Clust&See3.0 : clustering, module exploration and annotation

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However, large networks, such as protein interaction networks, are still difficult to analyze as a whole. Methods Here, we propose Clust&See3.0, a novel version of a Cytoscape app that has been developed to identify, visualize and manipulate network clusters and modules. It is now enriched with functionalities allowing custom annotations of nodes and computation of their statistical enrichments. Results As the wealth of multi-omics data is growing, such functionalities are highly valuable for a better understanding of biological module composition, as illustrated by the presented use case. Conclusions In summary, the originality of Clust&See3.0 lies in providing users with a complete tool for network clusters analyses: from cluster identification, visualization, node and cluster annotations to annotation statistical analyses. 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F1000Research 2024, 13 :994 ( https://doi.org/10.12688/f1000research.152711.2 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Software Tool Article Revised Clust&See3.0 : clustering, module exploration and annotation [version 2; peer review: 2 approved, 1 approved with reservations] Fabrice Lopez https://orcid.org/0000-0002-2401-0292 1 , Lionel Spinelli 1,2 , Christine Brun https://orcid.org/0000-0002-5563-6765 1,3 Fabrice Lopez https://orcid.org/0000-0002-2401-0292 1 , Lionel Spinelli 1,2 , Christine Brun https://orcid.org/0000-0002-5563-6765 1,3 PUBLISHED 14 Nov 2024 Author details Author details 1 TAGC (UMR1090), Aix-Marseille Université, INSERM, Turing Centre for Living Systems, Marseille, 13009, France 2 INSERM-CNRS, CIML, Turing Centre for Living Systems,, Aix-Marseille Univ, Marseille, France 3 CNRS, Marseille, 13009, France Fabrice Lopez Roles: Software Lionel Spinelli Roles: Formal Analysis, Methodology Christine Brun Roles: Conceptualization, Formal Analysis, Investigation, Supervision, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing OPEN PEER REVIEW DETAILS REVIEWER STATUS This article is included in the Cytoscape gateway. Abstract Background Cytoscape is an open-source software to visualize and analyze networks. However, large networks, such as protein interaction networks, are still difficult to analyze as a whole. Methods Here, we propose Clust&See3.0, a novel version of a Cytoscape app that has been developed to identify, visualize and manipulate network clusters and modules. It is now enriched with functionalities allowing custom annotations of nodes and computation of their statistical enrichments. Results As the wealth of multi-omics data is growing, such functionalities are highly valuable for a better understanding of biological module composition, as illustrated by the presented use case. Conclusions In summary, the originality of Clust&See3.0 lies in providing users with a complete tool for network clusters analyses: from cluster identification, visualization, node and cluster annotations to annotation statistical analyses. READ ALL READ LESS Keywords interaction networks, graph partitioning, clustering, visualization, cluster annotations, functional modules, statistical enrichment. Corresponding Author(s) Christine Brun ( [email protected] ) Close Corresponding author: Christine Brun Competing interests: No competing interests were disclosed. Grant information: Funding for article processing charges provided by Human Frontier Science Program grant RGP004/2023 to CB. Copyright: © 2024 Lopez F et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Lopez F, Spinelli L and Brun C. Clust&See3.0 : clustering, module exploration and annotation [version 2; peer review: 2 approved, 1 approved with reservations] . F1000Research 2024, 13 :994 ( https://doi.org/10.12688/f1000research.152711.2 ) First published: 02 Sep 2024, 13 :994 ( https://doi.org/10.12688/f1000research.152711.1 ) Latest published: 14 Nov 2024, 13 :994 ( https://doi.org/10.12688/f1000research.152711.2 ) Revised Amendments from Version 1 We added some elements of comparison with other tools. We added some elements of comparison with other tools. See the authors' detailed response to the review by Lun Hu See the authors' detailed response to the review by Ju Xiang See the authors' detailed response to the review by Anooja Ali READ REVIEWER RESPONSES Introduction Several years ago, we proposed Clust&See, a Cytoscape 1 plug-in that aims to facilitate network clustering and analysis for biologists by providing several original functionalities within a single framework. 2 Mainly, the tool allows decomposing a network into disjoint or overlapping clusters using several in-house algorithms, 3 – 5 visualizing those clusters as metanodes linked by several types of edges/relationships, and manipulating the clusters for further detailed visualization, exploration, analyses and comparisons. We have now developed Clust&See3.0 to fit Cytoscape3 new API and added functionalities allowing the user to annotate the nodes and the clusters with orthogonal data as well as to compute statistical enrichments for those annotations. Although Cytoscape Apps allowing the identification of clusters such as ClusterMaker, 6 cluster annotations and computation of enrichment statistics such as BinGO, 7 or combinations of those as proposed by Functional Enrichment Collection ( https://apps.cytoscape.org/apps/functionalenrichmentcollection ) do exist, those proposing in a single tool the identification of clusters and their annotation with custom annotation files are seldom. We therefore felt there was room for a tool such as Clust&See3.0, that performs all in a single workflow. We here provide examples of Clust&See3.0 usage on a protein-protein interaction network, where annotation enrichments are used (i) to functionally annotate the clusters with the Gene Ontology terms describing node functions in order to globally investigate the network and proceed with protein function prediction, and (ii) to discover features associated to clusters by the integration of data on nodes. Methods Implementation Briefly, the classic use of Clust&See 2 breaks down into the following phases: Decompose a network As in its first version, Clust&See proposes to partition an imported network using three algorithms: FT (Fusion-Transfer), an ascending hierarchical method fusing two clusters iteratively if the fusion results in a modularity gain 3 ; TFIT (iterated Transfer-Fusion), 4 a multi-level algorithm in which a vertex transfer procedure is performed to the best adjacent cluster while modularity increases, to finally compute a quotient graph. Both algorithms generate strict network partitions where clusters have no node in common. The third one, OCG (Overlapping Cluster Generator) 5 is an ascending hierarchical method fusing two clusters at each step while modularity increases, starting from an overlapping class system. This leads to overlapping clusters where some nodes belong to several clusters. Cluster visualization and exploration For each cluster in a partition, Clust&See provides detailed information about the cluster: a visualization of the cluster's sub-network, and its main topological features (number of nodes, edges, density, etc.) Each cluster can be viewed and explored independently, either in a compact mode as a metanode, or in an extended mode as the sub-network constituting the cluster ( Figure 1 ). The decomposition of the complete network can be viewed as a set of metanodes linked by edges, the thickness of which is proportional to the number of links between pairs of nodes of the linked metanodes. When clusters are overlapping, another type of link is added between the metanodes to represent the nodes shared between clusters/metanodes. The thickness of this link is then proportional to the number of shared nodes. From this map, the metanodes can be individually switched to the extended mode to visualize the corresponding sub-network, and back. Finally, the user can build a custom map by iteratively adding sub-networks and/or metanodes to obtain a global view of the partition ( Figure 1 , central panel). Figure 1. Clust&See3.0 interface. The central panel shows (1) the starting network in the top left corner, (2) the quotient graph after TFit partitioning where modules are represented by blue nodes with the icon of the corresponding sub-network inside. The size of the node reflects the number of nodes contained by the module. The blue links correspond to the edges linking the modules, their width reflecting the number of involved interactions. (3) In the right low corner, module 60 is shown as an expanded network. As in the former version of Clust&See, the right panel shows the details of the subnetworks forming the modules with their characteristics. The lower panel (former Data panel) contains the results of the different Cluster and Annotation analyses. The novel version of Clust&See3.0 now allows the user to annotate and analyze the nodes and the clusters as follows: Importing node annotations In order to annotate the clusters, Clust&See3.0 allows importing one or more annotation lists, composed of associations between a network node and one or more terms of any nature (see the Use case). The annotation file must be a text file and must contain at least two columns: one contains the node identifier (one identifier per line), the second one, the list of terms associated with this identifier, separated by a comma, a semicolon or a tab. The columns can also be separated by a comma, semicolon or tab, as long as the column separator is not the same as the term separator. The annotation import dialog box lets the user select the 2 columns of interest and eliminate any header lines. After import, Clust&See3.0 provides the number and percentage of annotated nodes in the network. Then the annotation process can be performed. Annotation rules, statistical enrichment Two types of enrichment can be computed for each annotation term, using the whole graph as background: • A one-sided hypergeometric hypothesis test, the null hypothesis of which corresponds to the proportional distribution of the annotation terms between the nodes inside and the nodes outside the cluster. When the p-value of the hypergeometric test is sufficiently low to reject the null hypothesis, this indicates that the nodes of the cluster carry the term more frequently than expected by chance, thus pointing toward a potential enrichment. It should be noted that since this test is applied to all clusters, the Benjamini-Hochberg procedure 8 is used to correct for the multiple testing effect on the p-values. The default value is set to p-values < 5.10 -2 . • A majority rule, corresponding to a minimum percentage (to be chosen by the user) of nodes annotated to the term among the annotated nodes of the cluster. The default value is set to 50%. Cluster and annotation analyses For each annotation list, the user can perform a statistical analysis of the clusters and the annotation terms, with different goals. At first, a global analysis of the annotations of the partition can be performed. When choosing the “Cluster list” tab, Clust&See3.0 provides for each cluster, (i) the number of nodes in the cluster that have received at least one annotation term, (ii) the number of annotation terms appearing at least once in the cluster, (iii) the number of terms that are statistically enriched in the cluster with a hypergeometric test, or that annotate a majority of cluster nodes ( i.e. with a “majority rule”). For both tests, thresholds are set by the user ( Figure 1 ). Finally, (iv) the terms that are enriched in clusters are shown on demand ( Figure 2A ). This first type of