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1,145 results for “somatic”
Basket Study of Neratinib in Participants With Solid Tumors Harboring Somatic HER2 or EGFR Exon 18 Mutations
ClinicalTrials.gov study NCT01953926. IPD Sharing: YES. Countries: 13. Publications: 10.
Exposure-Based Treatment for Undifferentiated Somatic Symptom Disorder
ClinicalTrials.gov study NCT04511286. IPD Sharing: NO. Countries: 1. Publications: 1.
Data from: Suppression of Huntington’s disease somatic instability by transcriptional repression and direct CAG repeat binding
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Lingering legacies: Past growth and parental experience influence somatic growth in a fish population
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Evaluating somatic cell count, the California mastitis test, and infrared thermography for subclinical mastitis detection in meat ewes
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Data from: Inheritance of somatic mutations can affect fitness in monkeyflowers
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UCSB SONGS Mitigation Monitoring: Reef Survey - Fish Fecundity, Feeding, and Somatic Growth
These data describe annual estimates of the fecundity, gut fullness, and somatic growth of five fish species at three subtidal reefs collected as part of the San Onofre Nuclear Generating Station (SONGS) Mitigation Monitoring Program. Data collection began in 2009 at an artificial reef (Wheeler North Reef in Orange County, CA) and two natural reference reefs (San Mateo Kelp in Orange County, CA and Barn Kelp in San Diego County, CA). Fish were collected annually at each reef between May and October.
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. </p>
Somatic copy number and structural variation in RPE-1 cells with induced chromosomal instability
<p><span><span><span><span><span><span><span><span><span><span><span>The chromosome breakage-fusion-bridge (BFB) cycle is a mutational process that produces gene amplification and genome instability. Signatures of BFB cycles can be observed in cancer genomes alongside chromothripsis, another catastrophic mutational phenomenon. Here, we explain this association by elucidating a mutational cascade, downstream of <a>th</a></span></span></span></span></span></span></span></span></span></span></span><span><span><span><span><span><span><span><span><span><span><span>e single cell division error of chromosome bridge formation, that rapidly generates extreme genomic complexity. We show that actomyosin forces are required for initial bridge breakage and mutagenesis, following which chromothripsis accumulates with aberrant interphase replication of bridge DNA. This is then followed by an unexpected burst of DNA replication in the next mitosis, generating extensive DNA damage. During this second cell division, broken bridge chromosomes frequently mis-segregate and form micronuclei, promoting additional chromothripsis. We <a>fu</a></span></span></span></span></span></span></span></span></span></span></span><span><span><span><span><span><span><span><span><span><span><span>rther show that this mutational cascade generates the continuing evolution and sub-clonal heterogeneity characteristic of many human cancers.</span></span></span></span></span></span></span></span></span></span></span></p>
Uncoupling of programmed DNA cleavage and repair jeopardizes the assembly of the Paramecium somatic genome
<p>In the ciliate <i>Paramecium</i>, the precise excision of numerous Internal Eliminated Sequences (IESs) from the somatic genome is essential at each sexual cycle. DNA double strands breaks (DSBs) are introduced by the PiggyMac endonuclease, and repaired in a highly concerted manner by the Non-Homologous End Joining pathway (NHEJ), as illustrated by the complete inhibition of DNA cleavage when Ku70/80 proteins are missing. We show here that expression of a DNA binding-deficient Ku70 mutant (Ku70-6E) permits DNA cleavage but not DSB repair, leading to accumulation of unrepaired DSBs. When wildtype and mutant Ku are co-expressed, the DSBs induced by Ku70-6E can be repaired by wildtype Ku, which uncouples DNA repair from the cleavage step. High-throughput sequencing of the developing MAC genome in these conditions reveals the presence of extremities healed by <i>de novo</i> telomere addition and numerous translocations between IES-flanking sequences. We conclude that coupling the two steps of IES excision ensures that both extremities are maintained together throughout the process, and propose that Ku assists PiggyMac during assembly of the synaptic pre-cleavage complex.</p>
