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154 results for “off-target”
Probiotics reshape the coral microbiome in situ without detectable off-targeted effects in the surrounding environment.
<p>The R code scripts and Supplementary data files from the paper: "Probiotics reshape the coral microbiome in situ without detectable off-targeted effects in the surrounding environment," accepted in Communications Biology. All R code and data necessary to reproduce the published results are available. </p>
GWAS Summary Statistics for Publication: Identifying novel genetic and phenotypic associations to genomic features by leveraging off-target reads in exome sequencing data
<p>This dataset contains summary statistics for genome-wide association studies (GWAS) conducted on genomic features derived from off-target reads in whole-exome sequencing (WES) data. The study utilized tools like Seeing Beyond the Target (SBT) and ImReP to construct novel phenotypic features from unmapped reads in ~50,000 participants in the UK Biobank. Features include mitochondrial DNA (mtDNA) copy number, ribosomal DNA (rDNA) copy number (5S, 18S, 28S), immune repertoire metrics (e.g., T-cell receptor alpha diversity), and microvial genome load (viral and fungal).</p> <p>Summary statistics can be used for replication studies, meta-analyses, or further exploration of these phenotypes.</p>
KiSSim: Predicting off-targets from structural similarities in the kinome
<p><strong>KiSSim: Predicting off-targets from structural similarities in the kinome</strong></p> <p><strong>Project description.</strong></p> <p>KiSSim (Kinase Structural Similarity) is a novel fingerprint designed specifically for kinase pockets, allowing for similarity studies across the structurally covered kinome. The kinase fingerprint is based on the <a href="https://klifs.net/">KLIFS</a> pocket alignment, which defines 85 pocket residues for all kinase structures. This enables a residue-by-residue comparison without a computationally expensive alignment step.</p> <p>The pocket fingerprint encodes each pocket residue’s spatial and physicochemical properties. The spatial properties describe the residue’s position in relation to the kinase pocket center and important kinase subpockets, i.e. the hinge region, the DFG region, and the front pocket. The physicochemical properties encompass for each residue its size and pharmacophoric features, solvent exposure, and side chain orientation.</p> <p>Some datasets are not part of the `kissim_app` GitHub repository due to their size but can be downloaded from here to the respective kissim_app folders.</p> <p><strong>Data.</strong></p> <ul> <li>`20210902_KLIFS_HUMAN.tar.gz` --- save in `kissim_app/data/external/structures`</li> <li>`complete_SiteAlign.txt.gz` --- save in `kissim_app/data/external/sitealign`</li> </ul> <p><strong>Results.</strong></p> <ul> <li>`results.tar.bz2`--- save as `kissim_app/results`</li> </ul> <p>These are the KiSSim results: fingerprints, feature/fingerprint distances, kinase matrices, and kinase trees for structures in all (`all`), DFG-in (`dfg_in`), and DFG-out (`dfg_out`) conformation. In the case of the DFG-in conformation, we also have KiSSim runs with fingerprint subsets based on only residues that interact with certain ligands in KLIFS IFPs: Erlotinib (`dfg_in_IRE`), Imatinib (`dfg_in_STI`), Bosutinib (`dfg_in_DB8`), and Dopamapimod (`dfg_in_B96`). The folder contains README with a detailed file list.</p> <p><strong>Usage.</strong></p> <p>This dataset can be used to run the notebooks available on <a href="https://github.com/volkamerlab/kissim_app">https://github.com/volkamerlab/kissim_app</a>.</p> <ol> <li>Clone the kissim_app repository.</li> <li>Download the files provided here.</li> <li>If applicable, extract the archive content to the folders as indicated above and run the notebooks.</li> </ol> <pre><code class="language-bash">cd /path/to/your/download tar -xvf results.tar.bz2 -C /path/to/kissim_app/ tar -xvf 20210902_KLIFS_HUMAN.tar.bz2 -C /path/to/kissim_app/data/external/structures/ # In case you want the raw SiteAlign data mv complete_SiteAlign.txt.gz /path/to/kissim_app/data/external/sitealign</code></pre> <p><strong>Citation.</strong></p> <p>These datasets are part of the KiSSim publication: TBA</p>
Predictive models for off-target binding profiles generation
<p>Models for predicting off-target binding, built with Conformal Prediction, and the <a href="http://cpsign-docs.genettasoft.com">CPSign software</a>. The dataset is part of an upcoming publication (Manuscript in preparation), which will provide more details.</p> <p>The dataset is a GZipped Tar archive, with the models as Java Archive (JAR) files. For every JAR-file, there is also a corresponding audit log, with the extension ".audit.json", produced by the workflow software (<a href="http://scipipe.org">SciPipe</a>) used to train the models. This audit file contains all the shell commands used in the workflow that produced the models.</p>
Supplementary Datasets for "Genome-wide CRISPR off-target prediction and optimization using RNA-DNA interaction fingerprints"
<p>Supplementary Datasets for "Genome-wide CRISPR off-target prediction and optimization using RNA-DNA interaction fingerprints". The deposition contains training/testing datasets used in the article.</p>
Data from: Off-target drift of the herbicide dicamba disrupts plant-pollinator interactions via novel pathways
Open the record for dataset details and reuse information.
