title: 360min RNA Rep1

status: Public on May 27 2020

city: St. Louis

first: Jennifer

middle: E.

last: Phillips-Cremins

state: MO

address: 4515 McKinley Ave

department: Bioengineering

institute: Washington University in St. Louis

description:
Ribosome depleted RNA
360min RNA Rep1

data_processing:
>> RNA-Seq<<
RNA-seq reads were mapped to the RefSeq transcriptome (transcriptome fasta downloaded from the UCSC genome browser on July 28, 2017) using Salmon. In accordance with the TruSeq Stranded Total RNA Library Preparation, mapping was done using the -ISR flag. The resulting TPM quantifications for each RefSeq transcript are provided.
Genome_build: refSeq mm9
Supplementary_files_format_and_content: Estimated transcripts per million (TPM) across all conditions generated using the Salmon package (see processing steps).
>> 5C-Seq <<
Paired-end reads were aligned to the 5C primer pseudo-genome using Bowtie, allowing only reads with one unique alignment to pass filtering. Only reads for which one paired end mapped to a forward/left-forward primer and the other end mapped to a reverse/left-reverse primer were tallied as true counts.  5C is subject to specific biases, such as primer GC content resulting in annealing/PCR biases, that methods such as HiC are not. This manifests in primer-primer pairs with mapped counts that are orders of magnitude higher than the neighboring primer-primer pairs – such an extreme enrichment of single primer-primer pairs does not resemble the broader distribution of elevated counts, spanning clusters of neighboring primer-primer pairs, that exists at bonafide looping interactions across 5C and HiC data. Therefore, we decided to remove these biased primer-primer pairs before proceeding with interaction analysis. This was done by calculating for each primer-primer pair the median count of itself and the 24 primer-primer pairs nearest to the primer-primer pair in question (i.e. a scipy.ndimage.generic_filter window of size 5 was passed over the primer-primer pair matrix and the median of each window was recorded). If the count of one primer-primer pair was greater than eight-fold higher its neighborhood median then it was flagged as a high spatial outlier and removed. This process was performed for all primer-primer pairs, except for those in the 5C region surrounding the Arc gene for which the 8-fold threshold was found to be too stringent due to low region complexity and a 100-fold threshold was utilized instead.  After high-outlier removal, primer-primer pair counts were quantile normalized across all 12 replicates (4 per condition) as previously described (Beagan 2016). For plotting purposes quantile normalized counts were merged across replicates via summation, whereas for loop calling analysis all replicates were kept separate. Primer-primer pair counts were then converted to fragment-fragment interaction counts by averaging the primer-primer counts that mapped to each fragment-fragment pair (max of 2 if both a forward/left-forward and a reverse/left-reverse primer were able to be designed to both fragments and were not trimmed during outlier removal). We then divided our 5C regions into adjacent 4 kb bins and computed the relative interaction frequency of two bins (i,j) by summing the counts of all fragment-fragment interactions for which the coordinates of one of the constituent fragments overlapped (at least partially) a 12 kb window surrounding the center of the 4 kb ith bin and the other constituent fragment overlapped the 12 kb window surrounding the center if the jth bin. Binned count matrices were then matrix balanced using the ICE algorithm, at which point we considered each entry (i,j) to represent the ‘Relative Interaction Frequency’ (Fig. 1D) of the 4 kb bins i and j. Finally, the background contact domain ‘expected’ signal was calculated using the donut background model as previously described and used to normalize the relative interaction frequency data for the background interaction frequency present at each bin-bin pair. The resulting background-normalized interaction frequency (“observed over expected”) counts were fit with a logistic distribution from which p-values were computed for each bin-bin pair and converted into ‘Background-corrected Interaction Scores’ (interaction score = -10*log2(p-value), Fig. 1H) as previously described. Interaction scores have proven to be informatively comparable across replicates and conditions and as such were used for most visualization analysis and all loop-calling analysis to follow.   We applied the 3DeFDR analysis package to our dataset in order to identify differential interactions across the TTX and Bic conditions (4 replicates of each). Briefly, 3DeFDR identifies differential interactions and empirically estimates a false discovery rate (eFDR) for each identified dynamic looping class. Interactions are only considered for analysis if the interaction scores of all 8 replicates across both conditions surpassed a ‘significance threshold’. Interactions are classified as ‘TTX-only’ if all 4 interaction scores of the TTX replicates surpassed the interaction scores of the Bic replicates by more than a specified ‘difference threshold’; ‘Bic-only’ interactions are classified in the same manner. Those interactions that pass the significance threshold but are not classified as Bic-only or TTX-only are classified as ‘Constitutive’. Finally, classified interactions (single matrix entries / heatmap pixels) are clustered based on spatial proximity into ‘loops’; loops for which the number of constituent interactions/pixels fell below a ‘size threshold’ are removed. The 3DeFDR package simulates null replicate sets (i.e. 8 replicates of the same cell type/condition so that there should be no differential looping events) using on a negative binomial counts generating function parameterized using the mean-variance relationships that exist in the real data. eFDR is computed separately for each differential looping class as the total number of interactions/pixels called in that class on a simulated null replicate set over the total number of loops called as that class on the original real replicate set.   We utilized the ‘non-adaptive’ functionality option of the 3DeFDR analysis package, which sweeps across a wide range of difference threshold and calculates an eFDR for each looping class at each iteration. 250 simulated null replicate sets of 8 replicates were generated based on mean-variance relationships underlying the real TTX replicates. We utilized the default 3DeFDR initialization parameters with the exception of ‘bin_properties’ (a tunable parameter that specifies the distance scales over which fragment level interactions are stratified prior to fitting the negative binomial counts generating function to those interactions). This parameter was adjusted to capture the full extent of our regional matrices: (1) for close-range interactions (0-150 kb), we stratified the interactions using fine-grained, 12 kb-sized sliding windows with a 4 kb step, (2) for mid-range interactions (151-600 kb), we stratified the interactions into 24 kb-sized sliding windows with an 8 kb step, and (3) for longer range interactions (601-2500 kb), we stratified the interactions into coarse-grained, 60 kb-sized sliding windows with a 24 kb step. Through this approach we achieved an eFDR of 6.6% for bic-only (activity-induced) loops utilizing a difference threshold of 6.75, a significance threshold of -10*log2(0.08) (i.e. a p-value of 0.08 resulting from the logistic fit to the observed over expected data), and a size threshold of 5.
Genome_build: mm9 5C primer psuedogenome
Supplementary_files_format_and_content: BED_mm9_ERneuro_plus_Synapt.bed is the bed file containing the coordinates for each 5C primer used.  '[Rep_name]-Activity-Synaptic.counts' contains the number of reads mapped to each pair of 5C primers (see 5C processing) for each replicate.  '5C_Loop_Calls.xlsx' contains the list of all loops called within 5C regions, sorted by looping classification (TTX-only, constitutive, or Bic-only). Columns 2-7 are the upstream (2-4) and downstream (5-7) anchor coordinates of each loop in bed format. Columns 8-17 represent the average binned and ba

source_name: E18 Cortex

growth_protocol: Murine cortical neurons were cultured using a protocol established previously in Shepherd et al., 2006. Cortices were dissected from E18 WT C57/BL6 mouse embryos. Cortices were then dissociated in DNase (0.01%; Sigma-Aldrich, St. Louis, MO) and papain (0.067%; Worthington Biochemicals, Lakewood, NJ), then triturated with a fire-polished glass pipette to obtain a single-cell suspension. Cells were pelleted at 1000xg for 4 min, the supernatant removed, and cells resuspended and counted with a TC-20 cell counter (Bio-Rad, Hercules, CA). Neurons were plated in 6-cm dishes (Greiner Bio-One, Monroe, NC) coated with poly-L-lysine (0.2 mg/mL; Sigma-Aldrich) at a density of 200,000 cells/mL. Neurons were initially plated in Neurobasal media containing 5% horse serum (NM5), 2% GlutaMAX, 2% B-27, and 1% penicillin/streptomycin (Thermo Fisher Scientific) in a 37ºC incubator with 5% CO2. On DIV4, neurons were fed via half media exchange with astrocyte-conditioned Neurobasal media containing 1% horse serum (NM1), GlutaMAX, and penicillin/streptomycin, 2% B-27, and 5 µM cytosine β-D-arabinofuranoside (AraC; Sigma-Aldrich). Neurons were fed with astrocyte-conditioned NM1 media every three days thereafter.

extract_protocol:
At DIV5 and DIV16, 900,000 neurons were lysed in 1 mL Trizol (Thermo Fisher Scientific 15596026). Lysates were snap frozen and stored at -80oc until use. Total RNA was then isolated using the mirVana miRNA Isolation Kit (Thermo Fisher Scientific AM1561) according to manufacturer’s protocol and eluted from the spin-column using 100 uL nuclease-free water. Samples were DNase treated (Thermo Fisher Scientific AM1906) and tested for quality using an Agilent Bioanalyzer RNA chip. All samples produced an RNA Integrity Number (RIN) greater than 9.
We utilized the TruSeq Stranded Total RNA Library Prep Kit with Ribo-Zero Gold (Illumina RS-122-2301) and prepared each RNA sample for sequencing according to the manufacturer’s protocol. cDNA libraries were amplified across 15 PCR cycles followed by AMPure XP Bead clean-up (1:1 bead:solution ratio). Finally, the library sizes were confirmed to be between 200-500 bp using the BioAnalyzer before sequencing 75 bp paired-end reads on the Illumina NextSeq500. In order to minimize and identify technical variation, three replicates spanning two culture batches were prepared, pooled, and sequenced on three flow cells to depths of greater than 60 million reads per library.

treatment_protocol: At DIV15, neurons were treated for 24 hours with either 1 uM Tetrodotoxin (TTX) or 10 uM Bicuculline (Bic) via addition to the cell culture media or left untreated.

tag: days in vitro (div)

value: 1 uM Tetrodotoxin (24 hr) followed by 10 uM Bicuculline (360 min)


title: 360min RNA Rep2

type: SRA

country: USA

description:
Ribosome depleted RNA
360min RNA Rep2

library_source: transcriptomic

molecule: total RNA

organism: Mus musculus

tag: strain

tag: treatment


title: Tsix +/- differentiation day 7 (replicate 1)

status: Public on Jun 25 2020

city: Boston

first: Eric

last: Aeby

state: Massachusetts

address: 185 Cambridge Street

department: Department of Molecular Biology and Department of Genetics

institute: Howard Hughes Medical Institute, Massachusetts General HospitalMassachusetts General Hospital, Harvard Medical School

data_processing:
Adaptors were trimmed using cutadapt
Reads were aligned to mm9 genome using bowtie2 and novoalign
allele specific reads were called by custom script (Pinter et al., 2012)
Metagene analysis was done by deeptools2 (Ramirez et al., 2016) and custom python scripts
Strand-specific PRO-seq coverage files (bigwig format) were generated by using uniquely mapped reads normalized by fragments per million with deeptools
Genome_build: mm9
Supplementary_files_format_and_content: bigwig files for strand-specific (fwd:watson, rev:crick strand) and RPKM normalized

source_name: embryonic stem cells

growth_protocol: Mouse ES cells were grown on gelatin coated plates in 2i+LIF-containing medium or differentiated accordingly

extract_protocol:
Total RNA was isolated using TRIzol extraction,biotin-labeled RNA was enriched using strepatividing pulldown
Library preparation is combined to PROseq experiment (three individual streptavidin enrichment steps) and was performed as described in (Kwak et al. 2013 and Mahat et al. 2016); Strand-specific RNA sequencing, multiplexed

treatment_protocol: Nuclei were isolated, permeabilized for nuclear run-on. Nascent transcripts were labeled with Biotin-11-ATP, Biotin-11-CTP, Biotin-11-GTP, Biotin-11-UTP

tag: genotype

value: Tsix +/-

tag: developmental stage

value: differentiation day 7

tag: Sex

value: female

value: C57BL/6J x CAST/EiJ hybrid


title: 020min RNA Rep2

description:
Ribosome depleted RNA
020min RNA Rep2

value: 1 uM Tetrodotoxin (24 hr) followed by 10 uM Bicuculline (20 min)


title: 020min RNA Rep1

description:
Ribosome depleted RNA
020min RNA Rep1


title: 060min RNA Rep1

description:
Ribosome depleted RNA
060min RNA Rep1

value: 1 uM Tetrodotoxin (24 hr) followed by 10 uM Bicuculline (60 min)


