV)


title: E14_Day6_H3K4me1_ChIP-seq

data_processing:
Sequence reads were obtained and mapped to reference genomes (mm9, hg19, susScr2) using Bowtie. Only reads with one or fewer mismatches and uniquely mapped onto the genome were retained.
Genome Build:
E14_H3K4me1_d6.bed.gz: mm9


title: Footprint for rapamycin treated PC3 cells, replicate #2.

data_processing:
Each sequencing run resulted in approximately 20-25 million raw reads per sample, of which 5-12 million unique reads were used for subsequent analysis. Ribosome footprint and RNA-Seq sequencing reads were aligned against a library of transcripts from the UCSC Known Genes database GRCh37/hg19. The first 25 nucleotides of each read were aligned using Bowtie and this initial alignment was then extended to encompass the full fragment-derived portion of the sequencing read while excluding the linker sequence. Read density profiles were then constructed for the canonical transcript of each gene, using only reads with 0 or 1 total mismatches between the read sequence and the reference sequence, comprised of the transcript fragment followed by the linker sequence. Footprint reads were assigned to an A site nucleotide at position +15 to +17 of the alignment, based on the total fragment length; mRNA reads were assigned to the first nucleotide of the alignment. The average read density per codon was then computed for the coding sequence of each transcript, excluding the first 15 and last 5 codons, which can display atypical ribosome accumulation.
Average read density was used as a measure of mRNA abundance (RNA-Seq reads) and of protein synthesis (ribosome profiling reads). For most analyses, genes were filtered to require at least 256 reads in the relevant RNA-Seq samples. Translational efficiency was computed as the ratio of ribosome footprint read density to RNA-Seq read density, scaled to normalize the translational efficiency of the median gene to 1.0 after excluding regulated genes (log2 fold-change +/- 1.5 after normalizing for the all-gene median). Changes in protein synthesis, mRNA abundance, and translational efficiency were similarly computed as the ratio of read densities between different samples, normalized to give the median gene a ratio of 1.0. This normalization corrects for differences in the absolute number of sequencing reads obtained for different libraries. 5333 (replicate #1) and 3977 (replicate #2) unique mRNAs passed a preset read threshold of 256 reads for single-gene quantification for all treatment conditions.
processed data file contains per gene read count, columns: 1) UCSC ID, 2) Length of trimmed CDS, 3) Read count in trimmed CDS
Genome Build:
fp_rapa_b_qexpr.txt: UCSC GRCh37/hg19

value: ribosome protected RNA


title: E14_Day6_H3K27ac_ChIP-seq

data_processing:
Sequence reads were obtained and mapped to reference genomes (mm9, hg19, susScr2) using Bowtie. Only reads with one or fewer mismatches and uniquely mapped onto the genome were retained.
Genome Build:
E14_H3K27ac_d6.bed.gz: mm9


title: E14_Day6_H2AZ_ChIP-seq

data_processing:
Sequence reads were obtained and mapped to reference genomes (mm9, hg19, susScr2) using Bowtie. Only reads with one or fewer mismatches and uniquely mapped onto the genome were retained.
Genome Build:
E14_H2AZ_d6.bed.gz: mm9


title: E14_Day4_H3K4me3_ChIP-seq

data_processing:
Sequence reads were obtained and mapped to reference genomes (mm9, hg19, susScr2) using Bowtie. Only reads with one or fewer mismatches and uniquely mapped onto the genome were retained.
Genome Build:
E14_H3K4me3_d4.bed.gz: mm9

source_name: mESC E14 under Activin A differentiation, day 4


title: E14_Day4_H3K4me2_ChIP-seq

data_processing:
Sequence reads were obtained and mapped to reference genomes (mm9, hg19, susScr2) using Bowtie. Only reads with one or fewer mismatches and uniquely mapped onto the genome were retained.
Genome Build:
E14_H3K4me2_d4.bed.gz: mm9

value: mESC E14 under Activin A differentiation, day 4


title: E14_Day4_H3K4me1_ChIP-seq

data_processing:
Sequence reads were obtained and mapped to reference genomes (mm9, hg19, susScr2) using Bowtie. Only reads with one or fewer mismatches and uniquely mapped onto the genome were retained.
Genome Build:
E14_H3K4me1_d4.bed.gz: mm9


title: Ago 2 immunoprecipitated RNA extracted from MCMV infected NIH-3T3 cells


title: E14_Day4_H3K9me3_ChIP-seq

data_processing:
Sequence reads were obtained and mapped to reference genomes (mm9, hg19, susScr2) using Bowtie. Only reads with one or fewer mismatches and uniquely mapped onto the genome were retained.
Genome Build:
E14_H3K9me3_d4.bed.gz: mm9


title: E14_Day4_RNA-seq

data_processing:
Sequence reads were obtained and mapped to reference genomes (mm9, hg19, susScr2) using Bowtie. Only reads with one or fewer mismatches and uniquely mapped onto the genome were retained.
Genome Build:
E14_RNA_d4.bam.gz: mm9
E14_RNA_d4_genes.expr.gz: mm9
E14_RNA_d4_transcripts.expr.gz: mm9
E14_RNA_d4_transcripts.gtf.gz: mm9


title: E14_Day4_H3K27ac_ChIP-seq

data_processing:
Sequence reads were obtained and mapped to reference genomes (mm9, hg19, susScr2) using Bowtie. Only reads with one or fewer mismatches and uniquely mapped onto the genome were retained.
Genome Build:
E14_H3K27ac_d4.bed.gz: mm9


title: E14_Day4_H2AZ_ChIP-seq

data_processing:
Sequence reads were obtained and mapped to reference genomes (mm9, hg19, susScr2) using Bowtie. Only reads with one or fewer mismatches and uniquely mapped onto the genome were retained.
Genome Build:
E14_H2AZ_d4.bed.gz: mm9


title: Footprint for vehicle treated PC3 cells, replicate #1.

data_processing:
Each sequencing run resulted in approximately 20-25 million raw reads per sample, of which 5-12 million unique reads were used for subsequent analysis. Ribosome footprint and RNA-Seq sequencing reads were aligned against a library of transcripts from the UCSC Known Genes database GRCh37/hg19. The first 25 nucleotides of each read were aligned using Bowtie and this initial alignment was then extended to encompass the full fragment-derived portion of the sequencing read while excluding the linker sequence. Read density profiles were then constructed for the canonical transcript of each gene, using only reads with 0 or 1 total mismatches between the read sequence and the reference sequence, comprised of the transcript fragment followed by the linker sequence. Footprint reads were assigned to an A site nucleotide at position +15 to +17 of the alignment, based on the total fragment length; mRNA reads were assigned to the first nucleotide of the alignment. The average read density per codon was then computed for the coding sequence of each transcript, excluding the first 15 and last 5 codons, which can display atypical ribosome accumulation.
Average read density was used as a measure of mRNA abundance (RNA-Seq reads) and of protein synthesis (ribosome profiling reads). For most analyses, genes were filtered to require at least 256 reads in the relevant RNA-Seq samples. Translational efficiency was computed as the ratio of ribosome footprint read density to RNA-Seq read density, scaled to normalize the translational efficiency of the median gene to 1.0 after excluding regulated genes (log2 fold-change +/- 1.5 after normalizing for the all-gene median). Changes in protein synthesis, mRNA abundance, and translational efficiency were similarly computed as the ratio of read densities between different samples, normalized to give the median gene a ratio of 1.0. This normalization corrects for differences in the absolute number of sequencing reads obtained for different libraries. 5333 (replicate #1) and 3977 (replicate #2) unique mRNAs passed a preset read threshold of 256 reads for single-gene quantification for all treatment conditions.
processed data file contains per gene read count, columns: 1) UCSC ID, 2) Length of trimmed CDS, 3) Read count in trimmed CDS
Genome Build:
fp_dmso_a_qexpr.txt: UCSC GRCh37/hg19


title: Footprint for rapamycin treated PC3 cells, replicate #1.