Target deconvolution of the HDAC pharmacopoeia highlights MBLAC2 as a common off-target
<p>This dataset contains the untargeted lipidomics data for the publication Lechner et al. 2022 "Target deconvolution of the HDAC pharmacopoeia highlights MBLAC2 as a common off-target". The dataset has also been submitted to MetaboLight repository with ID "MTBLS3557". Please refer to the MetaboLight repository for the most up-to-date datasets. </p> <p>Publication abstract:</p> <p>Histone deacetylase (HDAC) targeting drugs have entered the pharmacopoeia in the 2000s. However, some enigmatic phenotypes suggest off-target engagement. Here, we developed a quantitative chemical proteomics assay using immobilized HDAC inhibitors and mass spectrometry that we deployed to establish the target landscape of 53 drugs. The assay covers 9 of the 11 human zinc dependent HDACs, questions the reported selectivity of some widely-used molecules, notably for HDAC6, and delineates how the composition of HDAC complexes influences drug potency. Unexpectedly, metallo-beta-lactamase domain-containing protein 2 (MBLAC2) featured as a frequent off-target of hydroxamate drugs. This poorly characterized palmitoyl-CoA hydrolase is inhibited by 24 HDAC inhibitors at low nM potency. MBLAC2 enzymatic inhibition and knock down led to the accumulation of extracellular vesicles. Given the importance of extracellular vesicle biology in neurological diseases and cancer, this HDAC-independent drug effect may qualify MBLAC2 as a target for drug discovery.</p>
Summary statistics from "Impact of medication on blood transcriptome reveals off-target regulations of beta-blockers"
<p>The zipped csv-file is comma-delimited and contains the following columns:</p> <p>identifier: Probe number and associated active substance<br> PROBE_ID.Adult: Probe number<br> logFC.Adult: log fold change of specific probe in LIFE-Adult<br> SE.Adult: Stand error for probe in LIFE-Adult<br> P.Value.Adult: p-value for probe in LIFE-Adult<br> qval.Adult: q-value for probe in LIFE-Adult<br> medi.Adult: associated active substance in in LIFE-Adult<br> P.Value.Heart: p-value for probe in LIFE-Heart<br> logFC.Heart: p-value for probe in LIFE-Heart<br> SE.Heart: Standard error for probe in LIFE-Heart<br> qval.Heart: q-value for probe in LIFE-Heart<br> medi.Heart: associated active substance in LIFE-Heart<br> symbol_INGENUITY: Gene associated with probe according to Ingenuity<br> description_INGENUITY: Description of gene associated with probe according to Ingenuity</p> <p>For LIFE-Adult the results were calculated for all probes associated with the relevant 83 active substances described. For LIFE-Heart results were only calculated for probes that were also available LIFE-Adult.</p>
Off-target integron activity leads to rapid plasmid compensatory evolution in response to antibiotic selection pressure
Integrons are mobile genetic elements that have played an important role in the dissemination of antibiotic resistance. As shown previously (Souque et al, 2021), the integron can generate under stress combinatorial variation in resistance cassette expression by cassette re-shuffling, accelerating the evolution of resistance. However, the flexibility of the integron integrase site recognition motif hints at potential off-target effects of the integrase on the rest of the genome that may have important evolutionary consequences. Here we test this hypothesis by selecting for increased piperacillin resistance populations of <em>P.aeruginosa</em> with a mobile integron containing a hard-to-mobilise beta-lactamase cassette to minimize the potential for adaptive cassette re-shuffling. We found that integron activity can both decrease overall survival rate but also improve the fitness of the surviving populations. Off-target inversions mediated by the integron accelerated plasmid adaptation by disrupting costly conjugative genes otherwise mutated in control populations lacking a functional integrase. Plasmids containing integron-mediated inversions were associated with lower plasmid costs and higher stability than plasmids carrying mutations, albeit at a cost of reduced conjugative ability. These findings highlight the potential for integrons to create structural variation that can drive bacterial evolution, and they provide an interesting example showing how antibiotic pressure can drive the loss of conjugative genes.
