|
| Status |
Public on Apr 16, 2024 |
| Title |
IL4_1h_R2 |
| Sample type |
SRA |
| |
|
| Source name |
mouse bone marrow derived macrophages
|
| Organism |
Mus musculus |
| Characteristics |
cell type: BMDM
treatment: 1h DMSO followed by 1h IL4 10ng/mL
genotype: wild type
|
| Treatment protocol |
LPS (10 ng/ml) treatment for 30min, 1h, 2h or 4h, IL4 (10 ng/ml) for 30min, 1h, 2h or 4h
|
| Extracted molecule |
total RNA |
| Extraction protocol |
chromatin-associated-RNA-seq libraries were prepared for NextSeq500 sequencing following standard protocols.
Library preparation was performed following the SMART-seq2 protocol (Picelli et al. 2014b). Briefly, 5 ng of chromatin associated RNA were reverse transcribed with template-switching using random primers and an LNA-containing template-switching oligo (TSO). The resulting cDNA was pre-amplified, purified and tagmented with Tn5 transposase produced in-house. cDNA fragments generated after tagmentation were gap-repaired, enriched by PCR and purified to create the final cDNA library for Illumina NextSeq500 sequencing.
RNAseq_Smartseq2
|
| |
|
| Library strategy |
RNA-Seq |
| Library source |
transcriptomic single cell |
| Library selection |
cDNA |
| Instrument model |
Illumina NextSeq 500 |
| |
|
| Description |
Chromatin Associated RNA
|
| Data processing |
Reads were quality filtered according to the illumina pipeline
Single-end reads were mapped after adapter trimming to the mouse genome assembly mm10 (Illumina's iGenomes reference annotation downloaded from UCSC http://support.illumina.com/sequencing/sequencing_software/igenome.html), and the Refseq transcript annotation (ncbiRefSeqCurated 2017/11/16) using topHat2 (TopHat v2.1.1) (Trapnell et al. 2012) with parameters: --max-multihits 1 --b2-very-sensitive).
Reads mapping to the ENCODE black-list regions (https://github.com/Boyle-Lab/Blacklist) (Amemiya et al. 2019) were removed using standard bedtools operations (bedtools v2.29.2).
Per gene read counts were retrieved using standard R/Bioconductor packages (e.g. GenomicRanges and GenomicAlignment together with the proper GFF Refseq annotation ncbiRefSeqCurated 2017/11/16).
Sample normalization was achieved by selecting invariant genes across samples/conditions (Gualdrini et al. 2016). Briefly, we modelled the frequency distribution of the differences in reads counts across samples with a log-normal distribution. Peaks laying within 1σ of the best fitted mean difference were considered as invariant and used to normalize the samples. Differentially regulated genes were selected using DESEq2 (R/Bioconductor package version 1.26.0; R version 3.6.2) after turning off the default normalization DESEq2 applies.
Assembly: mm10
Supplementary files format and content: .bw are BigWig normalised files
|
| |
|
| Submission date |
Nov 29, 2022 |
| Last update date |
Apr 16, 2024 |
| Contact name |
Francesco Gualdrini |
| E-mail(s) |
Francesco.gualdrini@ieo.it
|
| Organization name |
European Institute of Oncology
|
| Department |
Department of Experimental Oncology
|
| Lab |
Transcriptional Control in Inflammation and Cancer
|
| Street address |
Via Adamello, 16
|
| City |
Milan |
| ZIP/Postal code |
20139 |
| Country |
Italy |
| |
|
| Platform ID |
GPL19057 |
| Series (2) |
| GSE218968 |
Unbiased profiling of clinical kinase inhibitors’ effects in activated macrophages using chromatin modifications as high-content readouts [Chromatin associated RNA-seq] |
| GSE219240 |
Unbiased profiling of clinical kinase inhibitors’ effects in activated macrophages using chromatin modifications as high-content readouts |
|
| Relations |
| BioSample |
SAMN31925332 |
| SRA |
SRX18411246 |