title: 005min RNA Rep1

description:
Ribosome depleted RNA
005min RNA Rep1

value: 1 uM Tetrodotoxin (24 hr) followed by 10 uM Bicuculline (5 min)


title: Tsix +/- day 0 (replicate 1)

value: undifferentiated ES cells


title: 060min RNA Rep2

description:
Ribosome depleted RNA
060min RNA Rep2


title: Tsix +/- day 0 (replicate 2)


title: 000min RNA Rep2

description:
Ribosome depleted RNA
000min RNA Rep2

value: 1 uM Tetrodotoxin (24 hr) followed by 10 uM Bicuculline (0 min)


title: 060min 5C

description:
Chromosome-conformation-capture-carbon-copy
BED_mm9_ERneuro_plus_Synapt.bed

library_source: genomic

data_processing:
Library strategy: 5C-seq
>> RNA-Seq<<
RNA-seq reads were mapped to the RefSeq transcriptome (transcriptome fasta downloaded from the UCSC genome browser on July 28, 2017) using Salmon. In accordance with the TruSeq Stranded Total RNA Library Preparation, mapping was done using the -ISR flag. The resulting TPM quantifications for each RefSeq transcript are provided.
Genome_build: refSeq mm9
Supplementary_files_format_and_content: Estimated transcripts per million (TPM) across all conditions generated using the Salmon package (see processing steps).
>> 5C-Seq <<
Paired-end reads were aligned to the 5C primer pseudo-genome using Bowtie, allowing only reads with one unique alignment to pass filtering. Only reads for which one paired end mapped to a forward/left-forward primer and the other end mapped to a reverse/left-reverse primer were tallied as true counts.  5C is subject to specific biases, such as primer GC content resulting in annealing/PCR biases, that methods such as HiC are not. This manifests in primer-primer pairs with mapped counts that are orders of magnitude higher than the neighboring primer-primer pairs – such an extreme enrichment of single primer-primer pairs does not resemble the broader distribution of elevated counts, spanning clusters of neighboring primer-primer pairs, that exists at bonafide looping interactions across 5C and HiC data. Therefore, we decided to remove these biased primer-primer pairs before proceeding with interaction analysis. This was done by calculating for each primer-primer pair the median count of itself and the 24 primer-primer pairs nearest to the primer-primer pair in question (i.e. a scipy.ndimage.generic_filter window of size 5 was passed over the primer-primer pair matrix and the median of each window was recorded). If the count of one primer-primer pair was greater than eight-fold higher its neighborhood median then it was flagged as a high spatial outlier and removed. This process was performed for all primer-primer pairs, except for those in the 5C region surrounding the Arc gene for which the 8-fold threshold was found to be too stringent due to low region complexity and a 100-fold threshold was utilized instead.  After high-outlier removal, primer-primer pair counts were quantile normalized across all 12 replicates (4 per condition) as previously described (Beagan 2016). For plotting purposes quantile normalized counts were merged across replicates via summation, whereas for loop calling analysis all replicates were kept separate. Primer-primer pair counts were then converted to fragment-fragment interaction counts by averaging the primer-primer counts that mapped to each fragment-fragment pair (max of 2 if both a forward/left-forward and a reverse/left-reverse primer were able to be designed to both fragments and were not trimmed during outlier removal). We then divided our 5C regions into adjacent 4 kb bins and computed the relative interaction frequency of two bins (i,j) by summing the counts of all fragment-fragment interactions for which the coordinates of one of the constituent fragments overlapped (at least partially) a 12 kb window surrounding the center of the 4 kb ith bin and the other constituent fragment overlapped the 12 kb window surrounding the center if the jth bin. Binned count matrices were then matrix balanced using the ICE algorithm, at which point we considered each entry (i,j) to represent the ‘Relative Interaction Frequency’ (Fig. 1D) of the 4 kb bins i and j. Finally, the background contact domain ‘expected’ signal was calculated using the donut background model as previously described and used to normalize the relative interaction frequency data for the background interaction frequency present at each bin-bin pair. The resulting background-normalized interaction frequency (“observed over expected”) counts were fit with a logistic distribution from which p-values were computed for each bin-bin pair and converted into ‘Background-corrected Interaction Scores’ (interaction score = -10*log2(p-value), Fig. 1H) as previously described. Interaction scores have proven to be informatively comparable across replicates and conditions and as such were used for most visualization analysis and all loop-calling analysis to follow.   We applied the 3DeFDR analysis package to our dataset in order to identify differential interactions across the TTX and Bic conditions (4 replicates of each). Briefly, 3DeFDR identifies differential interactions and empirically estimates a false discovery rate (eFDR) for each identified dynamic looping class. Interactions are only considered for analysis if the interaction scores of all 8 replicates across both conditions surpassed a ‘significance threshold’. Interactions are classified as ‘TTX-only’ if all 4 interaction scores of the TTX replicates surpassed the interaction scores of the Bic replicates by more than a specified ‘difference threshold’; ‘Bic-only’ interactions are classified in the same manner. Those interactions that pass the significance threshold but are not classified as Bic-only or TTX-only are classified as ‘Constitutive’. Finally, classified interactions (single matrix entries / heatmap pixels) are clustered based on spatial proximity into ‘loops’; loops for which the number of constituent interactions/pixels fell below a ‘size threshold’ are removed. The 3DeFDR package simulates null replicate sets (i.e. 8 replicates of the same cell type/condition so that there should be no differential looping events) using on a negative binomial counts generating function parameterized using the mean-variance relationships that exist in the real data. eFDR is computed separately for each differential looping class as the total number of interactions/pixels called in that class on a simulated null replicate set over the total number of loops called as that class on the original real replicate set.   We utilized the ‘non-adaptive’ functionality option of the 3DeFDR analysis package, which sweeps across a wide range of difference threshold and calculates an eFDR for each looping class at each iteration. 250 simulated null replicate sets of 8 replicates were generated based on mean-variance relationships underlying the real TTX replicates. We utilized the default 3DeFDR initialization parameters with the exception of ‘bin_properties’ (a tunable parameter that specifies the distance scales over which fragment level interactions are stratified prior to fitting the negative binomial counts generating function to those interactions). This parameter was adjusted to capture the full extent of our regional matrices: (1) for close-range interactions (0-150 kb), we stratified the interactions using fine-grained, 12 kb-sized sliding windows with a 4 kb step, (2) for mid-range interactions (151-600 kb), we stratified the interactions into 24 kb-sized sliding windows with an 8 kb step, and (3) for longer range interactions (601-2500 kb), we stratified the interactions into coarse-grained, 60 kb-sized sliding windows with a 24 kb step. Through this approach we achieved an eFDR of 6.6% for bic-only (activity-induced) loops utilizing a difference threshold of 6.75, a significance threshold of -10*log2(0.08) (i.e. a p-value of 0.08 resulting from the logistic fit to the observed over expected data), and a size threshold of 5.
Genome_build: mm9 5C primer psuedogenome
Supplementary_files_format_and_content: BED_mm9_ERneuro_plus_Synapt.bed is the bed file containing the coordinates for each 5C primer used.  '[Rep_name]-Activity-Synaptic.counts' contains the number of reads mapped to each pair of 5C primers (see 5C processing) for each replicate.  '5C_Loop_Calls.xlsx' contains the list of all loops called within 5C regions, sorted by looping classification (TTX-only, constitutive, or Bic-only). Columns 2-7 are the upstream (2-4) and downstream (5-7) anchor coordinates of each loop in bed format. Columns 8-17 represent

molecule: genomic DNA

extract_protocol:
Neuronal cultures were formaldehyde fixed as described for ChIP-seq and stored at -80oC. For each condition (Bic, Untreat, TTX), in situ 3C was performed on 4 replicates (divided evenly across two animal/culture batches) of 4-5 million cells. Briefly, cells were thawed on ice and resuspended (gently) in 250 uL of lysis buffer (10 mM Tris-HCl pH 8.0, 10 mM NaCl, 0.2% Igepal CA630) with 50 uL protease inhibitors (Sigma P8340). Cell suspension was incubated on ice for 15 minutes and pelleted. Pelleted nuclei were washed once in lysis buffer (resuspension and spin), then resuspended and incubated in 50 uL of 0.5% SDS at 62oC for 10 min. SDS was inactivated via the addition of 145 uL H2O, 25 uL 10% Triton X-100, and incubation at 37oC for 15 min. Subsequently, chromatin was digested overnight at 37oC with the addition of 25 uL 10X NEBuffer2 and 100U (5 uL) of HindIII (NEB, R0104S), followed by 20 min incubation at 62oC to inactivate the HindIII. Chromatin was re-ligated via the addition of 100 uL 10% Triton X-100, 120 uL NEB T4 DNA Ligation buffer (NEB B0202S), 12 uL 10 mg/mL BSA, 718 uL H2O, and 2000 U (5 uL) of T4 DNA Ligase (NEB M0202S) and incubation at 16oC for 2 hours (NOTE: This is a deviation from in situ HiC (Rao et al. 2010) in order to promote sticky-end ligation over blunt-end). Following ligation nuclei were pelleted, resuspended in 300 uL of 10 mM Tris-HCl (pH 8.0), 0.5 M NaCl, 1% SDS, plus 25 uL of 20 mg/mL proteinase K (NEB P8107), and incubated at 65oC for 4 hours at which point an additional 25 uL of proteinase K was added and incubated overnight. 3C templates were isolated next day via RNaseA treatment, phenol-chloroform extraction, ethanol precipitation, and Amicon filtration (Millipore MFC5030BKS) (for more details see Beagan et al. 2016). Template size distribution and quantity were assessed with a 0.8% agarose gel.
5C primers were designed according to the double-alternating design scheme using the My5C primer design software (http://my5c.umassmed.edu/my5Cprimers/5C.php)10 with universal “Emulsion” primer tails. Regions were designed to capture TAD structures immediately surrounding the genes of interest (Bdnf, Fos, Arc, Neurexin-1, Neuroligin-3, Synaptotagmin-1) in published mouse cortex HiC data. 5C reactions were carried out as previously described. 600 ng (~200,000 genome copies) of 3C template for each replicate was mixed with 1 fmole of each 5C primer and 0.9 ug of salmon sperm DNA in 1x NEB4 buffer, denatured at 95oC for 5 min, then incubated at 55oc for 16 hours. Primers which had then annealed in adjacent positions were ligated through the addition of 10 U (20 uL) Taq ligase (NEB M0208L) and incubation at 55oC for 1 hour then 75oC for 10 min. Successfully ligated primer-primer pairs were amplified using primers designed to the universal tails (FOR = CCTCTC TATGGGCAGTCGGTGAT, REV = CTGCCCCGGGTTCCTCATTCTCT) across 30 PCR cycles using Phusion High-Fidelity Polymerase. Presence of a single PCR product at 100 bp was confirmed via agarose gel, then residual DNA <100 bp was removed through AmpureXP bead cleanup at a ratio of 2:1 beads:DNA (vol/vol). 100 ng of the resulting 5C product was prepared for sequencing on the Illumina NextSeq 500 using the NEBNext Ultra DNA Library Prep Kit (NEB E7370) following the manufacturer’s instructions with the following parameter selections: during size selection, 70 uL of AMPure beads was added at the first step and 25 at the second step; linkered fragments were amplified using 8 PCR cycles. A single band at 220 bp in each final library was confirmed using an Agilent DNA 1000 Bioanalyzer chip, and library concentration was determined using the KAPA Illumina Library Quantification Kit (#KK4835). Finally, libraries were evenly pooled and sequenced on the Illumina NextSeq 500 using 37 bp paired-end reads to read depths of between 11 and 30 million reads per replicate.


title: 000min RNA Rep1

description:
Ribosome depleted RNA
000min RNA Rep1


title: 020min 5C


title: 000min 5C


title: Ctrl_1h-3

status: Public on Sep 04 2020

city: Vienna

first: Anna

middle: Christina

last: Obenauf

address: Campus-Vienna-Biocenter 1

country: Austria

institute: Research Institute of Molecular Pathology

data_processing:
library strategy: SLAMseq (Thiol (SH)-Linked Alkylation for the Metabolic sequencing of RNA)
Reads were aligned against the human genome (hg38) with STAR.
reads were processed using SlamDunk v0.2.4 running the full analysis procedure (slamdunk all) and stringency filter set to 2 (requiring ≥ 2 T>C conversions per read)
For details see: http://t-neumann.github.io/slamdunk/docs.html
Genome_build: hg38
Supplementary_files_format_and_content: TSV table containing UTR coordinates named by EntrezID, raw and normalized read counts, as well as T>C conversion statistics.