CRISPR-Cas9 off-targeting assessment with nucleic acid duplex energy parameters
<p>CRISPR-Cas9 off-targeting assessment with nucleic acid duplex energy parameters</p> <p>Collected and generated data for the paper</p> <p>## Data Tables</p> <p>Off-target score data for the ROC analysis using Haeussler dataset [2].</p> <p>Data from the table below is used to generate the Figure-2, Table-1 and Supplementary Figure-1 in the corresponding paper [1]. Don't forget to cite the corresponding studies as well if you use this table.</p> <ul> <li><strong>Haeussler_mm6_scores.csv.gz</strong>: This table includes the off-targeting scores of 1167036 off-target sequences, computed with CRISPRoff[1], CCTop[3], CFD[4], Cropit[5], Elevation (Elevation-score)[6], MIT[2,7] and VfoldCAS[8] methods. Off-target data has been taken from the Haeussler dataset [2].</li> </ul> <p>Analysis with CIRCLE-seq dataset [9]</p> <p>Data in all the three tables below has been generated to analyze the CIRCLE-seq dataset [9]. This data is further used to generate the Figure-3, Figure-4, and Supplementary Figure-4 in the corresponding paper. Don't forget to cite the corresponding studies as well if you use these tables.</p> <ul> <li> <p><strong>CIRCLEseq_known_off_scores.csv.gz</strong>: This table is used when generating the Figure-3 in the paper. It includes the 7 different off-targeting scores of CIRCLE-seq reported off-target sequences and the read counts from CIRCLE-seq experiments.</p> </li> <li> <p><strong>CIRCLEseq_mm6_off_scores.csv.gz</strong>: This table is used when generating the Figure-4 in the paper. It includes the 7 different off-targeting scores of RIsearch2(v2.1)[10] based off-target predictions for CIRCLE-seq gRNAs.</p> </li> <li> <p><strong>CIRCLEseq_specificities.csv.gz</strong>: This table is used when generating the Supplementary Figure-4 in the supplementary document of the paper. It includes the specificty scores of CIRCLE-seq gRNAs, computed with CRISPRspec[1], MIT[2,7], MIT*[1,2,7] and Elevation (Elevation-aggregate)[6] methods.</p> </li> </ul> <p>Analysis with SITE-seq dataset [11]</p> <p>Data in all the three tables below has been generated to analyze the SITE-seq dataset [11]. This data is further used to generate the Figure-5, Supplementary Figure-2 and Supplementary Figure-3 in the corresponding paper. Don't forget to cite the corresponding studies as well if you use these tables.</p> <ul> <li> <p><strong>SITEseq_known_off_scores.csv.gz</strong>: This table is used when generating the Supplementary Figure-2 in the supplementary document of the paper. It includes the 7 different off-targeting scores of SITE-seq reported off-target sequences and the read counts from SITE-seq experiments.</p> </li> <li> <p><strong>SITEseq_mm6_off_scores.csv.gz</strong>: This table is used when generating the Supplementary Figure-3 in the supplementary document of the paper. It includes the 7 different off-targeting scores of RIsearch2(v2.1) based off-target predictions for SITE-seq gRNAs.</p> </li> <li> <p><strong>SITEseq_specificities.csv.gz</strong>: This table is used when generating the Figure-5 in the paper. It includes the 4 different specificty scores of SITE-seq gRNAs.