analysis allows getting a global view of the annotation distribution and to quickly identify clusters that are enriched for annotation terms of interest. Figure 2. Details of the different tabs of the Data panel. (A) “Cluster List” shows the annotation of all clusters; (B) “Cluster Analysis”, all the annotations of cluster 60; (C) “Annotation term analysis”, all the clusters annotated to GO:2001136; (D) “Node list”, the detail of the annotations of each node of the cluster. The node names highlighted in green are those annotated to the term of interest, here GO:2001136. The second approach concerns the details of statistical analyses by cluster by choosing the “Cluster analysis” tab. Here, the user can select a particular cluster for a detailed study of the annotations of its nodes. Clust&See3.0 then lists the terms annotating the cluster proteins and provides (i) the number of nodes annotated to the term, (ii) the percentage of nodes annotated to the selected term among the total number of cluster nodes, (iii) the p-value of the term according to the hypergeometric test, (iv) the percentage of nodes annotated to the selected term among the total number of annotated cluster nodes ( Figure 2B ). The third approach concerns the details of the statistical analyses by annotation term. When choosing the “Annotation term analysis” tab, the user can select a particular annotation term and Clust&See3.0 reports for each cluster that contains proteins annotated to this term, the same features than previously ( Figure 2C ). This type of analysis allows having a detailed view on the distribution of a particular annotation term among all the clusters composing the network. The tables displaying the results for the clusters are dynamic and can be used to identify the clusters selected in the map and, in the panel detailing the characteristics of the clusters. The reverse is also true: a selected cluster in the map or in the detailed panel is selected in the annotation statistical results tables. Operation The minimum system requirements for use of the Clust&See3.0 Cytoscape app include: Hardware: Memory: 8 GB Monitor: 1600×900 (HD+) resolution Software: Java 11 and above Cytoscape 3.8.0 and above Use Case Annotation mode for network and cluster exploration, and for function prediction We will illustrate how to use Clust&See3.0 by partitioning and annotating the human reference interactome network (HuRI) 9 with Gene Ontology 10 (network and annotation files are available at https://doi.org/10.5281/zenodo.12570870 ). We first partitioned the largest connected component of the HuRI network that contains 8149 nodes and 52016 edges, with the TFit algorithm. 4 Sixty-seven modules were obtained among which 20 contain more than 4 nodes ( Figure 1 ). After loading the annotation file that contains the list of IDs of the Biological Process Gene Ontology (BP GO) associated to all human genes/proteins, the number of annotated nodes in the network is indicated: 88% of the proteins of HuRI have functional annotations in the BP GO. • “Cluster list” tab In the “C&S Partition” table, under the “Cluster list” tab, all clusters are shown ( Figure 2A ). As we empirically consider that the smallest clusters are not suitable for computing statistics on annotations, Clust&See3.0 proposes to hide them for clarity’ sake with a check box “Hide small clusters”. The number of enriched terms per cluster according to the chosen statistic (“Hypergeometric law” or “Majority rule”) is indicated, and their GO IDs are available in the “Total annotations” column ( Figure 2A ) (choose “Annotate cluster X/Remove cluster X annotations” or “Annotate all clusters/Remove all cluster annotations”). The list of annotation terms also appears by right clicking on a cluster of interest on the Partition view, under “Clust&See>Annotate cluster”. At this stage, custom annotations can be added manually by the user to tag a cluster of interest. This annotation will also appear in the list of annotation terms in the “Total annotations” column of the “Cluster list” table. This may help having a global quantitative view of the cluster annotations and further analyses. The individual investigation of a particular cluster starts also under this tab. For instance, Cluster 60 contains 8/9 nodes annotated (number given in the “Nodes” column), and solely 1 term is statistically enriched among the 40 terms (number given in the “Annotation terms” column) that annotate the proteins of the cluster, when the hypergeometric law with a corrected p-value threshold set at 5.10 −2 is chosen. • “Cluster analysis” tab The term GO:2001136 is enriched among the annotations of the proteins of cluster 60 with a corrected p-value of 4,57.10 -4 , available in “Hyperg p-value” column under the “Cluster analysis” tab ( Figure 2B ). The most relevant term ( i.e. with the lowest p-value) is easily found by using the ranking column containing the corrected p-values for the terms annotating all the proteins of cluster 60. The term GO:2001136 that corresponds to “negative regulation of endocytic recycling”, is annotating 25% of the annotated proteins of the cluster (number in the “Majority percent” column). Two other terms are also annotating 25% of the cluster’s proteins, but with highest p-values i.e. less significant ones. These are GO:0007264 “small GTPase-mediated signal transduction” and GO:0051056 “regulation of small GTPase mediated signal transduction”. Then, switching to the “C&S Details” Table ( Figure 2D ), under the “Node” tab, that shows the detailed annotations of the nodes of the cluster, we see that the three terms are associated to the same two genes encoding the proteins involved in these functions: ENSG00000175220 (ARHGAP1) and ENSG00000241484 (ARHGAP8). By expanding the cluster 60 on the network panel ( Figure 1 , central panel), it can be seen that these two proteins do not interact directly but with the product of ENSG00000141985 (SH3GL1), a protein regulating endocytosis by recruiting proteins to the membrane. • “Annotation term analysis” tab Then, wondering whether other clusters are also annotated to “negative regulation of endocytic recycling”, we switched to the “Annotation term analysis” tab to focus on a particular annotation and find all clusters enriched for this term. By entering the ID GO:2001136 in the dedicated frame, we found that none of the clusters but cluster 60 is annotated to this term, either using the hypergeometric law or the majority rule computations ( Figure 2C ). The user can therefore choose to annotate this cluster with this enriched term and, if appropriate, to transfer the annotation/function to any not yet annotated node of the cluster. Notably, the nodes contributing to cluster’s annotations are detailed in the “C&S Details” table ( Figure 2D ). Export of the annotations and computations as text files are available at each step, as well as a matrix of the whole results under the “Cluster annotation matrix” tab, for further analysis. Conclusion/Discussion Clust&See3.0 is a Cytoscape app that allows (i) clustering the nodes of any network, (ii) annotating the clusters with any annotation terms, and (iii) computing their enrichment significance in a single pipeline. This versatility of Clust&See3.0 is constituting its advantage compared to other existing Cytoscape enrichment plug-ins. Not only Clust&See3.0 allows as in its previous version loading any partition of the network, even not generated by the app, as long as the graph is completely covered by clusters, but it also allows using any type of data as node annotations, such as user’s experimental or curated data. In contrast, most of the existing apps are mainly centered on Gene Ontology terms, Reactome 11 and KEGG 12 annotations only. Moreover, whereas Clust&See3.0 provides enrichment analyses automatically on all clusters of the investigated partition at once, the other tools such as BinGO 6 only computes statistical enrichments on a single cluster at a time. Second, the results of Clust&See3.0 permit to get a global view of the distribution of the annotations between the whole set of clusters, as well as their statistical value. For all these reasons, we think Clust&See3.0 will be a valuable tool for the community. Ethics and consent Ethics and consent not required. Data availability Zenodo: Clust&See3.0 : clustering, module exploration and annotation. https://doi.org/10.5281/zenodo.12570870 . 13 The project contains the following underlying data: • go_annot.txt: The protein annotation file extracted from Gene Ontology Biological Process database. 10 • HuRI_CC.txt: The network file containing the largest connect component of the human reference interactome network. 9 Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0). Software availability • Software available from: https://apps.cytoscape.org/apps/clustnsee3 • Source code available from: https://github.com/fafa13/ClustnSee-3 • Archived source code at time of publication: https://doi.org/10.5281/zenodo.13220735 . 14 • License: GNU General Public Licence, V3 References 1. Shannon P, Markiel A, Ozier O, et al. : Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003; 13 : 2498–2504. PubMed Abstract | Publisher Full Text | Free Full Text 2. Spinelli L, Gambette P, Chapple CE, et al. : Clust&See: a Cytoscape plugin for the identification, visualization and manipulation of network clusters. Biosystems. 2013; 113 : 91–95. Publisher Full Text 3. Guénoche A: Consensus of partitions: a constructive approach. ADAC. 2011; 5 : 215–229. Publisher Full Text 4. Gambette P, Guénoche A: Bootstrap clustering for graph partitioning. RAIRO - Operations Research. 2012; 45 : 339–352. Publisher Full Text 5. Becker E, Robisson B, Chapple CE, et al. : Multifunctional proteins revealed by overlapping clustering in protein interaction network. Bioinformatics. 2012; 28 : 84–90. PubMed Abstract | Publisher Full Text | Free Full Text 6. Morris JH, Apeltsin L, Newman AM, et al. : clusterMaker: a multi-algorithm clustering plugin for Cytoscape. BMC Bioinformatics. 2011 Nov 9; 12 : 436. PubMed Abstract | Publisher Full Text | Free Full Text 7. Maere S, Heymans K, Kuiper M: BiNGO: a Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks. Bioinformatics. 2005; 21 : 3448–3449. PubMed Abstract | Publisher Full Text 8. Benjamini Y, Hochberg Y: Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society: Series B (Methodological). 1995; 57 : 289–300. Publisher Full Text 9. Luck K, Kim D-K, Lambourne L, et al. : A reference map of the human binary protein interactome. Nature. 2020; 580 : 402–408. PubMed Abstract | Publisher Full Text | Free Full Text 10. Ashburner M, Ball CA, Blake JA, et al. : Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000; 25 : 25–29. PubMed Abstract | Publisher Full Text | Free Full Text 11. Milacic M, Beavers D, Conley P, et al. : The Reactome Pathway Knowledgebase 2024. Nucleic Acids Res. 2024 Jan 5; 52 (D1): D672–D678. PubMed Abstract | Publisher Full Text | Free Full Text 12. Kanehisa M, Furumichi M, Sato Y, et al. : KEGG: biological systems database as a model of the real world. Nucleic Acids Res. 2024 Oct 17: gkae909. PubMed Abstract | Publisher Full Text 13. Lopez F, Spinelli L, Brun C: Clust&See3.0 : clustering, module exploration and annotation. [Dataset]. Zenodo. 2024. Publisher Full Text 14. Theories and Approaches of Genomic Complexity: Cytoscape app ClustnSee 3. Zenodo. 