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38 results for “somatic hypermutation”

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zenodo36/100

Dataset used for "Somatic hypermutation analysis for improved identification of B cell clonal families from next-generation sequencing data"

<p>Each simulated dataset was generated using the AbSim R package (version 0.2.6) in a B cell single-lineage fashion. Each B cell clone simulation begins with a random selection from sets of IGHV, IGHD, and IGHJ germline sequences to produce a unique V(D)J recombination event. Then, clones are made by introducing mutations using a local nucleotide context-dependent model (S5F model) along a phylogenetic tree in which branching events occur stochastically.&nbsp;</p>

opencc-by-4.0Apr 2020View details →
zenodo36/100

Somatic hypermutation-mediated paratope flexibility improves the cross-reactivity of human malaria antibodies -- Molecular Dynamics dataset

<p>4493 Manuscript Data<br>====================</p> <p>author: Anton Hanke<br>size of uncompressed folder: ~19Gb.<br>DOI: 10.5281/zenodo.11470585</p> <p># Standard MD simulation data</p> <p>Standard Simulations were generated with gromacs 2021.5 using the charmm36m forcefield Juli 2021 release tarball (https://mackerell.umaryland.edu/download.php?filename=CHARMM_ff_params_files/charmm36-jul2021.ff.tgz).<br>Post processed (PBC) simulations are structured as follows:<br>Mature generally refers to the wildtype 4493 antibody.</p> <p>- simulations/standardMD<br>&nbsp; |<br>&nbsp; |- prod.mdp &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;example production mdp file used to run all production simulations.<br>&nbsp; |<br>&nbsp; |- mature &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;Mature simulation set. (folder and file naming the same in all simulation directories)<br>&nbsp; | &nbsp;|- {peptide}_{replicate}_prod.gro &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; {peptide} = peptide; {replicate} = standard MD replicate<br>&nbsp; | &nbsp;|- {peptide}_{replicate}_prod.tpr<br>&nbsp; | &nbsp;`- {peptide}_{replicate}_prod_align_noPBC.xtc (10Frames/ns)<br>&nbsp; |<br>&nbsp; |- mature_rerun &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;Additional set of replicates with the wildtype 4493.<br>&nbsp; |- matureCapped &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;Set of simulations with termini capped peptides<br>&nbsp; |- mature_nanpv2 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; Set of simulations with NPDP similar positioning of NANP<br>&nbsp; |- wo_pep &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;Set of simulations without peptides for germline and mature<br>&nbsp; `- germline &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;Set of germline simulations.</p> <p><br># RAMD simulation data</p> <p>RAMD simulations were generated with gromacs_2020.5 patched with RAMDv2 modified to account for the connected multiple ligand groups.<br>(Source code provided as tar file ./sw/gromacs_ramd_patchv2.tar.gz)</p> <p>Not all trajectories contain an unbinding event (gromacs CUDA bug.).&nbsp;<br>These trajectories were not considered in the analysis of simulations.&nbsp;</p> <p>- simulations/ramd<br>&nbsp; |<br>&nbsp; |- prod.mdp &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; Exemplary production mdp file with RAMD settings, these were used in all trajectories w/<br>&nbsp; | &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; differing RAMD random seed.<br>&nbsp; |<br>&nbsp; |- mature_2.625kcalmolA_4.0_3.0<br>&nbsp; | &nbsp;|- {peptide}_{replicate}_prod_{startFrame}.gro &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; {peptide} = peptide; {replicate} = standard MD replicate; {startFrame} = Frame in standard MD used to start simulation.<br>&nbsp; | &nbsp;|- {peptide}_{replicate}_prod_{startFrame}.tpr<br>&nbsp; | &nbsp;|- {peptide}_{replicate}_prod_{startFrame}.ndx<br>&nbsp; | &nbsp;|- {peptide}_{replicate}_prod_{startFrame}_align_noPBC.xtc &nbsp; &nbsp; &nbsp; (100Frames/ns) Files omited due to size -- available on request.<br>&nbsp; | &nbsp;`- {peptide}_{replicate}_prod_{startFrame}_lastframe.pdb &nbsp; &nbsp; &nbsp; &nbsp; Last frame of the processed RAMD trajectory.<br>&nbsp; `- gl_2.625kcalmolA_4.0_3.0</p> <p><br># Analysis</p> <p>- analysis<br>&nbsp; |<br>&nbsp; |- entropie &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; Quasi harmonic entropy estimation.