organism: Homo sapiens

growth_protocol: Cells were cultured as suspension cells in RPMI-1640 supplemented with 10% FBS, 2 mM L-glutamine, 50 U/mL penicillin and 50 mg/mL streptomycin.

extract_protocol:
Total RNA isolation from cells was performed with an in-house paramagnetic bead-based purification protocol.
3'UTR sequencing libraries were generated from 500ng total RNA using a commercial kit (QuantSeq 3′ mRNA-Seq Library Prep Kit FWD for Illumina, Lexogen).

treatment_protocol: Cells were pre-treated with GSK-LSD1 inhibitor for 30min and 4SU labeling of newly synthesized transcripts was performed for 60 or 360min.

tag: cell line

value: PeTa

tag: cell type

value: Merkel cell carcinoma cell line

value: DMSO

tag: 4su labeling time before harvest (min)


title: Ctrl_1h-2


title: 005min 5C


title: Untreat RNA Rep1

data_processing:
RNA-seq: RNA-seq reads were mapped to the RefSeq transcriptome (transcriptome fasta downloaded from the UCSC genome browser on July 28, 2017) using Salmon. In accordance with the TruSeq Stranded Total RNA Library Preparation, mapping was done using the -ISR flag. The resulting TPM quantifications for each RefSeq transcript are provided.
Supplementary_files_format_and_content: RNA-seq: Estimated transcripts per million (TPM) across all conditions generated using the Salmon package (see processing steps).
ChIP-seq: H3K27ac ChIP-seq reads were aligned to the mm9 genome using Bowtie. Reads with more than two possible alignments were removed (-m2 flag utilized). IP libraries across the Bic, Untreat, and TTX conditions were downsampled to 38 million reads, while input libraries were downsampled to 44 million reads. Peaks were identified using MACS2 5 with a p-value cutoff parameter of 1x10e-8 and the broadpeak flag also invoked with a broadpeak cutoff of 1x10e-8. Between 44000 and 47000 peaks were called per condition.
Supplementary_files_format_and_content: ChIP-seq: Bed files of peak calls generated using the MACS2 package with a p-value and broad-cutoff of 1E-8 (see data processing steps).
5C-seq: Paired-end reads were aligned to the 5C primer pseudo-genome using Bowtie, allowing only reads with one unique alignment to pass filtering. Only reads for which one paired end mapped to a forward/left-forward primer and the other end mapped to a reverse/left-reverse primer were tallied as true counts.  5C is subject to specific biases, such as primer GC content resulting in annealing/PCR biases, that methods such as HiC are not. This manifests in primer-primer pairs with mapped counts that are orders of magnitude higher than the neighboring primer-primer pairs – such an extreme enrichment of single primer-primer pairs does not resemble the broader distribution of elevated counts, spanning clusters of neighboring primer-primer pairs, that exists at bonafide looping interactions across 5C and HiC data. Therefore, we decided to remove these biased primer-primer pairs before proceeding with interaction analysis. This was done by calculating for each primer-primer pair the median count of itself and the 24 primer-primer pairs nearest to the primer-primer pair in question (i.e. a scipy.ndimage.generic_filter window of size 5 was passed over the primer-primer pair matrix and the median of each window was recorded). If the count of one primer-primer pair was greater than eight-fold higher its neighborhood median then it was flagged as a high spatial outlier and removed. This process was performed for all primer-primer pairs, except for those in the 5C region surrounding the Arc gene for which the 8-fold threshold was found to be too stringent due to low region complexity and a 100-fold threshold was utilized instead.  After high-outlier removal, primer-primer pair counts were quantile normalized across all 12 replicates (4 per condition) as previously described (Beagan 2016). For plotting purposes quantile normalized counts were merged across replicates via summation, whereas for loop calling analysis all replicates were kept separate. Primer-primer pair counts were then converted to fragment-fragment interaction counts by averaging the primer-primer counts that mapped to each fragment-fragment pair (max of 2 if both a forward/left-forward and a reverse/left-reverse primer were able to be designed to both fragments and were not trimmed during outlier removal). We then divided our 5C regions into adjacent 4 kb bins and computed the relative interaction frequency of two bins (i,j) by summing the counts of all fragment-fragment interactions for which the coordinates of one of the constituent fragments overlapped (at least partially) a 12 kb window surrounding the center of the 4 kb ith bin and the other constituent fragment overlapped the 12 kb window surrounding the center if the jth bin. Binned count matrices were then matrix balanced using the ICE algorithm, at which point we considered each entry (i,j) to represent the ‘Relative Interaction Frequency’ (Fig. 1D) of the 4 kb bins i and j. Finally, the background contact domain ‘expected’ signal was calculated using the donut background model as previously described and used to normalize the relative interaction frequency data for the background interaction frequency present at each bin-bin pair. The resulting background-normalized interaction frequency (“observed over expected”) counts were fit with a logistic distribution from which p-values were computed for each bin-bin pair and converted into ‘Background-corrected Interaction Scores’ (interaction score = -10*log2(p-value), Fig. 1H) as previously described. Interaction scores have proven to be informatively comparable across replicates and conditions and as such were used for most visualization analysis and all loop-calling analysis to follow.   We applied the 3DeFDR analysis package to our dataset in order to identify differential interactions across the TTX and Bic conditions (4 replicates of each). Briefly, 3DeFDR identifies differential interactions and empirically estimates a false discovery rate (eFDR) for each identified dynamic looping class. Interactions are only considered for analysis if the interaction scores of all 8 replicates across both conditions surpassed a ‘significance threshold’. Interactions are classified as ‘TTX-only’ if all 4 interaction scores of the TTX replicates surpassed the interaction scores of the Bic replicates by more than a specified ‘difference threshold’; ‘Bic-only’ interactions are classified in the same manner. Those interactions that pass the significance threshold but are not classified as Bic-only or TTX-only are classified as ‘Constitutive’. Finally, classified interactions (single matrix entries / heatmap pixels) are clustered based on spatial proximity into ‘loops’; loops for which the number of constituent interactions/pixels fell below a ‘size threshold’ are removed. The 3DeFDR package simulates null replicate sets (i.e. 8 replicates of the same cell type/condition so that there should be no differential looping events) using on a negative binomial counts generating function parameterized using the mean-variance relationships that exist in the real data. eFDR is computed separately for each differential looping class as the total number of interactions/pixels called in that class on a simulated null replicate set over the total number of loops called as that class on the original real replicate set.   We utilized the ‘non-adaptive’ functionality option of the 3DeFDR analysis package, which sweeps across a wide range of difference threshold and calculates an eFDR for each looping class at each iteration. 250 simulated null replicate sets of 8 replicates were generated based on mean-variance relationships underlying the real TTX replicates. We utilized the default 3DeFDR initialization parameters with the exception of ‘bin_properties’ (a tunable parameter that specifies the distance scales over which fragment level interactions are stratified prior to fitting the negative binomial counts generating function to those interactions). This parameter was adjusted to capture the full extent of our regional matrices: (1) for close-range interactions (0-150 kb), we stratified the interactions using fine-grained, 12 kb-sized sliding windows with a 4 kb step, (2) for mid-range interactions (151-600 kb), we stratified the interactions into 24 kb-sized sliding windows with an 8 kb step, and (3) for longer range interactions (601-2500 kb), we stratified the interactions into coarse-grained, 60 kb-sized sliding windows with a 24 kb step. Through this approach we achieved an eFDR of 6.6% for bic-only (activity-induced) loops utilizing a difference threshold of 6.75, a significance threshold of -10*log2(0.08) (i.e. a p-value of 0.08 resulting from the logistic fit to the observed o

extract_protocol:
RNA-seq: At DIV5 and DIV16, 900,000 neurons were lysed in 1 mL Trizol (Thermo Fisher Scientific 15596026). Lysates were snap frozen and stored at -80oc until use. Total RNA was then isolated using the mirVana miRNA Isolation Kit (Thermo Fisher Scientific AM1561) according to manufacturer’s protocol and eluted from the spin-column using 100 uL nuclease-free water. Samples were DNase treated (Thermo Fisher Scientific AM1906) and tested for quality using an Agilent Bioanalyzer RNA chip. All samples produced an RNA Integrity Number (RIN) greater than 9.
RNA-seq:We utilized the TruSeq Stranded Total RNA Library Prep Kit with Ribo-Zero Gold (Illumina RS-122-2301) and prepared each RNA sample for sequencing according to the manufacturer’s protocol. cDNA libraries were amplified across 15 PCR cycles followed by AMPure XP Bead clean-up (1:1 bead:solution ratio). Finally, the library sizes were confirmed to be between 200-500 bp using the BioAnalyzer before sequencing 75 bp paired-end reads on the Illumina NextSeq500. In order to minimize and identify technical variation, three replicates spanning two culture batches were prepared, pooled, and sequenced on three flow cells to depths of greater than 60 million reads per library.
ChIP-seq: At DIV16, neuronal cultures were fixed in 1% formaldehyde for 10 minutes (room temp) via the addition (1:10 vol/vol) of the following fixation solution: 50 mM Hepes-KOH (pH 7.5), 100 mM NaCl, 1 mM EDTA, 0.5 mM EGTA, 11% Formaldehyde. Fixation was quenched via the addition of 2.5 M glycine (1:20 vol/vol) and scraped into pellets of 8 million cells. Each pellet was washed once with cold PBS, flash frozen, and stored at -80oC. Immunoprecipitation was performed as described previously (Beagan et al., 2016) with slight modifications. Briefly, IP reactions were prepared a day prior to cell lysis by combining 20 uL of protein A and protein G conjugated agarose beads (Invitrogen# 15918-014 and 15920-010, respectively) with 10 uL of anti-H3K27ac antibody (Abcam# ab4729) in 1 mL of cold PBS and rotated overnight. The next day cell pellets were resuspended in 1 mL lysis buffer (10 mM Tris pH 8.0, 10 mM NaCl, 0.2% NP-40/Igepal, Protease Inhibitor, PMSF) and incubated on ice for 10 min. Cells were further lysed with 30 strokes of a dounce homogenizer (pestle A) and then nuclei were pelleted. Nuclei were lysed on ice in 50 mM Tris pH 8.0, 10 mM EDTA, 1% SDS, Protease Inhibitor, PMSF for 20 min. SDS concentration was reduced before sonication by the addition of 300 uL IP Dilution Buffer (20 mM Tris pH 8.0, 2 mM EDTA, 150 mM NaCl, 1% Triston X-100, 0.01% SDS, Protease Inhibitor, PMSF), after which samples were sonicated for 60 minutes (30 seconds on, 30 seconds off cycle, 100% amplitude) using a Qsonica Q800R2 Sonicator. Insoluble fractions were removed via spin, and the supernatant was removed of non-specific binding chromatin via rotation with preclearing solution (3.7 mL IP Dilution Buffer, 0.5 mL Nuclear Lysis Buffer, 175 uL of Agarose Protein A/G beads, and 50 ug Rabbit IgG) for 2 hours at 4°C. Beads were pelleted and 4.7 mL of supernatant was removed. 200 uL of supernatant was retained as input control (stored at -20°C) while the remaining 4.5 mL was transferred to the beads that had been pre-bound with the H3K27ac antibody overnight; the IP reaction then rotated overnight again at 4°C. Bound bead complexes were washed once with 1 mL IP Wash Buffer 1 (20 mM Tris pH 8.0, 2 mM EDTA, 50 mM NaCl, 1% Triton X-100, 0.1% SDS), twice with 1 mL High-Salt Buffer (20 mM Tris pH 8.0, 2 mM EDTA, 500 mM NaCl, 1% Triton X-100, 0.01% SDS), once with IP Wash Buffer 2 (10 mM Tris pH 8.0, 1 mM EDTA, 0.25 M LiCl, 1% NP-40/Igepal, 1% Na-deoxycholate), and finally twice with 1x TE. Complexes were eluted by twice resuspending bound beads in 110 uL Elution Buffer (100 mM NaHCO3, 1% SDS), pelleting the beads after each elution and transferring 100 uL supernatant to a new tube. Finally, 12 uL of 5M NaCl and 20 ug RNase A were added to both 200 uL IP and input samples and incubated at 65 degrees for 1 hour, followed by the addition of 60 ug of Proteinase K and overnight incubation at 65 degrees. DNA was isolated via phenol-chloroform extraction and ethanol precipitation and concentration was quantified using Qubit fluorometer.
ChIP-seq libraries were prepared for sequencing using the NEBNext Ultra II DNA Library Prep Kit (NEB# E7645S), following manufacturer’s protocol with the following user-chosen specifications. 3 ng DNA from all IP and input samples was used as starting material. NEBNext Adaptors were diluted 15x in 10 mM Tris-HCL, pH 8.0 with 10 mM NaCl prior to adaptor ligation. Large DNA fragments were removed via a size selection by adding 15 uL of AMPure XP beads at the first bead addition step and 87 uL of beads at the second bead addition step. Size-selected DNA was amplified using 9 cycles of PCR enrichment. The size-range of the final libraries was confirmed to be between 200-1000 bp using an Agilent Bioanalyzer High Sensitivity DNA test. H3K27ac enrichment was confirmed prior to sequencing by querying the IP/input qPCR enrichment of primer pairs designed to the Arc, Synaptotagmin-1, and Tcf25 promoter regions. Library concentrations were calculated and normalized using the KAPA Illumina Library Quantification Kit (#KK4835) so that libraries could be equally pooled before sequencing 75 bp single-end reads on the NextSeq500. All IP libraries were sequenced to a depth greater than 48 million reads and all input libraries were sequenced to greater than 67 million reads.
5C-seq: Neuronal cultures were formaldehyde fixed as described for ChIP-seq and stored at -80oC. For each condition (Bic, Untreat, TTX), in situ 3C was performed on 4 replicates (divided evenly across two animal/culture batches) of 4-5 million cells. Briefly, cells were thawed on ice and resuspended (gently) in 250 uL of lysis buffer (10 mM Tris-HCl pH 8.0, 10 mM NaCl, 0.2% Igepal CA630) with 50 uL protease inhibitors (Sigma P8340). Cell suspension was incubated on ice for 15 minutes and pelleted. Pelleted nuclei were washed once in lysis buffer (resuspension and spin), then resuspended and incubated in 50 uL of 0.5% SDS at 62oC for 10 min. SDS was inactivated via the addition of 145 uL H2O, 25 uL 10% Triton X-100, and incubation at 37oC for 15 min. Subsequently, chromatin was digested overnight at 37oC with the addition of 25 uL 10X NEBuffer2 and 100U (5 uL) of HindIII (NEB, R0104S), followed by 20 min incubation at 62oC to inactivate the HindIII. Chromatin was re-ligated via the addition of 100 uL 10% Triton X-100, 120 uL NEB T4 DNA Ligation buffer (NEB B0202S), 12 uL 10 mg/mL BSA, 718 uL H2O, and 2000 U (5 uL) of T4 DNA Ligase (NEB M0202S) and incubation at 16oC for 2 hours (NOTE: This is a deviation from in situ HiC (Rao et al. 2010) in order to promote sticky-end ligation over blunt-end). Following ligation nuclei were pelleted, resuspended in 300 uL of 10 mM Tris-HCl (pH 8.0), 0.5 M NaCl, 1% SDS, plus 25 uL of 20 mg/mL proteinase K (NEB P8107), and incubated at 65oC for 4 hours at which point an additional 25 uL of proteinase K was added and incubated overnight. 3C templates were isolated next day via RNaseA treatment, phenol-chloroform extraction, ethanol precipitation, and Amicon filtration (Millipore MFC5030BKS) (for more details see Beagan et al. 2016). Template size distribution and quantity were assessed with a 0.8% agarose gel.
5C primers were designed according to the double-alternating design scheme using the My5C primer design software (http://my5c.umassmed.edu/my5Cprimers/5C.php)10 with universal “Emulsion” primer tails. Regions were designed to capture TAD structures immediately surrounding the genes of interest (Bdnf, Fos, Arc, Neurexin-1, Neuroligin-3, Synaptotagmin-1) in published mouse cortex HiC data. 5C reactions were carried out as previously described. 600 ng (~200,000 genome copies) of 3C template for each replicate was mixed with 1 fmole of each


