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      Deciphering cell–cell interactions and communication from gene expression

      review-article

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          Abstract

          Cell–cell interactions orchestrate organismal development, homeostasis and single-cell functions. When cells do not properly interact or improperly decode molecular messages, disease ensues. Thus, the identification and quantification of intercellular signalling pathways has become a common analysis performed across diverse disciplines. The expansion of protein–protein interaction databases and recent advances in RNA sequencing technologies have enabled routine analyses of intercellular signalling from gene expression measurements of bulk and single-cell data sets. In particular, ligand–receptor pairs can be used to infer intercellular communication from the coordinated expression of their cognate genes. In this Review, we highlight discoveries enabled by analyses of cell–cell interactions from transcriptomic data and review the methods and tools used in this context.

          Abstract

          Cell–cell interactions and communication can be inferred from RNA sequencing data of, for example, ligand–receptor pairs. The authors review insights gained and the methods and tools used in studies of cell–cell interactions based on transcriptomic data.

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          Most cited references175

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          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.
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            KEGG: kyoto encyclopedia of genes and genomes.

            M Kanehisa (2000)
            KEGG (Kyoto Encyclopedia of Genes and Genomes) is a knowledge base for systematic analysis of gene functions, linking genomic information with higher order functional information. The genomic information is stored in the GENES database, which is a collection of gene catalogs for all the completely sequenced genomes and some partial genomes with up-to-date annotation of gene functions. The higher order functional information is stored in the PATHWAY database, which contains graphical representations of cellular processes, such as metabolism, membrane transport, signal transduction and cell cycle. The PATHWAY database is supplemented by a set of ortholog group tables for the information about conserved subpathways (pathway motifs), which are often encoded by positionally coupled genes on the chromosome and which are especially useful in predicting gene functions. A third database in KEGG is LIGAND for the information about chemical compounds, enzyme molecules and enzymatic reactions. KEGG provides Java graphics tools for browsing genome maps, comparing two genome maps and manipulating expression maps, as well as computational tools for sequence comparison, graph comparison and path computation. The KEGG databases are daily updated and made freely available (http://www. genome.ad.jp/kegg/).
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              Is Open Access

              STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets

              Abstract Proteins and their functional interactions form the backbone of the cellular machinery. Their connectivity network needs to be considered for the full understanding of biological phenomena, but the available information on protein–protein associations is incomplete and exhibits varying levels of annotation granularity and reliability. The STRING database aims to collect, score and integrate all publicly available sources of protein–protein interaction information, and to complement these with computational predictions. Its goal is to achieve a comprehensive and objective global network, including direct (physical) as well as indirect (functional) interactions. The latest version of STRING (11.0) more than doubles the number of organisms it covers, to 5090. The most important new feature is an option to upload entire, genome-wide datasets as input, allowing users to visualize subsets as interaction networks and to perform gene-set enrichment analysis on the entire input. For the enrichment analysis, STRING implements well-known classification systems such as Gene Ontology and KEGG, but also offers additional, new classification systems based on high-throughput text-mining as well as on a hierarchical clustering of the association network itself. The STRING resource is available online at https://string-db.org/.
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                Author and article information

                Contributors
                oharismendy@ucsd.edu
                nlewisres@ucsd.edu
                Journal
                Nat Rev Genet
                Nat Rev Genet
                Nature Reviews. Genetics
                Nature Publishing Group UK (London )
                1471-0056
                1471-0064
                9 November 2020
                : 1-18
                Affiliations
                [1 ]GRID grid.266100.3, ISNI 0000 0001 2107 4242, Department of Pediatrics, , University of California, San Diego, ; La Jolla, CA USA
                [2 ]GRID grid.266100.3, ISNI 0000 0001 2107 4242, Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, ; La Jolla, CA USA
                [3 ]GRID grid.266100.3, ISNI 0000 0001 2107 4242, Bioinformatics and Systems Biology Graduate Program, , University of California, San Diego, ; La Jolla, CA USA
                [4 ]GRID grid.266100.3, ISNI 0000 0001 2107 4242, Division of Biomedical Informatics, , University of California, San Diego, ; La Jolla, CA USA
                [5 ]GRID grid.266100.3, ISNI 0000 0001 2107 4242, Moores Cancer Center, , University of California, San Diego, ; La Jolla, CA USA
                [6 ]GRID grid.266100.3, ISNI 0000 0001 2107 4242, Department of Bioengineering, , University of California, San Diego, ; La Jolla, CA USA
                Author information
                http://orcid.org/0000-0002-1546-9165
                http://orcid.org/0000-0003-2913-6051
                http://orcid.org/0000-0002-8098-9888
                Article
                292
                10.1038/s41576-020-00292-x
                7649713
                33168968
                4ed7f8c9-11ac-49fc-b3d8-fd61acc17382
                © Springer Nature Limited 2020

                This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

                History
                : 25 September 2020
                Categories
                Review Article

                rna sequencing,systems biology,computational biology and bioinformatics

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