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| Status |
Public on Jun 10, 2017 |
| Title |
HiC-TCC_NIPBL_Rep1 |
| Sample type |
SRA |
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| Source name |
TCC_NIPBL
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| Organism |
Mus musculus |
| Characteristics |
strain: C57BL/6J
genotype: Ttr-cre/Esr1(+/wt);Nipbl(flox/flox)
treatment: tamoxifen
age: 12 weeks
tissue: liver
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| Treatment protocol |
Treated 12 week old mice were injected with 1mg Tamoxifen (100μl of 10mg/ml Tamoxifen in corn oil) on 5 consecutive days. After keeping these mice for another 5 days without injection, they were sacrificed and the hepatocytes were harvested.
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| Extracted molecule |
genomic DNA |
| Extraction protocol |
Liver was dissected and the left lateral lobe was prepped for a two-step perfusion adapted from (Li et al 2010 & Goncalves et al 2007): First, the liver was perfused with an EDTA-containing buffer to remove Ca2+ from the tissue in order to weaken the integrity of the desmosomes, which were subsequently digested using a Ca2+ rich buffer containing collagenase. The freed hepatocytes were rinsed through a cell strainer and washed four times with ice-cold Ca2+ rich buffer without collagenase. For each wash, the cells were spun at low centrifugal force (60g for 1min) to enrich intact hepatocytes and reduce non-mesenchymal debris. Part of each sample was fixed with 1% PFA for 10 minutes at room temperature. Fixed and unfixed hepatocytes were aliquoted and frozen in liN2 for later use.
Hi-C: Roughly 100 million fixed hepatocytes per sample were processed according to (Kalhor et al, 2012) using HindIII. TCC libraries were PCR-amplified (12 cycles) and size selected. Equimolar pools of libraries were sequenced; ChIP-seq: Fixed aliquots of hepatocytes were hypotonically lysed and sonicated in 1% SDS/TE. An aliquot of each sample was reverse cross-linked in order to determine chromatin concentration and sonication efficiency. 20μg chromatin per sample was diluted in RIPA and incubated with 1.5μg of either αH3k4me3 antibody (C15410003-50, Diagenode) or αH3K27Ac antibody (ab4729, Abcam) at 4°C, overnight. The antibodies were retrieved with Dynabeads (IgA, Invitrogen) and bound chromatin was washed and eluted. After reverse cross-linking, the amount of ChIPped and input DNA was determined with Qubit (Thermo Fisher). The libraries were prepared with NEBNext® ChIP-Seq Library Prep Kit for Illumina®. After amplification and size selection with E-Gel® SizeSelect™ (Thermo Fisher) their size-distributions were determined with Bioanalyzer. Equimolar pools of libraries were sequenced with Illumina HiSeq2000 (50bp, single end). RNA-seq: RNA integrity was tested with Bioanalyzer (Agilent RNA Nano Kit) and ribosomal RNA was removed using Ribo-Zero rRNA Removal Kit (Illumina) prior to library preparation. Strand-specific libraries were prepared with NEBNext® Ultra™ Directional RNA Library Prep Kit for Illumina®. After amplification and size selection with Agencourt AMPure XP beads (Beckmann Coulter) their size-distributions were determined with Bioanalyzer. Equimolar pools of libraries were sequenced with Illumina HiSeq2000 (50bp, single end).
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| Library strategy |
Hi-C |
| Library source |
genomic |
| Library selection |
other |
| Instrument model |
Illumina HiSeq 2000 |
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| Description |
Tethered Chromosome Capture (TCC) variant of Hi-C
NIPBL.1kb.cool.HDF5
NIPBL.2kb.cool.HDF5
NIPBL.5kb.cool.HDF5
NIPBL.10kb.cool.HDF5
NIPBL.20kb.cool.HDF5
NIPBL.40kb.cool.HDF5
NIPBL.50kb.cool.HDF5
NIPBL.100kb.cool.HDF5
NIPBL.200kb.cool.HDF5
NIPBL.500kb.cool.HDF5
NIPBL.1000kb.cool.HDF5
eigs.20kb.tsv
eigs.100kb.tsv
tads.lavaburst.corner.NIPBL.20kb.txt
tads.lavaburst.modularity.NIPBL.20kb.txt
tads.lavaburst.modularity.boundaryProbability.20kb.txt
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| Data processing |
Hi-C. Alignment: Hi-C reads were mapped using bowtie 2.2.8 and the iterative mapping strategy (Imakaev et al, 2011) implemented in the hiclib package. Filtering: PCR duplicates and reads mapped to multiple or zero locations were removed. After binning, low-coverage bins were removed using a maximum allowed median absolute deviation (MAD) filter on log genomic coverage, set to five MADs. To remove the short-range Hi-C artifacts we also filter contacts mapping to the same or adjacent genomic bins (i.e., first two diagonals). Binning: the filtered reads pairs were binned into 20kb and 100kb contact matrices using the cooler library and stored as binary cool HDF5 files. Normalization: the filtered 20kb and 100kb contacts matrices were then normalized using the iterative correction procedure (IC), such that the genome-wide sum of contact probability for each row/column equals 1.0.
