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Sample GSM6090065 Query DataSets for GSM6090065
Status Public on Jun 22, 2024
Title GCB_B18hi_1
Sample type SRA
 
Source name spleen
Organism Mus musculus
Characteristics tissue: spleen
cell type: GCB
genotype: B6.129P2-Ightm1Mnz
growth protocol: -
treatment: SRBC injected (d0,d5), harvest d10
sorted: B220+, Fas+, CD38-
Extracted molecule genomic DNA
Extraction protocol cells were harvested and gen.DNA prepared by PCI extraction and EtOH precipitation
2 rounds of PCRs were used to add adapter sequences and dual barcoding for paired-end sequencing on Illumina platform
 
Library strategy OTHER
Library source genomic
Library selection other
Instrument model Illumina MiSeq
 
Description gen.DNA was purified and used for 2 rounds of PCRs attaching adapters and dual barcoding on 5' and 3' ends of the library
Dual barcoded libraries were multiplexed and sequenced on Illumina platforms with paired-end seq readmode
MutPE-seq
Data processing Reads were trimmed for standard adapters with cutadapt. Poor quality (Q<25) 3’ bases were trimmed with trimmomatic by averaging over a sliding window of 5nt. Read pairs were then filtered for minimum remaining length (200nt for read 1, 100nt for read 2) using cutadapt. Read mates were merged down to make combined single-end reads with FLASH allowing 10% mismatch between the mates.
Obvious erroneous mergers were removed by selecting combined reads with lengths within ±30nt of the amplicon length using cutadapt, The remaining combined reads were aligned with Bowtie2, using the “–very-sensitive-local” alignment mode and only the fixed variable region sequence and its immediate vicinity as reference.
Alignments were split into strata by the number of mismatches reported, using a custom Perl script. For each stratum a pile-up was generated with samtools (Li et al., 2009) taking into account only bases with quality of at least 30.
The pileups were then quantified with a custom Python script and the resulting mutation counts were processed and visualized with custom scripts in R (v3.5.1), with the help of additional R packages (ggplot2 , ggrepel, patchwork).
Background mutation profiles were controlled for by subtracting the corresponding mutation frequencies in control samples from the frequencies in the samples of interest, at each position and for each substitution type. Annotation of hot and cold spots was created by means of regex search for the corresponding patterns in the reference sequence.
Code for the workflow and the custom scripts is available on Github at https://github.com/PavriLab/IgH_VDJ_MutPE .
Assembly: NCBI mm9, NCBI HG38, custom IGH genome (see github)
Supplementary files format and content: bedGraph format files showing coverage tracks that can be visualized in genome browser
Library strategy: MutPE-seq
 
Submission date May 02, 2022
Last update date Jun 22, 2024
Contact name Maximilian Christian von der Linde
E-mail(s) maximilian.linde@imp.ac.at, max_vdl@outlook.com
Phone +4368181646556
Organization name Institute of Molecular Pathology
Lab Pavri GRP
Street address Campus-vienna-biocenter 1, IMP, Pavri GRP
City Vienna
State/province Vienna
ZIP/Postal code 1030
Country Austria
 
Platform ID GPL16417
Series (2)
GSE202039 High-resolution transcriptional analysis of immunoglobulin variable regions reveals the absence of direct relationships between somatic hypermutation, nascent transcription and epigenetic marks [MutPE]
GSE202042 High-resolution transcriptional analysis of immunoglobulin variable regions reveals the absence of direct relationships between somatic hypermutation, nascent transcription and epigenetic marks
Relations
BioSample SAMN28036777
SRA SRX15106353

Supplementary file Size Download File type/resource
GSM6090065_86756_B18_GCB1.aln.point.bedGraph.gz 5.3 Kb (ftp)(http) BEDGRAPH
SRA Run SelectorHelp
Raw data are available in SRA
Processed data provided as supplementary file
Processed data are available on Series record

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