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human secretome annotations  (Human Protein Atlas)

 
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    Human Protein Atlas human secretome annotations
    Enriched and neat plasma proteins cluster into four main groups based on protein intensities. Cluster 1 contains proteins found in neat plasma and shared across all methods, while Cluster 2 consists of proteins identified in all five enrichment methods. Cluster 3 includes proteins with medium intensity, mostly detected in all methods except ENRICHiST and neat. Cluster 4 consists primarily of low-intensity proteins. Each cluster is enriched with distinct Gene Ontology terms, cellular localizations, and tissue associations. (A) Hierarchical clustering of proteins detected in at least 10% of all samples, based on log 2 intensity. (B) Enrichment analysis of proteins in each cluster with the PANTHER GO-Slim databases , for cellular components and biological processes. Results were first filtered to retain terms with over 2-fold enrichment and a minimum of ten proteins per term. The top five terms with the lowest FDR values were selected for each cluster. (C) Subcellular location of proteins in each cluster based on The Human Protein Atlas (HPA) annotations. Only the top 20 locations are shown. The inset highlights the top five <t>secretome</t> annotations among all identified proteins. (D) Number of proteins annotated as tissue-enriched in the HPA transcriptomics data set. The 20 tissues with the highest total count of tissue-enriched proteins are shown.
    Human Secretome Annotations, supplied by Human Protein Atlas, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/human secretome annotations/product/Human Protein Atlas
    Average 86 stars, based on 1 article reviews
    human secretome annotations - by Bioz Stars, 2026-06
    86/100 stars

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    1) Product Images from "Automated Mag-Net Enrichment Unlocks Deep and Cost-Effective LC–MS Plasma Proteomics"

    Article Title: Automated Mag-Net Enrichment Unlocks Deep and Cost-Effective LC–MS Plasma Proteomics

    Journal: Journal of Proteome Research

    doi: 10.1021/acs.jproteome.5c00420

    Enriched and neat plasma proteins cluster into four main groups based on protein intensities. Cluster 1 contains proteins found in neat plasma and shared across all methods, while Cluster 2 consists of proteins identified in all five enrichment methods. Cluster 3 includes proteins with medium intensity, mostly detected in all methods except ENRICHiST and neat. Cluster 4 consists primarily of low-intensity proteins. Each cluster is enriched with distinct Gene Ontology terms, cellular localizations, and tissue associations. (A) Hierarchical clustering of proteins detected in at least 10% of all samples, based on log 2 intensity. (B) Enrichment analysis of proteins in each cluster with the PANTHER GO-Slim databases , for cellular components and biological processes. Results were first filtered to retain terms with over 2-fold enrichment and a minimum of ten proteins per term. The top five terms with the lowest FDR values were selected for each cluster. (C) Subcellular location of proteins in each cluster based on The Human Protein Atlas (HPA) annotations. Only the top 20 locations are shown. The inset highlights the top five secretome annotations among all identified proteins. (D) Number of proteins annotated as tissue-enriched in the HPA transcriptomics data set. The 20 tissues with the highest total count of tissue-enriched proteins are shown.
    Figure Legend Snippet: Enriched and neat plasma proteins cluster into four main groups based on protein intensities. Cluster 1 contains proteins found in neat plasma and shared across all methods, while Cluster 2 consists of proteins identified in all five enrichment methods. Cluster 3 includes proteins with medium intensity, mostly detected in all methods except ENRICHiST and neat. Cluster 4 consists primarily of low-intensity proteins. Each cluster is enriched with distinct Gene Ontology terms, cellular localizations, and tissue associations. (A) Hierarchical clustering of proteins detected in at least 10% of all samples, based on log 2 intensity. (B) Enrichment analysis of proteins in each cluster with the PANTHER GO-Slim databases , for cellular components and biological processes. Results were first filtered to retain terms with over 2-fold enrichment and a minimum of ten proteins per term. The top five terms with the lowest FDR values were selected for each cluster. (C) Subcellular location of proteins in each cluster based on The Human Protein Atlas (HPA) annotations. Only the top 20 locations are shown. The inset highlights the top five secretome annotations among all identified proteins. (D) Number of proteins annotated as tissue-enriched in the HPA transcriptomics data set. The 20 tissues with the highest total count of tissue-enriched proteins are shown.

