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6 results for “gene prediction and annotation”
Predicted genes annotated with Prokka for the 623 archaeal UBA genomes
<p>Gene prediction and annotation for the 623 archaeal UBA metagenome-assembled genomes using Prokka v1.12 with Pfam v31 and UniProt databases created on April 17, 2017 according to the Prokka instructions.</p> <p>These genomes are described in:</p> <p>Parks DH, et al. 2017. Recovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life. Nat Microbiol, doi:10.1038/s41564-017-0012-7/ .</p> <p>https://www.nature.com/articles/s41564-017-0012-7</p>
Data from: A modified GC-specific MAKER gene annotation method reveals improved and novel gene predictions of high and low GC content in Oryza sativa
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Data from: Repertoire-wide gene structure analyses: a case study comparing automatically predicted and manually annotated gene models
The location and modular structure of eukaryotic protein-coding genes in genomic sequences can be automatically predicted by gene annotation algorithms. These predictions are often used for comparative studies on gene structure, gene repertoires, and genome evolution. However, automatic annotation algorithms do not yet correctly identify all genes within a genome, and manual annotation is often necessary to obtain accurate gene models and gene sets. As manual annotation is time-consuming, only a fraction of the gene models in a genome is typically manually annotated, and this fraction often differs between species. To assess the impact of manual annotation efforts on genome-wide analyses of gene structural properties, we compared the structural properties of protein-coding genes in seven diverse insect species sequenced by the i5k initiative. Our results show that the subset of genes chosen for manual annotation by a research community (3.5-7% of gene models) may have structural properties (e.g., lengths and exon counts) that are not necessarily representative for a species' gene set as a whole. Nonetheless, the structural properties of automatically generated gene models are only altered marginally (if at all) through manual annotation. Major correlative trends, for example a negative correlation between genome size and exonic proportion, can be inferred from either the automatically predicted or manually annotated gene models alike. Vice versa, some previously reported trends did not appear in either the automatic or manually annotated gene sets, pointing towards insect-specific gene structural peculiarities. In our analysis of gene structural properties, automatically predicted gene models proved to be sufficiently reliable to recover the same gene-repertoire-wide correlative trends that we found when focusing on manually annotated gene models only. We acknowledge that analyses on the individual gene level clearly benefit from manual curation. However, as genome sequencing and annotation projects often differ in the extent of their manual annotation and curation efforts, our results indicate that comparative studies analyzing gene structural properties in these genomes can nonetheless be justifiable and informative.
Data from: Gene prediction and annotation in Penstemon (Plantaginaceae): a workflow for marker development from extremely low-coverage genome sequencing
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Data from: Repertoire-wide gene structure analyses: a case study comparing automatically predicted and manually annotated gene models
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Contradictory of gene expression signatures for clinical prognostic prediction caused by update of reference genome and annotation
GEO Series GSE143486. Homo sapiens. 30 samples. Type: Expression profiling by high throughput sequencing.
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.
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.