title: TTX RNA Rep 3

value: 1 uM Tetrodotoxin


title: TTX RNA Rep2


title: DIV5 RNA Rep3


title: DIV5 RNA Rep2


title: DIV5 RNA Rep1


title: Untreat RNA Rep3


title: Untreat RNA Rep2


title: TTX RNA Rep1


title: Bic RNA Rep2

value: 10 uM Bicuculline


title: Bic RNA Rep 3


title: Untreat 5C Rep4

data_processing:
Library strategy: 5C-seq
RNA-seq: RNA-seq reads were mapped to the RefSeq transcriptome (transcriptome fasta downloaded from the UCSC genome browser on July 28, 2017) using Salmon. In accordance with the TruSeq Stranded Total RNA Library Preparation, mapping was done using the -ISR flag. The resulting TPM quantifications for each RefSeq transcript are provided.
Supplementary_files_format_and_content: RNA-seq: Estimated transcripts per million (TPM) across all conditions generated using the Salmon package (see processing steps).
ChIP-seq: H3K27ac ChIP-seq reads were aligned to the mm9 genome using Bowtie. Reads with more than two possible alignments were removed (-m2 flag utilized). IP libraries across the Bic, Untreat, and TTX conditions were downsampled to 38 million reads, while input libraries were downsampled to 44 million reads. Peaks were identified using MACS2 5 with a p-value cutoff parameter of 1x10e-8 and the broadpeak flag also invoked with a broadpeak cutoff of 1x10e-8. Between 44000 and 47000 peaks were called per condition.
Supplementary_files_format_and_content: ChIP-seq: Bed files of peak calls generated using the MACS2 package with a p-value and broad-cutoff of 1E-8 (see data processing steps).
5C-seq: Paired-end reads were aligned to the 5C primer pseudo-genome using Bowtie, allowing only reads with one unique alignment to pass filtering. Only reads for which one paired end mapped to a forward/left-forward primer and the other end mapped to a reverse/left-reverse primer were tallied as true counts.  5C is subject to specific biases, such as primer GC content resulting in annealing/PCR biases, that methods such as HiC are not. This manifests in primer-primer pairs with mapped counts that are orders of magnitude higher than the neighboring primer-primer pairs – such an extreme enrichment of single primer-primer pairs does not resemble the broader distribution of elevated counts, spanning clusters of neighboring primer-primer pairs, that exists at bonafide looping interactions across 5C and HiC data. Therefore, we decided to remove these biased primer-primer pairs before proceeding with interaction analysis. This was done by calculating for each primer-primer pair the median count of itself and the 24 primer-primer pairs nearest to the primer-primer pair in question (i.e. a scipy.ndimage.generic_filter window of size 5 was passed over the primer-primer pair matrix and the median of each window was recorded). If the count of one primer-primer pair was greater than eight-fold higher its neighborhood median then it was flagged as a high spatial outlier and removed. This process was performed for all primer-primer pairs, except for those in the 5C region surrounding the Arc gene for which the 8-fold threshold was found to be too stringent due to low region complexity and a 100-fold threshold was utilized instead.  After high-outlier removal, primer-primer pair counts were quantile normalized across all 12 replicates (4 per condition) as previously described (Beagan 2016). For plotting purposes quantile normalized counts were merged across replicates via summation, whereas for loop calling analysis all replicates were kept separate. Primer-primer pair counts were then converted to fragment-fragment interaction counts by averaging the primer-primer counts that mapped to each fragment-fragment pair (max of 2 if both a forward/left-forward and a reverse/left-reverse primer were able to be designed to both fragments and were not trimmed during outlier removal). We then divided our 5C regions into adjacent 4 kb bins and computed the relative interaction frequency of two bins (i,j) by summing the counts of all fragment-fragment interactions for which the coordinates of one of the constituent fragments overlapped (at least partially) a 12 kb window surrounding the center of the 4 kb ith bin and the other constituent fragment overlapped the 12 kb window surrounding the center if the jth bin. Binned count matrices were then matrix balanced using the ICE algorithm, at which point we considered each entry (i,j) to represent the ‘Relative Interaction Frequency’ (Fig. 1D) of the 4 kb bins i and j. Finally, the background contact domain ‘expected’ signal was calculated using the donut background model as previously described and used to normalize the relative interaction frequency data for the background interaction frequency present at each bin-bin pair. The resulting background-normalized interaction frequency (“observed over expected”) counts were fit with a logistic distribution from which p-values were computed for each bin-bin pair and converted into ‘Background-corrected Interaction Scores’ (interaction score = -10*log2(p-value), Fig. 1H) as previously described. Interaction scores have proven to be informatively comparable across replicates and conditions and as such were used for most visualization analysis and all loop-calling analysis to follow.   We applied the 3DeFDR analysis package to our dataset in order to identify differential interactions across the TTX and Bic conditions (4 replicates of each). Briefly, 3DeFDR identifies differential interactions and empirically estimates a false discovery rate (eFDR) for each identified dynamic looping class. Interactions are only considered for analysis if the interaction scores of all 8 replicates across both conditions surpassed a ‘significance threshold’. Interactions are classified as ‘TTX-only’ if all 4 interaction scores of the TTX replicates surpassed the interaction scores of the Bic replicates by more than a specified ‘difference threshold’; ‘Bic-only’ interactions are classified in the same manner. Those interactions that pass the significance threshold but are not classified as Bic-only or TTX-only are classified as ‘Constitutive’. Finally, classified interactions (single matrix entries / heatmap pixels) are clustered based on spatial proximity into ‘loops’; loops for which the number of constituent interactions/pixels fell below a ‘size threshold’ are removed. The 3DeFDR package simulates null replicate sets (i.e. 8 replicates of the same cell type/condition so that there should be no differential looping events) using on a negative binomial counts generating function parameterized using the mean-variance relationships that exist in the real data. eFDR is computed separately for each differential looping class as the total number of interactions/pixels called in that class on a simulated null replicate set over the total number of loops called as that class on the original real replicate set.   We utilized the ‘non-adaptive’ functionality option of the 3DeFDR analysis package, which sweeps across a wide range of difference threshold and calculates an eFDR for each looping class at each iteration. 250 simulated null replicate sets of 8 replicates were generated based on mean-variance relationships underlying the real TTX replicates. We utilized the default 3DeFDR initialization parameters with the exception of ‘bin_properties’ (a tunable parameter that specifies the distance scales over which fragment level interactions are stratified prior to fitting the negative binomial counts generating function to those interactions). This parameter was adjusted to capture the full extent of our regional matrices: (1) for close-range interactions (0-150 kb), we stratified the interactions using fine-grained, 12 kb-sized sliding windows with a 4 kb step, (2) for mid-range interactions (151-600 kb), we stratified the interactions into 24 kb-sized sliding windows with an 8 kb step, and (3) for longer range interactions (601-2500 kb), we stratified the interactions into coarse-grained, 60 kb-sized sliding windows with a 24 kb step. Through this approach we achieved an eFDR of 6.6% for bic-only (activity-induced) loops utilizing a difference threshold of 6.75, a significance threshold of -10*log2(0.08) (i.e. a p-value of 0.08 resulting from the logis


title: 360min 5C


title: TTX 5C Rep 4


title: Untreat 5C Rep1


title: TTX 5C Rep 3


title: Bic 5C Rep 4


title: Untreat 5C Rep3


title: TTX 5C Rep2


title: Bic 5C Rep1


title: TTX H3K27ac ChIP Input


title: Bic 5C Rep 3


title: TTX H3K27ac ChIP IP


title: Untreat H3K27ac ChIP Input


title: Bic 5C Rep2


title: Untreat H3K27ac ChIP IP


title: Bic H3K27ac ChIP IP


title: Bic RNA Rep1


title: Untreat 5C Rep2


title: HODay7replicate3

status: Public on Oct 23 2019

city: Ann Arbor

first: Rajasree

last: Menon

state: Michigan

address: 4544E, MSRB2, Catherine Street, University of Michigan

department: Department of Computational  Medicine and Bioinformatics

institute: University of Michigan

description:
HO model (30% body surface area dorsal burn and Achilles tenotomy) was used in C57 and LysmCre/Tgfb1fx/fx mice
Day 7
HO_Day7_Markers

data_processing:
Cell Ranger Single Cell Software Suite 1.3 was used to perform sample de-multiplexing, barcode processing, and single cell gene counting (Alignment, Barcoding and UMI Count) at the University of Michigan Biomedical Core Facilities DNA Sequencing Core.
Seurat R package was used to perform unsupervised clustering and identification of cell-type specific markers.
Genome_build: GRCh38
Supplementary_files_format_and_content: barcodes.tsv, genes.tsv, and matrix.mtx files.

