|
| Status |
Public on Jun 22, 2024 |
| Title |
priBd3_B18hi_AIDER_1 |
| Sample type |
SRA |
| |
|
| Source name |
spleen
|
| Organism |
Mus musculus |
| Characteristics |
tissue: spleen
cell type: MACS CD43 depleted primary B cells + S2
genotype: B6.Gt(ROSA)26Sortm1(AID-ER)Mnz Ightm1Mnz Igktm1(neoR)Rsky
growth protocol: full RPMI
treatment: IL4, LPS, RP105
sorted: -
|
| 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 |
SAMN28036782 |
| SRA |
SRX15106348 |
