GEO Accession viewer

NCBI > GEO > Accession Display

Not logged in | Login

GEO help: Mouse over screen elements for information.

Sample GSM2060162

Query DataSets for GSM2060162

Status

Public on Jun 01, 2016

Title

CTL R-2HG replicate2

Sample type

SRA

Source name

sorted mouse bone marrow cells

Organism

Mus musculus

Characteristics

strain: C57BL6
tranduction: empty vector

Extracted molecule

genomic DNA

Extraction protocol

0.3 – 1 µg of DNA was used for RRBS library preparation using a published protocol with minor modifications (Smith et al, 2009). Briefly, genomic DNA was digested with MspI (NEB, Ipswich, MA, USA) end-repaired and A-tailed with the Klenow-fragment enzyme (NEB), and ligated (NEB) with 5mC-methylated Illumina paired end sequencing adapters (Illumina Inc., San Diego, CA). Fragments in a range of 40 to 220 bps insert size were gel-purified (NuSieve 3:1 Agarose, Lonza, Allenda, NJ, USA). Libraries were bisulfite converted using the EZ DNA Methylation™ Kit (ZymoResearch, Irvine, CA, USA) and amplified using PfuTurboCx polymerase (Agilent, Santa Clara, CA, USA). Each library was sequenced on a separate lane using an Illumina HiScanSQ instrument with version 2.5 sequencing chemistry. Libraries were spiked with 45% PhiX DNA to counteract the imbalance in nucleotide representation.

Library strategy

Bisulfite-Seq

Library source

genomic

Library selection

Reduced Representation

Instrument model

Illumina HiScanSQ

Data processing

Trim Galore (Babraham Bioinformatics) were used in RRBS mode to analyze the quality of the raw data sets (fastq files) and to remove adapter sequence contaminations.
Quality trimmed fastq files (phred score >20) were applied as input data for BSMAP aligner mapping short reads (50mers) to the murine reference genome (mm9) followed by calling methylation sites (RRBS mode, Lister protocol).
Results from methylation calling were further processed using the R package methylKit 0.9.1. methylKit quantifies, compares and annotates methylation calls (CpG) at single nucleotide and genomic interval levels, respectively.
For clustering of DMRs, the reads were filtered to contain at least one CpG. Only sequences with a coverage of 20 or more reads were included in the analysis. In addition, all sequences that showed no methylation at all were filtered out.
For determination of DMRs logistic regression and the SLIM-adjustment within the methylkit package was used on the filtered dataset and sequences with at least 40 % difference of methylation were called differentially methylated. For the analysis of DMRs in the vicinity of genes genomic intervals were defined as 500nt windows sliding in steps of 250 nucleotides across the entire genome. The threshold to count for a “CpG” islet is a minimum of 5 CpGs per interval.
To estimate the differential methylation status of samples being analyzed, the overall methylation rate was used to calculate a relative difference among these samples. Genomic intervals (CpG islets) with at least 25% difference in their methylation status were called differentially methylated.
Genome_build: mm9
Supplementary_files_format_and_content: Text files containing differentially methylated regions

Submission date

Feb 11, 2016

Last update date

May 15, 2019

Contact name

Anuhar Chaturvedi

E-mail(s)

chaturvedi.anuhar@mh-hannover.de

Phone

00495115329778

Organization name

Hannover Medical School