</p> </li> </ul> <p>Specificity-Efficiency Analysis</p> <p>This data is used to generate the Figure-6 and Supplementary Figure-5 in the corresponding paper. Don't forget to cite the corresponding studies as well if you use these tables.</p> <ul> <li><strong>Doench_Wang_specificity_grps.csv.gz</strong>: This table includes the specificity group of 3802 gRNA/on-target sequences, computed with CRISPRspec and MIT methods. gRNA sequence and modulation frequency data have been taken from the Haeussler dataset [2].</li> </ul> <p>## Citation</p> <p>If you find this data useful for your research, please cite the following works where appropriate:</p> <ol> <li>[Our citation comes here]</li> <li>Haeussler, M., Schonig, K., Eckert, H., Eschstruth, A., Mianne, J., Renaud, J.B., Schneider-Maunoury, S., Shkumatava, A., Teboul, L., Kent, J., Joly, J.S., Concordet, J.P.: Evaluation of off-target and on-target scoring algorithms and integration into the guide RNA selection tool CRISPOR. Genome Biol. 17(1), 148 (2016). <a href="https://www.ncbi.nlm.nih.gov/pubmed/27380939">PMID 27380939</a></li> <li>Stemmer, M., Thumberger, T., Del Sol Keyer, M., Wittbrodt, J., Mateo, J.L.: CCTop: An Intuitive, Flexible and Reliable CRISPR/Cas9 Target Prediction Tool. PLoS ONE 10(4), 0124633 (2015). <a href="https://www.ncbi.nlm.nih.gov/pubmed/25909470">PMID 25909470</a></li> <li>Doench, J.G., Fusi, N., Sullender, M., Hegde, M., Vaimberg, E.W., Donovan, K.F., Smith, I., Tothova, Z., Wilen, C., Orchard, R., Virgin, H.W., Listgarten, J., Root, D.E.: Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9. Nat. Biotechnol. 34(2), 184–191 (2016). <a href="https://www.ncbi.nlm.nih.gov/pubmed/26780180">PMID 26780180</a></li> <li>Singh, R., Kuscu, C., Quinlan, A., Qi, Y., Adli, M.: Cas9-chromatin binding information enables more accurate CRISPR off-target prediction. Nucleic Acids Res. 43(18), 118 (2015). <a href="https://www.ncbi.nlm.nih.gov/pubmed/26032770">PMID 26032770</a></li> <li>Listgarten, J., Weinstein, M., Kleinstiver, B.P., Sousa, A.A., Joung, J.K., Crawford, J., Gao, K., Hoang, L., Elibol, M., Doench, J.G., Fusi, N.: Prediction of off-target activities for the end-to-end design of CRISPR guide RNAs. Nature Biomedical Engineering 2, 38–47 (2018). <a href="https://www.ncbi.nlm.nih.gov/pubmed/29998038">PMID 29998038</a></li> <li>Hsu, P.D., Scott, D.A., Weinstein, J.A., Ran, F.A., Konermann, S., Agarwala, V., Li, Y., Fine, E.J., Wu, X., Shalem, O., Cradick, T.J., Marraffini, L.A., Bao, G., Zhang, F.: DNA targeting specificity of RNA-guided Cas9 nucleases. Nat. Biotechnol. 31(9), 827–832 (2013). <a href="https://www.ncbi.nlm.nih.gov/pubmed/23873081">PMID 23873081</a></li> <li>Xu, X., Duan, D., Chen, S.J.: CRISPR-Cas9 cleavage efficiency correlates strongly with target-sgRNA folding stability: from physical mechanism to off-target assessment. Sci Rep 7(1), 143 (2017). <a href="https://www.ncbi.nlm.nih.gov/pubmed/28273945">PMID 28273945</a></li> <li>Tsai, S.Q., Nguyen, N.T., Malagon-Lopez, J., Topkar, V.V., Aryee, M.J., Joung, J.K.: CIRCLE-seq: a highly sensitive in vitro screen for genome-wide CRISPR-Cas9 nuclease off-targets. Nat. Methods 14(6), 607–614 (2017). <a href="https://www.ncbi.nlm.nih.gov/pubmed/28459458">PMID 28459458</a></li> <li>Alkan, F., Wenzel, A., Palasca, O., Kerpedjiev, P., Rudebeck, A.F., Stadler, P.F., Hofacker, I.L., Gorodkin, J.: RIsearch2: suffix