2024. Publisher Full Text Comments on this article Comments (0) Version 2 VERSION 2 PUBLISHED 02 Sep 2024 ADD YOUR COMMENT Comment Author details Author details 1 TAGC (UMR1090), Aix-Marseille Université, INSERM, Turing Centre for Living Systems, Marseille, 13009, France 2 INSERM-CNRS, CIML, Turing Centre for Living Systems,, Aix-Marseille Univ, Marseille, France 3 CNRS, Marseille, 13009, France Fabrice Lopez Roles: Software Lionel Spinelli Roles: Formal Analysis, Methodology Christine Brun Roles: Conceptualization, Formal Analysis, Investigation, Supervision, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information Funding for article processing charges provided by Human Frontier Science Program grant RGP004/2023 to CB. Article Versions (2) version 2 Revised Published: 14 Nov 2024, 13:994 https://doi.org/10.12688/f1000research.152711.2 version 1 Published: 02 Sep 2024, 13:994 https://doi.org/10.12688/f1000research.152711.1 Copyright © 2024 Lopez F et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article Lopez F, Spinelli L and Brun C. Clust&See3.0 : clustering, module exploration and annotation [version 2; peer review: 2 approved, 1 approved with reservations] . F1000Research 2024, 13 :994 ( https://doi.org/10.12688/f1000research.152711.2 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 2 VERSION 2 PUBLISHED 14 Nov 2024 Revised Views 0 Cite How to cite this report: Hu L. Reviewer Report For: Clust&See3.0 : clustering, module exploration and annotation [version 2; peer review: 2 approved, 1 approved with reservations] . F1000Research 2024, 13 :994 ( https://doi.org/10.5256/f1000research.174224.r340656 ) The direct URL for this report is: https://f1000research.com/articles/13-994/v2#referee-response-340656 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 13 Dec 2024 Lun Hu , Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, China Approved VIEWS 0 https://doi.org/10.5256/f1000research.174224.r340656 All of my concerns have ... Continue reading READ ALL All of my concerns have been addressed in this revision. Competing Interests: No competing interests were disclosed. Reviewer Expertise: Complex network analysis I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Hu L. Reviewer Report For: Clust&See3.0 : clustering, module exploration and annotation [version 2; peer review: 2 approved, 1 approved with reservations] . F1000Research 2024, 13 :994 ( https://doi.org/10.5256/f1000research.174224.r340656 ) The direct URL for this report is: https://f1000research.com/articles/13-994/v2#referee-response-340656 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Ali A. Reviewer Report For: Clust&See3.0 : clustering, module exploration and annotation [version 2; peer review: 2 approved, 1 approved with reservations] . F1000Research 2024, 13 :994 ( https://doi.org/10.5256/f1000research.174224.r340655 ) The direct URL for this report is: https://f1000research.com/articles/13-994/v2#referee-response-340655 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 19 Nov 2024 Anooja Ali , REVA University, Bengaluru, Karnataka, India Approved VIEWS 0 https://doi.org/10.5256/f1000research.174224.r340655 No ... Continue reading READ ALL No further comments Competing Interests: No competing interests were disclosed. Reviewer Expertise: Bioinformatics, Deep learning , Computer Vision, Data mining, Computational Biology I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Ali A. Reviewer Report For: Clust&See3.0 : clustering, module exploration and annotation [version 2; peer review: 2 approved, 1 approved with reservations] . F1000Research 2024, 13 :994 ( https://doi.org/10.5256/f1000research.174224.r340655 ) The direct URL for this report is: https://f1000research.com/articles/13-994/v2#referee-response-340655 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Version 1 VERSION 1 PUBLISHED 02 Sep 2024 Views 0 Cite How to cite this report: Hu L. Reviewer Report For: Clust&See3.0 : clustering, module exploration and annotation [version 2; peer review: 2 approved, 1 approved with reservations] . F1000Research 2024, 13 :994 ( https://doi.org/10.5256/f1000research.167505.r320337 ) The direct URL for this report is: https://f1000research.com/articles/13-994/v1#referee-response-320337 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 31 Oct 2024 Lun Hu , Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, China Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.167505.r320337 In this paper, the authors propose Clust&See3.0, a novel version of a Cytoscape app that has been developed to identify, visualize and manipulate network clusters and modules. Here are some comments that may be helpful. 1. The introduction section needs to ... Continue reading READ ALL In this paper, the authors propose Clust&See3.0, a novel version of a Cytoscape app that has been developed to identify, visualize and manipulate network clusters and modules. Here are some comments that may be helpful. 1. The introduction section needs to be further strengthened. Since the proposed Clust&See is a Cytoscape for network clustering and analysis of biological data, the authors should provide relevant background and current needs. This will help to better illustrate the significance and applicability of the Cytoscape. 2. In the methods section, for the use of Clust&See3, the authors should provide a general process. Particularly, the differences between Clust&See3 and Clust&See should be emphasized. 3. The novel version of cluster&see3.0 allows users to annotate and analyze nodes and clusters, which is indeed very favorable. However, in the paper, the authors should focus on the advantages and implications of doing so. 4. In the conclusion section, the authors should briefly discuss the limitations and future improvements of Clust&See3.0. Is the rationale for developing the new software tool clearly explained? Yes Is the description of the software tool technically sound? No Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others? No Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool? No Are the conclusions about the tool and its performance adequately supported by the findings presented in the article? No Competing Interests: No competing interests were disclosed. Reviewer Expertise: Complex network analysis I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Hu L. Reviewer Report For: Clust&See3.0 : clustering, module exploration and annotation [version 2; peer review: 2 approved, 1 approved with reservations] . F1000Research 2024, 13 :994 ( https://doi.org/10.5256/f1000research.167505.r320337 ) The direct URL for this report is: https://f1000research.com/articles/13-994/v1#referee-response-320337 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 27 Nov 2024 Christine Brun , TAGC (UMR1090), Aix-Marseille Université, INSERM, Turing Centre for Living Systems, Marseille, 13009, France 27 Nov 2024 Author Response Dear Referee, We thank you for your positive review of our manuscript and acknowledge your remarks on our work. Please find below our point-by-point response. The ... Continue reading Dear Referee, We thank you for your positive review of our manuscript and acknowledge your remarks on our work. Please find below our point-by-point response. The introduction section needs to be further strengthened. Since the proposed Clust&See is a Cytoscape for network clustering and analysis of biological data, the authors should provide relevant background and current needs. This will help to better illustrate the significance and applicability of the Cytoscape. The current revised version of the introduction is answering this remark. The introduction has been modified to underline the needs. “Although Cytoscape Apps allowing the identification of clusters such as ClusterMaker 6 , cluster annotations and computation of enrichment statistics such as BinGO 7 , or combinations of those as proposed by Functional Enrichment Collection ( https://apps.cytoscape.org/apps/functionalenrichmentcollection) do exist, those proposing in a single tool the identification of clusters and their annotation with custom annotation files are seldom. We therefore felt there was room for a tool such as Clust&See3.0, that performs all in a single workflow.” In the methods section, for the use of Clust&See3, the authors should provide a general process. Particularly, the differences between Clust&See3 and Clust&See should be emphasized. The Method section is following the general process: 1- Decompose a network, 2- Cluster Visualization and exploration, 3- Importing nodes annotations, 4- Annotation rules, statistical enrichments, 5- Cluster and annotation analyses. While the two first steps were already possible with the first version of Clust&See, the step 3 to 5 are specific to Cluts&See3.0. Clust&See3.0 provides the handling of the annotations. In fact, Clust&See is included within Clust&See3.0 but has been re-coded to fulfill the Cytoscape3 format. 3. The novel version of cluster&see3.0 allows users to annotate and analyze nodes and clusters, which is indeed very favorable. However, in the paper, the authors should focus on the advantages and implications of doing so. As explained in the manuscript, the advantage of annotating clusters with Clust&See3.0 is the possibility of using custom annotations, allowing the analysis to exactly fit the will and the question of the user. Then, “For each annotation list, the user can perform a statistical analysis of the clusters and the annotation terms, with different goals. At first, a global analysis of the annotations of the partition can be performed. “…”This first type of analysis allows getting a global view of the annotation distribution and to quickly identify clusters that are enriched for annotation terms of interest.” 4. In the conclusion section, the authors should briefly discuss the limitations and future improvements of Clust&See3.0. The limitations of Clust&See3.0 could reside in the performances of the clustering algorithms. However, all our partitioning/clustering algorithms have been assessed previously by us and others. For instance, the performances of the 3 algorithms (i.e. FT, TFit, OCG) have been recognized to outperform 4 other algorithms on different biological datasets by measuring 5 performance indices (1). The comparison of OCG performances with other algorithms has also been performed by us, in the original publication (2) and by others (3). 1. Sharma P, Ahmed HA, Roy S, Bhattacharyya DK. Unsupervised methods for finding protein complexes from PPI networks. Netw Model Anal Health Inform Bioinforma. 2 juin 2015;4(1):8. 2. Becker E, Robisson B, Chapple CE, Guénoche A, Brun C. Multifunctional proteins revealed by overlapping clustering in protein interaction network. Bioinformatics. 1 janv 2012;28(1):84‑90. 