    Techniques Used: Clinical Proteomics, Transcriptomics



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    human secretome annotations  (Human Protein Atlas)
    86
    Human Protein Atlas human secretome annotations
    Enriched and neat plasma proteins cluster into four main groups based on protein intensities. Cluster 1 contains proteins found in neat plasma and shared across all methods, while Cluster 2 consists of proteins identified in all five enrichment methods. Cluster 3 includes proteins with medium intensity, mostly detected in all methods except ENRICHiST and neat. Cluster 4 consists primarily of low-intensity proteins. Each cluster is enriched with distinct Gene Ontology terms, cellular localizations, and tissue associations. (A) Hierarchical clustering of proteins detected in at least 10% of all samples, based on log 2 intensity. (B) Enrichment analysis of proteins in each cluster with the PANTHER GO-Slim databases , for cellular components and biological processes. Results were first filtered to retain terms with over 2-fold enrichment and a minimum of ten proteins per term. The top five terms with the lowest FDR values were selected for each cluster. (C) Subcellular location of proteins in each cluster based on The Human Protein Atlas (HPA) annotations. Only the top 20 locations are shown. The inset highlights the top five <t>secretome</t> annotations among all identified proteins. (D) Number of proteins annotated as tissue-enriched in the HPA transcriptomics data set. The 20 tissues with the highest total count of tissue-enriched proteins are shown.
    Human Secretome Annotations, supplied by Human Protein Atlas, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/human secretome annotations/product/Human Protein Atlas
    Average 86 stars, based on 1 article reviews
    human secretome annotations - by Bioz Stars, 2026-06
    86/100 stars
    		  Buy from Supplier

    Image Search Results


    Enriched and neat plasma proteins cluster into four main groups based on protein intensities. Cluster 1 contains proteins found in neat plasma and shared across all methods, while Cluster 2 consists of proteins identified in all five enrichment methods. Cluster 3 includes proteins with medium intensity, mostly detected in all methods except ENRICHiST and neat. Cluster 4 consists primarily of low-intensity proteins. Each cluster is enriched with distinct Gene Ontology terms, cellular localizations, and tissue associations. (A) Hierarchical clustering of proteins detected in at least 10% of all samples, based on log 2 intensity. (B) Enrichment analysis of proteins in each cluster with the PANTHER GO-Slim databases , for cellular components and biological processes. Results were first filtered to retain terms with over 2-fold enrichment and a minimum of ten proteins per term. The top five terms with the lowest FDR values were selected for each cluster. (C) Subcellular location of proteins in each cluster based on The Human Protein Atlas (HPA) annotations. Only the top 20 locations are shown. The inset highlights the top five secretome annotations among all identified proteins. (D) Number of proteins annotated as tissue-enriched in the HPA transcriptomics data set. The 20 tissues with the highest total count of tissue-enriched proteins are shown.

    Journal: Journal of Proteome Research

    Article Title: Automated Mag-Net Enrichment Unlocks Deep and Cost-Effective LC–MS Plasma Proteomics

    doi: 10.1021/acs.jproteome.5c00420

    Figure Lengend Snippet: Enriched and neat plasma proteins cluster into four main groups based on protein intensities. Cluster 1 contains proteins found in neat plasma and shared across all methods, while Cluster 2 consists of proteins identified in all five enrichment methods. Cluster 3 includes proteins with medium intensity, mostly detected in all methods except ENRICHiST and neat. Cluster 4 consists primarily of low-intensity proteins. Each cluster is enriched with distinct Gene Ontology terms, cellular localizations, and tissue associations. (A) Hierarchical clustering of proteins detected in at least 10% of all samples, based on log 2 intensity. (B) Enrichment analysis of proteins in each cluster with the PANTHER GO-Slim databases , for cellular components and biological processes. Results were first filtered to retain terms with over 2-fold enrichment and a minimum of ten proteins per term. The top five terms with the lowest FDR values were selected for each cluster. (C) Subcellular location of proteins in each cluster based on The Human Protein Atlas (HPA) annotations. Only the top 20 locations are shown. The inset highlights the top five secretome annotations among all identified proteins. (D) Number of proteins annotated as tissue-enriched in the HPA transcriptomics data set. The 20 tissues with the highest total count of tissue-enriched proteins are shown.

    Article Snippet: The subcellular location of proteins, gene-level RNA expression data, single cell-type expression data, and the human secretome annotations were downloaded from the Human Protein Atlas (HPA, version 24.0, proteinatlas.org ).

    Techniques: Clinical Proteomics, Transcriptomics