data_processing:
Each sequencing run resulted in approximately 20-25 million raw reads per sample, of which 5-12 million unique reads were used for subsequent analysis. Ribosome footprint and RNA-Seq sequencing reads were aligned against a library of transcripts from the UCSC Known Genes database GRCh37/hg19. The first 25 nucleotides of each read were aligned using Bowtie and this initial alignment was then extended to encompass the full fragment-derived portion of the sequencing read while excluding the linker sequence. Read density profiles were then constructed for the canonical transcript of each gene, using only reads with 0 or 1 total mismatches between the read sequence and the reference sequence, comprised of the transcript fragment followed by the linker sequence. Footprint reads were assigned to an A site nucleotide at position +15 to +17 of the alignment, based on the total fragment length; mRNA reads were assigned to the first nucleotide of the alignment. The average read density per codon was then computed for the coding sequence of each transcript, excluding the first 15 and last 5 codons, which can display atypical ribosome accumulation.
Average read density was used as a measure of mRNA abundance (RNA-Seq reads) and of protein synthesis (ribosome profiling reads). For most analyses, genes were filtered to require at least 256 reads in the relevant RNA-Seq samples. Translational efficiency was computed as the ratio of ribosome footprint read density to RNA-Seq read density, scaled to normalize the translational efficiency of the median gene to 1.0 after excluding regulated genes (log2 fold-change +/- 1.5 after normalizing for the all-gene median). Changes in protein synthesis, mRNA abundance, and translational efficiency were similarly computed as the ratio of read densities between different samples, normalized to give the median gene a ratio of 1.0. This normalization corrects for differences in the absolute number of sequencing reads obtained for different libraries. 5333 (replicate #1) and 3977 (replicate #2) unique mRNAs passed a preset read threshold of 256 reads for single-gene quantification for all treatment conditions.
processed data file contains per gene read count, columns: 1) UCSC ID, 2) Length of trimmed CDS, 3) Read count in trimmed CDS
Genome Build:
fp_rapa_a_qexpr.txt: UCSC GRCh37/hg19


title: E14_Day4_H3K27me3_ChIP-seq

data_processing:
Sequence reads were obtained and mapped to reference genomes (mm9, hg19, susScr2) using Bowtie. Only reads with one or fewer mismatches and uniquely mapped onto the genome were retained.
Genome Build:
E14_H3K27me3_d4.bed.gz: mm9


title: E14_Day0_RNA-seq

data_processing:
Sequence reads were obtained and mapped to reference genomes (mm9, hg19, susScr2) using Bowtie. Only reads with one or fewer mismatches and uniquely mapped onto the genome were retained.
Genome Build:
E14_RNA.bam.gz: mm9
E14_RNA_genes.expr.gz: mm9
E14_RNA_transcripts.expr.gz: mm9
E14_RNA_transcripts.gtf.gz: mm9

source_name: mouse embryonic stem cells (E14)

value: mouse embryonic stem cells (E14)


title: E14_Day4_H3K36me3_ChIP-seq

data_processing:
Sequence reads were obtained and mapped to reference genomes (mm9, hg19, susScr2) using Bowtie. Only reads with one or fewer mismatches and uniquely mapped onto the genome were retained.
Genome Build:
E14_H3K36me3_d4.bed.gz: mm9


title: E14_Day0_H3K27ac_ChIP-seq

data_processing:
Sequence reads were obtained and mapped to reference genomes (mm9, hg19, susScr2) using Bowtie. Only reads with one or fewer mismatches and uniquely mapped onto the genome were retained.
Genome Build:
E14_H3K27ac.bed.gz: mm9


title: E14_Day0_H3K4me3_ChIP-seq

data_processing:
Sequence reads were obtained and mapped to reference genomes (mm9, hg19, susScr2) using Bowtie. Only reads with one or fewer mismatches and uniquely mapped onto the genome were retained.
Genome Build:
E14_H3K4me3.bed.gz: mm9


title: E14_Day0_H3K4me2_ChIP-seq

data_processing:
Sequence reads were obtained and mapped to reference genomes (mm9, hg19, susScr2) using Bowtie. Only reads with one or fewer mismatches and uniquely mapped onto the genome were retained.
Genome Build:
E14_H3K4me2.bed.gz: mm9


title: E14_Day0_H3K4me1_ChIP-seq

data_processing:
Sequence reads were obtained and mapped to reference genomes (mm9, hg19, susScr2) using Bowtie. Only reads with one or fewer mismatches and uniquely mapped onto the genome were retained.
Genome Build:
E14_H3K4me1.bed.gz: mm9


title: E14_Day0_H3K36me3_ChIP-seq

data_processing:
Sequence reads were obtained and mapped to reference genomes (mm9, hg19, susScr2) using Bowtie. Only reads with one or fewer mismatches and uniquely mapped onto the genome were retained.
Genome Build:
E14_H3K36me3.bed.gz: mm9


title: E14_Day0_H2AZ_ChIP-seq

data_processing:
Sequence reads were obtained and mapped to reference genomes (mm9, hg19, susScr2) using Bowtie. Only reads with one or fewer mismatches and uniquely mapped onto the genome were retained.
Genome Build:
E14_H2AZ.bed.gz: mm9


title: Total RNA extracted from mock infected NIH-3T3 cells

data_processing: Counts: Raw reads were first processed using the Dustmasker program (Morgulis A et al., 2006. A fast and symmetric DUST implementation to mask low-complexity DNA sequences. J Comput Biol 13:1028-1040) to filter out low complexity reads, and then trimmed using the FASTX-Toolkit (http://hannonlab.cshl.edu/fastx_toolkit) to remove the 3' adaptor. Only reads from 15 to 32 nt were kept for further analysis. Remaining sequences with counts included.


title: E14_TAF1_ChIP-seq

data_processing:
Sequence reads were obtained and mapped to reference genomes (mm9, hg19, susScr2) using Bowtie. Only reads with one or fewer mismatches and uniquely mapped onto the genome were retained.
Genome Build:
E14_TAF1.bed.gz: mm9


title: piPSC_IgG_ChIP-seq

data_processing:
Sequence reads were obtained and mapped to reference genomes (mm9, hg19, susScr2) using Bowtie. Only reads with one or fewer mismatches and uniquely mapped onto the genome were retained.
Genome Build:
piPS_IgG.bed.gz: susScr2

organism: Sus scrofa

source_name: pig induced pluripotent stem cells

value: pig induced pluripotent stem cells

value: IgG


title: piPSC_RNA-seq

data_processing:
Sequence reads were obtained and mapped to reference genomes (mm9, hg19, susScr2) using Bowtie. Only reads with one or fewer mismatches and uniquely mapped onto the genome were retained.
Genome Build:
piPS_RNA.bam.gz: susScr2
piPS_RNA_genes.expr.gz: susScr2
piPS_RNA_transcripts.expr.gz: susScr2
piPS_RNA_transcripts.gtf.gz: susScr2


title: piPSC_OCT4_ChIP-seq

data_processing:
Sequence reads were obtained and mapped to reference genomes (mm9, hg19, susScr2) using Bowtie. Only reads with one or fewer mismatches and uniquely mapped onto the genome were retained.
Genome Build:
piPS_OCT4.bed.gz: susScr2


title: piPSC_p300_ChIP-seq

data_processing:
Sequence reads were obtained and mapped to reference genomes (mm9, hg19, susScr2) using Bowtie. Only reads with one or fewer mismatches and uniquely mapped onto the genome were retained.
Genome Build:
piPS_P300.bed.gz: susScr2


title: piPSC_NANOG_ChIP-seq

data_processing:
Sequence reads were obtained and mapped to reference genomes (mm9, hg19, susScr2) using Bowtie. Only reads with one or fewer mismatches and uniquely mapped onto the genome were retained.
Genome Build:
piPS_NANOG.bed.gz: susScr2

value: NANOG


title: piPSC_TAF1_ChIP-seq

data_processing:
Sequence reads were obtained and mapped to reference genomes (mm9, hg19, susScr2) using Bowtie. Only reads with one or fewer mismatches and uniquely mapped onto the genome were retained.
Genome Build:
piPS_TAF1.bed.gz: susScr2


title: piPSC_H3K4me2_ChIP-seq

data_processing:
Sequence reads were obtained and mapped to reference genomes (mm9, hg19, susScr2) using Bowtie. Only reads with one or fewer mismatches and uniquely mapped onto the genome were retained.
Genome Build:
piPS_H3K4me2.bed.gz: susScr2


title: piPSC_H3K9me3_ChIP-seq

data_processing:
Sequence reads were obtained and mapped to reference genomes (mm9, hg19, susScr2) using Bowtie. Only reads with one or fewer mismatches and uniquely mapped onto the genome were retained.
Genome Build:
piPS_H3K9me3.bed.gz: susScr2


title: piPSC_H3K27me3_ChIP-seq

data_processing:
Sequence reads were obtained and mapped to reference genomes (mm9, hg19, susScr2) using Bowtie. Only reads with one or fewer mismatches and uniquely mapped onto the genome were retained.
Genome Build:
piPS_H3K27me3.bed.gz: susScr2


title: piPSC_H3K4me1_ChIP-seq

data_processing:
Sequence reads were obtained and mapped to reference genomes (mm9, hg19, susScr2) using Bowtie. Only reads with one or fewer mismatches and uniquely mapped onto the genome were retained.
Genome Build:
piPS_H3K4me1.bed.gz: susScr2


title: piPSC_H3K27ac_ChIP-seq

data_processing:
Sequence reads were obtained and mapped to reference genomes (mm9, hg19, susScr2) using Bowtie. Only reads with one or fewer mismatches and uniquely mapped onto the genome were retained.
Genome Build:
piPS_H3K27ac.bed.gz: susScr2


title: HNRPA2B1_HITSCLIP_2

first: Hani

last: Goodarzi

department: Biochemistry and Biophysics

description: small RNA

data_processing:
Library strategy: CLIP-Seq
The raw data was aligned to hg18 human genome build using bowtie. ChIPseeqer (Giannopoulou and Elemeneto, 2011) was used to process the reads, find peak coordinates and create randomly generated peaks.
Genome Build:
HNRNPA2_rep2_real_random_peak_coordinates.txt: hg18
HNRNPA2_rep2_real_random_peaks.fa: hg18
HNRNPA2_rep2_real_random_peak_coordinates.txt: The discovered peaks along with their randomly generated counterparts (real peaks are set to '1' and random ones to '0'). The associated sequences are provided in the HNRNPA2_rep2_real_random_peaks.fa file.

