7 Since then, each new release has included both more data and new website functionalities, and major milestones consist of a gene‐centric database with information on all human genes predicted by Ensembl 8 and addition of transcriptomics data based on high‐throughput mRNA sequencing. ![]() The HPA was initiated in 2003, and launched a first version of the public database in 2005, containing protein expression data based on approximately 700 antibodies. Several recent achievements are a first draft of a tissue‐based atlas, 4 a sub‐cellular atlas, 5 and a pathology atlas. ![]() This approach allows looking at single proteins and lists of proteins belonging to structures such as organs and organelles, or categorizing proteins based on expression level and tissue distribution, for example, housekeeping proteins and tissue elevated proteins. Under this premise, the goal of the publicly available HPA is to reveal the spatial distribution and expression of every human protein in different human tissues, cancer types, and cell lines. 3 Part of the HPP initiative is the Human Protein Atlas (HPA) project, focusing on antibody‐based proteomics and integrated omics.Īn “atlas” is defined as a collection of maps or charts that gives a comprehensive view on a certain subject. Recent efforts include the Human Proteome Map 1 and the Proteomics DB 2 based on mass spectrometry of human tissues as well as the initiative from the HUPO Human Proteome Project (HPP), whose more stringent guidelines resulted in a more accurate map. Ever since the completion of the human genome sequence, the ultimate goal has been to understand the dynamic expression of the approximately 20,000 protein‐coding genes and to generate a map of the human proteome. mutatedEntities else 0 print ( ' ' + str ( deleted_count ) + ' entities deleted' )įor more examples, checkout sample-app python project in atlas-examples module.Proteins are the essential building blocks of life, and resolving the spatial distribution of all human proteins on an organ, tissue, cellular, and sub‐cellular level will greatly increase our understanding of human biology in health and disease. delete_entities_by_guids () deleted_count = len ( resp. guid ) print ( ' created test_view: guid=' + guid_view ) print ( ' created test_view.test_col1: guid=' + guid_view_col1 ) print ( ' created test_view.test_col2: guid=' + guid_view_col1 ) print ( ' created test_view lineage: guid=' + guid_process ) print ( ' created test_col1 lineage: guid=' + guid_col1_lineage ) print ( ' created test_col2 lineage: guid=' + guid_col2_lineage ) # Step 5: Finally, cleanup by deleting entities created above print ( 'Deleting entities' ) resp = client. create_entities ( entities_info ) guid_view = resp. add_referenced_entity ( test_view_col2 ) print ( 'Creating test_view' ) resp = client. ![]() add_referenced_entity ( test_view_col1 ) entities_info. add_referenced_entity ( test_view ) entities_info. Python atlas_example.py # atlas_example.py import time from apache_client import AtlasClient from apache_ import AtlasEntity, AtlasEntityWithExtInfo, AtlasEntitiesWithExtInfo, AtlasRelatedObjectId from apache_ import EntityOperation # Step 1: create a client to connect to Apache Atlas server client = AtlasClient ( ', ( 'admin', 'atlasR0cks!' )) # For Kerberos authentication, use HTTPKerberosAuth as shown below # from requests_kerberos import HTTPKerberosAuth # client = AtlasClient(' HTTPKerberosAuth()) # to disable SSL certificate validation (not recommended for production use!) # = False # Step 2: Let's create a database entity test_db = AtlasEntity ( entities_info = AtlasEntitiesWithExtInfo () entities_info. Verify if apache-atlas client is installed: > pip list Use the package manager pip to install Python client for Apache Atlas.
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