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      Performance Assessment of the Network Reconstruction Approaches on Various Interactomes

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          Abstract

          Beyond the list of molecules, there is a necessity to collectively consider multiple sets of omic data and to reconstruct the connections between the molecules. Especially, pathway reconstruction is crucial to understanding disease biology because abnormal cellular signaling may be pathological. The main challenge is how to integrate the data together in an accurate way. In this study, we aim to comparatively analyze the performance of a set of network reconstruction algorithms on multiple reference interactomes. We first explored several human protein interactomes, including PathwayCommons, OmniPath, HIPPIE, iRefWeb, STRING, and ConsensusPathDB. The comparison is based on the coverage of each interactome in terms of cancer driver proteins, structural information of protein interactions, and the bias toward well-studied proteins. We next used these interactomes to evaluate the performance of network reconstruction algorithms including all-pair shortest path, heat diffusion with flux, personalized PageRank with flux, and prize-collecting Steiner forest (PCSF) approaches. Each approach has its own merits and weaknesses. Among them, PCSF had the most balanced performance in terms of precision and recall scores when 28 pathways from NetPath were reconstructed using the listed algorithms. Additionally, the reference interactome affects the performance of the network reconstruction approaches. The coverage and disease- or tissue-specificity of each interactome may vary, which may result in differences in the reconstructed networks.

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          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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            The Protein Data Bank.

            The Protein Data Bank (PDB; http://www.rcsb.org/pdb/ ) is the single worldwide archive of structural data of biological macromolecules. This paper describes the goals of the PDB, the systems in place for data deposition and access, how to obtain further information, and near-term plans for the future development of the resource.
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              The STRING database in 2021: customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets

              Abstract Cellular life depends on a complex web of functional associations between biomolecules. Among these associations, protein–protein interactions are particularly important due to their versatility, specificity and adaptability. The STRING database aims to integrate all known and predicted associations between proteins, including both physical interactions as well as functional associations. To achieve this, STRING collects and scores evidence from a number of sources: (i) automated text mining of the scientific literature, (ii) databases of interaction experiments and annotated complexes/pathways, (iii) computational interaction predictions from co-expression and from conserved genomic context and (iv) systematic transfers of interaction evidence from one organism to another. STRING aims for wide coverage; the upcoming version 11.5 of the resource will contain more than 14 000 organisms. In this update paper, we describe changes to the text-mining system, a new scoring-mode for physical interactions, as well as extensive user interface features for customizing, extending and sharing protein networks. In addition, we describe how to query STRING with genome-wide, experimental data, including the automated detection of enriched functionalities and potential biases in the user's query data. The STRING resource is available online, at https://string-db.org/.
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                Author and article information

                Contributors
                Journal
                Front Mol Biosci
                Front Mol Biosci
                Front. Mol. Biosci.
                Frontiers in Molecular Biosciences
                Frontiers Media S.A.
                2296-889X
                05 October 2021
                2021
                : 8
                : 666705
                Affiliations
                [ 1 ]Graduate School of Informatics, Middle East Technical University, Ankara, Turkey
                [ 2 ]Foot and Mouth Diseases Institute, Ministry of Agriculture and Forestry, Ankara, Turkey
                [ 3 ]Chemical and Biological Engineering, College of Engineering, Koc University, Istanbul, Turkey
                [ 4 ]School of Medicine, Koc University, Istanbul, Turkey
                Author notes

                Edited by: Arzucan Ozgur, Boğaziçi University, Turkey

                Reviewed by: Hilal Kazan, Antalya Bilim University, Turkey

                Cesim Erten, Antalya Bilim University, Turkey

                Eileen Marie Hanna, Lebanese American University, Lebanon

                *Correspondence: Nurcan Tuncbag, ntuncbag@ 123456ku.edu.tr

                This article was submitted to Biological Modeling and Simulation, a section of the journal Frontiers in Molecular Biosciences

                Article
                666705
                10.3389/fmolb.2021.666705
                8523993
                34676243
                22686c51-5c56-44aa-8a0f-b994e7ca0257
                Copyright © 2021 Arici and Tuncbag.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 10 February 2021
                : 14 July 2021
                Categories
                Molecular Biosciences
                Original Research

                protein-protein interactions,interactome,network reconstruction,heat diffusion,personalized pagerank,prize-collecting steiner forest,pathway reconstruction

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