growth_protocol: MDA-MB-231 cells at 50% confluency were transfected with tagged HNRPA2B1 (Origene) using Lipofectamin LTX reagent (Invitrogen) according to manufacturer’s recommendations.

extract_protocol: Libraries were constructed according to HITS-CLIP protocol (Chi et al., 2009, Nature, 460: 479-86).

treatment_protocol: Seventy-two hours post-transfection, cells were UV-crosslinked and lysed. Samples were collected and treated based on HITS-CLIP protocol (Chi et al., 2009, Nature, 460: 479-86).

tag: antibody

tag: antibody vendor

value: Origene


title: piPSC_H3K4me3_ChIP-seq

data_processing:
Sequence reads were obtained and mapped to reference genomes (mm9, hg19, susScr2) using Bowtie. Only reads with one or fewer mismatches and uniquely mapped onto the genome were retained.
Genome Build:
piPS_H3K4me3.bed.gz: susScr2


title: HNRPA2B1_HITSCLIP_1

description: small RNA

data_processing:
Library strategy: CLIP-Seq
The raw data was aligned to hg18 human genome build using bowtie. ChIPseeqer (Giannopoulou and Elemeneto, 2011) was used to process the reads, find peak coordinates and create randomly generated peaks.
Genome Build:
HNRNPA2_rep1_real_random_peak_coordinates.txt: hg18
HNRNPA2_rep1_real_random_peaks.fa: hg18
HNRNPA2_rep1_real_random_peak_coordinates.txt: The discovered peaks along with their randomly generated counterparts (real peaks are set to '1' and random ones to '0'). The associated sequences are provided in the HNRNPA2_rep1_real_random_peaks.fa file.


title: piPSC_H3K36me3_ChIP-seq

data_processing:
Sequence reads were obtained and mapped to reference genomes (mm9, hg19, susScr2) using Bowtie. Only reads with one or fewer mismatches and uniquely mapped onto the genome were retained.
Genome Build:
piPS_H3K36me3.bed.gz: susScr2


title: piPSC_H2AZ_ChIP-seq

data_processing:
Sequence reads were obtained and mapped to reference genomes (mm9, hg19, susScr2) using Bowtie. Only reads with one or fewer mismatches and uniquely mapped onto the genome were retained.
Genome Build:
piPS_H2AZ.bed.gz: susScr2


title: E14_IgG_ChIP-seq

data_processing:
Sequence reads were obtained and mapped to reference genomes (mm9, hg19, susScr2) using Bowtie. Only reads with one or fewer mismatches and uniquely mapped onto the genome were retained.
Genome Build:
E14_IgGR.bed.gz: mm9


title: 4-7hr PolII ChIP-Seq ChIP Oregon-R strain Replicate 2

status: Public on Feb 27 2012

city: Toronto

first: DCC

last: modENCODE

state: Ontario

address: MaRS Centre, South Tower, 101 College Street, Suite 800

country: Canada

institute: Ontario Institute for Cancer Research

description: ChIP DNA; Antibody information listed below: official name: Anti-RNA polymerase II clone CTD4H8;target name: RNA polymerase II;host: Mouse;antigen: unknown;clonal: Monoclonal;purified: Protein A/G;company: Upstate-Millipore;catalog: 05-623;short description: Anti-RNA polymerase II clone CTD4H8. Peptide containing 10 repeats of YSPT[pS]PS corresponding to the carboxyl-terminal domain of RNA polymerase II. Purified mouse IgG1 in buffer containing 0.02 M phosphate buffer pH 7.6 0.25 M NaCl and 0.1% Sodium Azide before the addition of glycerol to 30%.

data_processing: Illumina_seq_maq_processing:DM:1 protocol. Using the MAQ command-line tool v.0.7.1, reads are converted from Illumina FASTQ to Sanger FASTQ to Binary FASTQ. The Drosophila genome v.5.1 is then converted into binary fasta format. Then the reads are mapped back to the Drosophila genome using maq 0.7.1 with a maximum of 3 mismatches per read and a mapview file is created which is then read into a local relational database.  Processed data are obtained using following parameters: genome version is 5.1   ChIPseq analysis BEDGRAPH Generation:DM:1 protocol. BEDGRAPH Track Generation:    * Tracks are read into SPP and informative tags are selected using SPP's select.informative.tags function.    * Local sequencing anomalies are removed with SPP's remove.local.tag.anomalies function.    * Input subtracted ChIP density bedgraph track is output with a step size of 100 and a bandwidth of 100 using SPP's get.smoothed.tag.density function. The tags are shifted 1/2 of the optimal shift size discovered with get.binding.characteristics.    * To combine replicates, all the reads were pooled together and the tag shift was estimated as the mean tag shift between the replicates.

organism: Drosophila melanogaster

growth_protocol: Using newly eclosed females fattened on wet yeast paste, collect embryos for up to four hours on apple juice plates. To ensure that females have not been holding embryos, precollect embryos for one hour. Wash embryos off the four-hour collection dish with PBS; dechorionate in 50% bleach for 3 minutes, and collect dechorionated embryos with a fine nylon mesh screen.

extract_protocol:
Cross linking and chromatin extraction protocol for Drosophila ChIP experiments.
Chromatin IP of a chromatin extract with an antibody.
Generation of a genomic library suitable for Illumina sequencing, using the standard Illumina protocol.  This includes a 150-200bp size selection step.
Load sample onto flow cell at a (usually 2-8 pM, variable) concentration and run on an Illumina Genome Analyzer II for single-end sequencing for 36 or 76 nt. Image data is then deconvoluted using the most current versions of Firecrest, Bustard, and Gerald (or equivalents).

tag: strain

value: Oregon-R(official name :Oregon-R-modENCODE genotype :wild type )

tag: developmental stage

value: Embryo 4-7h

tag: genotype

value: wild type


title: 4-7hr ORC ChIP-Seq ChIP Oregon-R strain Replicate 3

description: ChIP DNA; Antibody information listed below: official name: dORC2;target name: Drosophila ORC2p;host: Rabbit;antigen: purified full length protein;clonal: Polyclonal;purified: Crude;company: Lab;catalog: Stephen P. Bell MIT;reference: Austin et al 1999


title: 4-7hr PolII ChIP-Seq ChIP Oregon-R strain Replicate 1


title: 4-7hr ORC ChIP-Seq ChIP Oregon-R strain Replicate 1

source_name: 4-7hr_ORC ChIP-Seq ChIP Oregon-R strain Replicate 1


title: Genomic DNA of patient P440 blood [SOLiD v3+]

city: Cologne

first: Axel

last: HILLMER

address: Kerpener Str. 62

department: Institute of Pathology

country: Germany

institute: University of Cologne

description:
genomic DNA hydrosheared in 7.9 kb fragments (std dev: 767), 2x25bp paired-end (mate pair) sequences
GSM700323 and GSM700324 contain identical processed data files since 2 GEO Sample records have been generated based on sequencers.

data_processing: Raw data provided. For the project, sequence tags were mapped to the human reference sequence (NCBI Build 36) allowing 2 color code mismatches and paired using SOLiD System Analysis Pipeline Tool, Corona Lite v0.31 (Applied Biosystems) except for library DHH014 which was mapped/paired by Bioscope v1.0.1 (Applied Biosystems). If sequence tags had multiple mapping locations and one of them was located in the expected distance and orientation to its mate, this location was chosen by a process termed 'rescue'. Discordant paired-end tags (PETs) were defined and clustered to call structural variations as described in the readme file.

source_name: mononuclear blood cells

growth_protocol: mononuclear cells extracted by standard protocol from patient blood samples

extract_protocol: Genomic DNA was hydrosheared, EcoP15I recognition sites were methylated and EcoP15I CAP adaptors were ligated to the ends of DNA fragments. The methylated DNA constructs were separated on agarose gel and 5, 7, or 9 Kb sized fragments were selected for ligation resulting in circularized products where 5' and 3' ends of the DNA fragments were connected by an internal biotinylated adaptor with two flanking non-methylated EcoP15I CAP adaptors. Constructs were digested by methylation sensitive EcoP15I to release 5' and 3' paired-end tag (PET) constructs. Sequencing adaptors were ligated to the PET constructs, which were then amplified by PCR and sequenced by the Applied Biosystems SOLiD system.

treatment_protocol: no treatment

tag: tumor type

value: chronic myelogenous leukemia, remission, same patient as P145

value: blood


title: 4-7hr ORC ChIP-Seq Input Oregon-R strain

description: input DNA

data_processing: Illumina_seq_maq_processing:DM:1 protocol. Using the MAQ command-line tool v.0.7.1, reads are converted from Illumina FASTQ to Sanger FASTQ to Binary FASTQ. The Drosophila genome v.5.1 is then converted into binary fasta format. Then the reads are mapped back to the Drosophila genome using maq 0.7.1 with a maximum of 3 mismatches per read and a mapview file is created which is then read into a local relational database.  Processed data are obtained using following parameters: genome version is 5.1   ChIPseq analysis WIG Generation:DM:1 protocol. WIG Track Generation:    * Tracks are read into SPP and informative tags are selected using SPP's select.informative.tags function.    * Local sequencing anomalies are removed with SPP's remove.local.tag.anomalies function.    * Input subtracted ChIP density wiggle track is output with a step size of 100 and a bandwidth of 100 using SPP's get.smoothed.tag.density function. The tags are shifted 1/2 of the optimal shift size discovered with get.binding.characteristics.    * To combine replicates, all the reads were pooled together and the tag shift was estimated as the mean tag shift between the replicates.