3. Ding Z, Zhang X, Sun D, Luo B. Overlapping Community Detection based on Network Decomposition. Sci Rep. 12 avr 2016;6(1):24115. Is the rationale for developing the new software tool clearly explained? Yes Is the description of the software tool technically sound? No Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others? No As stated in the manuscript, the code is available on github and zenodo : https://github.com/fafa13/ClustnSee-3 , https://doi.org/10.5281/zenodo.13220735 Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool? No Are the conclusions about the tool and its performance adequately supported by the findings presented in the article? No On the behalf of all authors, Christine Brun Dear Referee, We thank you for your positive review of our manuscript and acknowledge your remarks on our work. Please find below our point-by-point response. The introduction section needs to be further strengthened. Since the proposed Clust&See is a Cytoscape for network clustering and analysis of biological data, the authors should provide relevant background and current needs. This will help to better illustrate the significance and applicability of the Cytoscape. The current revised version of the introduction is answering this remark. The introduction has been modified to underline the needs. “Although Cytoscape Apps allowing the identification of clusters such as ClusterMaker 6 , cluster annotations and computation of enrichment statistics such as BinGO 7 , or combinations of those as proposed by Functional Enrichment Collection ( https://apps.cytoscape.org/apps/functionalenrichmentcollection) do exist, those proposing in a single tool the identification of clusters and their annotation with custom annotation files are seldom. We therefore felt there was room for a tool such as Clust&See3.0, that performs all in a single workflow.” In the methods section, for the use of Clust&See3, the authors should provide a general process. Particularly, the differences between Clust&See3 and Clust&See should be emphasized. The Method section is following the general process: 1- Decompose a network, 2- Cluster Visualization and exploration, 3- Importing nodes annotations, 4- Annotation rules, statistical enrichments, 5- Cluster and annotation analyses. While the two first steps were already possible with the first version of Clust&See, the step 3 to 5 are specific to Cluts&See3.0. Clust&See3.0 provides the handling of the annotations. In fact, Clust&See is included within Clust&See3.0 but has been re-coded to fulfill the Cytoscape3 format. 3. The novel version of cluster&see3.0 allows users to annotate and analyze nodes and clusters, which is indeed very favorable. However, in the paper, the authors should focus on the advantages and implications of doing so. As explained in the manuscript, the advantage of annotating clusters with Clust&See3.0 is the possibility of using custom annotations, allowing the analysis to exactly fit the will and the question of the user. Then, “For each annotation list, the user can perform a statistical analysis of the clusters and the annotation terms, with different goals. At first, a global analysis of the annotations of the partition can be performed. “…”This first type of analysis allows getting a global view of the annotation distribution and to quickly identify clusters that are enriched for annotation terms of interest.” 4. In the conclusion section, the authors should briefly discuss the limitations and future improvements of Clust&See3.0. The limitations of Clust&See3.0 could reside in the performances of the clustering algorithms. However, all our partitioning/clustering algorithms have been assessed previously by us and others. For instance, the performances of the 3 algorithms (i.e. FT, TFit, OCG) have been recognized to outperform 4 other algorithms on different biological datasets by measuring 5 performance indices (1). The comparison of OCG performances with other algorithms has also been performed by us, in the original publication (2) and by others (3). 1. Sharma P, Ahmed HA, Roy S, Bhattacharyya DK. Unsupervised methods for finding protein complexes from PPI networks. Netw Model Anal Health Inform Bioinforma. 2 juin 2015;4(1):8. 2. Becker E, Robisson B, Chapple CE, Guénoche A, Brun C. Multifunctional proteins revealed by overlapping clustering in protein interaction network. Bioinformatics. 1 janv 2012;28(1):84‑90. 3. Ding Z, Zhang X, Sun D, Luo B. Overlapping Community Detection based on Network Decomposition. Sci Rep. 12 avr 2016;6(1):24115. Is the rationale for developing the new software tool clearly explained? Yes Is the description of the software tool technically sound? No Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others? No As stated in the manuscript, the code is available on github and zenodo : https://github.com/fafa13/ClustnSee-3 , https://doi.org/10.5281/zenodo.13220735 Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool? No Are the conclusions about the tool and its performance adequately supported by the findings presented in the article? No On the behalf of all authors, Christine Brun Competing Interests: No competing interests were disclosed. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 27 Nov 2024 Christine Brun , TAGC (UMR1090), Aix-Marseille Université, INSERM, Turing Centre for Living Systems, Marseille, 13009, France 27 Nov 2024 Author Response Dear Referee, We thank you for your positive review of our manuscript and acknowledge your remarks on our work. Please find below our point-by-point response. The ... Continue reading Dear Referee, We thank you for your positive review of our manuscript and acknowledge your remarks on our work. Please find below our point-by-point response. The introduction section needs to be further strengthened. Since the proposed Clust&See is a Cytoscape for network clustering and analysis of biological data, the authors should provide relevant background and current needs. This will help to better illustrate the significance and applicability of the Cytoscape. The current revised version of the introduction is answering this remark. The introduction has been modified to underline the needs. “Although Cytoscape Apps allowing the identification of clusters such as ClusterMaker 6 , cluster annotations and computation of enrichment statistics such as BinGO 7 , or combinations of those as proposed by Functional Enrichment Collection ( https://apps.cytoscape.org/apps/functionalenrichmentcollection) do exist, those proposing in a single tool the identification of clusters and their annotation with custom annotation files are seldom. We therefore felt there was room for a tool such as Clust&See3.0, that performs all in a single workflow.” In the methods section, for the use of Clust&See3, the authors should provide a general process. Particularly, the differences between Clust&See3 and Clust&See should be emphasized. The Method section is following the general process: 1- Decompose a network, 2- Cluster Visualization and exploration, 3- Importing nodes annotations, 4- Annotation rules, statistical enrichments, 5- Cluster and annotation analyses. While the two first steps were already possible with the first version of Clust&See, the step 3 to 5 are specific to Cluts&See3.0. Clust&See3.0 provides the handling of the annotations. In fact, Clust&See is included within Clust&See3.0 but has been re-coded to fulfill the Cytoscape3 format. 3. The novel version of cluster&see3.0 allows users to annotate and analyze nodes and clusters, which is indeed very favorable. However, in the paper, the authors should focus on the advantages and implications of doing so. As explained in the manuscript, the advantage of annotating clusters with Clust&See3.0 is the possibility of using custom annotations, allowing the analysis to exactly fit the will and the question of the user. Then, “For each annotation list, the user can perform a statistical analysis of the clusters and the annotation terms, with different goals. At first, a global analysis of the annotations of the partition can be performed. “…”This first type of analysis allows getting a global view of the annotation distribution and to quickly identify clusters that are enriched for annotation terms of interest.” 4. In the conclusion section, the authors should briefly discuss the limitations and future improvements of Clust&See3.0. The limitations of Clust&See3.0 could reside in the performances of the clustering algorithms. However, all our partitioning/clustering algorithms have been assessed previously by us and others. For instance, the performances of the 3 algorithms (i.e. FT, TFit, OCG) have been recognized to outperform 4 other algorithms on different biological datasets by measuring 5 performance indices (1). The comparison of OCG performances with other algorithms has also been performed by us, in the original publication (2) and by others (3). 1. Sharma P, Ahmed HA, Roy S, Bhattacharyya DK. Unsupervised methods for finding protein complexes from PPI networks. Netw Model Anal Health Inform Bioinforma. 2 juin 2015;4(1):8. 2. Becker E, Robisson B, Chapple CE, Guénoche A, Brun C. Multifunctional proteins revealed by overlapping clustering in protein interaction network. Bioinformatics. 1 janv 2012;28(1):84‑90. 3. Ding Z, Zhang X, Sun D, Luo B. Overlapping Community Detection based on Network Decomposition. Sci Rep. 12 avr 2016;6(1):24115. Is the rationale for developing the new software tool clearly explained? Yes Is the description of the software tool technically sound? No Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others? No As stated in the manuscript, the code is available on github and zenodo : https://github.com/fafa13/ClustnSee-3 , https://doi.org/10.5281/zenodo.13220735 Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool? No Are the conclusions about the tool and its performance adequately supported by the findings presented in the article? No On the behalf of all authors, Christine Brun Dear Referee, We thank you for your positive review of our manuscript and acknowledge your remarks on our work. Please find below our point-by-point response. The introduction section needs to be further strengthened. Since the proposed Clust&See is a Cytoscape for network clustering and analysis of biological data, the authors should provide relevant background and current needs. This will help to better illustrate the significance and applicability of the Cytoscape. The current revised version of the introduction is answering this remark. The introduction has been modified to underline the needs. “Although Cytoscape Apps allowing the identification of clusters such as ClusterMaker 6 , cluster annotations and computation of enrichment statistics such as BinGO 7 , or combinations of those as proposed by Functional Enrichment Collection ( https://apps.cytoscape.org/apps/functionalenrichmentcollection) do exist, those proposing in a single tool the identification of clusters and their annotation with custom annotation files are seldom. We therefore felt there was room for a tool such as Clust&See3.0, that performs all in a single workflow.” In the methods section, for the use of Clust&See3, the authors should provide a general process. Particularly, the differences between Clust&See3 and Clust&See should be emphasized. The Method section is following the general process: 1- Decompose a network, 2- Cluster Visualization and exploration, 3- Importing nodes annotations, 4- Annotation rules, statistical enrichments, 5- Cluster and annotation analyses. While the two first steps were already possible with the first version of Clust&See, the step 3 to 5 are specific to Cluts&See3.0. Clust&See3.0 provides the handling of the annotations. In fact, Clust&See is included within Clust&See3.0 but has been re-coded to fulfill the Cytoscape3 format. 3. The novel version of cluster&see3.0 allows users to annotate and analyze nodes and clusters, which is indeed very favorable. However, in the paper, the authors should focus on the advantages and implications of doing so. As explained in the manuscript, the advantage of annotating clusters with Clust&See3.0 is the possibility of using custom annotations, allowing the analysis to exactly fit the will and the question of the user. Then, “For each annotation list, the user can perform a statistical analysis of the clusters and the annotation terms, with different goals. At first, a global analysis of the annotations of the partition can be performed. “…”This first type of analysis allows getting a global view of the annotation distribution and to quickly identify clusters that are enriched for annotation terms of interest.” 4. In the conclusion section, the authors should briefly discuss the limitations and future improvements of Clust&See3.0. The limitations of Clust&See3.0 could reside in the performances of the clustering algorithms. However, all our partitioning/clustering algorithms have been assessed previously by us and others. For instance, the performances of the 3 algorithms (i.e. FT, TFit, OCG) have been recognized to outperform 4 other algorithms on different biological datasets by measuring 5 performance indices (1). The comparison of OCG performances with other algorithms has also been performed by us, in the original publication (2) and by others (3). 