STmut: a framework for visualizing somatic alterations in spatial transcriptomics data of cancer
<p>Scripts generating figures of the paper titled "STmut: a framework for visualizing somatic alterations in spatial transcriptomics data of cancer".</p>
Data from: Value-related learning in the olfactory bulb occurs through pathway-dependent peri-somatic inhibition of mitral cells
<p>Associating values to environmental cues is a critical aspect of learning from experiences, allowing animals to predict and maximise future rewards. Value-related signals in the brain were once considered a property of higher sensory regions, but their wide distribution across many brain regions is increasingly recognised. Here, we investigate how reward-related signals begin to be incorporated, mechanistically, at the earliest stage of olfactory processing, namely, in the olfactory bulb. In head-fixed mice performing Go/No-Go discrimination of closely related olfactory mixtures, rewarded odours evoke widespread inhibition in one class of output neurons, that is, in mitral cells but not tufted cells. The temporal characteristics of this reward-related inhibition suggest it is odour-driven, but it is also context-dependent since it is absent during pseudo-conditioning and pharmacological silencing of the piriform cortex. Further, the reward-related modulation is present in the somata but not in the apical dendritic tuft of mitral cells, suggesting an involvement of circuit component located deep in the olfactory bulb. Depth-resolved imaging from granule cell dendritic gemmules suggests that granule cells that target mitral cells receive a reward-related extrinsic drive. Thus, our study supports the notion that value-related modulation of olfactory signals is a characteristic of olfactory processing in the primary olfactory area and narrows down the possible underlying mechanisms to deeper circuit components that contact mitral cells peri-somatically.</p>
Somatic mutations alter the differentiation outcomes of iPSC-derived neurons (Metadata and AnnData/H5AD files)
<p><strong>Data S1:</strong> Metadata information for the 828,937 processed cells from the DN dataset: donor identity, cell type annotation, pool identifier, 10x sample, time point and replicate information. Related to STAR Methods: Reanalysis of pooled single-cell data (DA).</p> <p><strong>Data S2-S4:</strong> AnnData/H5AD files containing the single-cell gene expression matrices and the metadata for day 11, day 30 and day 52, respectively. The gene expression is normalised and log-transformed, but not scaled. md5 files are also included. Related to STAR Methods: DE analysis between failed and successful lines.</p> <p>#File names:</p> <p><strong>File-Data S1: </strong>suppData1.RDS</p> <p><strong>File-Data S2: </strong>allpools.scanpy.D11.wMetaClustUmapGraph.exprLogNormNotScaled_notKO.h5ad</p> <p><strong>File-Data S3: </strong>allpools.scanpy.D30.wMetaClustUmapGraph.exprLogNormNotScaled_notKO.h5ad</p> <p><strong>File-Data S4: </strong>allpools.scanpy.D52.wMetaClustUmapGraph.exprLogNormNotScaled_notKO.h5ad</p>
Training Data for 'somatic variants discovery'
<p>The data provided here are part of a Galaxy Training Network tutorial created by Björn Grüning to detect hCNVS in defined WES chromosomal regions.</p>
Common anti-cancer therapies induce somatic mutations in stem cells of healthy tissue
<p>Genome-wide mutation analyses have revealed that specific anti-cancer drugs are highly mutagenic to cancer cells, but the mutational impact of anti-cancer therapies on normal cells is not known. Here, we examine genome-wide somatic mutation patterns in 42 healthy adult stem cells (ASCs) of the colon or the liver from 14 colorectal cancer patients (mean of 3.2 ASC per donor) that received systemic chemotherapy and/or radiotherapy. The platinum-based chemo-drug Oxaliplatin induces on average 535±260 mutations in colon ASC, while 5-FU shows a complete mutagenic absence in most colon ASCs. In contrast with the colon, normal liver ASCs escape mutagenesis from systemic treatment. Radiation results in the accumulation of 50-100 5-10,000bp deletions and structural rearrangements in colon ASCs. Thus, while chemotherapies are highly effective at killing cancer cells, their systemic use also increases the mutational burden of long-lived normal stem cells responsible for tissue renewal thereby increasing the risk for developing second cancers.</p>