title: Genomic DNA of patient P145 blood

description: genomic DNA hydrosheared in 8.8 kb fragments (std dev: 542 bp below median; 562 above median), 2x25bp paired-end (mate pair) sequences

value: chronic myelogenous leukemia, chronic phase, same patient as P440


title: 4-7hr ORC ChIP-Seq ChIP Oregon-R strain Replicate 2


title: Adult mouse brain

city: Charlottesville,

first: Anindya

last: Dutta

email: ad8q@virginia.edu, pk7z@virginia.edu

state: VA

address: Room no. 1232, Jordan Hall

department: MD-BIOC BC/Mole Genetics~School of medicine

institute: University of Virginia

description:
extra chromosomal circular DNA sequencing
Pair End

data_processing:
Identification of microDNA by paired-end sequencing of MDA products: microDNA were computationally identified by two different methods: Island method and split read method (in-house developed). All sequence tags were mapped on the reference genome (build mm9 for mouse and hg19 for human) by using the Novoalign software. Only those paired tags that could be mapped uniquely and in opposite orientation to each other were considered for the identification of islands in the "island method". The sequence coverage of each base pair was profiled for each chromosome, and islands (potential circles) delineated where there were two consecutive sequenced bases. In other words an "island" is a contiguous portion of the genome where a cluster of PE-tags were mapped uniquely and in the opposite orientation to its pair. Paired End tags that had one tag mapping uniquely to an island, while the other was unmapped but passed the sequence quality filter, were considered separately for the validation of circles. We generated a database of hypothetical junctional tags that could be created by ligation of the ends of islands to generate circles. The unmapped tags described above were matched against these hypothetical junctional tags. If the mapped tag of a PE read falls in an island and the un-mapped tag matches with a hypothetical junctional tag generated by ligation of the ends of the island, we annotated that island as a circle. This approach underestimates the number of true microDNAs because further sequence coverage is likely to reveal more junctional tags ligating the ends of islands. The "split read" method extracted the paired end tags where one end was mapped (M) and other end was un-mapped (UM) but had good base quality score. The UM tags were split into two equal halves and mapped individually by using Novoalign software. Those split pairs which were mapped on the same strand (F-F or R-R) with no mismatch were considered for the identification of circles.  The "Island method" and the "split-read method" both are dependent on the junctional tags for the identification of circles. >80% of the circles were identified by both algorithms. The "Island method" failed to detect circles 1) with low base coverage, 2) with the start and the end of the circle contaminated by reads from contaminating linear DNA or lost due to sequencing errors and 3) that were overlapping with each other. On the other hand the "Split-read method" failed to detect circles if the two split reads do not map uniquely to the genome. Processed data include the microDNA co-ordinate (chromosome name, start position of circle and end position of circle) mm9 assembly for mouse and hg19 assembly for human samples. The fourth column in the processed data is base pair coverage (abundance).
Genome Build:
AMB.miDNA.txt: mm9

source_name: 6 Months old adult mice

extract_protocol: MicroDNA library preparation and sequencing: Extrachromosomal DNA was prepared from isolated nuclei by HiSpeed Plasmid Midi Kit, Qiagen, 12643. Nuclei were suspended in 10 ml Buffer P1 containing 10 ng/μl RNase A and lysed by adding 10 ml Buffer P2. Genomic DNA was precipitated on ice after the addition of 10 ml Buffer P3. The precipitate was removed by QIAfilter Cartridge and the clear lysate was loaded onto an equilibrated HiSpeed Midi Tip. After washing a tip with 20 ml Buffer QC, DNA was eluted with 5 ml Buffer QF. Eluted DNA was precipitaed by adding 3.5 ml isopropanol and centrifuged immediately at 15,000 x g for 30 min at 4°C. DNA pellet was washed with 2 ml 70% ethanol and dissolved in 20 μl Buffer EB. The extrachromosomal DNA was treated sequentially with 10 ng/μl RNase A and 200 ng/μl proteinase K (reaction volume: 20 μl/μg DNA), followed by phenol-chloroform extraction and ethanol precipitation. The fraction was then digested with 0.4 U/μl ATP-dependent DNase (reaction volume:250 μl/μg DNA) (Epicentre, E3101K) to remove linear double-stranded DNA. This treatment cycle was repeated twice for some preparations. ATP-dependent DNase-resistant DNA (extra chromosomal circular DNA (eccDNA) fraction) was purified by QIAquick PCR Purification Kit (Qiagen, 28104) to further remove oligonucleotides and DNA of >10 kb (including mitochondrial DNA). The eccDNA was then amplified by REPLI-g Mini Kit using Multiple Displacement Amplification. 5 μg of Amplified DNA was nebulized and used for paired-end (PE) library construction as described. Paired-end high-throughput sequencing was performed according to the manufacturer's protocol (Illumina).

treatment_protocol: Nuclei were extracted from tissues and cell lines. All the reagents used were pre-chilled and the entire procedure was performed on ice. Tissues and cells were homogenized by douncing 50 times in 10 mL of Nuclear Extraction Buffer. After homogenization, samples were layered on 18 mL of Sucrose Cushion in 40 mL ultracentrifuge tubes (Beckman, 25 × 89 mm, 344058) and spun at 30,000 x g for 45 min at 4°C (Beckman, L8-70M, SW28 rotor). Supernatant, including debris, was removed, and 200 μl of chilled 1 × PBS was added to the tube. After 20 min incubation on ice, the nuclei were dissociated by pipetting, and recovered by centrifugation at 500 x g for 5 min.

tag: tissue/cell lines

value: Adult mouse brain

tag: dna type

value: Circular DNA


title: Embryo mouse brain sample

data_processing:
Identification of microDNA by paired-end sequencing of MDA products: microDNA were computationally identified by two different methods: Island method and split read method (in-house developed). All sequence tags were mapped on the reference genome (build mm9 for mouse and hg19 for human) by using the Novoalign software. Only those paired tags that could be mapped uniquely and in opposite orientation to each other were considered for the identification of islands in the "island method". The sequence coverage of each base pair was profiled for each chromosome, and islands (potential circles) delineated where there were two consecutive sequenced bases. In other words an "island" is a contiguous portion of the genome where a cluster of PE-tags were mapped uniquely and in the opposite orientation to its pair. Paired End tags that had one tag mapping uniquely to an island, while the other was unmapped but passed the sequence quality filter, were considered separately for the validation of circles. We generated a database of hypothetical junctional tags that could be created by ligation of the ends of islands to generate circles. The unmapped tags described above were matched against these hypothetical junctional tags. If the mapped tag of a PE read falls in an island and the un-mapped tag matches with a hypothetical junctional tag generated by ligation of the ends of the island, we annotated that island as a circle. This approach underestimates the number of true microDNAs because further sequence coverage is likely to reveal more junctional tags ligating the ends of islands. The "split read" method extracted the paired end tags where one end was mapped (M) and other end was un-mapped (UM) but had good base quality score. The UM tags were split into two equal halves and mapped individually by using Novoalign software. Those split pairs which were mapped on the same strand (F-F or R-R) with no mismatch were considered for the identification of circles.  The "Island method" and the "split-read method" both are dependent on the junctional tags for the identification of circles. >80% of the circles were identified by both algorithms. The "Island method" failed to detect circles 1) with low base coverage, 2) with the start and the end of the circle contaminated by reads from contaminating linear DNA or lost due to sequencing errors and 3) that were overlapping with each other. On the other hand the "Split-read method" failed to detect circles if the two split reads do not map uniquely to the genome. Processed data include the microDNA co-ordinate (chromosome name, start position of circle and end position of circle) mm9 assembly for mouse and hg19 assembly for human samples. The fourth column in the processed data is base pair coverage (abundance).
Genome Build:
Genomic.miDNA.txt: mm9