source_name: Tenotomy site of a burn/tenotomy mouse

extract_protocol: Single-cell 3’ library generation was performed on the 10x Genomics Chromium Controller following the manufacturers protocol for the v2 reagent kit (10X Genomics, Pleasanton, CA, USA). Cell suspensions were loaded onto a Chromium Single-Cell A chip along with reverse transcription (RT) master mix and single cell 3’ gel beads, aiming for 2000-6000 cells per channel. In this experiment, 8700 cells were encapsulated into emulsion droplets at a concentration of 700-1200 cells/ul which targets 5000 single cells with an expected multiplet rate of 3.9%. Following generation of single-cell gel bead-in-emulsions (GEMs), reverse transcription was performed and the resulting Post GEM-RT product was cleaned up using DynaBeads MyOne Silane beads (ThermoFisher Scientific, Waltham, MA, USA).  The cDNA was amplified, SPRIselect (Beckman Coulter, Brea, CA, USA) cleaned and quantified then enzymatically fragmented and size selected using SPRIselect beads to optimize the cDNA amplicon size prior to library construction. An additional round of double-sided SPRI bead cleanup is performed after end repair and A-tailing.  Another single-sided cleanup is done after adapter ligation.  Indexes were added during PCR amplification and a final double-sided SPRI cleanup was performed. Libraries were quantified by Kapa qPCR for Illumina Adapters (Roche) and size was determined by Agilent tapestation D1000 tapes. Read 1 primer sequence are added to the molecules during GEM incubation. P5, P7 and sample index and read 2 primer sequence are added during library construction via end repair, A-tailing, adaptor ligation and PCR. Libraries were generated with unique sample indices (SI) for each sample.

treatment_protocol:
A partial-thickness scald burn injury was administered to animals according to a previously described protocol by a single surgeon.3,57 Briefly, mice were anesthetized with inhaled isoflurane. Dorsal hair was closely clipped, and an aluminum block heated to 60°C was exposed to the dorsal region over 30% of the total body surface area for 18 s to achieve a partial-thickness burn injury. Each mouse then received a concurrent sterile dorsal hind limb tendon transection at the midpoint of the Achilles tendon (Achilles tenotomy) with placement of a single 5-0 vicryl suture to close the skin. Pain management was achieved with subcutaneous injections of buprenorphine (Buprenex, Reckitt Benckiser Pharmaceuticals) every 12 hr for 2 days.
Post injury harvested tissue samples were digested for 45 minutes in 0.3% Type 1 Collagenase and 0.4% Dispase II (Gibco) in Roswell Park Memorial Institute (RPMI) medium at 37°C under constant agitation at 120rpm. Digestions were subsequently quenched with 10% FBS RPMI and filtered through 40μm sterile strainers. Cells were then washed in PBS with 0.04% BSA, counted and resuspended at a concentration of ~1000 cells/ul. Cell viability was assessed with Trypan blue exclusion on a Countess II (Thermo Fisher Scientific) automated counter and only samples with >85% viability were processed for further sequencing.

tag: timepoint

value: cells recruited to injury site at day 7


title: HODay7replicate1

value: A partial-thickness scald burn injury was administered to animals


title: HODay7replicate2


title: TTX 5C Rep1


title: HODay7replicate4

status: Public on Oct 26 2019

description:
HO_Day7_Markers
A partial-thickness scald burn injury was administered to animals according to a previously described protocol by a single surgeon.3,57 Briefly, mice were anesthetized with inhaled isoflurane. Dorsal hair was closely clipped, and an aluminum block heated to 60°C was exposed to the dorsal region over 30% of the total body surface area for 18 s to achieve a partial-thickness burn injury. Each mouse then received a concurrent sterile dorsal hind limb tendon transection at the midpoint of the Achilles tendon (Achilles tenotomy) with placement of a single 5-0 vicryl suture to close the skin. Pain management was achieved with subcutaneous injections of buprenorphine (Buprenex, Reckitt Benckiser Pharmaceuticals) every 12 hr for 2 days.
Post injury harvested tissue samples were digested for 45 minutes in 0.3% Type 1 Collagenase and 0.4% Dispase II (Gibco) in Roswell Park Memorial Institute (RPMI) medium at 37°C under constant agitation at 120rpm. Digestions were subsequently quenched with 10% FBS RPMI and filtered through 40μm sterile strainers. Cells were then washed in PBS with 0.04% BSA, counted and resuspended at a concentration of ~1000 cells/ul. Cell viability was assessed with Trypan blue exclusion on a Countess II (Thermo Fisher Scientific) automated counter and only samples with >85% viability were processed for further sequencing.
Libraries were sequenced on a HiSeq 4000,  (Illumina, San Diego, CA, USA) using a HiSeq 4000 PE Cluster Kit (PN PE-410-1001) with HiSeq 4000 SBS Kit (100 cycles, PN FC-410-1002)reagents, loaded at 200 pM following Illumina’s denaturing and dilution recommendations.   The run configuration was 26 x 8 x 98 cycles for Read 1, Index and Read 2, respectively.

data_processing:
Cell Ranger Single Cell Software Suite 1.3 was used to perform sample de-multiplexing, barcode processing, and single cell gene counting (Alignment, Barcoding and UMI Count) at the University of Michigan Biomedical Core Facilities DNA Sequencing Core
Genome_build: mm10
Seurat R package was used to perform unsupervised clustering and identification of cell-type specific markers.
Supplementary_files_format_and_content: barcodes.tsv, genes.tsv, and matrix.mtx files

source_name: Day 7

tag: sample type

tag: biosource

value: HO model (30% body surface area dorsal burn and Achilles tenotomy) was used in C57 and LysmCre/Tgfb1fx/fx mice


title: Bic H3K27ac ChIP Input


title: cfDNA_3204766_Cirrhosis_alone

status: Public on Aug 16 2019

type: genomic

city: Rochester

first: Keith

middle: Darrell

last: Robertson

state: MN

address: 200 First Street SW, Stabile 12-70

department: Pharmacology

institute: Mayo Clinic

hyb_protocol: Manufacturer's recommendation

scan_protocol: Manufacturer's recommendation

data_processing: minfi (SWAN)

label: Cy3, Cy5

source_name: plasma

label_protocol: Manufacturer's recommendation

extract_protocol: QIAamp Circulating Nucleic Acid Kit

tag: disease state

tag: tissue

value: plasma


title: cfDNA_6354977_Cirrhosis_alone


title: cfDNA_7059663_Cirrhosis_with_HCC


title: cfDNA_7194609_Cirrhosis_alone


title: cfDNA_3449101_Cirrhosis_alone


title: cfDNA_3984068_Cirrhosis_with_HCC


title: Q49: UNKNOWN

status: Public on Apr 10 2020

city: Omaha

first: Pranita

last: Atri

state: NE

address: S 42nd and Emile St

department: Biochemistry

institute: UNIVERSITY OF NEBRASKA MEDICAL CENTER

hyb_protocol: Bisulfite converted D was amplified, fragmented and hybridised to Illumina Infinium Human MethylationEPIC (850K) Beadchip using standard Illumina protocol

scan_protocol: Arrays were imaged using BeadArray Reader using standard recommended Illumina scanner setting

data_processing: Rnbeads Version 2.0.0

source_name: UNKNOWN

label_protocol: Standard Illumina Protocol

extract_protocol: Isolated genomic D using the RecoverAll kit

value: UNKNOWN

tag: age (years)

value: F

tag: subgroup


title: cfDNA_5214939_Cirrhosis_with_HCC


title: cfDNA_4456725_Cirrhosis_alone


title: cfDNA_3817068_Cirrhosis_with_HCC


title: cfDNA_5358548_Cirrhosis_alone


title: cfDNA_2431273_Cirrhosis_alone


title: cfDNA_7193884_Cirrhosis_alone


title: cfDNA_6334538_Cirrhosis_alone


title: cfDNA_7038848_Cirrhosis_alone


title: cfDNA_8566121_Cirrhosis_with_HCC


title: cfDNA_7257350_Cirrhosis_alone


title: cfDNA_7381838_Cirrhosis_with_HCC


title: cfDNA_7374554_Cirrhosis_with_HCC


title: cfDNA_7405893_Cirrhosis_with_HCC


title: cfDNA_7310494_Cirrhosis_with_HCC


title: cfDNA_3506109_Cirrhosis_with_HCC


title: cfDNA_7217711_Cirrhosis_alone


title: cfDNA_6032742_Cirrhosis_with_HCC


title: cfDNA_6279828_Cirrhosis_alone


title: cfDNA_7260493_Cirrhosis_with_HCC


title: cfDNA_8480077_Cirrhosis_alone


title: cfDNA_6422622_Cirrhosis_alone


title: cfDNA_3288399_Cirrhosis_with_HCC


title: Rep5_LADL_BL

status: Public on Jun 24 2019

data_processing:
Library strategy: 5C-seq
The 37 bp pair-ended sequencing reads for Replicates 1 and 2 were directly mapped to a pseudo-genome consisting of 5C primer sequences with Bowtie (http://bowtie-bio.sourceforge.net/index.shtml) (Langmead, 2010) using parameters --tryhard and -m 2 and --trim5 6. Only reads with one unique alignment were considered for downstream analyses. Interactions were counted when both paired-end reads could be uniquely mapped to the 5C primer pseudo-genome. Only interactions between forward-reverse primer pairs were tallied as a true count. Counts were converted to contact matrices for each genomic region queried (i.e. ~1-3.5 Megabase regions around developmentally regulated genes Sox2, Klf4, Nanog, Dppa2, Rex1. Outlier values were removed if they are greater than the sum of 30 surrounding pixels by 32-fold. To correct for bias related to intrinsic properties of restriction fragments (i.e. G-C content; fragment size) and procedural batch effects, we converted our raw .counts data to ‘Observed’ values through a series of pre-processing steps. Briefly, raw .counts matrices were (i) quantile normalized across biological conditions, (ii) binned into 4 kilobase(kb)-sized bins, and (iII) matrix balanced using ICE balancing algorithm using default parameters (Imakaev et al 2012). .    The 75 bp end of pair-ended sequencing reads from Replicates 3, 4, and 5 were independently mapped to the pseudo-genome consisting of 5C primer sequences. The length of these reads required additional processing. First, only the reads containing the HindIII recognition sequence (AAGCTT) were split into two sub-reads corresponding to the 20 bp of sequence immediately 3’ and immediately 5’ of the HindIII cut site, respectively. To assign specific 5C primer-primer ligations to each 75 bp end, subreads were mapped to the pseudogenome consisting of 5C primers using Bowtie with parameters --tryhard and -m 2. To identify the 5C “monomers” that have only one 5C ligation junction, we compared the primer-primer ligations between the paired reads. If both paired reads have the same primer-primer ligation junction, we classified them as a “monomer” and constructed counts files for downstream analysis as described above.
Genome_build: mm9
Supplementary_files_format_and_content: The processed data files ending with “_iced.counts” contain the following information for each bin-bin pair:  “Bin1 ID” (unique identifier of the downstream bin), “Bin2 ID” (unique identifier of the upstream bin),  “Counts” (quantile normalized and binned/smoothed counts after ICE balancing).    The raw primer bed file (BED_6697-mm9-pluripotent.bed) contains the following information for each individual primer used in the 5C experiment: “Chromosome” (chromosome containing the 5C region), “Start Site” (genomic coordinate of the start of the primer fragment), “End Site” (genomic coordinate of the end of the primer fragment), “Primer ID” (unique identifier within our primer set), “Orientations” (primer orientations)    The binned primer bed file (4000_pixelmap.bed) contain the following information for each bin: “Chromosome” (chromosome containing the 5C region), “Start Site” (genomic coordinate of the start of the bin), “End Site” (genomic coordinate of the end of the bin), “Bin ID” (unique identifier within our primer set).    The raw counts files (all other processed files ending with “.counts” were generated by first aligning paired-end reads or monomer-parsed subreads to a pseudo-genome consisting of all 5C primers using Bowtie (http://bowtie-bio.sourceforge.net/index.shtml) (Langmead, 2010). Only reads with one unique alignment were considered for downstream analyses. Interactions were counted when both paired-end reads could be uniquely mapped to the 5C primer pseudo-genome. Only interactions between forward-reverse primer pairs were tallied as a true count.

source_name: mouse embryonic stem cells v6.5

growth_protocol: Mouse embryonic stem cells v6.5 and iPSC were cultured in serum media as previously described in Beagan et al 2016. The cells were routinely grown on feeder layer, and passaged twice on gelatin-coated culture dishes before crosslink.