<br>&nbsp; | &nbsp;|- inp &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; Concatenated &amp; Bootstrapped, coarse-grained and aligned trajectories of all systems<br>&nbsp; | &nbsp;|- out &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; CPPTRAJ runs to calculate QHE on the bootstrapped trajectories<br>&nbsp; | &nbsp;|- run_complex.sh &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;Script running analysis.<br>&nbsp; | &nbsp;|- ana.py &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;Script to calculte average and std of QHE for each system. (generates *.out *.tsv *.png)<br>&nbsp; | &nbsp;|- cg.py &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; Script used to bootstrap, coarse-grain align and build average structure with.<br>&nbsp; | &nbsp;`- delta_entropies.ods &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; Excel file used to calculate Tab 1. in Main text of paper from entropies.out.<br>&nbsp; |<br>&nbsp; |- mmpbsa &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; MMPBSA calculations (MM + SolvEnergy) with gmx_MMPBSA<br>&nbsp; | &nbsp;|- inp &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; Input trajectories and topologies processed for MMPBSA<br>&nbsp; | &nbsp;|- out/gmx_mmpbsa &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;Output directories in which gmx_MMPBSA was run.<br>&nbsp; | &nbsp;| &nbsp;` *.dat &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; Output files containing calculated energy terms from gmx_MMPBSA.<br>&nbsp; | &nbsp;|- mmpbsa.in &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; MMPBSA input file used to run analysis.<br>&nbsp; | &nbsp;|- plot_results.py &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; Python script to plot correlation of MMPBSA output with experimental data<br>&nbsp; | &nbsp;|- pca_eig_extr.py &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; Script to reduce simulations to regions of high probability density within trajectory (not used in the present analysis)<br>&nbsp; | &nbsp;|- slurm-91315023.out &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;Log file of the analysis run<br>&nbsp; | &nbsp;`- run_mmpbsa.sh &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; Shell script to run the MMPBSA analysis (generates input and output file trees).<br>&nbsp; |<br>&nbsp; `- ramd &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; RAMD analysation.<br>&nbsp; &nbsp; &nbsp;|- run.sh &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;Shell script to run the analysis<br>&nbsp; &nbsp; &nbsp;|- run_ramd_ana.py &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; Python script called by `run.sh` to run the analysis using `ramdAnalysis.py`<br>&nbsp; &nbsp; &nbsp;|- contact_clusters.py &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; Python script to generate plots based on output of the analysis.<br>&nbsp; &nbsp; &nbsp;|- ramdAnalysis.py &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; Python module containing analysis classes called/used within `run_ramd_ana.py`<br>&nbsp; &nbsp; &nbsp;| &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;Based on tauRAMD &amp; Fingerprint analysis by Dr. Daria Khokh (https://doi.org/10.1021%2Facs.jctc.8b00230; https://doi.org/10.1063%2F5.0019088)<br>&nbsp; &nbsp; &nbsp;|- abrun.* &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; Log files from the present run<br>&nbsp; &nbsp; &nbsp;|- *.svg; *.png &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;Analysis output files.<br>&nbsp; &nbsp; &nbsp;|- tramd_patchv2/ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;Output PDB structures from the analysis (excluded due to size, available on request)<br>&nbsp; &nbsp; &nbsp;`- representatives.pse &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; Pymol session of cluster representatives along unbinding for germline and wildtype with contact probabilities within the<br>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; cluster mapped as b-factor.</p> <p># Figures</p> <p>- figure_pdbs &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; PDB files (and pymol sessions) used to generate figures in the papers main text.</p>

opencc-by-4.0Jul 2024View details →
dryad36/100

B cell receptor parent-child pairs for studying somatic hypermutation

Open the record for dataset details and reuse information.

publicJun 2025View details →
geo24/100

Regulated somatic hypermutation enhances antibody affinity maturation

GEO Series GSE287123. Mus musculus. 27 samples. Type: Expression profiling by high throughput sequencing; Other.

openGEO-OpenJan 2025View details →
geo24/100

A broad atlas of somatic hypermutation allows prediction of activation-induced deaminase targets.