source_name: 13.5 days old Embryo mouse brain

value: Embryo mouse brain sample

value: Genomic DNA


title: NIH3T3 mouse cell lines

data_processing:
Identification of microDNA by paired-end sequencing of MDA products: microDNA were computationally identified by two different methods: Island method and split read method (in-house developed). All sequence tags were mapped on the reference genome (build mm9 for mouse and hg19 for human) by using the Novoalign software. Only those paired tags that could be mapped uniquely and in opposite orientation to each other were considered for the identification of islands in the "island method". The sequence coverage of each base pair was profiled for each chromosome, and islands (potential circles) delineated where there were two consecutive sequenced bases. In other words an "island" is a contiguous portion of the genome where a cluster of PE-tags were mapped uniquely and in the opposite orientation to its pair. Paired End tags that had one tag mapping uniquely to an island, while the other was unmapped but passed the sequence quality filter, were considered separately for the validation of circles. We generated a database of hypothetical junctional tags that could be created by ligation of the ends of islands to generate circles. The unmapped tags described above were matched against these hypothetical junctional tags. If the mapped tag of a PE read falls in an island and the un-mapped tag matches with a hypothetical junctional tag generated by ligation of the ends of the island, we annotated that island as a circle. This approach underestimates the number of true microDNAs because further sequence coverage is likely to reveal more junctional tags ligating the ends of islands. The "split read" method extracted the paired end tags where one end was mapped (M) and other end was un-mapped (UM) but had good base quality score. The UM tags were split into two equal halves and mapped individually by using Novoalign software. Those split pairs which were mapped on the same strand (F-F or R-R) with no mismatch were considered for the identification of circles.  The "Island method" and the "split-read method" both are dependent on the junctional tags for the identification of circles. >80% of the circles were identified by both algorithms. The "Island method" failed to detect circles 1) with low base coverage, 2) with the start and the end of the circle contaminated by reads from contaminating linear DNA or lost due to sequencing errors and 3) that were overlapping with each other. On the other hand the "Split-read method" failed to detect circles if the two split reads do not map uniquely to the genome. Processed data include the microDNA co-ordinate (chromosome name, start position of circle and end position of circle) mm9 assembly for mouse and hg19 assembly for human samples. The fourth column in the processed data is base pair coverage (abundance).
Genome Build:
NIH3T3.miDNA.txt: mm9

source_name: Mouse embryonic fibroblast cell lines

value: NIH3T3 mouse cell lines


title: Genomic DNA of patient P440 blood [SOLiD v2]

description:
genomic DNA hydrosheared in 9.2 kb fragments (std dev: 560 bp below median; 502 above median), 2x25bp paired-end (mate pair) sequences
GSM700323 and GSM700324 contain identical processed data files since 2 GEO Sample records have been generated based on sequencers.


title: Genomic DNA of patient P022 blood

description: genomic DNA hydrosheared in 5.1 kb fragments (std dev: 700 bp below median; 442 above median), 2x25bp paired-end (mate pair) sequences, F3 tag sequence

value: chronic myelogenous leukemia, blast crisis


title: Embryo mouse brain sample 3

data_processing:
Identification of microDNA by paired-end sequencing of MDA products: microDNA were computationally identified by two different methods: Island method and split read method (in-house developed). All sequence tags were mapped on the reference genome (build mm9 for mouse and hg19 for human) by using the Novoalign software. Only those paired tags that could be mapped uniquely and in opposite orientation to each other were considered for the identification of islands in the "island method". The sequence coverage of each base pair was profiled for each chromosome, and islands (potential circles) delineated where there were two consecutive sequenced bases. In other words an "island" is a contiguous portion of the genome where a cluster of PE-tags were mapped uniquely and in the opposite orientation to its pair. Paired End tags that had one tag mapping uniquely to an island, while the other was unmapped but passed the sequence quality filter, were considered separately for the validation of circles. We generated a database of hypothetical junctional tags that could be created by ligation of the ends of islands to generate circles. The unmapped tags described above were matched against these hypothetical junctional tags. If the mapped tag of a PE read falls in an island and the un-mapped tag matches with a hypothetical junctional tag generated by ligation of the ends of the island, we annotated that island as a circle. This approach underestimates the number of true microDNAs because further sequence coverage is likely to reveal more junctional tags ligating the ends of islands. The "split read" method extracted the paired end tags where one end was mapped (M) and other end was un-mapped (UM) but had good base quality score. The UM tags were split into two equal halves and mapped individually by using Novoalign software. Those split pairs which were mapped on the same strand (F-F or R-R) with no mismatch were considered for the identification of circles.  The "Island method" and the "split-read method" both are dependent on the junctional tags for the identification of circles. >80% of the circles were identified by both algorithms. The "Island method" failed to detect circles 1) with low base coverage, 2) with the start and the end of the circle contaminated by reads from contaminating linear DNA or lost due to sequencing errors and 3) that were overlapping with each other. On the other hand the "Split-read method" failed to detect circles if the two split reads do not map uniquely to the genome. Processed data include the microDNA co-ordinate (chromosome name, start position of circle and end position of circle) mm9 assembly for mouse and hg19 assembly for human samples. The fourth column in the processed data is base pair coverage (abundance).
Genome Build:
EMB3.miDNA.txt: mm9

value: Embryo mouse brain sample 3


title: Embryo mouse heart

data_processing:
Identification of microDNA by paired-end sequencing of MDA products: microDNA were computationally identified by two different methods: Island method and split read method (in-house developed). All sequence tags were mapped on the reference genome (build mm9 for mouse and hg19 for human) by using the Novoalign software. Only those paired tags that could be mapped uniquely and in opposite orientation to each other were considered for the identification of islands in the "island method". The sequence coverage of each base pair was profiled for each chromosome, and islands (potential circles) delineated where there were two consecutive sequenced bases. In other words an "island" is a contiguous portion of the genome where a cluster of PE-tags were mapped uniquely and in the opposite orientation to its pair. Paired End tags that had one tag mapping uniquely to an island, while the other was unmapped but passed the sequence quality filter, were considered separately for the validation of circles. We generated a database of hypothetical junctional tags that could be created by ligation of the ends of islands to generate circles. The unmapped tags described above were matched against these hypothetical junctional tags. If the mapped tag of a PE read falls in an island and the un-mapped tag matches with a hypothetical junctional tag generated by ligation of the ends of the island, we annotated that island as a circle. This approach underestimates the number of true microDNAs because further sequence coverage is likely to reveal more junctional tags ligating the ends of islands. The "split read" method extracted the paired end tags where one end was mapped (M) and other end was un-mapped (UM) but had good base quality score. The UM tags were split into two equal halves and mapped individually by using Novoalign software. Those split pairs which were mapped on the same strand (F-F or R-R) with no mismatch were considered for the identification of circles.  The "Island method" and the "split-read method" both are dependent on the junctional tags for the identification of circles. >80% of the circles were identified by both algorithms. The "Island method" failed to detect circles 1) with low base coverage, 2) with the start and the end of the circle contaminated by reads from contaminating linear DNA or lost due to sequencing errors and 3) that were overlapping with each other. On the other hand the "Split-read method" failed to detect circles if the two split reads do not map uniquely to the genome. Processed data include the microDNA co-ordinate (chromosome name, start position of circle and end position of circle) mm9 assembly for mouse and hg19 assembly for human samples. The fourth column in the processed data is base pair coverage (abundance).
Genome Build:
EMH.miDNA.txt: mm9

source_name: 13.5 days old Embryo mouse heart

value: Embryo mouse heart


title: 4-7hr PolII ChIP-Seq Input Oregon-R strain

description: input DNA


title: Embryo mouse liver

data_processing:
Identification of microDNA by paired-end sequencing of MDA products: microDNA were computationally identified by two different methods: Island method and split read method (in-house developed). All sequence tags were mapped on the reference genome (build mm9 for mouse and hg19 for human) by using the Novoalign software. Only those paired tags that could be mapped uniquely and in opposite orientation to each other were considered for the identification of islands in the "island method". The sequence coverage of each base pair was profiled for each chromosome, and islands (potential circles) delineated where there were two consecutive sequenced bases. In other words an "island" is a contiguous portion of the genome where a cluster of PE-tags were mapped uniquely and in the opposite orientation to its pair. Paired End tags that had one tag mapping uniquely to an island, while the other was unmapped but passed the sequence quality filter, were considered separately for the validation of circles. We generated a database of hypothetical junctional tags that could be created by ligation of the ends of islands to generate circles. The unmapped tags described above were matched against these hypothetical junctional tags. If the mapped tag of a PE read falls in an island and the un-mapped tag matches with a hypothetical junctional tag generated by ligation of the ends of the island, we annotated that island as a circle. This approach underestimates the number of true microDNAs because further sequence coverage is likely to reveal more junctional tags ligating the ends of islands. The "split read" method extracted the paired end tags where one end was mapped (M) and other end was un-mapped (UM) but had good base quality score. The UM tags were split into two equal halves and mapped individually by using Novoalign software. Those split pairs which were mapped on the same strand (F-F or R-R) with no mismatch were considered for the identification of circles.  The "Island method" and the "split-read method" both are dependent on the junctional tags for the identification of circles. >80% of the circles were identified by both algorithms. The "Island method" failed to detect circles 1) with low base coverage, 2) with the start and the end of the circle contaminated by reads from contaminating linear DNA or lost due to sequencing errors and 3) that were overlapping with each other. On the other hand the "Split-read method" failed to detect circles if the two split reads do not map uniquely to the genome. Processed data include the microDNA co-ordinate (chromosome name, start position of circle and end position of circle) mm9 assembly for mouse and hg19 assembly for human samples. The fourth column in the processed data is base pair coverage (abundance).
Genome Build:
EML.miDNA.txt: mm9

source_name: 13.5 days old Embryo mouse liver

value: Embryo mouse liver


title: Genomic DNA of patient P308 blood

description: genomic DNA hydrosheared in 6.9 kb fragments (std dev: 504 bp below median; 410 above median), 2x25bp paired-end (mate pair) sequences

