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Status |
Public on Apr 16, 2024 |
Title |
Refametinib_2hLPS_R2 |
Sample type |
SRA |
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Source name |
mouse bone marrow derived macrophages
|
Organism |
Mus musculus |
Characteristics |
cell type: BMDM treatment: 1h pre-treatment with Refametinib followed by 2h stimulation with 10ng/mL LPS genotype: wild type
|
Treatment protocol |
LPS (10 ng/ml) treatment for 2h
|
Extracted molecule |
polyA RNA |
Extraction protocol |
Total RNA was extracted from BMDM cells using the Zymo Quick-RNA kit (Zymo Research code R1055). Library preparation was performed following the SMART-seq2 protocol (Picelli et al. 2014b). Briefly, 5 ng of total RNA were reverse transcribed with template-switching using oligo(dT) 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.
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Library strategy |
RNA-Seq |
Library source |
transcriptomic |
Library selection |
cDNA |
Instrument model |
Illumina NovaSeq 6000 |
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|
Description |
RNAseq
|
Data processing |
Reads were quality filtered according to the illumina pipeline Pair-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
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Submission date |
Dec 02, 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 |
GPL24247 |
Series (2) |
GSE219229 |
Unbiased profiling of clinical kinase inhibitors’ effects in activated macrophages using chromatin modifications as high-content readouts [RNA-Seq] |
GSE219240 |
Unbiased profiling of clinical kinase inhibitors’ effects in activated macrophages using chromatin modifications as high-content readouts |
|
Relations |
BioSample |
SAMN31994874 |
SRA |
SRX18466778 |