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74 results for “hypermutators”

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zenodo40/100

Code and Data associated with "Discovery of positive and purifying selection in metagenomic time series of hypermutator microbial populations"

<p>Code and data sufficient to reproduce analyses in&nbsp;&quot;Discovery of positive and purifying selection in metagenomic time series of hypermutator microbial populations&quot;.</p>

opencc-by-4.0Jul 2022View details →
ClinicalTrials.gov40/100

NIMBUS: Nivolumab Plus Ipilimumab in Metastatic Hypermutated HER2-negative Breast Cancer

ClinicalTrials.gov study NCT03789110. IPD Sharing: YES. Countries: 1. Publications: 1.

controlledIPD-YESFeb 2026View details →
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

Hypermutator emergence in experimental Escherichia coli populations is stress type dependent

<p class="Textbody"><span>Genotypes exhibiting an increased mutation rate, called hypermutators, can propagate in microbial populations because they can have an advantage due to the higher supply of beneficial mutations needed for adaptation. Although this is a frequently observed phenomenon in natural and laboratory populations, little is known about the influence of parameters such as the degree of maladaptation, stress intensity and the genetic architecture for adaptation on the emergence of hypermutators. To address this knowledge gap, we measured the emergence of hypermutators over ~1000 generations in experimental <em>Escherichia coli</em> populations exposed to different levels of osmotic or antibiotic stress. Our stress types were chosen based on the assumption that the genetic architecture for adaptation differs between them. Indeed, we show that the size of the genetic basis for adaptation is larger for osmotic stress compared to antibiotic stress. During our experiment, we observed an increased emergence of hypermutators in populations exposed to osmotic stress but not in those exposed to antibiotic stress, indicating that hypermutator emergence rates are stress-type-dependent. These results support our hypothesis that hypermutator emergence is linked to the size of the genetic basis for adaptation. In addition, we identified other parameters that covaried with stress type (stress level and IS transposition rates) that might have contributed to an increased hypermutator provision and selection. Our results provide a first comparison of hypermutator emergence rates under varying stress conditions and point towards complex interactions of multiple stress-related factors on the evolution of mutation rates.</span></p>

opencc-zeroApr 2023View details →
ClinicalTrials.gov36/100

Pembrolizumab in Ultramutated and Hypermutated Endometrial Cancer

ClinicalTrials.gov study NCT02899793. IPD Sharing: UNDECIDED. Countries: 1. Publications: 3.

restrictedIPD-UNDECIDEDFeb 2026View details →
ClinicalTrials.gov36/100

Pembrolizumab in MMR-Proficient Metastatic Colorectal Cancer Pharmacologically Primed to Trigger Hypermutation Status

ClinicalTrials.gov study NCT03519412. IPD Sharing: Not stated. Countries: 1. Publications: 6.

restrictedIPD-UNDECIDEDFeb 2026View details →
dryad36/100

Hypermutator emergence in experimental Escherichia coli populations is stress type dependent

Open the record for dataset details and reuse information.

publicApr 2023View 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 →
ClinicalTrials.gov32/100

Pilot Study of Nivolumab in Pediatric Patients With Hypermutant Cancers

ClinicalTrials.gov study NCT02992964. IPD Sharing: NO. Countries: 5. Publications: 3.

closedIPD-NOFeb 2026View details →
dryad28/100

Data from: Evolution of mutation rates in hypermutable populations of Escherichia coli propagated at very small effective population size

Mutation is the ultimate source of the genetic variation—including variation for mutation rate itself—that fuels evolution. Natural selection can raise or lower the genomic mutation rate of a population by changing the frequencies of mutation rate modifier alleles associated with beneficial and deleterious mutations. Existing theory and observations suggest that where selection is minimized, rapid systematic evolution of mutation rate either up or down is unlikely. Here, we report systematic evolution of higher and lower mutation rates in replicate hypermutable Escherichia coli populations experimentally propagated at very small effective size—a circumstance under which selection is greatly reduced. Several populations went extinct during this experiment, and these populations tended to evolve elevated mutation rates. In contrast, populations that survived to the end of the experiment tended to evolve decreased mutation rates. We discuss the relevance of our results to current ideas about the evolution, maintenance and consequences of high mutation rates.

opencc-zeroDec 2016View details →
ClinicalTrials.gov28/100

Nivolumab Ipilimumab in Patients With hyperMutated Cancers Detected in Blood (NIMBLe)

ClinicalTrials.gov study NCT03461952. IPD Sharing: NO. Countries: 2. Publications: 0.

closedIPD-NOFeb 2026View details →
ClinicalTrials.gov28/100

Nivolumab in Patients With IDH-Mutant Gliomas With and Without Hypermutator Phenotype

ClinicalTrials.gov study NCT03718767. IPD Sharing: YES. Countries: 1. Publications: 0.

controlledIPD-YESFeb 2026View details →
dryad28/100

Data from: Evolution of mutation rates in hypermutable populations of Escherichia coli propagated at very small effective population size

Open the record for dataset details and reuse information.

publicFeb 2017View details →
geo24/100

Megabase Length Hypermutation Accompanies Human Structural Variation at 17p11.2 [II]

GEO Series GSE125208. Homo sapiens. 24 samples. Type: Genome variation profiling by array.

openGEO-OpenJan 2019View details →
geo24/100

Mutability and hypermutation antagonize immunoglobulin codon optimality; Analysis of human tonsil B cells.

GEO Series GSE260943. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing; Other.

openGEO-OpenOct 2024View 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

Multi-omics informed mechanism-based model of meropenem and tobramycin against hypermutable Pseudomonas aeruginosa

GEO Series GSE270916. Pseudomonas aeruginosa. 48 samples. Type: Other.

openGEO-OpenJun 2025View details →
geo24/100

Megabase Length Hypermutation Accompanies Human Structural Variation at 17p11.2 [III]

GEO Series GSE125209. Homo sapiens. 71 samples. Type: Genome variation profiling by array.

openGEO-OpenJan 2019View details →
geo24/100

Megabase Length Hypermutation Accompanies Human Structural Variation at 17p11.2 [I]

GEO Series GSE125207. Homo sapiens. 31 samples. Type: Genome variation profiling by array.

openGEO-OpenJan 2019View details →

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International Brain Laboratory public data

The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.

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Last verified 2026-04-29Open record