value: chronic myelogenous leukemia, chronic phase


title: Embryo mouse brain sample 2

data_processing:
Identification of microDNA by paired-end sequencing of MDA products: microDNA were computationally identified by two different methods: Island method and split read method (in-house developed). All sequence tags were mapped on the reference genome (build mm9 for mouse and hg19 for human) by using the Novoalign software. Only those paired tags that could be mapped uniquely and in opposite orientation to each other were considered for the identification of islands in the "island method". The sequence coverage of each base pair was profiled for each chromosome, and islands (potential circles) delineated where there were two consecutive sequenced bases. In other words an "island" is a contiguous portion of the genome where a cluster of PE-tags were mapped uniquely and in the opposite orientation to its pair. Paired End tags that had one tag mapping uniquely to an island, while the other was unmapped but passed the sequence quality filter, were considered separately for the validation of circles. We generated a database of hypothetical junctional tags that could be created by ligation of the ends of islands to generate circles. The unmapped tags described above were matched against these hypothetical junctional tags. If the mapped tag of a PE read falls in an island and the un-mapped tag matches with a hypothetical junctional tag generated by ligation of the ends of the island, we annotated that island as a circle. This approach underestimates the number of true microDNAs because further sequence coverage is likely to reveal more junctional tags ligating the ends of islands. The "split read" method extracted the paired end tags where one end was mapped (M) and other end was un-mapped (UM) but had good base quality score. The UM tags were split into two equal halves and mapped individually by using Novoalign software. Those split pairs which were mapped on the same strand (F-F or R-R) with no mismatch were considered for the identification of circles.  The "Island method" and the "split-read method" both are dependent on the junctional tags for the identification of circles. >80% of the circles were identified by both algorithms. The "Island method" failed to detect circles 1) with low base coverage, 2) with the start and the end of the circle contaminated by reads from contaminating linear DNA or lost due to sequencing errors and 3) that were overlapping with each other. On the other hand the "Split-read method" failed to detect circles if the two split reads do not map uniquely to the genome. Processed data include the microDNA co-ordinate (chromosome name, start position of circle and end position of circle) mm9 assembly for mouse and hg19 assembly for human samples. The fourth column in the processed data is base pair coverage (abundance).
Genome Build:
EMB2.miDNA.txt: mm9

value: Embryo mouse brain sample 2


title: 4-7hr PolII ChIP-Seq ChIP Oregon-R strain Replicate 3

value: Oregon-R(official name :Oregon-R-modENCODE genotype :wild type )


title: Genomic DNA of patient P098 blood

description: genomic DNA hydrosheared in 9 kb fragments (std dev: 792 bp below median; 506 above median), 2x25bp paired-end (mate pair) sequences

value: chronic myelogenous leukemia, blast crisis, BCR-ABL1 mutation Y253F


title: GATA1 ESEP Differentiating Rep1A

city: Bronx

first: Sandeep

middle: Nagaraj

last: Wontakal

state: NY

address: 1300 Morris Park Ave Chanin 402

department: Cell Biology

institute: Albert Einstein College of Medicine

description: Chromatin immunoprecipitation was performed using an antibody against GATA-1

data_processing:
Peak detection was done using cisGenome and spp software.  Samples GATA1_ESEP_Diff_Rep2A and GATA1_ESEP_Diff_Rep2B were the same samples that were sequenced twice.
Genome Build:
GATA1_ESEP_Diff_Rep1A.bed: mm9

source_name: ES-cell derived erythroid progenitors

growth_protocol: Cells were grown in StemPro34 medium (Invitrogen) supplemented with 2u/ml Epogen (Amgen), 40ng/ml hIGF-1 (Sigma), 1mM dexamethasone (Sigma), 100ng/ml murine SCF (R&D Systems/Invitrogen), and 0.1% b-mercaptoethanol (Gibco) at 37° C in a humidified 10% CO2 atmosphere.

extract_protocol: Libraries were prepared as recemmended by Illumina.

treatment_protocol: ES-EP cells were differentiated for 24 hours by culturing in StemPro34 media supplemented with 10u/ml Epogen, 10mg/ml insulin (Sigma), 3mM mifepristone (Sigma), and 0.1% b-mercaptoethanol.

tag: chip antibody

value: polyclonal rabbit a-GATA-1 produced in-house (H Im et al. PNAS 2005)

tag: growth condition

value: Differentiating

value: ES-cell derived erythroid progenitors

tag: cell line

value: ES-EP


title: GATA1 ESEP Differentiating Rep2

data_processing:
Peak detection was done using cisGenome and spp software.  Samples GATA1_ESEP_Diff_Rep2A and GATA1_ESEP_Diff_Rep2B were the same samples that were sequenced twice.
Genome Build:
GATA1_ESEP_Diff_Rep2.bed: mm9

tag: cell type


title: GATA1 ESEP Proliferating Rep1

data_processing:
Peak detection was done using cisGenome and spp software.  Samples GATA1_ESEP_Diff_Rep2A and GATA1_ESEP_Diff_Rep2B were the same samples that were sequenced twice.
Genome Build:
GATA1_ESEP_Rep1.bed: mm9

value: Proliferating


title: GATA1 ESEP Proliferating Rep2

data_processing:
Peak detection was done using cisGenome and spp software.  Samples GATA1_ESEP_Diff_Rep2A and GATA1_ESEP_Diff_Rep2B were the same samples that were sequenced twice.
Genome Build:
GATA1_ESEP_Rep2.bed: mm9


title: GATA1 ESEP Differentiating Rep1B

data_processing:
Peak detection was done using cisGenome and spp software.  Samples GATA1_ESEP_Diff_Rep2A and GATA1_ESEP_Diff_Rep2B were the same samples that were sequenced twice.
Genome Build:
GATA1_ESEP_Diff_Rep1B.bed: mm9


title: ESEP Differentiating Input

description: Input DNA that was used for chromatin immunoprecipitation

data_processing:
Peak detection was done using cisGenome and spp software.  Samples GATA1_ESEP_Diff_Rep2A and GATA1_ESEP_Diff_Rep2B were the same samples that were sequenced twice.
Genome Build:
ESEP_Diff_Input.bed: mm9

value: none, input DNA


title: U937 human cell lines

data_processing:
Identification of microDNA by paired-end sequencing of MDA products: microDNA were computationally identified by two different methods: Island method and split read method (in-house developed). All sequence tags were mapped on the reference genome (build mm9 for mouse and hg19 for human) by using the Novoalign software. Only those paired tags that could be mapped uniquely and in opposite orientation to each other were considered for the identification of islands in the "island method". The sequence coverage of each base pair was profiled for each chromosome, and islands (potential circles) delineated where there were two consecutive sequenced bases. In other words an "island" is a contiguous portion of the genome where a cluster of PE-tags were mapped uniquely and in the opposite orientation to its pair. Paired End tags that had one tag mapping uniquely to an island, while the other was unmapped but passed the sequence quality filter, were considered separately for the validation of circles. We generated a database of hypothetical junctional tags that could be created by ligation of the ends of islands to generate circles. The unmapped tags described above were matched against these hypothetical junctional tags. If the mapped tag of a PE read falls in an island and the un-mapped tag matches with a hypothetical junctional tag generated by ligation of the ends of the island, we annotated that island as a circle. This approach underestimates the number of true microDNAs because further sequence coverage is likely to reveal more junctional tags ligating the ends of islands. The "split read" method extracted the paired end tags where one end was mapped (M) and other end was un-mapped (UM) but had good base quality score. The UM tags were split into two equal halves and mapped individually by using Novoalign software. Those split pairs which were mapped on the same strand (F-F or R-R) with no mismatch were considered for the identification of circles.  The "Island method" and the "split-read method" both are dependent on the junctional tags for the identification of circles. >80% of the circles were identified by both algorithms. The "Island method" failed to detect circles 1) with low base coverage, 2) with the start and the end of the circle contaminated by reads from contaminating linear DNA or lost due to sequencing errors and 3) that were overlapping with each other. On the other hand the "Split-read method" failed to detect circles if the two split reads do not map uniquely to the genome. Processed data include the microDNA co-ordinate (chromosome name, start position of circle and end position of circle) mm9 assembly for mouse and hg19 assembly for human samples. The fourth column in the processed data is base pair coverage (abundance).
Genome Build:
U937.miDNA.txt: hg19

