|
| Status |
Public on Aug 13, 2020 |
| Title |
ECHi-C Adipocyte D1 Experiment #2 |
| Sample type |
SRA |
| |
|
| Source name |
BM-hMSC-TERT4
|
| Organism |
Homo sapiens |
| Characteristics |
cell type: Adipocyte
Stage: Day 1
|
| Treatment protocol |
Two days post confluency (day 0), cells were induced to undergo adipocyte differentiation by exposing them to a adipogenic differentiation cocktail (DMEM supplemented with 10% fetal calf serum, 10ug/mL insulin, 1µM rosiglitazone, 100 mM dexamethasone, and 500 µM isobutylmethylxanthine). Medium was replaced on days 2, 4, 7 and 9.
|
| Growth protocol |
Telomerase-immortalized human mesenchymal stromal cells of bone marrow (BM-hMSC-TERT4) were grown under standard cell culture conditions in α-MEM supplemented with 10 % fetal calf serum (FCS) and 1 % penicillin/streptomycin (P/S).
|
| Extracted molecule |
genomic DNA |
| Extraction protocol |
Capture Hi-C of enhancers was performed on 500 ng Hi-C libraries using the custom-designed biotinylated RNA bait library and custom blockers according to the manufacturer’s instructions (SureSelect, Agilent). Post-capture PCR amplification using universal primer and 6 bp-index primers (New England Biolabs) were performed.
ECHi-C libraries were constructed from genomic DNA according to the manufacturer's instructions (Illumina).
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| |
|
| Library strategy |
Hi-C |
| Library source |
genomic |
| Library selection |
other |
| Instrument model |
Illumina HiSeq 1500 |
| |
|
| Description |
ECHiC_Lineages.counts.txt.gz
ECHiC_Adipogenesis.counts.txt.gz
|
| Data processing |
Base calling for all sequencing runs was performed using bcl2fastq (v2.20.0.422).
ChIP-seq: Reads were aligned using STAR (v2.3.1, --outSJfilterIntronMaxVsReadN 0 --outFilterMatchNmin 25 --outFilterMismatchNmax 2 --alignIntronMax 1). Duplicated reads/fragments were removed using Picard (v2.5.0). Reads/fragments were counted using HOMER (v4.10.3) in DNase I hypersensitive regions identified in a previous study (Rauch et al., 2019)
scATAC-seq: Reads (R1 and R2) were trimmed using TrimGalore (v. 0.6.0) and aligned aligned using STAR (v2.3.1, --outSJfilterIntronMaxVsReadN 0 --outFilterMatchNmin 25 --outFilterMismatchNmax 2 --alignIntronMax 1). Only short fragments (< 140bp) were kept , the short fragments were deduplicated using Picard (v2.5.0) and peaks were detected using MACS2 (v. 2.1.2). The cell barcodes (Index1) were quality filtered using FASTX toolkit (v. 0.0.13, -q 30 -p 80). The quality filtered barcodes were matched to the barcode white list (10X Genomics) and only barcodes with more than 10000 reads were kept. The raw aligned reads were split into cell-specific files using the filtered barcodes and individually deduplicated. Count matrices were generated by counting fragments for each cell using featureCounts (v.2.0.0) in peak detected regions. Cells and peaks were quality filtered and co-accessible peaks were identified using Cicero.
CUT&RUN: Reads trimmed with cutadpt and aligned using STAR (v2.3.1, --outSJfilterIntronMaxVsReadN 0 --outFilterMatchNmin 25 --outFilterMismatchNmax 2 --alignIntronMax 1).
RNA-seq: Reads were aligned using STAR (v2.3.1). Reads were counted using iRNA-seq (v1.0).
HiC: Reads were aligned and quality controlled using Hicup (v0.6.1). TADs were identified using HiTAD from TADlib (v0.3.1).
ECHiC: Reads were aligned and quality controlled using Hicup (v0.6.1). Interactions between virtual restriction fragments were detected using Chicago (v1.6.0) using custom weights calculated from high confidence interactions in the data.
Genome_build: hg19
Supplementary_files_format_and_content: ChIP-seq: Tab-delimited txt files containing normalized tag counts for each replicate in DNase I hypersensitive regions identified in a previous study (Rauch et al., 2019). For each replicate, read depth normalized bedGraph files are also supplied for filtered and deduplicated alignments.
Supplementary_files_format_and_content: scATAC-seq: Tab-deliminted txt file containing the peak co-accessibility (estimated with Cicero).
Supplementary_files_format_and_content: CUT&RUN: Tab-delimited txt files containing raw tag counts for each replicate in DNase I hypersensitive regions identified in a previous study (Rauch et al., 2019). For each replicate, read depth normalized bedGraph files are also supplied.
Supplementary_files_format_and_content: RNA-seq: Tab-delimited txt file containing raw tag counts for each replicate for all RefSeq genes . For each replicate, read depth normalized bedGraph files are also supplied.
Supplementary_files_format_and_content: Hi-C: The BED file contains all detected TADs and subTADs (identified using HiTAD).
Supplementary_files_format_and_content: ECHi-C: All interactions identified (using Chicago), as well as counts in all samples and annotation is also supplied in a tab-delimited txt file.
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| |
|
| Submission date |
Feb 26, 2020 |
| Last update date |
Aug 13, 2020 |
| Contact name |
Susanne Mandrup |
| E-mail(s) |
s.mandrup@bmb.sdu.dk
|
| Phone |
+45 6550 2340
|
| Organization name |
University of Southern Denmark
|
| Department |
Department of Biochemistry and Molecular Biologi
|
| Street address |
Campusvej 55
|
| City |
Odense M |
| State/province |
Fyn |
| ZIP/Postal code |
5230 |
| Country |
Denmark |
| |
|
| Platform ID |
GPL18460 |
| Series (1) |
| GSE140782 |
Highly connected enhancer communities control lineage-determining genes in human mesenchymal stem cells |
|
| Relations |
| BioSample |
SAMN14214203 |
| SRA |
SRX7807119 |
