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18
datasets available to search
ShareScore release 0.9.0
Dataset results
18 results for “Computational genomics”
Computed results for Bayesian genome scale modelling temperature effect on yeast metabolism
<p>This repository contains the computed results for reproducing the figures in the manuscript "Li G., et al. Bayesian genome scale modelling identifies thermal determinants of yeast metabolism". The scripts can be found in Github (<a href="https://github.com/Gangl2016/BayesianGEM">https://github.com/Gangl2016/BayesianGEM</a>)</p>
Genomic DNA transposition induced by human PGBD5 (Accompanying Scripts for Computational Analyses)
<p>We include the bash scripts, python code and command-line parameters used to prepare, map, and analyze NGS sequencing data generated from a modified version of flanking sequence exponential anchored (FLEA) PCR to identify genomic insertion locations of a transposable element reporter construct. In summary, we map these reads to a hybrid genome consisting in the human reference hg19 and the reporter plasmid, identify reads that span the insertion breakpoint and thus recover the genomic insertion loci. </p> <p>Please find more details in the supplied README. </p> <p> </p> <p>This collection of scripts corresponds to the data analysis in the following publication:</p> <p>Genomic DNA transposition induced by human PGBD5</p> <p>Anton Henssen, Elizabeth Henaff, Eileen Jiang, Amy Eisenberg, Julianne R. Carson, Camila Villasante, Mondira Ray, Eric Still, Melissa Burns, Jorge Gandara, Cedric Feschotte, Christopher E. Mason, Alex Kentsis</p> <p> </p> <p> </p>
Simulation dataset for "Computational pan-genome mapping and pairwise SNP-distance improve detection of Mycobacterium tuberculosis transmission clusters"
<p>Simulated Illumina reads for SNP distance method evaluation and comparison used in the article "Computational pan-genome mapping and pairwise SNP-distance improve detection of Mycobacterium tuberculosis transmission clusters".</p> <p>Details for simulation can be found at https://gitlab.com/rki_bioinformatics/panpasco/tree/master/simulation_dataset.</p>
Computationally defined and in vitro validated putative genomic safe harbour loci for transgene expression in human cells
<p>Selection of the target site is an inherent question for any project aiming for directed transgene integration. Genomic safe harbour (GSH) loci have been proposed as safe sites in the human genome for transgene integration. Although several sites have been characterised for transgene integration in the literature, most of these do not meet criteria set out for a GSH, and the limited set that do have not been characterised extensively. Here, we conducted a computational analysis using publicly available data to identify 25 unique putative GSH loci that reside in active chromosomal compartments. We validated stable transgene expression and minimal disruption of the native transcriptome in three GSH sites <em>in vitro</em> using human embryonic stem cells (hESCs) and their differentiated progeny. Furthermore, for easily targeted transgene expression, we have engineered constitutive landing pad expression constructs into the three validated GSH in hESCs.</p>
Computationally defined and in vitro validated putative genomic safe harbour loci for transgene expression in human cells
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Pre-computed MGCs from human microbiome reference genomes
<p>This dataset contains non-redundant metabolic gene clusters (MGCs) collected by running gutSMASH and BiG-MAP on a collection of unique high-quality reference genomes. This collection consist of MGCs predicted by gutSMASH using 1,520 genomes from the Culturable Genome Reference (CGR), 2,308 genomes from the Human Microbiome Project (HMP) and 414 Clostridia genomes as input and then filtered for redundancy using the family module of BiG-MAP. For more information: <a href="http://doi.org/10.1101/2021.02.25.432841">https://doi.org/10.1101/2021.02.25.432841</a></p> <p><strong>BiG-MAP_mg.pickle</strong> -> suitable for <strong>metagenome</strong> analyses</p> <p><strong>BiG-MAP_mt.pickle </strong>-> suitable for <strong>metatranscriptome </strong>analyses</p> <p>The files can be used as direct input in the third module of BiG-MAP (BiG-MAP.map.py: <a href="https://github.com/medema-group/BiG-MAP">https://github.com/medema-group/BiG-MAP</a>).</p>
Vanderbilt BME8901 Computational Genomics
<p>Vanderbilt BME8901 Computational Genomics</p>
Personalized Warfarin Dosing by Genomics and Computational Intelligence
ClinicalTrials.gov study NCT00872079. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Computational analysis of genome-wide DNA-methylation during the differentiation of human embryonic stem cells along the endodermal lineage
GEO Series GSE21715. Homo sapiens. 6 samples. Type: Expression profiling by array.