array-based large-scale prediction of RNA-RNA interactions and siRNA off-targets. Nucleic Acids Res. (2017). <a href="https://www.ncbi.nlm.nih.gov/pubmed/28108657">PMID 28108657</a></li> <li>Cameron, P., Fuller, C.K., Donohoue, P.D., Jones, B.N., Thompson, M.S., Carter, M.M., Gradia, S., Vidal, B., Garner, E., Slorach, E.M., Lau, E., Banh, L.M., Lied, A.M., Edwards, L.S., Settle, A.H., Capurso, D., Llaca, V., Deschamps, S., Cigan, M., Young, J.K., May, A.P.: Mapping the genomic landscape of CRISPR-Cas9 cleavage. Nat. Methods 14(6), 600–606 (2017). <a href="https://www.ncbi.nlm.nih.gov/pubmed/28459459">PMID 28459459</a></li> </ol> <p>## Contact</p> <p>ferro@rth.dk gorodkin@rth.dk</p>
Off-target integron activity leads to rapid plasmid compensatory evolution in response to antibiotic selection pressure
Open the record for dataset details and reuse information.
Sanger sequencing of target and off-target genomic regions for gene-edited iPSC clones with SETBP1 genetic variants
<p>This data set includes chromatograms generated using sanger sequencing of targeted regions of genomic DNA from clonal iPSC lines. The iPSC lines include clones generated using CRISPR/Cas9 homology directed repair to introduce genetic variants into <em>SETBP1,</em> and their wild-type controls. Additional files have been included in the data set to link chromatogram (ab1) files to specific iPSC clones for genomic regions across the variant in <em>SETBP1 (</em>SETBP1 clones genetic variant sanger sequencing.xslx)<em> </em>and top<em> </em>off-target sites (SETBP1 clones off-target sanger sequencing.xlsx). </p>
Hunting for the Off-Target Properties of Ticagrelor on Endothelial Function in Humans (HI-TECH)
ClinicalTrials.gov study NCT02587260. IPD Sharing: UNDECIDED. Countries: 4. Publications: 8.
Paralogs and off-target sequences improve phylogenetic resolution in a densely-sampled study of the breadfruit genus (Artocarpus, Moraceae)
Open the record for dataset details and reuse information.
Joint single-cell profiling of Cas9 edits and transcriptomes reveals on- and off-target effects on gene expression (RNA-seq)
GEO Series GSE313958. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
Innate Immune Response and Off-Target Mis-splicing Are Common Morpholino-Induced Side Effects in Xenopus
GEO Series GSE96655. Xenopus tropicalis. 36 samples. Type: Expression profiling by high throughput sequencing.
Sensitive and unbiased genome-wide profiling of base-editor-induced off-target activity with CHANGE-seq-BE [Hybrid Capture Sequencing for CBE and ABE]
GEO Series GSE308237. Homo sapiens. 36 samples. Type: Other.
Predicting off-target effects of antisense oligomers targeting bacterial mRNAs with the MASON webserver
GEO Series GSE199542. Salmonella enterica subsp. enterica serovar Typhimurium str. SL1344. 28 samples. Type: Expression profiling by high throughput sequencing.
Potential off-targets investigation of a lead antisense oligonucleotides targeting ABCA4 c.768G>T in retinal organoids
GEO Series GSE253344. Homo sapiens. 8 samples. Type: Expression profiling by high throughput sequencing.
Global analysis of off-target genes by GAPDH siRNA
GEO Series GSE41924. Homo sapiens. 3 samples. Type: Expression profiling by array.
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.