CWL run of Somatic Variant Calling Workflow (CWLProv 0.5.0 Research Object)
<p>The somatic variant calling workflow included in this case study is designed by <a href="http://bcb.io/">Blue Collar Bioinformatics (bcbio)</a>, a community-driven initiative to develop best-practice pipelines for variant calling, RNA-seq and small RNA analysis workflows. According to the documentation, the goal of this project is to facilitate the automated analysis of high throughput data by making the resources quantifiable, analyzable, scalable, accessible and reproducible.</p> <p>All the underlying tools are containerized, facilitating software use in the workflow. The somatic variant calling workflow defined in CWL is available on GitHub and equipped with a well defined test dataset.</p> <p>This dataset folder is a CWLProv Research Object that captures the Common Workflow Language execution provenance, see <a href="https://w3id.org/cwl/prov/0.5.0">https://w3id.org/cwl/prov/0.5.0</a> or use <a href="https://pypi.org/project/cwlprov/">https://pypi.org/project/cwlprov/</a> to explore</p> <p><strong>Steps to reproduce</strong></p> <p>To build the research object again, use Python 3 on macOS. Built on:</p> <ul> <li>Processor 2.8GHz Intel Core i7</li> <li>Memory: 16GB</li> <li>OS: macOS High Sierra, Version 10.13.3</li> <li>Storage: 250GB</li> </ul> <p>To run the workflow:<br> </p> <pre><code class="language-bash">pip3 install cwltool==1.0.20180912090223 git clone https://github.com/FarahZKhan/bcbio_test_cwlprov cd bcbio_test_cwlprov/somatic/somatic-workflow/ cwltool --provenance somaticwf_0.5.0_mac main-somatic.cwl main-somatic-samples.json</code></pre> <p>To package the research object:<br> </p> <pre><code class="language-bash">zip -r somaticwf_0.5.0_mac.zip somaticwf_0.5.0_mac/ sha256sum somaticwf_0.5.0_mac.zip > somaticwf_0.5.0_mac.zip.sha256</code></pre> <p>The <a href="https://github.com/FarahZKhan/bcbio_test_cwlprov">cloned git repository</a> is a fork of <a href="https://github.com/bcbio/test_bcbio_cwl">https://github.com/bcbio/test_bcbio_cwl</a>. It was obtained using:</p> <pre><code class="language-bash">wget -O test_bcbio_cwl.tar.gz https://github.com/bcbio/test_bcbio_cwl/archive/master.tar.gz</code></pre> <p>The content is from an archived version from the documentation here: <a href="https://bcbio-nextgen.readthedocs.io/en/latest/contents/cwl.html#install-bcbio-vm-with-containers">https://bcbio-nextgen.readthedocs.io/en/latest/contents/cwl.html#install-bcbio-vm-with-containers</a></p>
Data from: SMAD4 promotes somatic-germline contact during murine oocyte growth
<p>Development of the mammalian oocyte requires physical contact with the surrounding granulosa cells of the follicle, which provide it with essential nutrients and regulatory signals. This contact is achieved through specialized filopodia, termed transzonal projections (TZPs), that extend from the granulosa cells to the oocyte surface. Transforming growth factor (TGFβ) family ligands produced by the oocyte increase the number of TZPs, but how they do so is unknown. Using an inducible Cre recombinase strategy together with expression of green fluorescent protein to verify Cre activity in individual granulosa cells, we examined the effect of depleting the canonical TGFβ mediator, SMAD4. We observed a 20-50% decrease in the total number of TZPs in SMAD4-depleted granulosa cell-oocyte complexes, and a 50% decrease in the number of newly generated TZPs when the granulosa cells were reaggregated with granulosa cell-free wild-type oocytes. Three-dimensional image analysis revealed that TZPs of SMAD4-depleted cells were also longer than controls and more frequently oriented towards the oocyte. Strikingly, the transmembrane proteins, N-cadherin and Notch2, were reduced by 50% in these cells. SMAD4 may thus modulate a network of cell adhesion proteins that stabilize the attachment of TZPs to the oocyte, thereby amplifying signalling between the two cell types.</p>