Insplico: Effective computational tool for studying intron splicing order genome-wide with short and long RNA-seq reads
GEO Series GSE207459. Homo sapiens; Mus musculus. 10 samples. Type: Expression profiling by high throughput sequencing.
iRNA-seq: Computational method for genome wide assessment of acute transcriptional regulation from total RNA-seq data
GEO Series GSE60462. Homo sapiens. 10 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Expression profiling by high throughput sequencing.
Dissecting the effect of a 3D micro-scaffold on the transcriptome of Neural Stem Cells with computational genomics
GEO Series GSE150767. Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing.
A GENETIC, GENOMIC, AND COMPUTATIONAL RESOURCE FOR EXPLORING NEURAL CIRCUIT FUNCTION
GEO Series GSE116969. Drosophila melanogaster. 266 samples. Type: Expression profiling by high throughput sequencing.
Data from: Demographic inferences using short-read genomic data in an Approximate Bayesian Computation framework: in silico evaluation of power, biases, and proof of concept in Atlantic walrus
Approximate Bayesian Computation (ABC) is a powerful tool for model-based inference of demographic population histories from large genetic data sets. For most organisms its implementation has been hampered by the lack of sufficient genetic data. Genotyping-by-sequencing (GBS) provides cheap genome-scale data to fill this gap, but its potential has not fully been exploited. Here, we explored power, precision and biases of a coalescent-based ABC approach where GBS data were modeled with either a population mutation parameter (θ) or with a fixed sites (FS) approach, allowing single or several segregating sites per locus. With simulated data ranging from 500 to 50,000 loci a variety of demographic models could be reliably inferred across a range of timescales and migration scenarios. Posterior estimates were informative with 1,000 loci for migration and split time in simple population divergence models. In more complex models posterior distributions were wide and almost reverted to the uninformative prior even with 50,000 loci. ABC parameter estimates, however, were generally more accurate than an alternative composite-likelihood method. Bottleneck scenarios proved particularly difficult and only recent bottlenecks without recovery could be reliably detected and dated. Notably, minor allele frequency filters – usual practice for GBS data – negatively affected nearly all estimates. With this in mind, we used a combination of FS and θ approaches on empirical GBS data generated from the Atlantic walrus (Odobenus rosmarus rosmarus), collectively providing support for a population split before the last glacial maximum followed by asymmetrical migration and a range-wide bottleneck. Overall, this study evaluates the potential and limitations of GBS data in an ABC-coalescence framework and proposes a best-practice approach.
Data from: Demographic inferences using short-read genomic data in an Approximate Bayesian Computation framework: in silico evaluation of power, biases, and proof of concept in Atlantic walrus
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GENE-counter, a computational and statistical pipeline for assessing RNA-Seq data for genome-wide expression differences
GEO Series GSE25818. Arabidopsis thaliana. 6 samples. Type: Expression profiling by array.
A computational genomics approach to identify cis-regulatory modules - a case study using E2F1
GEO Series GSE5175. Homo sapiens. 6 samples. Type: Genome binding/occupancy profiling by genome tiling array.
Center for Computational and Genomic Medicine Analytic Tools
The Center for Computational and Genomic Medicine, established in 2018 with the recruitment of Yi Xing, PhD, drives biological discoveries and medical innovations by integrating genomics, big data, and computing. The Center contributes to CHOP's entire ecosystem of research, including collaborations with other key Centers of Emphasis, including the Center for Cellular and Molecular Therapeutics and the Department of Biomedical and Health Informatics. The Center provides an intellectual home for recruiting tenure track faculty who are not only adept at using existing technology, but also can develop new genomic technologies or computational tools that can be offered to a broader community of scientists. The Center has hybrid character - computational biology research will pave the way for new projects in wet laboratories. Under Dr. Xing's direction, the Center aims to serve as an engine for technological and biomedical innovation, providing tight interaction from both the basic science and technology side, as well as the translational and clinical side of genomics and computational biology.
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.