1. Sharma P, Ahmed HA, Roy S, Bhattacharyya DK. Unsupervised methods for finding protein complexes from PPI networks. Netw Model Anal Health Inform Bioinforma. 2 juin 2015;4(1):8. 2. Becker E, Robisson B, Chapple CE, Guénoche A, Brun C. Multifunctional proteins revealed by overlapping clustering in protein interaction network. Bioinformatics. 1 janv 2012;28(1):84‑90. 3. Ding Z, Zhang X, Sun D, Luo B. Overlapping Community Detection based on Network Decomposition. Sci Rep. 12 avr 2016;6(1):24115. Is the rationale for developing the new software tool clearly explained? Yes Is the description of the software tool technically sound? No Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others? No As stated in the manuscript, the code is available on github and zenodo : https://github.com/fafa13/ClustnSee-3 , https://doi.org/10.5281/zenodo.13220735 Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool? No Are the conclusions about the tool and its performance adequately supported by the findings presented in the article? No On the behalf of all authors, Christine Brun Competing Interests: No competing interests were disclosed. Close Report a concern COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Xiang J. Reviewer Report For: Clust&See3.0 : clustering, module exploration and annotation [version 2; peer review: 2 approved, 1 approved with reservations] . F1000Research 2024, 13 :994 ( https://doi.org/10.5256/f1000research.167505.r329845 ) The direct URL for this report is: https://f1000research.com/articles/13-994/v1#referee-response-329845 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 23 Oct 2024 Ju Xiang , Central South University, Changsha, Hunan, China Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.167505.r329845 The authors reported Clust&See3.0, a new version of Cytoscape app “Clust&See” for the identification, visualization and manipulation of network clusters. It provided functionalities allowing custom annotations and computation of statistical enrichments. It may be a useful tool of network cluster analyses. --It would be better ... Continue reading READ ALL The authors reported Clust&See3.0, a new version of Cytoscape app “Clust&See” for the identification, visualization and manipulation of network clusters. It provided functionalities allowing custom annotations and computation of statistical enrichments. It may be a useful tool of network cluster analyses. --It would be better to provide introduction for other tools of the same type in section introduction. --It is suggested to provide a table for comparing the different versions of Clust&See and other tools of the same type. --It is helpful for users to choose and use tools if the complexity of time and space for the algorithms in the tool can be provided. --Furthermore, it would be even better if comparisons with other tools could be provided. Is the rationale for developing the new software tool clearly explained? Yes Is the description of the software tool technically sound? Yes Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others? Partly Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool? Yes Are the conclusions about the tool and its performance adequately supported by the findings presented in the article? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: Bioinformatics, Data mining, ComputationalBiology I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Xiang J. Reviewer Report For: Clust&See3.0 : clustering, module exploration and annotation [version 2; peer review: 2 approved, 1 approved with reservations] . F1000Research 2024, 13 :994 ( https://doi.org/10.5256/f1000research.167505.r329845 ) The direct URL for this report is: https://f1000research.com/articles/13-994/v1#referee-response-329845 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 14 Nov 2024 Christine Brun , TAGC (UMR1090), Aix-Marseille Université, INSERM, Turing Centre for Living Systems, Marseille, 13009, France 14 Nov 2024 Author Response Dear Referee, We thank you for your positive review on our manuscript and acknowledge your remarks on our work. Please find below our point-by-point response. The authors ... Continue reading Dear Referee, We thank you for your positive review on our manuscript and acknowledge your remarks on our work. Please find below our point-by-point response. The authors reported Clust&See3.0, a new version of Cytoscape app “Clust&See” for the identification, visualization and manipulation of network clusters. It provided functionalities allowing custom annotations and computation of statistical enrichments. It may be a useful tool of network cluster analyses. We thank the reviewer to recognize the utility of our work. --It would be better to provide introduction for other tools of the same type in section introduction. Cytoscape Apps proposing in a single tool the identification of clusters AND their annotation with custom annotation files are seldom. We add this information in the introduction section in a new paragraph. “Although Cytoscape Apps allowing the identification of clusters such as ClusterMaker 6 , cluster annotations and computation of enrichment statistics such as BinGO 7 , or combinations of those as proposed by Functional Enrichment Collection ( https://apps.cytoscape.org/apps/functionalenrichmentcollection) do exist, those proposing in a single tool the identification of clusters and their annotation with custom annotation files are seldom. We therefore felt there was room for a tool such as Clust&See3.0, that performs all in a single workflow.” --It is suggested to provide a table for comparing the different versions of Clust&See and other tools of the same type. The previous Clust&See version is completely included in the new one. The differences between the two App versions lie in the statistical enrichment functionalities newly implemented in Clust&See3. Regarding other tools of the same type, the comparison is difficult for several reasons. As explain now in the text, other tools propose either to compute clusters, or to annotate them (one by one). Then collections such as Functional Enrichment Collection proposes to install several of these individual tools at once, but the user must perform the transfer of the results of the first tool to the second, etc., whereas Clust&See3.0 provided everything at once. Finally, CyCommunityDetection detect communities and annotate all clusters but not with custom annotations. In addition, it runs on a remote sever whereas Clust&See3.0 is locally installed, thus avoiding issues due to server accessibility or network connection. --It is helpful for users to choose and use tools if the complexity of time and space for the algorithms in the tool can be provided. OCG runs in O(n 3 ) with default parameters. The two others run faster. In an independent evaluation (9), we can read “OCG is an elite algorithm of high time efficiency…” --Furthermore, it would be even better if comparisons with other tools could be provided. As explained previously, a formal comparison is difficult, mainly because the purpose of the tools being different, the functionalities are different. However, all our partitioning/clustering algorithms have been assessed previously by us and others. For instance, the performances of the 3 algorithms (i.e. FT, TFit, OCG) have been recognized to outperform 4 other algorithms on different biological datasets by measuring 5 performance indices (1). The comparison of OCG performances with other algorithms has also been performed by us, in the original publication (2) and by others (3). 1. Sharma P, Ahmed HA, Roy S, Bhattacharyya DK. Unsupervised methods for finding protein complexes from PPI networks. Netw Model Anal Health Inform Bioinforma. 2 juin 2015;4(1):8. 2. Becker E, Robisson B, Chapple CE, Guénoche A, Brun C. Multifunctional proteins revealed by overlapping clustering in protein interaction network. Bioinformatics. 1 janv 2012;28(1):84‑90. 3. Ding Z, Zhang X, Sun D, Luo B. Overlapping Community Detection based on Network Decomposition. Sci Rep. 12 avr 2016;6(1):24115. On behalf of the authors, Christine Brun Dear Referee, We thank you for your positive review on our manuscript and acknowledge your remarks on our work. Please find below our point-by-point response. The authors reported Clust&See3.0, a new version of Cytoscape app “Clust&See” for the identification, visualization and manipulation of network clusters. It provided functionalities allowing custom annotations and computation of statistical enrichments. It may be a useful tool of network cluster analyses. We thank the reviewer to recognize the utility of our work. --It would be better to provide introduction for other tools of the same type in section introduction. Cytoscape Apps proposing in a single tool the identification of clusters AND their annotation with custom annotation files are seldom. We add this information in the introduction section in a new paragraph. “Although Cytoscape Apps allowing the identification of clusters such as ClusterMaker 6 , cluster annotations and computation of enrichment statistics such as BinGO 7 , or combinations of those as proposed by Functional Enrichment Collection ( https://apps.cytoscape.org/apps/functionalenrichmentcollection) do exist, those proposing in a single tool the identification of clusters and their annotation with custom annotation files are seldom. We therefore felt there was room for a tool such as Clust&See3.0, that performs all in a single workflow.” --It is suggested to provide a table for comparing the different versions of Clust&See and other tools of the same type. The previous Clust&See version is completely included in the new one. The differences between the two App versions lie in the statistical enrichment functionalities newly implemented in Clust&See3. Regarding other tools of the same type, the comparison is difficult for several reasons. As explain now in the text, other tools propose either to compute clusters, or to annotate them (one by one). Then collections such as Functional Enrichment Collection proposes to install several of these individual tools at once, but the user must perform the transfer of the results of the first tool to the second, etc., whereas Clust&See3.0 provided everything at once. Finally, CyCommunityDetection detect communities and annotate all clusters but not with custom annotations. In addition, it runs on a remote sever whereas Clust&See3.0 is locally installed, thus avoiding issues due to server accessibility or network connection. --It is helpful for users to choose and use tools if the complexity of time and space for the algorithms in the tool can be provided. OCG runs in O(n 3 ) with default parameters. The two others run faster. In an independent evaluation (9), we can read “OCG is an elite algorithm of high time efficiency…” --Furthermore, it would be even better if comparisons with other tools could be provided. As explained previously, a formal comparison is difficult, mainly because the purpose of the tools being different, the functionalities are different. However, all our partitioning/clustering algorithms have been assessed previously by us and others. For instance, the performances of the 3 algorithms (i.e. FT, TFit, OCG) have been recognized to outperform 4 other algorithms on different biological datasets by measuring 5 performance indices (1). The comparison of OCG performances with other algorithms has also been performed by us, in the original publication (2) and by others (3). 