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> |<br> |- prod.mdp example production mdp file used to run all production simulations.<br> |<br> |- mature Mature simulation set. (folder and file naming the same in all simulation directories)<br> | |- {peptide}_{replicate}_prod.gro {peptide} = peptide; {replicate} = standard MD replicate<br> | |- {peptide}_{replicate}_prod.tpr<br> | `- {peptide}_{replicate}_prod_align_noPBC.xtc (10Frames/ns)<br> |<br> |- mature_rerun Additional set of replicates with the wildtype 4493.<br> |- matureCapped Set of simulations with termini capped peptides<br> |- mature_nanpv2 Set of simulations with NPDP similar positioning of NANP<br> |- wo_pep Set of simulations without peptides for germline and mature<br> `- germline 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.). <br>These trajectories were not considered in the analysis of simulations. </p> <p>- simulations/ramd<br> |<br> |- prod.mdp Exemplary production mdp file with RAMD settings, these were used in all trajectories w/<br> | differing RAMD random seed.<br> |<br> |- mature_2.625kcalmolA_4.0_3.0<br> | |- {peptide}_{replicate}_prod_{startFrame}.gro {peptide} = peptide; {replicate} = standard MD replicate; {startFrame} = Frame in standard MD used to start simulation.<br> | |- {peptide}_{replicate}_prod_{startFrame}.tpr<br> | |- {peptide}_{replicate}_prod_{startFrame}.ndx<br> | |- {peptide}_{replicate}_prod_{startFrame}_align_noPBC.xtc (100Frames/ns) Files omited due to size -- available on request.<br> | `- {peptide}_{replicate}_prod_{startFrame}_lastframe.pdb Last frame of the processed RAMD trajectory.<br> `- gl_2.625kcalmolA_4.0_3.0</p> <p><br># Analysis</p> <p>- analysis<br> |<br> |- entropie Quasi harmonic entropy estimation.<br> | |- inp Concatenated & Bootstrapped, coarse-grained and aligned trajectories of all systems<br> | |- out CPPTRAJ runs to calculate QHE on the bootstrapped trajectories<br> | |- run_complex.sh Script running analysis.<br> | |- ana.py Script to calculte average and std of QHE for each system. (generates *.out *.tsv *.png)<br> | |- cg.py Script used to bootstrap, coarse-grain align and build average structure with.<br> | `- delta_entropies.ods Excel file used to calculate Tab 1. in Main text of paper from entropies.out.<br> |<br> |- mmpbsa MMPBSA calculations (MM + SolvEnergy) with gmx_MMPBSA<br> | |- inp Input trajectories and topologies processed for MMPBSA<br> | |- out/gmx_mmpbsa Output directories in which gmx_MMPBSA was run.<br> | | ` *.dat Output files containing calculated energy terms from gmx_MMPBSA.<br> | |- mmpbsa.in MMPBSA input file used to run analysis.<br> | |- plot_results.py Python script to plot correlation of MMPBSA output with experimental data<br> | |- pca_eig_extr.py Script to reduce simulations to regions of high probability density within trajectory (not used in the present analysis)<br> | |- slurm-91315023.out Log file of the analysis run<br> | `- run_mmpbsa.sh Shell script to run the MMPBSA analysis (generates input and output file trees).<br> |<br> `- ramd RAMD analysation.<br> |- run.sh Shell script to run the analysis<br> |- run_ramd_ana.py Python script called by `run.sh` to run the analysis using `ramdAnalysis.py`<br> |- contact_clusters.py Python script to generate plots based on output of the analysis.<br> |- ramdAnalysis.py Python module containing analysis classes called/used within `run_ramd_ana.py`<br> | Based on tauRAMD & Fingerprint analysis by Dr. Daria Khokh (https://doi.org/10.1021%2Facs.jctc.8b00230; https://doi.org/10.1063%2F5.0019088)<br> |- abrun.* Log files from the present run<br> |- *.svg; *.png Analysis output files.<br> |- tramd_patchv2/ Output PDB structures from the analysis (excluded due to size, available on request)<br> `- representatives.pse Pymol session of cluster representatives along unbinding for germline and wildtype with contact probabilities within the<br> cluster mapped as b-factor.</p> <p># Figures</p> <p>- figure_pdbs PDB files (and pymol sessions) used to generate figures in the papers main text.</p>
A multiscale functional map of somatic mutations in cancer integrating protein structure and network topology
<p>Source Data and Supplementary Data associated with the paper “A multiscale functional map of somatic mutations in cancer integrating protein structure and network topology” (DOI: https://doi.org/10.1101/2023.03.06.531441).</p>
Data and code archive for Andrews et al. 'Exposure to food insecurity increases energy storage and reduces somatic maintenance in European starlings'
<p>Data and code archive for Andrews et al. 'Exposure to food insecurity increases energy storage and reduces somatic maintenance in European starlings'</p> <p>The archive contains one R script and three .Rdata data files, relating to the mass data, the telomere data, and the food consumption data respectively.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.