SFLD (Structure-Function Linkage Database) is a hierarchical classification of enzymes that relates specific sequence-structure features to specific chemical capabilities.

SMART (a Simple Modular Architecture Research Tool) allows the identification and annotation of genetically mobile domains and the analysis of domain architectures. SMART is based at EMBL, Heidelberg, Germany.

HAMAP stands for High-quality Automated and Manual Annotation of Proteins. HAMAP profiles are manually created by expert curators. They identify proteins that are part of well-conserved protein families or subfamilies. HAMAP is based at the SIB Swiss Institute of Bioinformatics, Geneva, Switzerland.

The European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI) is international, innovative and interdisciplinary, and a champion of open data in the life sciences. The EMBL-EBI captures and presents globally comprehensive sequence data as part of the International Nucleotide Sequence Database Collaboration. Data provided to GBIF include geotagged environmental sequences with user-provided taxonomic identifications. This dataset contains INSDC sequences associated with environmental sample identifiers. The dataset is prepared periodically using the public ENA API (https://www.ebi.ac.uk/ena/portal/api/) by querying data with the search parameters: environmental_sample=True & host="" EMBL-EBI also publishes other records in separate datasets (https://www.gbif.org/publisher/ada9d123-ddb4-467d-8891-806ea8d94230). The data was then processed as follows: 1. Human sequences were excluded. 2. For non-CONTIG records, the sample accession number (when available) along with the scientific name were used to identify sequence records corresponding to the same individuals (or group of organism of the same species in the same sample). Only one record was kept for each scientific name/sample accession number. 3. Contigs and whole genome shotgun (WGS) records were added individually. 4. The records that were missing some information were excluded. Only records associated with a specimen voucher or records containing both a location AND a date were kept. 5. The records associated with the same vouchers are aggregated together. 6. A lot of records left corresponded to individual sequences or reads corresponding to the same organisms. In practise, these were "duplicate" occurrence records that weren't filtered out in STEP 2 because the sample accession sample was missing. To identify those potential duplicates, we grouped all the remaining records by scientific_name, collection_date, location, country, identified_by, collected_by and sample_accession (when available). Then we excluded the groups that contained more than 50 records. The rationale behind the choice of threshold is explained here: Deduplication v2 gbif/embl-adapter#10 (comment) 7. To improve the matching of the EBI scientific name to the GBIF backbone taxonomy, we incorporated the ENA taxonomic information. The kingdom, Phylum, Class, Order, Family, and genus were obtained from the ENA taxonomy checklist available here: http://ftp.ebi.ac.uk/pub/databases/ena/taxonomy/sdwca.zip More information available here: https://github.com/gbif/embl-adapter#readme You can find the mapping used to format the EMBL data to Darwin Core Archive here: https://github.com/gbif/embl-adapter/blob/master/DATAMAPPING.md

This dataset contains INSDC sequences associated with host organisms. The dataset is prepared periodically using the public ENA API (https://www.ebi.ac.uk/ena/portal/api/) using the methods described below.

EMBL-EBI also publishes other records in separate datasets (https://www.gbif.org/publisher/ada9d123-ddb4-467d-8891-806ea8d94230).

The data was then processed as follows:

1. Human sequences were excluded.

2. For non-CONTIG records, the sample accession number (when available) along with the scientific name were used to identify sequence records corresponding to the same individuals (or group of organism of the same species in the same sample). Only one record was kept for each scientific name/sample accession number.

3. Contigs and whole genome shotgun (WGS) records were added individually.

4. The records that were missing some information were excluded. Only records associated with a specimen voucher or records containing both a location AND a date were kept.

5. The records associated with the same vouchers are aggregated together.

6. A lot of records left corresponded to individual sequences or reads corresponding to the same organisms. In practise, these were "duplicate" occurrence records that weren't filtered out in STEP 2 because the sample accession sample was missing. To identify those potential duplicates, we grouped all the remaining records by `scientific_name`, `collection_date`, `location`, `country`, `identified_by`, `collected_by` and `sample_accession` (when available). Then we excluded the groups that contained more than 50 records. The rationale behind the choice of threshold is explained here: https://github.com/gbif/embl-adapter/issues/10#issuecomment-855757978

7. To improve the matching of the EBI scientific name to the GBIF backbone taxonomy, we incorporated the ENA taxonomic information. The kingdom, Phylum, Class, Order, Family, and genus were obtained from the ENA taxonomy checklist available here: http://ftp.ebi.ac.uk/pub/databases/ena/taxonomy/sdwca.zip

More information available here: https://github.com/gbif/embl-adapter#readme

You can find the mapping used to format the EMBL data to Darwin Core Archive here: https://github.com/gbif/embl-adapter/blob/master/DATAMAPPING.md

This dataset contains INSDC sequence records not associated with environmental sample identifiers or host organisms. The dataset is prepared periodically using the public ENA API (https://www.ebi.ac.uk/ena/portal/api/) by querying data with search parameters: `environmental_sample=False & host=""`

EMBL-EBI also publishes other records in separate datasets (https://www.gbif.org/publisher/ada9d123-ddb4-467d-8891-806ea8d94230).