source_name: Human histiocytic lymphoma cell line

value: U937 human cell lines

value: Circular DNA


title: HeLaS3 human cell lines

data_processing:
Identification of microDNA by paired-end sequencing of MDA products: microDNA were computationally identified by two different methods: Island method and split read method (in-house developed). All sequence tags were mapped on the reference genome (build mm9 for mouse and hg19 for human) by using the Novoalign software. Only those paired tags that could be mapped uniquely and in opposite orientation to each other were considered for the identification of islands in the "island method". The sequence coverage of each base pair was profiled for each chromosome, and islands (potential circles) delineated where there were two consecutive sequenced bases. In other words an "island" is a contiguous portion of the genome where a cluster of PE-tags were mapped uniquely and in the opposite orientation to its pair. Paired End tags that had one tag mapping uniquely to an island, while the other was unmapped but passed the sequence quality filter, were considered separately for the validation of circles. We generated a database of hypothetical junctional tags that could be created by ligation of the ends of islands to generate circles. The unmapped tags described above were matched against these hypothetical junctional tags. If the mapped tag of a PE read falls in an island and the un-mapped tag matches with a hypothetical junctional tag generated by ligation of the ends of the island, we annotated that island as a circle. This approach underestimates the number of true microDNAs because further sequence coverage is likely to reveal more junctional tags ligating the ends of islands. The "split read" method extracted the paired end tags where one end was mapped (M) and other end was un-mapped (UM) but had good base quality score. The UM tags were split into two equal halves and mapped individually by using Novoalign software. Those split pairs which were mapped on the same strand (F-F or R-R) with no mismatch were considered for the identification of circles.  The "Island method" and the "split-read method" both are dependent on the junctional tags for the identification of circles. >80% of the circles were identified by both algorithms. The "Island method" failed to detect circles 1) with low base coverage, 2) with the start and the end of the circle contaminated by reads from contaminating linear DNA or lost due to sequencing errors and 3) that were overlapping with each other. On the other hand the "Split-read method" failed to detect circles if the two split reads do not map uniquely to the genome. Processed data include the microDNA co-ordinate (chromosome name, start position of circle and end position of circle) mm9 assembly for mouse and hg19 assembly for human samples. The fourth column in the processed data is base pair coverage (abundance).
Genome Build:
HeLaS3.miDNA.txt: hg19

source_name: Human cervical adenocarinoma cell line

value: HeLaS3 human cell lines


title: IFNB Multi SeV10 Rep1 mRNA

status: Public on Feb 26 2012

city: Cambridge

first: Tarjei

middle: S

last: Mikkelsen

state: MA

address: 7 Cambridge Center

institute: Broad Institute

description: Multi-hit sampling mutagenesis of human interferon beta enhancer, reporter mRNA

data_processing: To infer the tag copy numbers in each Tag-Seq library, all sequence reads were examined, regardless of their quality scores. If the first ten nucleotides of a read perfectly matched one of the 13,000 or 27,000 designed tags and the remaining nucleotides matched the expected upstream MPRA construct sequence, this was counted as one occurrence of that tag. All reads that did not meet this criterion were discarded.

source_name: HEK293 cells

growth_protocol: HEK293T/17 cells (ATCC CRL-11268) were cultured in DMEM (Mediatech) supplemented with 10% FBS and L-glutamine/penicillin/streptomycin.

extract_protocol: Total RNA was isolated from cell lysates using RNeasy kits (Qiagen). mRNA was extracted from total RNA using MicroPoly(A)Puristâ¢ kits (Ambion) and treated with DNase I using the Turbo DNA-freeâ¢ kit (Ambion). First-strand cDNA was synthesized from 400-700 ng mRNA using High Capacity RNA-to-cDNA kits (Applied Biosystems).  Tag-Seq sequencing libraries were generated directly from 12% of a cDNA reaction or 50 ng plasmid DNA by 26 cycle PCR using Pfu Ultra HS DNA polymerase 2x master mix (Agilent) and primers AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCT and CAAGCAGAAGACGGCATACGAGATXXXXXXXXGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTCGAGGTGCCTAAAGG (where XXXXXXXX is a library-specific index sequence). The resultant PCR products were size-selected using 2% agarose E-Gel EX (Invitrogen).

tag: treatment

value: Sendai virus (MOI = 10, 16 hours)

tag: sample type

value: embyonic kidney cell line

tag: reporter

value: IFNB multi-hit


title: IFNB Multi SeV0 Rep1 mRNA

value: Mock virus infection


title: IFNB Single SeV10 Rep1 mRNA

description: Single-hit scanning mutagenesis of human interferon beta enhancer, reporter mRNA

value: IFNB single-hit


title: Embryo mouse brain sample 1

data_processing:
Identification of microDNA by paired-end sequencing of MDA products: microDNA were computationally identified by two different methods: Island method and split read method (in-house developed). All sequence tags were mapped on the reference genome (build mm9 for mouse and hg19 for human) by using the Novoalign software. Only those paired tags that could be mapped uniquely and in opposite orientation to each other were considered for the identification of islands in the "island method". The sequence coverage of each base pair was profiled for each chromosome, and islands (potential circles) delineated where there were two consecutive sequenced bases. In other words an "island" is a contiguous portion of the genome where a cluster of PE-tags were mapped uniquely and in the opposite orientation to its pair. Paired End tags that had one tag mapping uniquely to an island, while the other was unmapped but passed the sequence quality filter, were considered separately for the validation of circles. We generated a database of hypothetical junctional tags that could be created by ligation of the ends of islands to generate circles. The unmapped tags described above were matched against these hypothetical junctional tags. If the mapped tag of a PE read falls in an island and the un-mapped tag matches with a hypothetical junctional tag generated by ligation of the ends of the island, we annotated that island as a circle. This approach underestimates the number of true microDNAs because further sequence coverage is likely to reveal more junctional tags ligating the ends of islands. The "split read" method extracted the paired end tags where one end was mapped (M) and other end was un-mapped (UM) but had good base quality score. The UM tags were split into two equal halves and mapped individually by using Novoalign software. Those split pairs which were mapped on the same strand (F-F or R-R) with no mismatch were considered for the identification of circles.  The "Island method" and the "split-read method" both are dependent on the junctional tags for the identification of circles. >80% of the circles were identified by both algorithms. The "Island method" failed to detect circles 1) with low base coverage, 2) with the start and the end of the circle contaminated by reads from contaminating linear DNA or lost due to sequencing errors and 3) that were overlapping with each other. On the other hand the "Split-read method" failed to detect circles if the two split reads do not map uniquely to the genome. Processed data include the microDNA co-ordinate (chromosome name, start position of circle and end position of circle) mm9 assembly for mouse and hg19 assembly for human samples. The fourth column in the processed data is base pair coverage (abundance).
Genome Build:
EMB1.miDNA.txt: mm9

value: Embryo mouse brain sample 1


title: PHF20KO

city: Houston

first: kaifu

last: chen

address: 1 Baylor plaza, suite 450A

institute: Baylor College of Medicine

data_processing: Reads are mapped to genome (mm9) using ELAND. Reads that are uniquely mapped and have no more than 2 mismatches are selected for future analysis. MACS is used to call peaks and generate Wiggle files.

source_name: E14.5 embryo head

extract_protocol: ChIP was performed using the corresponding antibodies and protein A-conjugated magnetic beads (Invitrogen). Briefly, cells were cross-linked with 1% formaldehyde and lysed with the buffer containing 50mM Tris-HCl pH=8.0, 10mM EDTA, 1% SDS and protease inhibitors. Cell lysate was sonicated with Branson Digital Sonifier (Branson) and diluted with RIPA buffer by 10 fold (v/v) before incubated overnight with antibody-protein A-magnetic beads complexes. In the next day, beads were washed three times with RIPA buffer and twice with TE buffer. IP ed DNA was eluted from beads, de-crosslinked at 68 and purification by MinElute PCR Purification Kit (Qiagen). Purified DNA was subject to ChIP-sequencing analysis.

tag: tissue type

value: E14.5 embryo brain

tag: genotype/variation

value: PHF20 knockout

tag: mutant phenotype

value: perinatal lethality

tag: chip antibody vendor

value: Active Motif, Cat# 39167, Lot#  01609002

tag: chip antibody lot#

tag: chip antibody catalog#


title: CRE Single 100uM Rep1 mRNA

description: Single-hit scanning mutagenesis of CRE, reporter mRNA

value: 100 uM forskolin (5 hours)

value: CRE single-hit


title: CRE Single 100uM Rep2 mRNA


title: IFNB Multi SeV10 Rep1 Plasmid

description: Multi-hit sampling mutagenesis of human interferon beta enhancer, reporter plasmid

organism: Escherichia coli

source_name: Plasmid pool

value: plasmid pool


title: IFNB Multi SeV0 Rep1 Plasmid


title: CRE Multi 0uM Rep1 mRNA

description: Multi-hit sampling mutagenesis of CRE, reporter mRNA

value: DMSO (5 hours)

value: CRE multi-hit


title: CRE Multi 100uM Rep1 mRNA


title: CRE Multi 100uM Rep2 mRNA


title: CRE Multi 100uM Rep2 Plasmid

description: Multi-hit sampling mutagenesis of CRE, reporter plasmid


title: CRE Multi 100uM Rep1 Plasmid


title: CRE Multi 0uM Rep2 Plasmid


title: CRE Single 100uM Rep2 Plasmid

description: Single-hit scanning mutagenesis of CRE, reporter plasmid


title: CRE Multi 0uM Rep1 Plasmid


title: CRE Single 100uM Rep1 Plasmid


title: Endogenous mRNA from daf-2(e1370) mutant 3

status: Public on Feb 25 2012

city: Jena

first: JenAge

last: Project

address: Beutenbergstr. 11

institute: Leibniz Institute for Age Research - Fritz Lipmann Institute

description: polyA RNA purification followed be chemical fragmentation and reverse transcription using random hexamers (all describes in the manufacturer’s instruction)

data_processing: Mapping of reads with Bowtie to the respective genome (ce6 for C.elegans; mm9 for M.musculus samples) and to a splice site data set created using UCSC's RefSeq annotation. Counting of uniquely mapped reads and assignemt to RefSeq genes using R Statistical Language and Bioconductor.

