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19,342 results for “sequence”
Figure 2 in Diagnosability of mtDNA with Random Forests: Using sequence data to delimit subspecies
Figure 2. Illustration of steps in constructing a Random Forests ensemble of classification trees.
Data for "Shear Strain Evolution Spanning the 2020 Mw6.8 Elazığ and 2023 Mw7.8/Mw7.6 Kahramanmaraş Earthquake Sequence along the East Anatolian Fault Zone" manuscript
<p>Data necessary to support the analysis presented in "Shear Strain Evolution Spanning the 2020 Mw6.8 Elazığ and 2023 Mw7.8/Mw7.6 Kahramanmaraş Earthquake Sequence along the East Anatolian Fault Zone" manuscript</p>
Illumina Sequencing Data for "Phocaeicola vulgatus shapes the long-term growth dynamics and evolutionary adaptations of Clostridioides difficile"
<p>Illumina Sequencing Data for "<em>Phocaeicola vulgatus</em> shapes the long-term growth dynamics and evolutionary adaptations of <em>Clostridioides difficile</em>"</p>
Associated code and data for "A Practical Guideline for MicroRNA Sequencing Data Analysis in Chronic Lymphocytic Leukemia (doi: 10.1007/978-1-0716-4290-0_18)".
<p>This deposit contains the data, code, and analysis to recreate the results in the manuscript - Tuulikki Suomela, Liang Zhang, Julio Vera, Heiko Bruns, Xin Lai. A Practical Guideline for MicroRNA Sequencing Data Analysis in Chronic Lymphocytic Leukemia. Methods Mol. Biol., 2883, 403–426. <a href="https://www.researchgate.net/publication/387267721_A_Practical_Guideline_for_MicroRNA_Sequencing_Data_Analysis_in_Chronic_Lymphocytic_Leukemia">https://doi.org/10.1007/978-1-0716-4290-0_18</a>.</p> <p>The pipeline allows users to perform end-to-end analysis of bulk miRNA sequencing data, including quality control of FastQ files, mapping of read counts to miRNA genes using miRBase or Reference genome, quantification of miRNA read counts, differential gene expression analysis using DEseq2, gene set enrichment analysis using curated cancer hallmark gene sets, and identification of miRNA targets.</p> <p>If you have used the code for your research, please cite the original publication. Thank you very much.</p>
DNA large fragment deleting by compact, sequence-motif-free and specific TaqTth-hpRNA assisted with the microhomology-mediated end joining pathway
<p><span>A DNA editing tool TaqTth-hpRNA was developed in this study, composed of a compact recombinant TaqTth nuclease (832 aa) and a simple hairpin-RNA guiding probe (hpRNA). <em>In vitro</em> biochemical studies showed the TaqTth-hpRNA efficiently cleaves artificially synthesized ssDNA without stringent sequence motif like PAM. It can also cleave the genomic DNA of <em>E. coli</em> with ~80% efficiency. The TaqTth-hpRNA cleavage of genomic DNA in mammalian cells generated products with large fragment deletions mediated by the microhomology-mediated end joining (MMEJ) pathway. In addition, the cleavage was sensitive to mismatches in targeted regions, which was applied to specific damage of the <em>APP<sup>lon</sup></em> mutation in Alzheimer’s disease without disrupting the <em>APP<sup>wt</sup></em> locus. It is worth mentioning that the <em>APP<sup>lon</sup></em> sequence has only one base difference from that of <em>APP<sup>wt</sup></em>. The characteristics of small size, no PAM requirement, high specificity, and large deletion products make the TaqTth-hpRNA a potential therapeutic strategy for treating autosomal dominant disorders in the future.</span></p>
Transfer learning reveals sequence determinants of the quantitative response to transcription factor dosage