The data was then processed as follows:

1. Human sequences were excluded.

2. For non-CONTIG records, the sample accession number (when available) along with the scientific name were used to identify sequence records corresponding to the same individuals (or group of organism of the same species in the same sample). Only one record was kept for each scientific name/sample accession number.

3. Contigs and whole genome shotgun (WGS) records were added individually.

4. The records that were missing some information were excluded. Only records associated with a specimen voucher or records containing both a location AND a date were kept.

5. The records associated with the same vouchers are aggregated together.

6. A lot of records left corresponded to individual sequences or reads corresponding to the same organisms. In practise, these were "duplicate" occurrence records that weren't filtered out in STEP 2 because the sample accession sample was missing. To identify those potential duplicates, we grouped all the remaining records by `scientific_name`, `collection_date`, `location`, `country`, `identified_by`, `collected_by` and `sample_accession` (when available). Then we excluded the groups that contained more than 50 records. The rationale behind the choice of threshold is explained here: https://github.com/gbif/embl-adapter/issues/10#issuecomment-855757978

7. To improve the matching of the EBI scientific name to the GBIF backbone taxonomy, we incorporated the ENA taxonomic information. The kingdom, Phylum, Class, Order, Family, and genus were obtained from the ENA taxonomy checklist available here: http://ftp.ebi.ac.uk/pub/databases/ena/taxonomy/sdwca.zip

More information available here: https://github.com/gbif/embl-adapter#readme

You can find the mapping used to format the EMBL data to Darwin Core Archive here: https://github.com/gbif/embl-adapter/blob/master/DATAMAPPING.md

ChIP-Seq data for 154 CD4-positive, alpha-beta T cell sample(s). 355 run(s), 265 experiment(s), 250 analysis(s) on human genome GRCh37. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/blueprint_Epivar/protocols/README_chipseq_analysis_ebi_20160816

Overview of active ENA Data Hubs

Quantitative study of the N-terminal acetylome variations in Arabidopsis thaliana, looking at the effect of a N-acetyltransferase KO.

This data is apart of a project assessing transcriptional start site switching and UTR switching at translational level following hypoxia.

Bacterial meningitis is usually fatal without treatment and prompt and accurate diagnosis coupled with the timely administration of parenteral antibiotics, are necessary in order to save lives. The diagnosis can sometimes be delayed whilst samples are analysed in a laboratory using traditional methods of microscopy and antigen testing. The objective of our project is to define specific protein signatures in cerebrospinal fluid associated with Streptococcus pneumoniae infection which could lead to the development of assays or point-of-care devices to improve the speed and accuracy of diagnosis, and guide the clinicians in the treatment and prognosis of children with bacterial meningitis. The associated research paper is in preparation.

Gene Expression Omnibus. GEO is a public functional genomics data repository supporting MIAME-compliant data submissions. The GEO DataSets database stores original submitter-supplied records (Series, Samples and Platforms) as well as curated DataSets.

In this study, we compared the effects of two cytokine treatments on the proteome of human Th-1 cells. We used saturating doses of murine single-chain IL-27 (EBI3+p28, 10nM) and HyperIL-6 (20nM) and continuously stimulated cells of three donors with the two cytokines for 24h or left untreated.

Not available

Proteomic analysis of sorted peroxysomes (old, young, and middle-aged).

Dataset of the type ? from the database All - version N/A

Hepatocarcinoma is the third leading cause of death in cancer in the world. In recent years, research on CREB in hepatocellular carcinoma has become a hotspot, so our research group wants to use the mass spectrometry analysis what proteins can bind with CREB, and then explore the links between CREB and hepatocellular carcinoma.

Cohorts metadata for the Early Causes project.

Analysis of intact O-linked glycopeptides for SARS-Cov-2 S and human ACE2 protein by LC-MS

Putative tumor suppressor gene that may be implicated in the origin and progression of lung cancer