extract_protocol:
3C templates were generated using HindIII with a in situ 3C protocol similar to that previously described in Rao et al. 2014. 5C libraries were generated 3C templates using double alternating 5C primer design across ~1-3.5 Mb regions around Sox2, Klf4, Dppa2, Rex1, Nanog  (described previously Dostie and Dekker, 2007; Dostie et al., 2006; van Berkum and Dekker, 2009; Hnisz et al 2016).
Library was constructed using NEBNExt Ultra II DNA Library prep kit for Illumina kit according to manufacturer's protocol. Each library was quantitated using qPCR-based Kapa Library Quantification Kit, and equal molecular number of each libarary was pooled before sequencing.

value: 129SvJae x C57BL/6

tag: gender

value: male

value: V6.5 ES cells cultured under serum/LIF conditions

value: LADL cells targeting Klf4 and SE after 24hr 5mW/cm2 blue light exposure


title: Rep4_LADL_BL


title: Rep3_Onesided_guide_Dark

value: one sided guide control cells targeting Zfp462 promoter region only in dark


title: Rep4_EmptyTarget_Dark

value: Empty target control cells


title: Rep4_LADL_Dark

value: LADL cells targeting Klf4 and SE in Dark


title: Rep3_LADL_Dark


title: Rep2_EmptyAnchor_Dark

extract_protocol:
3C templates were generated using HindIII with a in situ 3C protocol similar to that previously described in Rao et al. 2014. 5C libraries were generated 3C templates using double alternating 5C primer design across ~1-3.5 Mb regions around Sox2, Klf4, Dppa2, Rex1, Nanog  (described previously Dostie and Dekker, 2007; Dostie et al., 2006; van Berkum and Dekker, 2009; Hnisz et al 2016).
Library was constructed using NEBNExt Ultra DNA Library prep kit for Illumina kit according to manufacturer's protocol. Each library was quantitated using qPCR-based Kapa Library Quantification Kit, and equal molecular number of each libarary was pooled before sequencing.

value: Empty anchor control cells targeting Klf4 and SE in Dark


title: Rep5_LADL_Dark


title: Rep2_EmptyAnchor_BL

value: Empty anchor control cells targeting Klf4 and SE after 24hr 1.5mW/cm2 blue light exposure


title: Rep3_Onesided_guide_BL

value: one sided guide control cells targeting Zfp462 promoter region only after 24hr 5mW/cm2 blue light exposure


title: cfDNA_3478829_Cirrhosis_with_HCC


title: 4hr_LADL_Dark_24hr

value: LADL cells targeting Klf4 and SE in Dark for 4hr expeirment


title: Rep3_LADL_BL


title: Rep2_LADL_BL

value: LADL cells targeting Klf4 and SE after 24hr 1.5mW/cm2 blue light exposure


title: 4hr_LADL_BL_24hr

value: LADL cells targeting Klf4 and SE after 4hr 5mW/cm2 blue light exposure for 4hr experiment


title: Rep1_EmptyTarget_Dark


title: Rep2_LADL_Dark


title: 4hr_EmptyTarget_Dark_24hr

value: Empty target control cells for 4hr experiment


title: Rep3_EmptyTarget_Dark


title: Rep1_LADL_Dark


title: Rep1_LADL_BL


title: vivo_P6_large_rep3

status: Public on May 28 2019

city: Fukuoka

first: So

last: Shimamoto

state: Fukuoka

address: Maidashi 3-1-1, Higashi-ku

department: Department of Developmental Stem Cell Biology

country: Japan

institute: Kyushu-university

data_processing:
RNA-seq reads were processed with the FASTX tool kit (v0.0.14, Patel and Jain, 2012) to remove short (<20 bp) and low quality (quality score <20) reads, followed by trimming of the adaptor sequence.
Processed reads were mapped to the mouse mm10 genome using TopHat2/Bowtie2 (v2.0.13, Kim et al., 2013).
Mapped reads were counted by featureCounts(v1.5.1, Liao et al., 2014)
RPM values were calculated using R software(v.3.4.4)
Genome_build: mm10
Supplementary_files_format_and_content: tab-delimited text files including RPM values for each sample.

molecule: polyA RNA

source_name: In vivo P6 large oocyte

growth_protocol: In vivo oocytes were isolated from stella-ECFP(SC) reporter transgenic ovaries at various developmental stages.  In vitro oocytes induced from mouse ESCs were isolated from reconstituted ovaries (rOvaries) at various date of culture as described previously (Hikabe et al., 2016) To produce one rOvary, 5000 Blimp1-mVenus (BV)-positive PGCLCs and 75,000 frozen-thawed E12.5 gonadal somatic cells were aggregated and cultured on Transwell-COL membranes (IVDi culture). WT ESCs and Foxo3dNES Tg ESCs were subjected to IVDi culture for 21 days under the normoxic (5% CO2 and air) or hypoxic condition (5% O2, 7% CO2 and 93%N2) to obtain in vitro D21 oocytes. 10 μM of Rock inhibitor (Y-27632) was added for the first 2 days in the IVDi culture.

extract_protocol:
A directional RNA-seq library was prepared as described previously (Hamazaki et al. 2015). Briefly, poly(A)+ RNAs were extracted from the oocytes by using a Dynabeads mRNA DIRECT Micro Kit (Invitrogen).
cDNA libraries were constructed from purified poly(A) RNA using a NEBNext Ultra Directional RNA Library Prep Kit for Illumina (NEB). cDNA libraries were amplified by 12-cycle PCR and checked for length distribution.

value: C57BL/6 x B6D2F1

value: oocyte


title: vivo_P6_large_rep2


title: vivo_P6_small_rep3

source_name: In vivo P6 small oocyte


title: vivo_P6_small_rep2


title: cfDNA_7171872_Cirrhosis_with_HCC

value: plasma


title: vivo_P6_small_rep1


title: cfDNA_7225035_Cirrhosis_with_HCC


title: vivo_P3_rep3

source_name: In vivo P3 oocyte


title: cfDNA_7356980_Cirrhosis_with_HCC


title: cfDNA_7143866_Cirrhosis_alone


title: vivo_P1_rep3

source_name: In vivo P1 oocyte


title: vivo_P3_rep2


title: cfDNA_6427323_Cirrhosis_alone


title: cfDNA_7382358_Cirrhosis_with_HCC


title: vivo_P3_rep1


title: cfDNA_7740238_Cirrhosis_with_HCC


title: vivo_P1_rep1


title: cfDNA_8867406_Cirrhosis_alone


title: cfDNA_6322820_Cirrhosis_alone


title: cfDNA_7248326_Cirrhosis_with_HCC


title: vivo_E18.5_rep2

source_name: In vivo E18.5 germ cell

value: Germ cell


title: vivo_E18.5_rep1


title: vivo_P1_rep2


title: cfDNA_7358782_Cirrhosis_with_HCC


title: vivo_P6_large_rep1


title: vivo_E14.5_rep3

source_name: In vivo E14.5 germ cell


title: vivo_E16.5_rep3

source_name: In vivo E16.5 germ cell


title: vivo_E16.5_rep2


title: cfDNA_7110403_Cirrhosis_with_HCC


title: cfDNA_7238895_Cirrhosis_alone


title: Male_3

status: Public on May 03 2019

city: Bronx

first: Marija

last: Kundakovic

state: NY

address: 441 E. Fordham Road

department: Biological Sciences

institute: Fordham University

data_processing:
The obtained sequences were adapter trimmed and aligned to the mouse reference genome (mm10 including small contigs) using the BWA-MEM software (arXiv:1303.3997v1).
We selected and used only uniquely aligned reads for the analysis. Reads aligned to mitochondrial DNA and PCR duplicate reads were eliminated from further analysis.
Read numbers were down-sampled so that peak calling was based on comparable numbers of reads for each sample.
We performed peak calling using MACS2 and calculated the irreproducible discovery rates (IDR) of the detected peaks between biological replicates to select the high confidence peaks (IDR < 0.05).
We selected peaks which were shared across at least two biological replicates with an IDR <0.05.
Genome_build: mm10

source_name: FACS-sorted neuronal (NeuN+) nuclei

extract_protocol:
Ventral hippocampus was dissected from the brain, snap frozen in liquid nitrogen, and total nuclei were extracted using sucrose gradient centrifugation. Neuronal (NeuN+) nuclei were isolated by fluorescence-activated nuclei sorting  with anti-NeuN antibody conjugated to AlexaFluor 488 on a FACSAria instrument. Detailed protocol for total nuclei isolation and purification of neuronal nuclei is provided in Kundakovic et al, 2017 (PMID:27113501).
ATAC-seq libraries were prepared according to Buenrostro et al. 2013 (PMID:24097267) with some modifications. Illumina Nextera DNA Library Prep kit (FC-121-1030) and Nextera Index kit (FC 121-1011) were used to prepare the libraries.

value: ventral hippocampus

tag: age

value: 11 weeks

value: male

tag: estrous cycle stage


title: Die_1

tag: strain

value: female

value: diestrus


title: vivo_E16.5_rep1


title: vivo_E14.5_rep2


title: Male_2


title: cfDNA_3746886_Cirrhosis_alone


title: Pro_2

value: proestrus


title: Pro_3


title: Die_2


title: vivo_E18.5_rep3


title: Male_1


title: Die_3


title: NRNA-9

data_processing:
Basecalls performed using CASAVA version 1.8
Sequenced reads were trimmed for adaptor sequence, and masked for low-complexity or low-quality sequence, then mapped to mm10 whole genome using STAR v2.6.1b with parameters -outFilterType BySJout --outFilterMultimapNmax 20 --alignSJoverhangMin 8  --alignSJDBoverhangMin 1 --outFilterMismatchNmax 999 --outFilterMismatchNoverReadLmax 0.04 --alignIntronMin 20  --alignIntronMax 1000000 --alignMatesGapMax 1000000 --sjdbGTFfile GENCODE_vM15.gtf
The generated counts were analyzed with DESeq2.
Genome_build: mm10
Supplementary_files_format_and_content: GeneCounts: column 1: gene ID, column 2: counts for unstranded RNA-seq, column 3: counts for the 1st read strand aligned with RNA (htseq-count option -s yes), column 4: counts for the 2nd read strand aligned with RNA (htseq-count option -s reverse)

source_name: male_FACS-sorted neuronal (NeuN+) nuclei

extract_protocol:
Ventral hippocampus was dissected from the brain, snap frozen in liquid nitrogen, and total nuclei were extracted using sucrose gradient centrifugation. Neuronal (NeuN+) nuclei were isolated by fluorescence-activated nuclei sorting  with anti-NeuN antibody conjugated to AlexaFluor 488 on a FACSAria instrument. Detailed protocol for total nuclei isolation and purification of neuronal nuclei is provided in Kundakovic et al, 2017 (PMID:27113501). Per each biological replicate, 250,000 neuronal nuclei were directly sorted into Trizol LS reagent. After the addition of chloroform, the aqueous phase was recovered and total nuclear RNA was further isolated and purified using the miRNeasy Micro kit reagents and the RNeasy MinElute spin column.
Nuclear RNA isolated from sorted neuronal nuclei (16-26 ng/sample) was used to construct cDNA sequencing library using the KAPA RNA HyperPrep Kit with RiboErase (KK8560, KAPA Biosystems), following general manufacturer's instructions. Briefly, rRNA was first depleted from total nuclear RNA by hybridization of complementary DNA oligonucleotides followed by treatment with RNase H and DNase. rRNA-depleted nuclear RNA was fragmented at 94°C for 5 minutes in the presence of Mg2+, followed by the first and second strand cDNA synthesis and A-tailing. Following the adapter ligation with the KAPA single-indexed adapters (KK8701), each library was amplified using 13 PCR cycles. After a bead-based cleanup, the library quality was examined using Bioanalyzer High-Sensitivity DNA Assay and the libraries were quantified by Qubit HS DNA kit prior to sequencing.

value: FACS-sorted neuronal (NeuN+) nuclei

tag: molecule subtype

value: nuclear RNA


title: vivo_E14.5_rep1


title: NRNA-5

source_name: female_diestrus_FACS-sorted neuronal (NeuN+) nuclei


title: NRNA-7

source_name: female_proestrus_FACS-sorted neuronal (NeuN+) nuclei


title: Pro_1


title: NRNA-6


title: cfDNA_3661371_Cirrhosis_alone


title: NRNA-4


title: NRNA-2


title: NRNA-3


title: NRNA-1


title: H3K4me3 ChIP-Seq rbbp-5(-)

status: Public on Feb 08 2019

city: Manchester

first: Gino

middle: Bernard

last: Poulin

state: Greater Manchester

address: Dover Street

country: United Kingdom

institute: University of Manchester

description: chip antibody: anti-H3K4me3 (AM39159)

data_processing:
== ce10 alignments ==
See the supplementary file GSE94639_GEO_DATA_PROCESSING_ce10.txt.
Genome_build: ce10
Supplementary_files_format_and_content: BW = GC-corrected, downsampled bigWigs for H3K4me1/2/3 and Inputs across N2, wdr-5, rbbp-5, and double mutants.