GEO Series GSE102944. Mus musculus. 9 samples. Type: Other.

openGEO-OpenJan 2018View details →
geo24/100

Huwe1 supports B-cell development, B-cell-dependent immunity, somatic hypermutation and class switch recombination by regulating proliferation

GEO Series GSE221351. Mus musculus. 12 samples. Type: Expression profiling by high throughput sequencing.

openGEO-OpenDec 2022View details →
geo24/100

SETD2 haploinsufficiency enhances germinal center-associated AICDA somatic hypermutation to drive B-cell lymphomagenesis

GEO Series GSE189867. Mus musculus. 62 samples. Type: Expression profiling by high throughput sequencing; Other; Genome binding/occupancy profiling by high throughput sequencing.

openGEO-OpenApr 2022View details →
geo24/100

Somatic hypermutation generates antibody specificities beyond the primary repertoire

GEO Series GSE283094. Mus musculus. 8 samples. Type: Other.

openGEO-OpenDec 2024View details →
ClinicalTrials.gov24/100

Durvalumab and Tremelimumab Combination in Somatically Hypermutated Recurrent Solid Tumors

ClinicalTrials.gov study NCT03911557. IPD Sharing: NO. Countries: 1. Publications: 0.

closedIPD-NOFeb 2026View details →
geo24/100

Dynamic regulation of somatic hypermutation in germinal centers enables rapid B cell clonal expansion without loss of affinity

GEO Series GSE285185. Mus musculus. 17 samples. Type: Expression profiling by high throughput sequencing.

openGEO-OpenMar 2025View details →
geo24/100

Immunoglobulin transcript sequence and somatic hypermutation computation from unselected RNA-seq reads in Chronic Lymphocytic Leukemia

GEO Series GSE66228. Homo sapiens. 17 samples. Type: Expression profiling by high throughput sequencing.

openGEO-OpenApr 2015View details →
geo20/100

Role of Dot1L and H3K79 methylation in regulating somatic hypermutation of immunoglobulin genes

GEO Series GSE167873. Homo sapiens. 4 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.

openGEO-OpenFeb 2021View details →
geo20/100

Regulation of somatic hypermutation by higher-order chromatin structure

GEO Series GSE273434. Homo sapiens. 141 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Other.

openGEO-OpenJun 2025View details →
geo20/100

Regulation of somatic hypermutation by higher-order chromatin structure [ChIP-Seq]

GEO Series GSE271542. Homo sapiens. 11 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.

openGEO-OpenJun 2025View details →
geo20/100

Regulation of somatic hypermutation by higher-order chromatin structure [MutPE-Seq]

GEO Series GSE271544. Homo sapiens. 57 samples. Type: Other.

openGEO-OpenJun 2025View details →
geo20/100

UNG shapes the specificity of AID-induced somatic hypermutation

GEO Series GSE39115. Mus musculus. 28 samples. Type: Other.

openGEO-OpenJul 2012View details →
geo20/100

HIRA-dependent H3.3 deposition and its modification H3.3K36me3 facilitate somatic hypermutation of immunoglobulin genes by maintaining the proper chromatin state and transcription

GEO Series GSE180899. Homo sapiens. 8 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.

openGEO-OpenJul 2021View details →
geo20/100

Topologically Associated Domains Delineate Susceptibility to Somatic Hypermutation

GEO Series GSE139810. Homo sapiens. 69 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Other.

openGEO-OpenDec 2019View details →
geo20/100

Regulation of somatic hypermutation by higher-order chromatin structure

GEO Series GSE271541. Homo sapiens. 2 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.

openGEO-OpenJun 2025View details →
geo20/100

UNG shapes the specifity of AID-induced somatic hypermutation in B cells

GEO Series GSE39114. Mus musculus. 10 samples. Type: Other.

openGEO-OpenJul 2012View details →

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