<p>Processed data and code for "Transfer learning reveals sequence determinants of the quantitative response to transcription factor dosage," Naqvi et al 2025.</p> <p>Directory is organized into the following subfolders, each tar'ed and gzipped:</p> <p><strong>data_analysis.tar.gz - Processed data for modulation of TWIST1 levels and calculation of RE responsiveness to TWIST1 dosage</strong></p> <ul> <li>atac_design.txt - design matrix for ATAC-seq TWIST1 titration samples</li> <li>all.sub.150bpclust.greater2.500bp.merge.TWIST1.titr.ATAC.counts.txt - ATAC-seq counts from all samples over all reproducible ATAC-seq peak regions, as defined in Naqvi et al 2023</li> <li>atac_deseq_fitmodels_moded50.R - R code for calculating new version of ED50 and response to full depletion from TWIST1 titration data (note, uses drm.R function from <a href="https://doi.org/10.5281/zenodo.7689948">10.5281/zenodo.7689948</a>, install drc() with this version to avoid errors)</li> </ul> <p><strong>baseline_models.tar.gz - Code and data for training baseline models to predict RE responsiveness to SOX9/TWIST1 dosage</strong></p> <ul> <li>{sox9|twist1}.{0v100|ed50}.{train|valid|test}.txt - Training/testing/validation data (ED50 or full TF depletion effect for SOX9 or TWIST1), split into train/test/validation folds</li> <li>HOCOMOCOv11_core_HUMAN_mono_jaspar_format.all.sub.150bpclust.greater2.500bp.merge.minus300bp.p01.maxscore.mat.cpg.gc.basemean.txt.gz - matrix of predictors for all REs. Quantitative encoding of PWM match for all HOCOMOCO motifs + CpG + GC content, plus unperturbed ATAC-seq signal</li> <li>train_baseline.R - R code to train baseline (LASSO regression or random forest) models using predictor matrix and the provided training data. <ul> <li>Note: training the random forest to predict full TF depletion is computationally intensive because it is across all REs, if doing this run on CPU for ~6 hrs. </li> </ul> </li> </ul> <p><strong>chrombpnet_models.tar.gz - Remainder of code, data, and models for fine-tuning and interpreting ChromBPNet mdoels to </strong><strong>predict RE responsiveness to SOX9/TWIST1 dosage</strong></p> <ul> <li>Fine-tuning code, data, models <ul> <li>{all|sox9.direct|twist1.bound.down}.{train|valid|test}.{ed50|0v100.log2fc}.txt - Training/testing/validation data (ED50 or full TF depletion effect for SOX9 or TWIST1), split into train/test/validation folds</li> <li>pretrained.unperturbed.chrombpnet.h5 - Pretrained model of unperturbed ATAC-seq signal in CNCCs, obtained by running ChromBPNet (https://github.com/kundajelab/chrombpnet) on DMSO-treated SOX9/TWIST1-tagged ATAC-seq data</li> <li>finetune_chrombpnet.py - code for fine-tuning the pretrained model for any of the relevant prediction tasks (ED50/ effect of full TF depletion for SOX9/TWIST1)</li> <li>best.model.chrombpnet.{0v100|ed50}.{sox9|twist1}.h5 - output of finetune_chrombpnet.py, best model after 10 training epochs for the indicated task</li> <li>chrombpnet.{0v100|ed50}.{sox9|twist1}.contrib.{h5|bw} - contribution scores for the indicated predictive model, obtained by running chrombpnet contribs_bw on the corresponding model h5 file.</li> <li>chrombpnet.{0v100|ed50}.{sox9|twist1}.contrib.modisco.{h5|bw} - TF-MoDIsCo output from the corresponding contribution score file</li> </ul> </li> <li>Interpretation code, data, models <ul> <li>contrib_h5_to_projshap_npy.py - code to convert contrib .h5 files into .npy files containing projected SHAP scores (required because the CWM matching code takes this format of contribution scores)</li> <li>sox9.direct.10col.bed, twist1.bound.down.10col.uniq.bed - regions over which CWMs will be matched (likely direct targets of each TF)</li> <li>match_cwms.py - Python code to match individual CWM instances. Takes as input: modisco .h5 file, SHAP .npy file, bed file of regions to be matched. Output is a bed file of all CWM matches (not pruned, contains many redundant matches).</li> <li>chrombpnet.ed50.{sox9|twist1}.contrib.perc05.matchperc10.allmatch.bed - output of match_cwms.py </li> <li>take_max_overlap.py - code to merge output of match_cwms.py into clusters, and then take the maximum (length-normalized) match score in each cluster as the representative CWM match of that cluster. Requires upstream bedtools commands to be piped in, see example usage in file. </li> <li>chrombpnet.ed50.{sox9|twist1}.contrib.perc05.matchperc10.allmatch.maxoverlap.bed - output of take_max_overlap.py. These CWM instances are the ones used throughout the paper.