organism: Caenorhabditis elegans

source_name: whole animal, daf-2(e1370) mutant

growth_protocol: C. elegans: Synchronisation with egg laying period, Worms were maintained on NGM agar plates and fed with E. coli OP50 , Worms were harvested for RNA isolation at the age of 5 days after L4 larvae stage; MEF: Cells were cultured in DMEM containing 25 mM Glucose and 10% FBS

extract_protocol: library was prepared using Illumina RNA sample prep kit (RS-100-0801), following the instructions

tag: genetic background

value: N2 var. Bristol

tag: genotype

tag: tissue

value: whole animal

tag: developmental stage

value: adult (5 days after L4)


title: PHF20WT

tag: strain

value: wild type

value: control

tag: chip antibody


title: Endogenous mRNA from  N2 var. Bristol (wildtype) 3

source_name: whole animal; N2 var Bristol


title: CRE Multi 0uM Rep2 mRNA

tag: cell line

value: embyonic kidney cell line


title: Endogenous mRNA from daf-2(e1370) mutant 2


title: IFNB Single SeV10 Rep1 Plasmid

description: Single-hit scanning mutagenesis of human interferon beta enhancer, reporter plasmid

value: IFNB single-hit


title: Endogenous mRNA from daf-2(e1370) mutant 1


title: Endogenous mRNA from  N2 var. Bristol (wildtype) 2


title: Endogenous mRNA from  N2 var. Bristol (wildtype) 1


title: Endogenous mRNA from MEF insr+/- knockout cells 2

source_name: murine embryonic fibroblasts (MEF), insr+/- knockout

value: insr+/-

tag: cell type

value: murine embryonic fibroblasts (MEF)

tag: passage


title: Endogenous mRNA from MEF insr+/- -lox cells 2

source_name: murine embryonic fibroblasts (MEF), insr+/- -lox


title: Endogenous mRNA from MEF insr+/- -lox cells 1


title: Endogenous mRNA from MEF irs1-/- knockout cells 2

source_name: murine embryonic fibroblasts (MEF), irs1-/- knockout

value: irs1 KO


title: Long_untreated

city: Bonn

first: Stefan

middle: Andreas

last: Juranek

state: NRW

address: Venusber-Campus 1

department: Medizinische Klinik III

institute: UK Bonn

description: Endogenous RNA

data_processing:
Long_untreated.txt; genome build: chromosome built 07/25/2008, plasmid pTT27 built 05/01/2009
Read mapping was done using an algorithm described previously (Yassour et al., http://www.ncbi.nlm.nih.gov/pubmed/20796282).The algorithm was applied with the following parameters: 1. A minimum match length of 17 nt between read and genome was required. 2. For the medium and long libraries a sequence at the 5’ or 3’ read end was only considered part of the adapter if it had at least seven matches to the corresponding A5 (starting from its 3’ end) or A3 adapter (starting from its 5’ end) sequence. For the short library we were less restrictive requiring a minimum of only three nt match. 3. A maximum of two mismatches and/or insertions/deletions were allowed for the whole read. 4. Read length was adapted to the Illumina output and was defined as 36 nt for the short library and 100 nt for the medium and long libraries. The Illumina software applied in the mapping of the medium and long libraries adds to the short read sequences that have a full A3 adapter random nucleotides at their 3’ ends up to the fixed read length. Thus to map such reads we first used cutadapt-0.9.3 (http://code.google.com/p/cutadapt) to identify the A3 adapter. We then replaced the random nucleotides stretches with polyA and allowed the SolexaMatch to identify polyA matches 3’ to the full adapter length in the analysis of the medium and long libraries. This was not required for the short library as the combination of the length of the A3 adapter (21 nt) and the minimal genomic match length (17 nt) exceeds the maximal read length (36 nt). In analyses that regarded reads that were mapped to multiple genomic locations we divided the read count by the corresponding number of genomic locations. Each one of the multiple genomic location was assigned this normalized value.

The Read annotation supplementary file (*.txt) lists all the reads that were mapped to the genome. The annotation of tRNA, mRNA, ncRNA, and rRNA follows the NCBI annotation. For rRNA however we expanded slightly the genes' boundaries based on the reads in the region.

The supplementary .txt file is tab-delimited and includes the following information:
Field 1 read  sequence
Field 2 read sequence with no adapter
Field 3 read length
Field 4 normalized copy number (divided by the number of genomic locations) (Field 5/Field 11)
Field 5 the copy number of the read
Field 6 chromosome
Field 7 start
Field 8 strand
Field 9 mismatch (0-no mismatch -1 one mismatch -2 two mismatches)
Field 10 adapter
Field 11 number of genomic locations
Field 12 annotation  (reads might have multiple annotations even within a specific genomic location); reads with no annotation are assigned 'None'.

organism: Thermus thermophilus

source_name: Thermus thermophilus HB8

growth_protocol: T. thermophilus HB8 cells were grown in LB media supplemented with 1 mM magnesium chloride and 1 mM calcium chloride shaking at 220 rpm at 75˚C

extract_protocol:
RNA was extracted using the guanidiniumisothiocyanate/phenol method, according to Chomczynski and Sacchi, Analytical Biochemistry, 1987.
Briefly, 200 µg of total RNA were separated on a 15% 15x17x0.08 cm denaturing polyacrylamide (19:1) gel. RNA size marker cocktail (20 µg, composed of 4 µg each of a 19, 24, 35, 50, and 100 nt (nucleotide) long oligoribonucleotides) was loaded in separate lanes with empty lanes between marker and sample loading. After electrophoresis (0.5x TBE, 30 W, 45 min), the size markers were visualized by 260 nm UV shadowing. The portion of the gel that represented the RNA size region between 19-35, 35-50, and 50-100 nt was cut out and the gel pieces were eluted for 12 h with 3 volumes (v/w) 0.3 M sodium chloride at 4ºC in a thermomixer shaking at 800 rpm. The RNA was precipitated using 3 volumes absolute ethanol and resuspended in water. The yield of fractionated RNA was approximately 1% relative to the total RNA input. Each of the three RNA size fractions was divided in two aliquots. One was directly used as input for the 3’ adapter ligation, whereas the second was treated with alkaline phosphatase (NEB) for 1 h at 37ºC in 1 x reaction buffer (NEB) to remove all 5’ or 3’ phosphoryl groups. Alkaline phosphatase was removed by phenol/chloroform extraction and the small RNAs were subsequently completely phosphorylated at the 5’ end by T4 polynucleotide kinase (NEB) for 1 h at 37ºC in 1x reaction buffer (NEB) with 2 mM ATP. Preadenylated 3’ adapter was ligated to the small RNAs in 20 µl reaction volume using 1 µg RNA, 2.5 µM 3’ adapter A3 rAppTCGTATGCCGTCTTCTGCTTG and 1 µg T4 Rnl2(1-249)K227Q (NEB) in RNA ligation buffer (50 mM Tris-HCl, pH 7.6, 10 mM magnesium chloride, 10 mM 2-mercaptoethanol, 0.1 mg/ml acetylated BSA (Sigma, B-8894), and 15% aqueous DMSO) at 4ºC for 12 h and subsequently separated by denaturing PAA gel electrophoresis. Ligated products (indicated by adapter-ligated size markers loaded in separate lanes, with one empty lane distance to the samples) were cut out and eluted as described above. After ethanol precipitation, the 5’ adapter was ligated to the small RNAs in a total reaction volume of 20 µl using 1 µg T4 Rnl1 (Fermentas), 5 µM 5’ adapter A5 r(GUUCAGAGUUCUACAGUCCGACGAUC), r indicating ribonucleotide, in RNA ligation buffer containing 0.2 mM ATP for 1 h at 37ºC. The ligated product was purified on a 12% denaturing PAA gel. The final product was reverse transcribed by SuperScript III reverse transcriptase according to manufacturer’s instructions (Invitrogen). 100 pmol of the reverse transcription primer (CAAGCAGAAGACGGCATACGA) was added to the purified RNAs taken up in 6.5 µl H2O and the mixture was denatured for 30 s at 90ºC and transferred directly to 50ºC. 7.5 µl of a reaction mixture containing 100 mM Tris-HCl (pH 8.3), 150 mM potassium chloride, 6 mM magnesium chloride, 20 mM DTT, and 1 mM dNTPs, was added and the samples were incubated for 3 min at 50ºC. 150 U reverse transcriptase was added and the reaction mixture was incubated at 42ºC for 60 min. To hydrolyze the RNA 100 mM potassium hydroxide was added, and the mixture was incubated for 10 min at 90ºC. The solution was neutralized by adding hydrochloric acid to 100 mM. The pH of the solution was checked by spotting 1 µl on pH paper. The cDNA was used for PCR amplification using a standard PCR protocol and primers compatible with Solexa sequencing (CAAGCAGAAGACGGCATACGA and AATGATACGGCGACCACCGACAGGTTCAGAGTTCTACAGTCCGA). The libraries were deep sequenced using the Solexa technology at the Rockefeller University Genomic Resource Center using standard protocol by Illumina.

tag: size

tag: od 600 nm at harvest

tag: treatment group

value: untreated