ChIP-seq. We processed ChIP-seq data following the steps of the ENCODE pipeline [https://github.com/ENCODE-DCC/chip-seq-pipeline]. Alignment: we used bwa 0.7.12 (program bwa aln with parameters: -q 5 -l 32 -k 2). Filtering: PCR duplicates were marked using picardtools 2.7.1. Unmapped reads, non-primary alignments, and low quality alignments (mapQ < 30) were removed using samtools 1.3. Cross-correlation analysis was performed using phantompeakqualtools. Peaks and signal tracks were generated using MACS2. For Rad21, Smc3 and CTCF ChIP-seq, we followed the same steps with the following variations: reads from pooled mouse hepatocyte chromatin and HEK human chromatin (internal control and calibration) were mapped to the combined mm9 and hg19 assemblies using the bwa mem program. After filtering, reads were divided into those that mapped uniquely to either mm9 or hg19. A database of CTCF motif instances was produced by performing a PWM scan of the canonical mouse CTCF motif against the mm9 genome using the fimo tool from the MEME suite with default parameters. Peak files with motif annotations were generated by intersecting the peak files against the motif database using bedtools 2.26.0.
RNA-seq. We mapped the RNA-seq data to mm9 reference mouse genome assembly and GENCODE vM1 transcriptome using STAR v.2.5.0a and adapted scripts from the ENCODE RNA-Seq pipeline [https://www.encodeproject.org/pipelines/ENCPL002LSE/]. To obtain the tracks of local transcription, we aggregated the uniquely mapped reads into RPKM-normalized bigWig files using the built-in STAR functionality. To find differentially expressed genes, we aggregated the read counts at the gene level using HTSeq with the "union" option and called DE genes with DESeq2. Alignment with STAR v2.5.0.a was done using parameters: --outFilterMultimapNmax 20 --alignSJoverhangMin 8 --alignSJDBoverhangMin 1 --outFilterMismatchNmax 999 --outFilterMismatchNoverReadLmax 0.04 --alignIntronMin 20 --alignIntronMax 1000000 --alignMatesGapMax 1000000 --outSAMunmapped Within --outFilterType BySJout --outSAMstrandField intronMotif --outSAMtype BAM SortedByCoordinate --quantMode TranscriptomeSAM --sjdbScore 1. Signal tracks were generated using STAR with parameters: --runMode inputAlignmentsFromBAM --outWigType bedGraph --outWigStrand Stranded --outWigReferencesPrefix chr.
Compartment eigenvectors. The compartment structure of Hi-C maps was detected using a modified procedure from (Imakaev et al, 2012). In short, compartments were quantified as the dominant eigenvector of the observed/expected 20kb and 100kb cis contacts maps upon subtraction of 1.0, as implemented in hiclib.
Domain detection. To identify contact domains, we used a segmentation that divides the genome into domains in such a way as to maximize a global domain scoring function. We used two different scoring functions: one was the corner score function from (Rao et al, 2014) and the other was based on network modularity, which is a metric widely used to detect communities in networks. The implementation of these and related algorithms is provided in the lavaburst package. To robustly call insulating boundaries across different conditions, we exploit the multi-resolution nature of the modularity score and compute the average marginal boundary scores on 20kb WT and ΔNipbl contact matrices sweeping over a range of gamma values to obtain a 1D boundary (i.e., insulation) track. Short intervals representing insulating loci were called by thresholding on the boundary score, and the common and unique loci to each condition were determined by interval intersection.
Genome_build: mm9
Supplementary_files_format_and_content: Cooler HDF5 files (extension .cool.HDF5) contain contact matrices and were generated using the cooler Python package; ChIP-seq signal track bigWigs were generated using MACS2, in units of SPMR; RNA-seq bigWig files were generated using STAR in units of RPM; Eigenvector text files are fixed step bedGraph-like files covering the whole genome with a column for the eigenvector in each condition (U=WT, T=TAM, N=NIPBL); TAD call text files are BED-like files where each row corresponds to a detected domain; the TAD boundary probabilities file is a bedGraph-like file covering the whole genome at 20kb with columns for the insulation tracks in conditions U=WT and N=NIPBL; The deseq2 CSV file contains estimated transcript abundances and differential expression statistics across the three conditions
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| Submission date |
Jan 11, 2017 |
| Last update date |
May 24, 2023 |
| Contact name |
Nezar Abdennur |
| E-mail(s) |
nezar@mit.edu
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| Organization name |
MIT
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| Lab |
Mirny
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| Street address |
77 Massachusetts Ave
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| City |
Cambridge |
| State/province |
MA |
| ZIP/Postal code |
02139 |
| Country |
USA |
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| Platform ID |
GPL13112 |
| Series (1) |
| GSE93431 |
Two independent modes of chromosome organization are revealed by cohesin removal |
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| Relations |
| Reanalyzed by |
GSE233377 |
| BioSample |
SAMN06217800 |
| SRA |
SRX2485044 |
| Supplementary file |
Size |
Download |
File type/resource |
| GSM2453283_NIPBL-R1.100kb.cool.HDF5.gz |
33.8 Mb |
(ftp)(http) |
HDF5 |
| GSM2453283_NIPBL-R1.10kb.cool.HDF5.gz |
80.5 Mb |
(ftp)(http) |
HDF5 |
| GSM2453283_NIPBL-R1.20kb.cool.HDF5.gz |
66.6 Mb |
(ftp)(http) |
HDF5 |
SRA Run Selector |
| Raw data are available in SRA |
| Processed data are available on Series record |
| Processed data provided as supplementary file |
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