</li> </ul> </li> </ul> <p><strong>modisco_reports.zip -</strong><strong> TF-MoDIsCo reports from running on the fine-tuned ChromBPNet models</strong></p> <ul> <li>modisco_report_{sox9|twist1}_{0v100|ed50}: folders containing images of discovered CWMs and HTMLs/PDFs of summarized reports from running TF-MoDisCo on the indicated fine-tuned ChromBPNet model</li> </ul> <p><strong>chrombpnet_models_supp.tar.gz - Alternative ChromBPNet mdoels to </strong><strong>predict SOX9/TWIST1 ED50 using varying definitons of direct targets</strong></p> <ul> <li> <p>best.model.chrombpnet.ed50.twist1.3hdn.h5 - TWIST1 direct targets defined using response to full 3h depletion (as was done for SOX9 throughout the rest of the paper)</p> </li> <li> <p>best.model.chrombpnet.ed50.sox9.v5chip.h5 - SOX9 direct targets defined using V5 ChIP-seq from SOX9-tagged lines (as was done for TWIST1 throughout the paper)</p> </li> </ul> <p><strong>mirny_model.tar.gz - Code and data for analyzing and fitting Mirny model of TF-nucleosome competition to observed RE dosage response curves</strong></p> <ul> <li>twist1.strong.multi.only.ed50.cutoff.true.hill.txt - ED50 and signed hill coefficients for all TWIST1-dependent REs with only buffering Coordinators (mostly one or two) and no other TFs' binding sites. "ed50_new" is the ED50 calculation used in this paper. </li> <li>twist1.strong.weak{1|2|3}.ed50.cutoff.true.hill.txt - ED50 and signed hill coefficients for all TWIST1-dependent REs with only buffering Coordinators (mostly one or two) and the indicated number of sensitizing (weak) Coordinators and no other TFs' binding sites. "ed50_new" is the ED50 calculation used in this paper. </li> <li>MirnyModelAnalysis.py - Python code for analysis of Mirny model of TF-nucleosome competition. Contains implementations of analytic solutions, as well as code to fit model to observed ED50 and hill coefficients in the provided data files.</li> </ul> <p><strong>nucleoatac.tar.gz - Output files from running NucleoATAC on merged ATAC-seq from each of 5 TWIST1 dosages</strong></p> <ul> <li>TWIST1_{dosage}_merge.nucmap_combined.bed.gz - see NucleoATAC docs for output format</li> </ul>
Human breast cancer PDTX models bulk and single cell RNA sequencing
<p>This dataset includes information relevant to the following manuscript from the labs of Prof. Carlos Caldas (University of Cambridge), and Dr. Long V. Nguyen (Princess Margaret Cancer Centre, University Health Network):</p> <p>Nguyen LV et al. Dynamics and plasticity of human breast cancer single cell-derived clones. Under consideration for publication.</p> <p>Bulk RNA sequencing raw count matrices are provided (RawCounts.csv) along with the normalized count matrices (LogCPMNormCounts.csv).</p> <p>Single cell RNA sequencing count matrix processed from R package metacell is provided (mat.pdx_LN_v2_filt.Rda), along with the mc and mc2d files with information on metacell partitions (mc.pdx_LN_v2_filt.Rda and mc2d.pdx_LN_v2_filt.Rda).</p> <p>Single cell RNA sequencing count matrices processed using Seurat are also provided separately for each PDTX model analysed (STG139.rds, STG201.rds, AB040.rds and IC07.rds).</p> <p>Code and information on data analysis is provided for reviewers in our unpublished manuscript and on Github (https://github.com/cclab-brca/clone-dynamics).</p>
Raw Sequence Reads of UF-FLMNH 171438
<p>Paired-end reads for UF-FLMNH 171438 used to construct a draft genome sequence and annotation for the common, or spectacled caiman <em>Caiman crocodilus</em></p>
Raw Sequence Reads of FL-FLMNH 175565
<p>Paired-end sequence reads of FL-FLMNH 175565 from <em>Alligator mississippiensis</em> used to evaluate the contigs assembled by IDBA-UD.</p>
A HKU5-related Coronavirus identified and assembled from Short-Read sequencing data of Gossypium Barbadense
<p>This is the complete annotated genome of the Merbecovirus identified from Gossypium Barbadense sequencing data, <a href="https://trace.ncbi.nlm.nih.gov/Traces/sra/?run=SRR5885860">SRR5885860</a></p>
FIG. 2 in Foraminiferal biostratigraphy, facies and sequence stratigraphy analysis across the K-Pg Boundary in Hazara, Lesser Himalayas (Dhudial Section)
FIG. 2. — Generalized stratigraphy of the study area (Shah 2009).