1. Sharma P, Ahmed HA, Roy S, Bhattacharyya DK. Unsupervised methods for finding protein complexes from PPI networks. Netw Model Anal Health Inform Bioinforma. 2 juin 2015;4(1):8. 2. Becker E, Robisson B, Chapple CE, Guénoche A, Brun C. Multifunctional proteins revealed by overlapping clustering in protein interaction network. Bioinformatics. 1 janv 2012;28(1):84‑90. 3. Ding Z, Zhang X, Sun D, Luo B. Overlapping Community Detection based on Network Decomposition. Sci Rep. 12 avr 2016;6(1):24115. On behalf of the authors, Christine Brun Competing Interests: No competing interests were disclosed. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 14 Nov 2024 Christine Brun , TAGC (UMR1090), Aix-Marseille Université, INSERM, Turing Centre for Living Systems, Marseille, 13009, France 14 Nov 2024 Author Response Dear Referee, We thank you for your positive review on our manuscript and acknowledge your remarks on our work. Please find below our point-by-point response. The authors ... Continue reading Dear Referee, We thank you for your positive review on our manuscript and acknowledge your remarks on our work. Please find below our point-by-point response. The authors reported Clust&See3.0, a new version of Cytoscape app “Clust&See” for the identification, visualization and manipulation of network clusters. It provided functionalities allowing custom annotations and computation of statistical enrichments. It may be a useful tool of network cluster analyses. We thank the reviewer to recognize the utility of our work. --It would be better to provide introduction for other tools of the same type in section introduction. Cytoscape Apps proposing in a single tool the identification of clusters AND their annotation with custom annotation files are seldom. We add this information in the introduction section in a new paragraph. “Although Cytoscape Apps allowing the identification of clusters such as ClusterMaker 6 , cluster annotations and computation of enrichment statistics such as BinGO 7 , or combinations of those as proposed by Functional Enrichment Collection ( https://apps.cytoscape.org/apps/functionalenrichmentcollection) do exist, those proposing in a single tool the identification of clusters and their annotation with custom annotation files are seldom. We therefore felt there was room for a tool such as Clust&See3.0, that performs all in a single workflow.” --It is suggested to provide a table for comparing the different versions of Clust&See and other tools of the same type. The previous Clust&See version is completely included in the new one. The differences between the two App versions lie in the statistical enrichment functionalities newly implemented in Clust&See3. Regarding other tools of the same type, the comparison is difficult for several reasons. As explain now in the text, other tools propose either to compute clusters, or to annotate them (one by one). Then collections such as Functional Enrichment Collection proposes to install several of these individual tools at once, but the user must perform the transfer of the results of the first tool to the second, etc., whereas Clust&See3.0 provided everything at once. Finally, CyCommunityDetection detect communities and annotate all clusters but not with custom annotations. In addition, it runs on a remote sever whereas Clust&See3.0 is locally installed, thus avoiding issues due to server accessibility or network connection. --It is helpful for users to choose and use tools if the complexity of time and space for the algorithms in the tool can be provided. OCG runs in O(n 3 ) with default parameters. The two others run faster. In an independent evaluation (9), we can read “OCG is an elite algorithm of high time efficiency…” --Furthermore, it would be even better if comparisons with other tools could be provided. As explained previously, a formal comparison is difficult, mainly because the purpose of the tools being different, the functionalities are different. However, all our partitioning/clustering algorithms have been assessed previously by us and others. For instance, the performances of the 3 algorithms (i.e. FT, TFit, OCG) have been recognized to outperform 4 other algorithms on different biological datasets by measuring 5 performance indices (1). The comparison of OCG performances with other algorithms has also been performed by us, in the original publication (2) and by others (3). 1. Sharma P, Ahmed HA, Roy S, Bhattacharyya DK. Unsupervised methods for finding protein complexes from PPI networks. Netw Model Anal Health Inform Bioinforma. 2 juin 2015;4(1):8. 2. Becker E, Robisson B, Chapple CE, Guénoche A, Brun C. Multifunctional proteins revealed by overlapping clustering in protein interaction network. Bioinformatics. 1 janv 2012;28(1):84‑90. 3. Ding Z, Zhang X, Sun D, Luo B. Overlapping Community Detection based on Network Decomposition. Sci Rep. 12 avr 2016;6(1):24115. On behalf of the authors, Christine Brun Dear Referee, We thank you for your positive review on our manuscript and acknowledge your remarks on our work. Please find below our point-by-point response. The authors reported Clust&See3.0, a new version of Cytoscape app “Clust&See” for the identification, visualization and manipulation of network clusters. It provided functionalities allowing custom annotations and computation of statistical enrichments. It may be a useful tool of network cluster analyses. We thank the reviewer to recognize the utility of our work. --It would be better to provide introduction for other tools of the same type in section introduction. Cytoscape Apps proposing in a single tool the identification of clusters AND their annotation with custom annotation files are seldom. We add this information in the introduction section in a new paragraph. “Although Cytoscape Apps allowing the identification of clusters such as ClusterMaker 6 , cluster annotations and computation of enrichment statistics such as BinGO 7 , or combinations of those as proposed by Functional Enrichment Collection ( https://apps.cytoscape.org/apps/functionalenrichmentcollection) do exist, those proposing in a single tool the identification of clusters and their annotation with custom annotation files are seldom. We therefore felt there was room for a tool such as Clust&See3.0, that performs all in a single workflow.” --It is suggested to provide a table for comparing the different versions of Clust&See and other tools of the same type. The previous Clust&See version is completely included in the new one. The differences between the two App versions lie in the statistical enrichment functionalities newly implemented in Clust&See3. Regarding other tools of the same type, the comparison is difficult for several reasons. As explain now in the text, other tools propose either to compute clusters, or to annotate them (one by one). Then collections such as Functional Enrichment Collection proposes to install several of these individual tools at once, but the user must perform the transfer of the results of the first tool to the second, etc., whereas Clust&See3.0 provided everything at once. Finally, CyCommunityDetection detect communities and annotate all clusters but not with custom annotations. In addition, it runs on a remote sever whereas Clust&See3.0 is locally installed, thus avoiding issues due to server accessibility or network connection. --It is helpful for users to choose and use tools if the complexity of time and space for the algorithms in the tool can be provided. OCG runs in O(n 3 ) with default parameters. The two others run faster. In an independent evaluation (9), we can read “OCG is an elite algorithm of high time efficiency…” --Furthermore, it would be even better if comparisons with other tools could be provided. As explained previously, a formal comparison is difficult, mainly because the purpose of the tools being different, the functionalities are different. However, all our partitioning/clustering algorithms have been assessed previously by us and others. For instance, the performances of the 3 algorithms (i.e. FT, TFit, OCG) have been recognized to outperform 4 other algorithms on different biological datasets by measuring 5 performance indices (1). The comparison of OCG performances with other algorithms has also been performed by us, in the original publication (2) and by others (3). 1. Sharma P, Ahmed HA, Roy S, Bhattacharyya DK. Unsupervised methods for finding protein complexes from PPI networks. Netw Model Anal Health Inform Bioinforma. 2 juin 2015;4(1):8. 2. Becker E, Robisson B, Chapple CE, Guénoche A, Brun C. Multifunctional proteins revealed by overlapping clustering in protein interaction network. Bioinformatics. 1 janv 2012;28(1):84‑90. 3. Ding Z, Zhang X, Sun D, Luo B. Overlapping Community Detection based on Network Decomposition. Sci Rep. 12 avr 2016;6(1):24115. On behalf of the authors, Christine Brun Competing Interests: No competing interests were disclosed. Close Report a concern COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Ali A. Reviewer Report For: Clust&See3.0 : clustering, module exploration and annotation [version 2; peer review: 2 approved, 1 approved with reservations] . F1000Research 2024, 13 :994 ( https://doi.org/10.5256/f1000research.167505.r320335 ) The direct URL for this report is: https://f1000research.com/articles/13-994/v1#referee-response-320335 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 10 Sep 2024 Anooja Ali , REVA University, Bengaluru, Karnataka, India Approved VIEWS 0 https://doi.org/10.5256/f1000research.167505.r320335 The authors propose Clust&See3.0, an improved version of Cytoscape, for visualizing and manipulating the bioinformatic network clusters. It has additional functionalities , when compared to Cytoscape like custom annotations of nodes. It provided more statistical analysis for cluster evaluation. ... Continue reading READ ALL The authors propose Clust&See3.0, an improved version of Cytoscape, for visualizing and manipulating the bioinformatic network clusters. It has additional functionalities , when compared to Cytoscape like custom annotations of nodes. It provided more statistical analysis for cluster evaluation. How does the user interface of Clust&See3.0 compare to previous versions in terms of usability and accessibility for biologists unfamiliar with network analysis? What are the key characteristics of clusters identified in large protein-protein interaction networks using Clust&See3.0? How do these characteristics correlate with biological functions? How does the annotation and enrichment analysis provided by Clust&See3.0 contribute to the accuracy of protein function predictions in complex biological networks? I hope the user feedback on Clust&See3.0's functionalities inform future updates and enhancements to the software. The citations refer to the aligners for creating a huge protein network based on functional similarities among proteins. The next citation uses cytoscape tool with KEGG pathway analysis and GO annotation tool for generating biclusters. It deals with the generation of gene ontology clusters for each category of MF, BP and CC from biclusters. Is the rationale for developing the new software tool clearly explained? Yes Is the description of the software tool technically sound? Yes Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others? Yes Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool? Yes Are the conclusions about the tool and its performance adequately supported by the findings presented in the article? Yes References 1. Ali A, Ajil A, Meenakshi Sundaram A, Joseph N: Detection of Gene Ontology Clusters Using Biclustering Algorithms. SN Computer Science . 