1
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: not found

      Learning complex subcellular distribution patterns of proteins via analysis of immunohistochemistry images

      1 , 2 , 3 , 1 , 2
      Bioinformatics
      Oxford University Press (OUP)

      Read this article at

      ScienceOpenPublisherPubMed
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Motivation

          Systematic and comprehensive analysis of protein subcellular location as a critical part of proteomics (‘location proteomics’) has been studied for many years, but annotating protein subcellular locations and understanding variation of the location patterns across various cell types and states is still challenging.

          Results

          In this work, we used immunohistochemistry images from the Human Protein Atlas as the source of subcellular location information, and built classification models for the complex protein spatial distribution in normal and cancerous tissues. The models can automatically estimate the fractions of protein in different subcellular locations, and can help to quantify the changes of protein distribution from normal to cancer tissues. In addition, we examined the extent to which different annotated protein pathways and complexes showed similarity in the locations of their member proteins, and then predicted new potential proteins for these networks.

          Availability and implementation

          The dataset and code are available at: www.csbio.sjtu.edu.cn/bioinf/complexsubcellularpatterns.

          Supplementary information

          Supplementary data are available at Bioinformatics online.

          Related collections

          Most cited references34

          • Record: found
          • Abstract: found
          • Article: not found

          Gene Ontology: tool for the unification of biology

          Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Finding scientific topics.

            A first step in identifying the content of a document is determining which topics that document addresses. We describe a generative model for documents, introduced by Blei, Ng, and Jordan [Blei, D. M., Ng, A. Y. & Jordan, M. I. (2003) J. Machine Learn. Res. 3, 993-1022], in which each document is generated by choosing a distribution over topics and then choosing each word in the document from a topic selected according to this distribution. We then present a Markov chain Monte Carlo algorithm for inference in this model. We use this algorithm to analyze abstracts from PNAS by using Bayesian model selection to establish the number of topics. We show that the extracted topics capture meaningful structure in the data, consistent with the class designations provided by the authors of the articles, and outline further applications of this analysis, including identifying "hot topics" by examining temporal dynamics and tagging abstracts to illustrate semantic content.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              A subcellular map of the human proteome

              Resolving the spatial distribution of the human proteome at a subcellular level greatly increases our understanding of human biology and disease. Here, we present a comprehensive image-based map of the subcellular protein distribution, the Cell Atlas, built by integrating transcriptomics and antibody-based immunofluorescence microscopy with validation by mass spectrometry. Mapping the in situ localization of 12,003 human proteins at a single-cell level to 30 subcellular structures enabled the definition of 13 major organelle proteomes. Exploration of the proteomes reveals single-cell variations of abundance or spatial distribution, and localization of approximately half of the proteins to multiple compartments. This subcellular map can be used to refine existing protein-protein interaction networks and provides an important resource to deconvolute the highly complex architecture of the human cell.
                Bookmark

                Author and article information

                Contributors
                Journal
                Bioinformatics
                Oxford University Press (OUP)
                1367-4803
                1460-2059
                March 15 2020
                March 01 2020
                November 13 2019
                March 15 2020
                March 01 2020
                November 13 2019
                : 36
                : 6
                : 1908-1914
                Affiliations
                [1 ]Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China
                [2 ]Department of Computational Biology, Carnegie Mellon University, Pittsburgh, PA 15213, USA
                [3 ]School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
                Article
                10.1093/bioinformatics/btz844
                31722369
                9344b891-ed5b-4c51-b06f-da351cba3f4f
                © 2019

                https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model

                History

                Comments

                Comment on this article