Database of liquefaction phenomena triggered by the March 2021 Thessaly, Greece, seismic sequence
<p>This archive contains data related to the paper “Floodplain evolution and its influence on liquefaction clustering: the case study of March 2021 Thessaly, Greece, seismic sequence” by George Papathanassiou, Sotiris Valkaniotis, Athanassios Ganas, Alexandros Stampolidis, Dimitra Rapti, Riccardo Caputo.</p> <p><br> Submitted to <em>Engineering Geology, Elsevier</em></p> <p> </p> <p><strong>Liquefaction_DB_20210303</strong> contains shapefiles and spreadsheet for the mapped liquefaction phenomena<br> <strong>UAS_Liquefaction_Surveys_Larisa_EQ</strong> contains detailed geospatial data for mapped liquefaction phenomena from UAS surveys</p>
PalaeoChip Arctic v1.0 reference and bait sequences.
<p>Organelle reference sequences of Holarctic fauna and flora from GenBank NCBI, as well as the sequences of the individual baits designed to target those referneces in the PalaeoChip Arctic v1.0 bait-set.</p>
A revised classification of Glossopetalon (Crossosomataceae) based on restriction site-associated DNA sequencing
Glossopetalon inhabits arid regions in the American west and northern Mexico on limestone substrates. The genus comprises four species: G. clokeyi ; G. pungens ; G. texense ; and G. spinescens . Three of the species are narrow endemics. The fourth, G. spinescens , is a widespread species with six recognized varieties. All six varieties are intricately branched shrubs that have been difficult to identify due to a lack of clearly delineating morphological characters. Characters typically used to differentiate the varieties of G. spinescens, such as stem coloration, leaf blade size, and presence of stipules, are highly variable within and among populations. A custom protocol of double digest restriction-site associated DNA sequencing (ddRAD) was used to resolve the phylogeny of Glossopetalon and address if population genetic data analyses (such as STRUCTURE, SVDquartets, and phylogenetic networks) support the recognition of six varieties of G. spinescens . Glossopetalon was fully supported as monophyletic and G. pungens was resolved sister to the remaining taxa in the genus. The varieties of G. spinescens were resolved as two distinct lineages corresponding to their biogeography – one to the northwest (lineage 1) and one to southeast (lineage 2). Glossopetalon clokeyi was resolved at the base of lineage 1 and G. texense was embedded within lineage 2 sister to var. spinescens . Taxonomic changes include the recognition of G. texense and G. clokeyi as varieties of G. spinescens and description of a unique population from northern Arizona as a new variety – G. spinescens var. goodwinii .