2023; 4 (3). Publisher Full Text 2. H V, Ramachandra Anooja, Ali P S, Ambili S, et al.: An Optimization on Bicluster Algorithm for Gene Expression Data. IEEE explore . 2023. Publisher Full Text 3. A, Ali H. V. Ramachandra, Sundaram A. Ajil, Ramakrishnan: Pareto Optimization Technique for Protein Motif Detection in Genomic Data Set. Springer link . 2023. Publisher Full Text Competing Interests: No competing interests were disclosed. Reviewer Expertise: Bioinformatics, Deep learning , Computer Vision, Data mining, Computational Biology I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Ali A. Reviewer Report For: Clust&See3.0 : clustering, module exploration and annotation [version 2; peer review: 2 approved, 1 approved with reservations] . F1000Research 2024, 13 :994 ( https://doi.org/10.5256/f1000research.167505.r320335 ) The direct URL for this report is: https://f1000research.com/articles/13-994/v1#referee-response-320335 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 14 Nov 2024 Christine Brun , TAGC (UMR1090), Aix-Marseille Université, INSERM, Turing Centre for Living Systems, Marseille, 13009, France 14 Nov 2024 Author Response Dear Referee, We thank you for your positive review on our manuscript and acknowledge your remarks on our work. Please find below our point-by-point response. The authors propose ... Continue reading Dear Referee, We thank you for your positive review on our manuscript and acknowledge your remarks on our work. Please find below our point-by-point response. The authors propose Clust&See3.0, an improved version of Cytoscape, for visualizing and manipulating the bioinformatic network clusters. It has additional functionalities , when compared to Cytoscape like custom annotations of nodes. It provided more statistical analysis for cluster evaluation. - How does the user interface of Clust&See3.0 compare to previous versions in terms of usability and accessibility for biologists unfamiliar with network analysis? Author response: The previous version of Clust&See had been developed as an app for Cytoscape2.8, which had a completely different architecture and is almost not used anymore. The new Clust&See3.0 has been completely re-developed to comply with Cytoscape3 architecture. Clust&See3.0 therefore benefits in term of usability and accessibility for biologists from the Cytoscape3 features. Note that Cytoscape3 is far more intuitive than Cytoscape2. In addition, to increase the usability of Clust&See3.0 user interface compared to the previous version, we have significantly improved the interconnection of graphical elements and their responsiveness to user actions, so that the user's experience can be enhanced. - What are the key characteristics of clusters identified in large protein-protein interaction networks using Clust&See3.0? How do these characteristics correlate with biological functions? Author response: Both versions of Clust&See propose the same 3 algorithms to identify clusters: FT, TFit and OCG. Most of the clusters found by these algorithms display a biological homogeneity, i.e. they contain proteins involved in the same pathway, the same biological process. This is illustrated by our own network biology analyses in different contexts (1–7) as well as by other authors: the algorithms provided by Clust&See have been used in more than 40 analyses (49 citations in Google Scholar since its first publication (8)). - How does the annotation and enrichment analysis provided by Clust&See3.0 contribute to the accuracy of protein function predictions in complex biological networks? Author response: The advantage of Clust&See3 is twofold. First, any annotation file can be loaded in Clust&See3, allowing the user to highlight clusters of interest at a glance. Second, according to the goal of the analysis, the user can choose between (i) the accuracy of the annotation using the hypergeometric law (e.g. to highlight clusters containing nodes annotated with rare terms) and (ii) the homogeneity of the annotation with the majority rule (e.g. to highlight clusters containing nodes annotated with the same terms). 1. Kim DK, Weller B, Lin CW, Sheykhkarimli D, Knapp JJ, Dugied G, et al. A proteome-scale map of the SARS-CoV-2-human contactome. Nat Biotechnol. janv 2023;41(1):140‑9. 2. Katsogiannou M, Andrieu C, Baylot V, Baudot A, Dusetti NJ, Gayet O, et al. The functional landscape of Hsp27 reveals new cellular processes such as DNA repair and alternative splicing and proposes novel anticancer targets. Mol Cell Proteomics. déc 2014;13(12):3585‑601. 3. Zanzoni A, Spinelli L, Braham S, Brun C. Perturbed human sub-networks by Fusobacterium nucleatum candidate virulence proteins. Microbiome. 10 août 2017;5(1):89. 4. Zanzoni A, Spinelli L, Ribeiro DM, Tartaglia GG, Brun C. Post-transcriptional regulatory patterns revealed by protein-RNA interactions. Sci Rep. 13 mars 2019;9(1):4302. 5. Zanzoni A, Brun C. Integration of quantitative proteomics data and interaction networks: Identification of dysregulated cellular functions during cancer progression. Methods. 2016;93:103‑9. 6. Ribeiro DM, Zanzoni A, Cipriano A, Delli Ponti R, Spinelli L, Ballarino M, et al. Protein complex scaffolding predicted as a prevalent function of long non-coding RNAs. Nucleic Acids Res. 25 janv 2018;46(2):917‑28. 7. Ribeiro DM, Prod’homme A, Teixeira A, Zanzoni A, Brun C. The role of 3’UTR-protein complexes in the regulation of protein multifunctionality and subcellular localization. Nucleic Acids Res. 9 juill 2020;48(12):6491‑502. 8. Spinelli L, Gambette P, Chapple CE, Robisson B, Baudot A, Garreta H, et al. Clust&See: a Cytoscape plugin for the identification, visualization and manipulation of network clusters. BioSystems. août 2013;113(2):91‑5. On behalf of the authors, Christine Brun Dear Referee, We thank you for your positive review on our manuscript and acknowledge your remarks on our work. Please find below our point-by-point response. The authors propose Clust&See3.0, an improved version of Cytoscape, for visualizing and manipulating the bioinformatic network clusters. It has additional functionalities , when compared to Cytoscape like custom annotations of nodes. It provided more statistical analysis for cluster evaluation. - How does the user interface of Clust&See3.0 compare to previous versions in terms of usability and accessibility for biologists unfamiliar with network analysis? Author response: The previous version of Clust&See had been developed as an app for Cytoscape2.8, which had a completely different architecture and is almost not used anymore. The new Clust&See3.0 has been completely re-developed to comply with Cytoscape3 architecture. Clust&See3.0 therefore benefits in term of usability and accessibility for biologists from the Cytoscape3 features. Note that Cytoscape3 is far more intuitive than Cytoscape2. In addition, to increase the usability of Clust&See3.0 user interface compared to the previous version, we have significantly improved the interconnection of graphical elements and their responsiveness to user actions, so that the user's experience can be enhanced. - What are the key characteristics of clusters identified in large protein-protein interaction networks using Clust&See3.0? How do these characteristics correlate with biological functions? Author response: Both versions of Clust&See propose the same 3 algorithms to identify clusters: FT, TFit and OCG. Most of the clusters found by these algorithms display a biological homogeneity, i.e. they contain proteins involved in the same pathway, the same biological process. This is illustrated by our own network biology analyses in different contexts (1–7) as well as by other authors: the algorithms provided by Clust&See have been used in more than 40 analyses (49 citations in Google Scholar since its first publication (8)). - How does the annotation and enrichment analysis provided by Clust&See3.0 contribute to the accuracy of protein function predictions in complex biological networks? Author response: The advantage of Clust&See3 is twofold. First, any annotation file can be loaded in Clust&See3, allowing the user to highlight clusters of interest at a glance. Second, according to the goal of the analysis, the user can choose between (i) the accuracy of the annotation using the hypergeometric law (e.g. to highlight clusters containing nodes annotated with rare terms) and (ii) the homogeneity of the annotation with the majority rule (e.g. to highlight clusters containing nodes annotated with the same terms). 1. Kim DK, Weller B, Lin CW, Sheykhkarimli D, Knapp JJ, Dugied G, et al. A proteome-scale map of the SARS-CoV-2-human contactome. Nat Biotechnol. janv 2023;41(1):140‑9. 2. Katsogiannou M, Andrieu C, Baylot V, Baudot A, Dusetti NJ, Gayet O, et al. The functional landscape of Hsp27 reveals new cellular processes such as DNA repair and alternative splicing and proposes novel anticancer targets. Mol Cell Proteomics. déc 2014;13(12):3585‑601. 3. Zanzoni A, Spinelli L, Braham S, Brun C. Perturbed human sub-networks by Fusobacterium nucleatum candidate virulence proteins. Microbiome. 10 août 2017;5(1):89. 4. Zanzoni A, Spinelli L, Ribeiro DM, Tartaglia GG, Brun C. Post-transcriptional regulatory patterns revealed by protein-RNA interactions. Sci Rep. 13 mars 2019;9(1):4302. 5. Zanzoni A, Brun C. Integration of quantitative proteomics data and interaction networks: Identification of dysregulated cellular functions during cancer progression. Methods. 2016;93:103‑9. 6. Ribeiro DM, Zanzoni A, Cipriano A, Delli Ponti R, Spinelli L, Ballarino M, et al. Protein complex scaffolding predicted as a prevalent function of long non-coding RNAs. Nucleic Acids Res. 25 janv 2018;46(2):917‑28. 7. Ribeiro DM, Prod’homme A, Teixeira A, Zanzoni A, Brun C. The role of 3’UTR-protein complexes in the regulation of protein multifunctionality and subcellular localization. Nucleic Acids Res. 9 juill 2020;48(12):6491‑502. 8. Spinelli L, Gambette P, Chapple CE, Robisson B, Baudot A, Garreta H, et al. Clust&See: a Cytoscape plugin for the identification, visualization and manipulation of network clusters. BioSystems. août 2013;113(2):91‑5. On behalf of the authors, Christine Brun Competing Interests: No competing interests were disclosed. