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| Status |
Public on Jun 21, 2021 |
| Title |
ATAC-5K-exp2 |
| Sample type |
SRA |
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| Source name |
E14 mouse embryonic stem cells
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| Organism |
Mus musculus |
| Characteristics |
cell line: E14 mouse embryonic stem cells
cell numbers: 5,000
library protocol: low-input ATAC&mRNA-seq
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| Growth protocol |
Mouse E14 embryonic stem cells (129/Ola background) were maintained in Glasgow Minimum Essential Medium (GMEM, Sigma) containing 15% fetal bovine serum, supplemented with 1× Pen-Strep (Gibco), 2 mM Glutamax (Gibco), 50 µM β-mercaptoethanol (Gibco), 0.1 mM nonessential amino acids (Gibco), 1 mM sodium pyruvate (Gibco), and Leukemia Inhibitory Factor (LIF, 1000U/ml, Millipore).
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| Extracted molecule |
genomic DNA |
| Extraction protocol |
For low-input ATAC&mRNA-seq, harvested cells were washed and then permeabilized with mild detergent to facilitate the entry of Tn5 into the nuclei to tagment open chromatin regions. Tagmented cells were then lysed and Dynabeads Oligo (dT)25 were added into the cell lysate to capture mRNA. After magnetic separation, tagmented genomic DNA in the supernatant was purified and further amplified with indexed PCR to construct ATAC-seq library, while mRNA captured on beads was reverse transcribed using the bead-bound oligo (dT) as primer. The mRNA/cDNA hybrids were then directly tagmented by Tn5, and after initial end extension, the tagmented cDNA was amplified with indexed PCR to prepare mRNA-seq library using Nextera XT DNA Library Preparation Kit. Omni-ATAC-seq was performed following the Omni-ATAC protocol (Corces et al., 2017).
Open chromatin regions and mRNA/cDNA hybrids were tagmented with Tn5 and then amplified with indexed PCR to prepare ATAC-seq and RNA-seq libraries.
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| Library strategy |
ATAC-seq |
| Library source |
genomic |
| Library selection |
other |
| Instrument model |
NextSeq 550 |
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| Description |
unified_ATAC_peaks.bed.gz
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| Data processing |
ATACseq processing: Raw reads were filtered to exclude pairs with average base quality <20. Adapters removed by Cutadapt with parameters "a CTGTCTCTTATA -O 5 -q 0”, then reads were filtered to exclude fragments with length <30bp. Filtered, trimmed read pairs were mapped against mm10 via Bowtie2 v2.1.0 with parameters “-X 2000 --fr --end-to-end --very-sensitive”, followed by filtering with samtools v1.3.1 at MAPQ5. Reads mapped to chrM were filtered out. Duplicate mapped read pairs were removed by Picard tools MarkDuplicates.jar v1.110. Only the 9bp at the 5’ end of each read was retained for downstream analysis.
ATACseq signal tracks: Coverage tracks for genome browser views were generated with BEDtools v2.24.0 genomeCoverageBed, depth-normalized to 10 million read ends per sample, then converted to bigWig format with UCSC utility bedGraphToBigWig.
ATACseq peaks: Peak calls per sample were made by MACS2 v2.1.1 with parameters “callpeak -g mm -q 0.0001 --keep-dup=all --nomodel --extsize 9", then BEDtools v2.24.0 mergeBed was used to merge neighboring peaks within 200bp. One single set of unified peaks was generated by collapsing called peaks and identifying only peak regions that overlap from at least 3 of 8 samples via BEDtools v2.24.0 unionBedGraphs, followed by merging neighboring peaks within 200bp with BEDtools v2.24.0 mergeBed. Unified peaks less than 50bp in width were discarded. Unified peaks not within canonical chromosomes (chr1-19, X, Y) were also discarded.
RNAseq processing: Raw reads were filtered to exclude pairs with average base quality <20. Filtered reads pairs were mapped against mm10 by STAR v2.5 with parameters "--outSAMattrIHstart 0 --outFilterType BySJout --alignSJoverhangMin 8 --limitBAMsortRAM 55000000000 --outSAMstrandField intronMotif --outFilterIntronMotifs RemoveNoncanonical". Counts per gene per sample were collected by featureCounts (Subread v1.5.0-p1) with parameters "-s0 -Sfr -p".
RNAseq signal tracks: Coverage tracks were generated by STAR v2.5 with parameters “--runMode inputAlignmentsFromBAM --outWigType bedGraph --outWigStrand Unstranded --outWigNorm RPM”, then converted to bigWig format with UCSC utility bedGraphToBigWig.
Genome_build: mm10
Supplementary_files_format_and_content: coverage tracks in bigWig format; peaks in BED format; RNAseq counts-per-gene in tab-delimited text format
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| Submission date |
Jan 25, 2021 |
| Last update date |
Jun 21, 2021 |
| Contact name |
ruifang li |
| E-mail(s) |
lir4@niehs.nih.gov
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| Organization name |
NIEHS
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| Street address |
111 T.W. Alexander Drive
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| City |
RTP |
| State/province |
NC |
| ZIP/Postal code |
27709 |
| Country |
USA |
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| Platform ID |
GPL21626 |
| Series (1) |
| GSE165478 |
Low-input ATAC&mRNA-Seq: a simple and robust method for simultaneous dual-omics profiling with low cell number |
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| Relations |
| BioSample |
SAMN17531204 |
| SRA |
SRX9933987 |
| Supplementary file |
Size |
Download |
File type/resource |
| GSM5034381_ATAC-5K-exp2.9mer_depthNorm.bigWig |
296.3 Mb |
(ftp)(http) |
BIGWIG |
| GSM5034381_ATAC-5K-exp2.peaks.bed.gz |
143.6 Kb |
(ftp)(http) |
BED |
SRA Run Selector |
| Raw data are available in SRA |
| Processed data provided as supplementary file |
| Processed data are available on Series record |
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