Resolution, validation and divergence of heterozygous haplotypes from pooled long read sequencing of the diamondback moth (Lepidoptera: Plutellidae)
<p>This data sets includes the full set of intermediate genome assemblies produced during our analyses. We make these available for researchers who may be interested in the variation between assembly results and variation within the study organism (<em>Plutella xylostella</em>) prior to removal during subsequent genome processing.</p>
Illumina TruSeq stranded mRNA sequences of Rhynchosporium commune isolate UK7 in plantae
<p>Transcription profiles were generated from the <em>Rhynchosporium commune</em> isolate UK7. RNA-seq experiments were conducted <em>in plantae</em> on the barley cultivar Beatrix (Viskosa 9 Pasadena, Saaten Union, breeders’ Reference NS01/2449). Leaves were collected at 9 and at 13 days post infection (dpi). All experiments were conducted in triplicates. Total RNA was extracted using TRIzol (Invitrogen Inc.) following the manufacturer’s recommendations. RNA integrity and quantity was assessed on a Bioanalyzer 2100 (Agilent) and a Qubit fluorometer (Life Technologies) and a Bioanalyzer 2100. Libraries were prepared using the TruSeq stranded mRNA sample prep kit (Illumina Inc.). Total RNA was ribosome-depleted by using polyA selection and reverse-transcribed into double-stranded cDNA.</p>
Illumina TruSeq stranded mRNA sequences of Rhynchosporium commune isolate UK7 in vitro
<p>Transcription profiles were generated from the <em>Rhynchosporium commune</em> isolate UK7. Cultures were grown either on Luria-Bertani (LBA) or Potato Dextrose Broth (PDB) media. Total RNA was extracted using TRIzol (Invitrogen Inc.) following the manufacturer’s recommendations. Experiments were conducted in triplicates. RNA integrity and quantity was assessed on a Bioanalyzer 2100 (Agilent) and a Qubit fluorometer (Life Technologies) and a Bioanalyzer 2100. Libraries were prepared using the TruSeq stranded mRNA sample prep kit (Illumina Inc.). Total RNA was ribosome-depleted by using polyA selection and reverse-transcribed into double-stranded cDNA.</p>
GapA, RecA and DnaX sequences used for species delineation and phylogenetic analysis
<p>Survey of soft rot Pectobacteriaceae along a river stream highlights various ecological behaviour among species: GapA, RecA and DnaX sequences used for species delineation and phylogenetic analysis (fasta file format).</p>
Raw data for "Condensates in RNA repeat sequences are heterogeneously organized and exhibit reptation dynamics"
<p>This is the raw data for the paper "Condensates in RNA repeat sequences are heterogeneously organized and exhibit reptation dynamics".</p> <p>There are 5 directories. Three correspond to the CAG repeats with different length (20, 31 and 47). The "scramble47" directory stores data for the scrambled sequence. "Electrostatics" has data for the electrostatics run (see details in Extended Data Fig. 9).</p> <p>Each directory of CAG contains multiple sub-directories corresponding to different concentrations.</p> <p> </p>
TESTAR sequence results
<p>TESTAR test results datasets extracted with TESTAR tool using CODEO desktop application as System Under Test (SUT). These datasets have been generated to be used as an example to be automatically generated and introduced locally in DECODER PKM, from H2020 DECODER Project.</p> <p>TESTAR tool is an open source tool (www.testar.org) for automated testing through graphical user interface (GUI) currently being developed by the Universitat Politecnica de Valencia and the Open University of the Netherlands.</p> <p>CODEO (https://www.sysgo.com/codeo) is an Eclipse-based IDE that facilitates embedded applications development, by providing all the components software engineers need. The architecture configurations can be esaily done through the CODEO graphical tools.</p> <p>PKM is the Persistent Knowledge Monitor developed as main infrastructure from H2020 DECODER Project (www.decoder-project.eu) under grant agreement number 824231.</p> <p>As TESTAR explores automatically the SUT, it will use the Document Object Model (DOM) information extracted from MyThaiStar SUT, to generate and save a TESTAR State Model in the OrientDB graph database. This model contains information about the Widgets, States and Actions, that were found in the SUT.</p> <p>- ArtefactTestResults_codeo_74_2021-12-14_13h38m39s.zip: For DECODER project purposes, TESTAR test results knowledge has been summarized and referenced in an artifact JSON file to adapt to PKM input requirements.</p> <p>- CODEO_TestResults.zip: All the information obtained through the DOM is used graphically and semantically to create screenshots, logs and html reports that indicate how TESTAR has navigated in the exploration and indicates if any error has been detected. </p>
ScienceDex guides
Understand access before you commit
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.