== ce6 alignments ==
Sequences: Standard Illumina software base-calling and quality-control filtering was applied
Sequences (75 nt reads, single end) were aligned to the C. elegans genome (ce6) using the BWA algorithm (v0.6.1, default parameters).
Aligns were extended in silico at their 3’-ends to a length of 200 bp, which is the average genomic fragment length in the size-selected library, and assigned to 32-nt bins along the genome. The resulting histograms (genomic “signal maps”) were stored in bigWIG files.
Peak locations were determined using the MACS algorithm (v1.4.2) with a cutoff of pvalue = 1E-7. --- or e.g.: Peak calling was done with the SICER script (SICER v1.1; FDR 1E-10, gap=600).
Signal maps and peak locations were used as input data to Active Motifs proprietary analysis program, which creates excel tables containing detailed information on sample comparison, peak metrics, peak locations and gene annotations
Genome_build: ce6
Supplementary_files_format_and_content: BW = signal map in bigWIG format (UCSC)

organism: Caenorhabditis elegans

source_name: late C. elegans embryos rbbp-5(-)

growth_protocol: A synchronised population of adult worms were grown on  a dish (245x245 mm) and embryos harvested by bleaching of young adults

extract_protocol:
Embryos were fixed with 2% formaldehyde for 30 min and quenched with 0.1 M Tris pH7.5.  Lysates were sonicated with a microtip sonicator to shear the DNA. Genomic DNA (Input) was prepared by treating aliquots of chromatin with RNase, proteinase K and heat for de-crosslinking, followed by ethanol precipitation. For each ChIP reaction antibody was first incubated overnight at 4 C then Protein A agarose beads were incubated overnight at 4 C. Immune complexes were washed, eluted from the beads with SDS buffer, and subjected to RNase and proteinase K treatment. Crosslinks were reversed by incubation overnight at 65 C, and ChIP DNA was purified by Qiaquick (Qiagen) extraction.
Illumina sequencing libraries were prepared from the ChIP and Input DNAs by the standard consecutive enzymatic steps of end-polishing, dA-addition, and adaptor ligation. After a final PCR amplification step, the resulting DNA libraries were quantified and sequenced on NextSeq 500.

value: whole embryo

tag: genotype


title: input DNA wdr-5(-)

description: chip antibody: N/A

source_name: late C. elegans embryos wdr-5(-)


title: H3K4me2 ChIP-Seq wdr-5(-)

description: chip antibody: anti-H3K4me2 (AM39141)


title: H3K4me3 ChIP-Seq wdr-5(-)

description: chip antibody: anti-H3K4me3 (AM39159)


title: input DNA rbbp-5(-)

description: chip antibody: N/A


title: H3K4me2 ChIP-Seq rbbp-5(-)

description: chip antibody: anti-H3K4me2 (AM39141)


title: H3K4me1 ChIP-Seq rbbp-5(-)

description: chip antibody: anti-H3K4me1 (AM39297)


title: H3K4me1 ChIP-Seq N2

description: chip antibody: anti-H3K4me1 (AM39297)

source_name: late C. elegans embryos N2

value: N2 wild type


title: H3K4me3 ChIP-Seq N2

description: chip antibody: anti-H3K4me3 (AM39159)


title: H3K4me2 ChIP-Seq N2

description: chip antibody: anti-H3K4me2 (AM39141)


title: NRNA-8

tag: Sex


title: H3K4me1 ChIP-Seq wdr-5(-)

description: chip antibody: anti-H3K4me1 (AM39297)


title: HCC1_input_chipseq

status: Public on Sep 09 2018

description: input

data_processing:
RRBS raw sequencing reads was aligned to hg19 and analyzed through a Streamlined Analysis and Annotation Pipeline (SAAP-RRBS)
RNA-seq raw reads were mapped to hg19. Transcripts were assembled using TopHat and quantified through Cufflinks. Fragments per kilobase of transcript per million mapped reads (FPKM) representing transcription level was generated using this package for subsequent comparison.
ChIP-seq raw reads were mapped to hg19. Peak calling was performed with SICER package and corrected for input (parameter setting: window size 200bp, gap size 200bp, effective genome size 0.74, FDR 0.01).
Genome_build: hg19
Supplementary_files_format_and_content: .txt files for the RNA-seq and RRBS samples; bed files for the ChIP-seq samples

source_name: HCC liver

extract_protocol:
Mnase digested nucleosomes
libraries were constructed according to illumina's instructions

tag: sample id

value: liver

value: HCC

tag: chip antibody


title: NL1_input_chipseq

description: input

source_name: normal liver

value: normal


title: Cirr1_input_chipseq

description: input

source_name: cirrhotic liver

value: cirrhosis


title: Cirr3_h3k27me3_chipseq


title: HCC3_input_chipseq

description: input


title: HCC3_h3k27me3_chipseq


title: Cirr3_input_chipseq

description: input


title: HCC1_h3k27me3_chipseq


title: Cirr1_h3k27me3_chipseq


title: HCC3_h3k4me3_chipseq

value: ab8580, abcam


title: Cirr3_h3k4me3_chipseq


title: HCC1_h3k4me3_chipseq


title: NL1_h3k4me3_h3k4me3_chipseq


title: Cirr1_h3k4me3_chipseq


title: HCC3_h3k27ac_chipseq

value: ab4729, abcam


title: NL1_h3k27me3_chipseq


title: Cirr3_h3k27ac_chipseq


title: input DNA N2

description: chip antibody: N/A


title: NL1_h3k27ac_h3k27ac_chipseq


title: HCC1_h3k4me1_chipseq

value: ab8896, abcam


title: HCC3_h3k4me1_chipseq


title: Cirr1_h3k4me1_chipseq


title: Cirr1_h3k27ac_chipseq


title: NL1_h3k4me1_h3k4me1_chipseq


title: Cirr3_h3k4me1_chipseq


title: HCC1_h3k27ac_chipseq


title: Cirr3_tab_rrbs

description:
DNA hydroxymethylation
RRBS_TAB.txt
674N

extract_protocol:
phenol-chloroform
libraries were constructed according to illumina's instructions


title: Cirr4_tab_rrbs

description:
DNA hydroxymethylation
RRBS_TAB.txt
649N


title: HCC2_tab_rrbs

description:
DNA hydroxymethylation
RRBS_TAB.txt
617T


title: Cirr2_tab_rrbs

description:
DNA hydroxymethylation
RRBS_TAB.txt
617N


title: HCC2_rrbs

description:
DNA methylation
RRBS_TAB.txt
617T


title: Cirr1_tab_rrbs

description:
DNA hydroxymethylation
RRBS_TAB.txt
661N


title: Cirr2_rrbs

description:
DNA methylation
RRBS_TAB.txt
617N


title: Cirr3_rrbs

description:
DNA methylation
RRBS_TAB.txt
674N


title: HCC3_rrbs

description:
DNA methylation
RRBS_TAB.txt
674T


title: HCC3_tab_rrbs

description:
DNA hydroxymethylation
RRBS_TAB.txt
674T


title: HCC4_rnaseq

description:
gene expression
RNA_seq.txt
649T

extract_protocol:
trizol
libraries were constructed according to illumina's instructions


title: HCC1_rrbs

description:
DNA methylation
RRBS_TAB.txt
661T


title: HCC1_tab_rrbs

description:
DNA hydroxymethylation
RRBS_TAB.txt
661T


title: NL1_rrbs

description:
DNA methylation
RRBS_TAB.txt
756


title: Cirr4_rnaseq

description:
gene expression
RNA_seq.txt
649N


title: Cirr4_rrbs

description:
DNA methylation
RRBS_TAB.txt
649N


title: NL2_rrbs

description:
DNA methylation
RRBS_TAB.txt
757


title: HCC4_tab_rrbs

description:
DNA hydroxymethylation
RRBS_TAB.txt
649T


title: Cirr1_rrbs

description:
DNA methylation
RRBS_TAB.txt
661N


title: HCC2_rnaseq

description:
gene expression
RNA_seq.txt
617T


title: Cirr3_rnaseq

description:
gene expression
RNA_seq.txt
674N


title: NL1_rnaseq

description:
gene expression
RNA_seq.txt
756


title: HCC1_rnaseq

description:
gene expression
RNA_seq.txt
661T


title: Cirr1_rnaseq

description:
gene expression
RNA_seq.txt
661N


title: Cirr2_rnaseq

description:
gene expression
RNA_seq.txt
617N


title: NL2_tab_rrbs

description:
DNA hydroxymethylation
RRBS_TAB.txt
757


title: HCC4_rrbs

description:
DNA methylation
RRBS_TAB.txt
649T


title: HCC3_rnaseq

description:
gene expression
RNA_seq.txt
674T


title: NL1_tab_rrbs

description:
DNA hydroxymethylation
RRBS_TAB.txt
756


title: Brain-FXS-Case1-5C

status: Public on Aug 29 2018

data_processing:
Library strategy: 5C
Paired-end reads were mapped to a pseudo-reference genome representing all possible 5C primer ligation junctions as previously described (Beagan et al., 2016, Beagan et al., 2017, Phillips-Cremins et al., 2013). Counts files were assembled by tallying the number of reads for each 5C primer junction. Raw 5C counts distributions were quantile normalized to address minor sequencing depth differences. Next, ligation-junction resolution counts were converted to restriction fragment resolution counts by averaging the reads from the ligation junctions tallied for each fragment. An NxN matrix of fragment-level 5C counts, where N is the number of fragments in the FMR1 region queried by the double alternating primers, was then binned into 4 kb bins with a 12 kb smoothing window using sum-binning. Finally, restriction fragment biases were normalized by applying matrix balancing as previously reported (Imakaev et al., 2012).
5C: Paired-end reads were mapped to a pseudo-reference genome representing all possible 5C primer ligation junctions as previously described (Beagan et al., 2016, Beagan et al., 2017, Phillips-Cremins et al., 2013). Counts files were assembled by tallying the number of reads for each 5C primer junction. Raw 5C counts distributions were quantile normalized to address minor sequencing depth differences. Next, ligation-junction resolution counts were converted to restriction fragment resolution counts by averaging the reads from the ligation junctions tallied for each fragment. An NxN matrix of fragment-level 5C counts, where N is the number of fragments in the FMR1 region queried by the double alternating primers, was then binned into 4 kb bins with a 12 kb smoothing window using sum-binning. Finally, restriction fragment biases were normalized by applying matrix balancing as previously reported (Imakaev et al., 2012).
Supplementary_files_format_and_content: 5C: The processed data files (end in .counts) contain the following information for each bin-bin pair:  “Bin1 ID” (unique identifier of the upstream bin), “Bin2 ID” (unique identifier of the downstream bin),  “Count” (the number of mapped reads to this bin-bin pair). To match Bin ID to genomic coordinates, the binned primer bed file (“4000_pixelmap.bed”) contains the following information for each bin: “Chromosome” (chromosome containing the 5C region), “Start” (genomic coordinate of the start of the bin), “End” (genomic coordinate of the end of the bin), “Bin ID” (unique identifier of bin). The counts in the processed data files represent normalized, balanced, binned and smoothed interaction counts between Bin1 and Bin2.
ChIP-Seq: Reads were mapped to the reference human genome (hg19) using bowtie. Reads were analyzed if they had two or fewer reportable alignments. To facilitate the comparison of ChIPseq libraries across cell types, the mapped reads were filtered to remove optical and PCR duplicates and then downsampled to equivalent read numbers across cellular states. Peaks were identified using Model-based Analysis for ChIP Sequencing v2.0 (MACS2). Default parameters were used with a p-value cutoff of p < 1E-8.
Supplementary_files_format_and_content: ChIP-Seq: The ChIP-seq processed data files are in the ENCODE narrowPeak format
Genome_build: hg19

source_name: Flash frozen brain tissue

growth_protocol: Cell lines were cultured according to manufacturer's instructions

extract_protocol: 3C templates were generated using HindIII with an in situ 3C protocol similar to that previously described in Rao et al. 2014. 5C libraries were generated from 3C templates using a double alternating primer design across a 6 Mb region around FMR1 (described previously Dostie and Dekker, 2007; Dostie et al., 2006; van Berkum and Dekker, 2009).

