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Sample GSM3983819

Query DataSets for GSM3983819

Status

Public on Jul 30, 2019

Title

82649: RNA-seq WT Peritoneal macrophages #2

Sample type

SRA

Source name

Peritoneal macrophages

Organism

Mus musculus

Characteristics

tissue: Peritoneal macrophages
genotype: Bhlhe40+/+Bhlhe41+/+
strain background: C57BL/6
cell isolation: Ex vivo, FACS-sorted from peritoneal peritoneal lavage
cell-sorting strategy: CD11b+ F4/80+ MHCII intermediate Tim4+

Extracted molecule

polyA RNA

Extraction protocol

Rneasy Plus Mini Kit, Dynabeads mRNA purification kit
NEBNext Ultra II

Library strategy

RNA-Seq

Library source

transcriptomic

Library selection

cDNA

Instrument model

Illumina HiSeq 2500

Data processing

ChIP-seq: For Bhlhe40 ChIP-seq, the reads were aligned to the mouse genome assembly version of July 2007 (NCBI37/mm9), using the Bowtie program version 1.0.0; for H3K27ac ChIP-seq samples reads were aligned to the mouse genome assembly version of July 2007 (NCBI37/mm9), using the Bowtie2 program (usegalaxy.eu; version 2.3.4.2). Resulting BAM files were processed with BAM to Wiggle (usegalaxy.eu; version Version 2.6.4) and Wig/BedGraph-to-bigWig (usegalaxy.eu; version Version 1.1.1) converters.
RNA-seq: Reads corresponding to mouse ribosomal RNAs (BK000964.1 and NR046144.1) were removed. The remaining reads were cut down to a read length of 44 nucleotides and aligned to the mouse transcriptome (genome assembly version of July 2007 NCBI37/mm9) using TopHat version 1.4.1 (Trapnell et al, 2009). The database generation of RefSeq-annotated genes was performed as previously described (Wöhner et al., 2016). To refine the annotation of immunoglobulin genes, the immunoglobulin l light-chain segments were replaced with their corresponding converted GRCm38.p3 annotations (Ensembl version 79) (Yates et al., 2016). The resulting number of genes was 24,732. For analysis of differential gene expression, the number of reads per gene was counted using HTseq version 0.5.3 (Anders et al, 2014) with the overlap resolution mode set to 'union'. The data sets were analyzed using the R package DESeq2 version 1.2.10 (Love et al, 2014). Sample normalizations and dispersion estimations were conducted using the default DESeq2 settings. Transcripts per million (TPM) were calculated from RNA-seq data, as described (Wagner et al, 2012).
Genome_build: MM9
Supplementary_files_format_and_content: RNA-seq: Excel table with 4 tabs: TPM values for all samples and output of DEseq2 for three pairwise comparisons, see ‘data processing step’ for details. ChIP-seq: Bigwig files provided for all samples, see ‘data processing step’ for details.

Submission date

Jul 29, 2019

Last update date

Jul 31, 2019

Contact name

Taras Kreslavsky

E-mail(s)

taras.kreslavskiy@ki.se

Phone

+46761124813

Organization name

Karolinska Institutet