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5 results for “meta-omics”
EukZoo, an aquatic protistan protein database for meta-omics studies.
<p>This database contain protein sequences of aquatic microbial eukaryotes, or protists. The purpose of this is to make a database that is of reasonable quality to serve as resource for both taxonomy and functional interpretation of metagenomic and metatranscriptomic studies of protists. The source of the sequences were mainly from Marine Microbial Eukaryotes Transcriptome Sequencing Project (MMETSP), and supplemented with various genomes and transcriptomes of organisms that were not a part of MMETSP.</p> <p>To use this database, one has to understand the main function of the three files here.</p> <p>(1) The protein sequences are stored in .faa file. You can build an alignment/search database out of that and search your meta-omics sequences against it. Each sequence in the FASTA file has an ID which always consists of two parts like this: "MMETSP0004_1234567". The text before the first underscore is the source ID of that sequence.</p> <p>(2) Taxonomy information of each source ID are stored in "EukZoo_taxonomy_table_v_0.2.tsv". One can use the information within in conjunction with database search results to assign taxonomy to sequences.</p> <p>(3) KEGG annotation of each sequence are stored in "EukZoo_KEGG_annotation_v_0.2.tsv". One can use the information within in conjunction with database search results to assign KEGG functional annotation (KO ID) to sequences.</p> <p>I also provide scripts to assign taxonomy and KEGG annotation from database search results. You can also find the scripts and explanations on how to use them on the <a href="https://github.com/zxl124/EukZoo-database">EukZoo GitHub page</a>. You will find details on how the database was created and curated on there as well.</p> <p>Please contact me at zhenfeng.liu1@gmail.com if you have any questions or requests. Thank you for your interest in EukZoo.</p>
MIntO: a Modular and Scalable Pipeline for Microbiome Metagenomic and Metatranscriptomic Meta-omics Data Integration
<p>To illustrate the use of MIntO, a set of 91 human fecal metagenomes from the Inflammatory Bowel Disease Multi’omics Database was selected (IBDMDB). We selected six participants diagnosed as non-IBD (P6018 (nIBD1), M2072 (nIBD2)); Crohn’s disease (H4006 (CD1) and H4020 (CD2)); and ulcerative colitis (H4019 (UC1) and H4035 (UC2)) that were followed for one year each. </p> <p>Here, we present the results from the <em>genome-based assembly-dependent</em> mode, where we used 91 metagenomic high-quality reads<strong> </strong>to recover 163 high-quality MAGs, which constituted a set of non-redundant genomes.</p>
Analysis data for ""Integration of time-series meta-omics data reveals how microbial ecosystems respond to disturbance""
<p>Analysis data for the manuscript: "Integration of meta-omics data reveals how microbial ecosystems respond to disturbance"</p> <p>Files used with the repository: https://git-r3lab.uni.lu/malte.herold/laots_niche_ecology_analysis/</p> <p>The archive was split into multiple parts for uploading to zenodo which need to be joined in order to extract the files:</p> <pre><code class="language-bash">cat resultsdir_laots.tar.gz.part_* > resultsdir_laots.tar.gz tar xvfz resultsdir_laots.tar.gz</code></pre> <p>Version 2 contains additional files generated in the revision.</p> <p> </p>
Table S1 - Table S7: Integrated meta-omic analyses of the gastrointestinal tract microbiome in patients undergoing allogeneic stem cell transplantation
<p> </p> <p><strong>Table S1. </strong>Number of reads per prokaryotic operational taxonomic unit (OTU) and sample from the cohort.<strong> </strong></p> <p><strong>Table S2. </strong>Number of reads per eukaryotic operational taxonomic unit (OTU) and sample from the cohort.</p> <p><strong>Table S3. </strong>Blood cell counts and clinical data from patient A07.<strong> </strong></p> <p><strong>Table S4. </strong>Number of reads per operational taxonomic unit (OTU) and sample from patient A07.<strong> </strong></p> <p><strong>Table S5. </strong>Numbers of identified antibiotic resistance genes and total number of genes. Numbers of identified antibiotic resistance genes in relation to total numbers of genes in samples from patient A07 before and after allo-HSCT and from four healthy individuals.<strong> </strong></p> <p><strong>Table S6. </strong>Statistics of the metagenomic and metatranscriptomic datasets and the co-assembled contigs.<strong> </strong></p> <p><strong>Table S7. </strong>Antibiotic resistance genes in population-level genomes and their expression in the pre- and post-treatment sample.</p>
Integrated meta-omics uncovering the mechanisms of Lactobacillus salivarius to improve Oreochromis niloticus intestine health
GEO Series GSE284663. Oreochromis niloticus. 6 samples. Type: Expression profiling by high throughput sequencing.
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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.