value: Flash frozen brain tissue


title: Brain-Control1-5C


title: GM04025_CTCF_IP_Rep2

description: GM04025_merged_IP_downsampled_1e-8_pval_thresh_peaks.narrowPeak

data_processing:
Reads were mapped to the reference human genome (hg19) using bowtie. Reads were analyzed if they had two or fewer reportable alignments. To facilitate the comparison of ChIPseq libraries across cell types, the mapped reads were filtered to remove optical and PCR duplicates and then downsampled to equivalent read numbers across cellular states. Peaks were identified using Model-based Analysis for ChIP Sequencing v2.0 (MACS2). Default parameters were used with a p-value cutoff of p < 1E-8.
5C: Paired-end reads were mapped to a pseudo-reference genome representing all possible 5C primer ligation junctions as previously described (Beagan et al., 2016, Beagan et al., 2017, Phillips-Cremins et al., 2013). Counts files were assembled by tallying the number of reads for each 5C primer junction. Raw 5C counts distributions were quantile normalized to address minor sequencing depth differences. Next, ligation-junction resolution counts were converted to restriction fragment resolution counts by averaging the reads from the ligation junctions tallied for each fragment. An NxN matrix of fragment-level 5C counts, where N is the number of fragments in the FMR1 region queried by the double alternating primers, was then binned into 4 kb bins with a 12 kb smoothing window using sum-binning. Finally, restriction fragment biases were normalized by applying matrix balancing as previously reported (Imakaev et al., 2012).
Supplementary_files_format_and_content: 5C: The processed data files (end in .counts) contain the following information for each bin-bin pair:  “Bin1 ID” (unique identifier of the upstream bin), “Bin2 ID” (unique identifier of the downstream bin),  “Count” (the number of mapped reads to this bin-bin pair). To match Bin ID to genomic coordinates, the binned primer bed file (“4000_pixelmap.bed”) contains the following information for each bin: “Chromosome” (chromosome containing the 5C region), “Start” (genomic coordinate of the start of the bin), “End” (genomic coordinate of the end of the bin), “Bin ID” (unique identifier of bin). The counts in the processed data files represent normalized, balanced, binned and smoothed interaction counts between Bin1 and Bin2.
ChIP-Seq: Reads were mapped to the reference human genome (hg19) using bowtie. Reads were analyzed if they had two or fewer reportable alignments. To facilitate the comparison of ChIPseq libraries across cell types, the mapped reads were filtered to remove optical and PCR duplicates and then downsampled to equivalent read numbers across cellular states. Peaks were identified using Model-based Analysis for ChIP Sequencing v2.0 (MACS2). Default parameters were used with a p-value cutoff of p < 1E-8.
Supplementary_files_format_and_content: ChIP-Seq: The ChIP-seq processed data files are in the ENCODE narrowPeak format
Genome_build: hg19

source_name: B-lymphocytes

extract_protocol:
20 million B-lymphocytes were fixed in serum-free RPMI 1640 in 1% formaldehyde for 10 minutes at room temperature. Fixation was terminated by adding glycine to a final concentration of 0.2 M and incubating for 5 minutes at room temperature. Cells were spun down for 5 minutes at 1,350 rpm at 4C, the supernatant removed and washed once with PBS.  Cells were lysed by incubating on ice for 10 minutes in 1 mL Cell lysis buffer (10 mM Tris pH 8.0, 10 mM NaCl, 0.2% NP-40/Igepal, Protease Inhibitor, PMSF). Lysed cells were pelleted for 5 minutes at 300xg at 4C and the resulting nuclei were lysed by re-suspending in 500 mL Nuclear Lysis Buffer (50 mM Tris pH 8.0, 10 mM EDTA, 1% SDS, Protease Inhibitor, PMSF) and incubated on ice for 20 min. Samples were sonicated after adding 300 μl IP Dilution Buffer (20 mM Tris pH 8.0, 2 mM EDTA, 150 mM NaCl, 1% Triton X-100, 0.01% SDS, Protease Inhibitor, PMSF) using a QSonica Q800R2 sonicator for 1 hour set at 100% amplitude, with pulse set to 30 seconds on and 30 seconds off. After sonication, samples were pelleted at 13,000 rpm for 5 min at 4C. The supernatant was transferred to pre-clearing reactions containing 3.7 mL IP Dilution Buffer, 500 μl Nuclear Lysis Buffer, 175 μl of a 1:1 ratio of ProteinA:ProteinG bead slurry and 50 ug of rabbit IgG. Samples were rotated at 4C for 2 hours.  Next, 200 μl was aliquoted from the pre-cleared chromatin as the whole cell extract ‘input’ control. The remainder of the DNA was added to the pre-bound IP reaction consisting of 1mL cold PBS, 20 μl Protein A, 20 μl Protein G, and 10 μl CTCF antibody. Pre-binding of the IP reaction was performed the previous night by rotation at 4C. Upon addition of DNA, IP reactions were rotated overnight at 4C.   IP reactions were pelleted at 4,000 RPM for 5 minutes at 4C. The supernatant was discarded and the bead pellet was washed once with IP Wash Buffer 1 (20mM Tris pH 8, 2mM EDTA, 50 mM NaCl, 1% Triton X-100, 0.1% SDS), twice with High Salt Buffer (20 mM Tris pH 8, 2mM EDTA, 500 mM NaCl, 1% Triton X-100, 0.01% SDS), once with IP Wash Buffer 2 (10 mM Tris pH 8, 1mM EDTA, 0.25 M LiCl, 1% NP-40/Igepal, 1% sodium deoxycholate) and twice with TE buffer (10mM Tris pH 8, 1mM EDTA pH 8). Washed beads were eluted twice with 100 mL of Elution buffer (100mM NaHCO3, 1%SDS, prepared fresh) by adding 110 μl buffer to each sample, spinning at 7,500 rpm for 2 minutes and removing 100 μl of supernatant as the final eluate. To degrade residual RNA, 2 μl RNAse A and 12 μl 5M NaCl were added to each IP and input sample and incubated at 65C for 1 hour. To reverse crosslinks, 3 μl of proteinase K were added to all samples and incubated overnight at 65C.   To extract DNA, 400 μl of 1X TE and 400 μl phenol:chloroform were added to each sample and vortexed for 60 seconds. Samples were then spun at 14,000 rpm for 10 min at 4C. The top phase was transferred to a new tube and  DNA was precipitated by adding 16 μl 5M NaCl, 6 μl glycogen, 1mL cold 100% ethanol and 40 μl 3M sodium acetate to each sample. Samples were incubated at -80C for 2 hours and then pelleted at 14,000 RPM for 1 hour at 4C. Pellets were resuspended in 1 μl cold 80% ethanol and spun for 30 min at 14,000 RPM at 4C. Supernatent was removed and pellets were dried on 37C heat block for 15 minutes. DNA pellets were dissolved in 20 μl 1X TE at 37C for 15 minutes.
Libraries were prepared for sequencing using the NEBNext Ultra II Library Prep Kit for Illumina following the manufacturers protocol. 0.5 ng of DNA was used for starting material, which was quantified using Qubit dsDNA HS Assay Kit. No size selection step was performed after adaptor ligation. Libraries were amplified over 11 PCR cycles using NEBNext Multiplex Oligos for Illumina. High sensitivity electrophoresis was performed on an Agilent 2100 Bioanalyzer to confirm the library size of 250 to 1200 bp. Library concentration was assayed via the KAPA Illumina Library Quantification Kit and diluted to equivalent concentrations. Pooled libraries were sequenced using a 75-cycle paired-end kit on a Illumina NextSeq 500.

tag: cell line

tag: antibody

value: CTCF

value: B-lymphocytes


title: GM09237_CTCF_Input_Rep2

description:
GM09237_Rep2_IP_downsampled_peaks.narrowPeak
GM09237_merged_IP_downsampled_1e-8_pval_thresh_peaks.narrowPeak

value: input


title: GM04025_CTCF_Input_Rep2

description:
GM04025_Rep2_IP_downsampled_peaks.narrowPeak
GM04025_merged_IP_downsampled_1e-8_pval_thresh_peaks.narrowPeak


title: GM09237_CTCF_IP_Rep2

description: GM09237_merged_IP_downsampled_1e-8_pval_thresh_peaks.narrowPeak


title: GM04025_CTCF_Input_Rep1

description:
GM04025_Rep1_IP_downsampled_peaks.narrowPeak
GM04025_merged_IP_downsampled_1e-8_pval_thresh_peaks.narrowPeak


title: GM04025_CTCF_IP_Rep1


title: GM09236_CTCF_Input_Rep2

description:
GM09236_Rep2_IP_downsampled_peaks.narrowPeak
GM09236_merged_IP_downsampled_1e-8_pval_thresh_peaks.narrowPeak


title: GM09236_CTCF_Input_Rep1

description:
GM09236_Rep1_IP_downsampled_peaks.narrowPeak
GM09236_merged_IP_downsampled_1e-8_pval_thresh_peaks.narrowPeak


title: GM09237_CTCF_IP_Rep1


title: GM09236_CTCF_IP_Rep2

description: GM09236_merged_IP_downsampled_1e-8_pval_thresh_peaks.narrowPeak


title: GM09236_CTCF_IP_Rep1


title: AG06103_5C_Rep1

source_name: Fibroblasts

value: Fibroblasts


title: NL2_rnaseq

description:
gene expression
RNA_seq.txt
757


title: GM04024_5C_Rep1


title: Brain-Control2-5C


title: GM09237_5C_Rep1


title: GM06897_5C_Rep1


title: GM04025_5C_Rep1


title: GM09236_5C_Rep1


title: Input-DNMT3B-2

status: Public on Jul 31 2018

data_processing:
ChIP-seq peak calling was performed with MACS2 package and corrected for input. Narrow peak calling was performed on sequences from the FLAG-ZBTB24 (FDR cut-off = 0.01) lines and broad peak calling on the FLAG-DNMT3B lines (FDR cut-off = 0.005)
Raw reads were mapped to the hg19 genome and transcripts were assembles using HISAT2. Transcripts from all RNAseq samples were merged using Stringtie and differential expression was, defining log2 fold change, was performed on pooled samples using CuffLinks.
Genome_build: hg19
Supplementary_files_format_and_content: peak bed, FPKM, TPM, foldchange

source_name: Cell line

growth_protocol: HCT116 cells were grown in McCoy's 5A supplemented with FBS and L-glutamine. Stable FLAG-expressing lines and shRNA lines were created by lentiviral infection followed by selection in puromycin (0.75µg/mL). For FLAG-tagged lines, single cell clones were isolated by limiting dilution and propagated post screening by Western and immunofluoresence.

extract_protocol:
Genomic DNA was isolated and purified from cells in culture using a serial phenol: chloroform extraction. Trizol reagent was used for extracting total RNA following the manufacturer’s protocol.
RNA samples are converted to Illumina sequencing libraries using Illumina’s Truseq Stranded Total RNA Library Preparation Kit;ChIP-seq library is prepared with ThruPLEX DNA-seq kit.

value: colorectal cancer cell line - HCT116

tag: stale expression

value: Input


title: GM06890_5C_Rep1


title: Brain-FXS-Case2-5C


title: Input-ZBTB24-5


title: Input-EMPTY2

value: FLAG-only


title: Input-EMPTY1


title: GM09237_CTCF_Input_Rep1

description:
GM09237_Rep1_IP_downsampled_peaks.narrowPeak
GM09237_merged_IP_downsampled_1e-8_pval_thresh_peaks.narrowPeak


title: FLAG-DNMT3B-2


title: FLAG-ZBTB24-11


title: Input-ZBTB24-7


title: Input-DNMT3B-1


title: Input-ZBTB24-11


title: FLAG-DNMT3B-1


title: FLAG-ZBTB24-7


title: FLAG-EMPTY1


title: FLAG-EMPTY2


title: gtf_FLAG-ZBTB24-11


title: gtf_FLAG-ZBTB24-5


title: gtf_FLAG-EMPTY1


title: gtf_WT

value: No tag or shRNA


title: gtf_FLAG-EMPTY2


title: gtf_3BKO

value: colorectal cancer cell line - HCT116 3BKO

value: DNMT3B genetic knockout


title: gtf_shZB3

value: shZBTB24 (shRNA against ZBTB24)


title: gtf_shZB1


title: gtf_shNT3

value: shNT (shRNA against no target control)