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 14 Nov 2024 Christine Brun , TAGC (UMR1090), Aix-Marseille Université, INSERM, Turing Centre for Living Systems, Marseille, 13009, France 14 Nov 2024 Author Response Dear Referee, We thank you for your positive review on our manuscript and acknowledge your remarks on our work. Please find below our point-by-point response. The authors propose ... Continue reading Dear Referee, We thank you for your positive review on our manuscript and acknowledge your remarks on our work. Please find below our point-by-point response. The authors propose Clust&See3.0, an improved version of Cytoscape, for visualizing and manipulating the bioinformatic network clusters. It has additional functionalities , when compared to Cytoscape like custom annotations of nodes. It provided more statistical analysis for cluster evaluation. - How does the user interface of Clust&See3.0 compare to previous versions in terms of usability and accessibility for biologists unfamiliar with network analysis? Author response: The previous version of Clust&See had been developed as an app for Cytoscape2.8, which had a completely different architecture and is almost not used anymore. The new Clust&See3.0 has been completely re-developed to comply with Cytoscape3 architecture. Clust&See3.0 therefore benefits in term of usability and accessibility for biologists from the Cytoscape3 features. Note that Cytoscape3 is far more intuitive than Cytoscape2. In addition, to increase the usability of Clust&See3.0 user interface compared to the previous version, we have significantly improved the interconnection of graphical elements and their responsiveness to user actions, so that the user's experience can be enhanced. - What are the key characteristics of clusters identified in large protein-protein interaction networks using Clust&See3.0? How do these characteristics correlate with biological functions? Author response: Both versions of Clust&See propose the same 3 algorithms to identify clusters: FT, TFit and OCG. Most of the clusters found by these algorithms display a biological homogeneity, i.e. they contain proteins involved in the same pathway, the same biological process. This is illustrated by our own network biology analyses in different contexts (1–7) as well as by other authors: the algorithms provided by Clust&See have been used in more than 40 analyses (49 citations in Google Scholar since its first publication (8)). - How does the annotation and enrichment analysis provided by Clust&See3.0 contribute to the accuracy of protein function predictions in complex biological networks? Author response: The advantage of Clust&See3 is twofold. First, any annotation file can be loaded in Clust&See3, allowing the user to highlight clusters of interest at a glance. Second, according to the goal of the analysis, the user can choose between (i) the accuracy of the annotation using the hypergeometric law (e.g. to highlight clusters containing nodes annotated with rare terms) and (ii) the homogeneity of the annotation with the majority rule (e.g. to highlight clusters containing nodes annotated with the same terms). 1. Kim DK, Weller B, Lin CW, Sheykhkarimli D, Knapp JJ, Dugied G, et al. A proteome-scale map of the SARS-CoV-2-human contactome. Nat Biotechnol. janv 2023;41(1):140‑9. 2. Katsogiannou M, Andrieu C, Baylot V, Baudot A, Dusetti NJ, Gayet O, et al. The functional landscape of Hsp27 reveals new cellular processes such as DNA repair and alternative splicing and proposes novel anticancer targets. Mol Cell Proteomics. déc 2014;13(12):3585‑601. 3. Zanzoni A, Spinelli L, Braham S, Brun C. Perturbed human sub-networks by Fusobacterium nucleatum candidate virulence proteins. Microbiome. 10 août 2017;5(1):89. 4. Zanzoni A, Spinelli L, Ribeiro DM, Tartaglia GG, Brun C. Post-transcriptional regulatory patterns revealed by protein-RNA interactions. Sci Rep. 13 mars 2019;9(1):4302. 5. Zanzoni A, Brun C. Integration of quantitative proteomics data and interaction networks: Identification of dysregulated cellular functions during cancer progression. Methods. 2016;93:103‑9. 6. Ribeiro DM, Zanzoni A, Cipriano A, Delli Ponti R, Spinelli L, Ballarino M, et al. Protein complex scaffolding predicted as a prevalent function of long non-coding RNAs. Nucleic Acids Res. 25 janv 2018;46(2):917‑28. 7. Ribeiro DM, Prod’homme A, Teixeira A, Zanzoni A, Brun C. The role of 3’UTR-protein complexes in the regulation of protein multifunctionality and subcellular localization. Nucleic Acids Res. 9 juill 2020;48(12):6491‑502. 8. Spinelli L, Gambette P, Chapple CE, Robisson B, Baudot A, Garreta H, et al. Clust&See: a Cytoscape plugin for the identification, visualization and manipulation of network clusters. BioSystems. août 2013;113(2):91‑5. On behalf of the authors, Christine Brun Dear Referee, We thank you for your positive review on our manuscript and acknowledge your remarks on our work. Please find below our point-by-point response. The authors propose Clust&See3.0, an improved version of Cytoscape, for visualizing and manipulating the bioinformatic network clusters. It has additional functionalities , when compared to Cytoscape like custom annotations of nodes. It provided more statistical analysis for cluster evaluation. - How does the user interface of Clust&See3.0 compare to previous versions in terms of usability and accessibility for biologists unfamiliar with network analysis? Author response: The previous version of Clust&See had been developed as an app for Cytoscape2.8, which had a completely different architecture and is almost not used anymore. The new Clust&See3.0 has been completely re-developed to comply with Cytoscape3 architecture. Clust&See3.0 therefore benefits in term of usability and accessibility for biologists from the Cytoscape3 features. Note that Cytoscape3 is far more intuitive than Cytoscape2. In addition, to increase the usability of Clust&See3.0 user interface compared to the previous version, we have significantly improved the interconnection of graphical elements and their responsiveness to user actions, so that the user's experience can be enhanced. - What are the key characteristics of clusters identified in large protein-protein interaction networks using Clust&See3.0? How do these characteristics correlate with biological functions? Author response: Both versions of Clust&See propose the same 3 algorithms to identify clusters: FT, TFit and OCG. Most of the clusters found by these algorithms display a biological homogeneity, i.e. they contain proteins involved in the same pathway, the same biological process. This is illustrated by our own network biology analyses in different contexts (1–7) as well as by other authors: the algorithms provided by Clust&See have been used in more than 40 analyses (49 citations in Google Scholar since its first publication (8)). - How does the annotation and enrichment analysis provided by Clust&See3.0 contribute to the accuracy of protein function predictions in complex biological networks? Author response: The advantage of Clust&See3 is twofold. First, any annotation file can be loaded in Clust&See3, allowing the user to highlight clusters of interest at a glance. Second, according to the goal of the analysis, the user can choose between (i) the accuracy of the annotation using the hypergeometric law (e.g. to highlight clusters containing nodes annotated with rare terms) and (ii) the homogeneity of the annotation with the majority rule (e.g. to highlight clusters containing nodes annotated with the same terms). 1. Kim DK, Weller B, Lin CW, Sheykhkarimli D, Knapp JJ, Dugied G, et al. A proteome-scale map of the SARS-CoV-2-human contactome. Nat Biotechnol. janv 2023;41(1):140‑9. 2. Katsogiannou M, Andrieu C, Baylot V, Baudot A, Dusetti NJ, Gayet O, et al. The functional landscape of Hsp27 reveals new cellular processes such as DNA repair and alternative splicing and proposes novel anticancer targets. Mol Cell Proteomics. déc 2014;13(12):3585‑601. 3. Zanzoni A, Spinelli L, Braham S, Brun C. Perturbed human sub-networks by Fusobacterium nucleatum candidate virulence proteins. Microbiome. 10 août 2017;5(1):89. 4. Zanzoni A, Spinelli L, Ribeiro DM, Tartaglia GG, Brun C. Post-transcriptional regulatory patterns revealed by protein-RNA interactions. Sci Rep. 13 mars 2019;9(1):4302. 5. Zanzoni A, Brun C. Integration of quantitative proteomics data and interaction networks: Identification of dysregulated cellular functions during cancer progression. Methods. 2016;93:103‑9. 6. Ribeiro DM, Zanzoni A, Cipriano A, Delli Ponti R, Spinelli L, Ballarino M, et al. Protein complex scaffolding predicted as a prevalent function of long non-coding RNAs. Nucleic Acids Res. 25 janv 2018;46(2):917‑28. 7. Ribeiro DM, Prod’homme A, Teixeira A, Zanzoni A, Brun C. The role of 3’UTR-protein complexes in the regulation of protein multifunctionality and subcellular localization. Nucleic Acids Res. 9 juill 2020;48(12):6491‑502. 8. Spinelli L, Gambette P, Chapple CE, Robisson B, Baudot A, Garreta H, et al. Clust&See: a Cytoscape plugin for the identification, visualization and manipulation of network clusters. BioSystems. août 2013;113(2):91‑5. On behalf of the authors, Christine Brun Competing Interests: No competing interests were disclosed. Close Report a concern COMMENT ON THIS REPORT Comments on this article Comments (0) Version 2 VERSION 2 PUBLISHED 02 Sep 2024 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 2 3 Version 2 (revision) 14 Nov 24 read read Version 1 02 Sep 24 read read read Anooja Ali , REVA University, Bengaluru, India Ju Xiang , Central South University, Changsha, China Lun Hu , Chinese Academy of Sciences, Urumqi, China Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2024 Hu L. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 13 Dec 2024 | for Version 2 Lun Hu , Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, China 0 Views copyright © 2024 Hu L. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions All of my concerns have been addressed in this revision. Competing Interests No competing interests were disclosed. Reviewer Expertise Complex network analysis I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (0) Hu L. Peer Review Report For: Clust&See3.0 : clustering, module exploration and annotation [version 2; peer review: 2 approved, 1 approved with reservations] . F1000Research 2024, 13 :994 ( https://doi.org/10.5256/f1000research.174224.r340656) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-994/v2#referee-response-340656 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2024 Ali A. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 19 Nov 2024 | for Version 2 Anooja Ali , REVA University, Bengaluru, Karnataka, India 0 Views copyright © 2024 Ali A. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions No further comments Competing Interests No competing interests were disclosed. Reviewer Expertise Bioinformatics, Deep learning , Computer Vision, Data mining, Computational Biology I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (0) Ali A. Peer Review Report For: Clust&See3.0 : clustering, module exploration and annotation [version 2; peer review: 2 approved, 1 approved with reservations] . F1000Research 2024, 13 :994 ( https://doi.org/10.5256/f1000research.174224.r340655) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-994/v2#referee-response-340655 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2024 Hu L. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 31 Oct 2024 | for Version 1 Lun Hu , Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, China 0 Views copyright © 2024 Hu L. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions In this paper, the authors propose Clust&See3.0, a novel version of a Cytoscape app that has been developed to identify, visualize and manipulate network clusters and modules. Here are some comments that may be helpful. 1. The introduction section needs to be further strengthened. Since the proposed Clust&See is a Cytoscape for network clustering and analysis of biological data, the authors should provide relevant background and current needs. This will help to better illustrate the significance and applicability of the Cytoscape. 2. In the methods section, for the use of Clust&See3, the authors should provide a general process. Particularly, the differences between Clust&See3 and Clust&See should be emphasized. 3. The novel version of cluster&see3.0 allows users to annotate and analyze nodes and clusters, which is indeed very favorable. However, in the paper, the authors should focus on the advantages and implications of doing so. 4. In the conclusion section, the authors should briefly discuss the limitations and future improvements of Clust&See3.0. Is the rationale for developing the new software tool clearly explained? Yes Is the description of the software tool technically sound? No Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others? No Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool? No Are the conclusions about the tool and its performance adequately supported by the findings presented in the article? No Competing Interests No competing interests were disclosed. Reviewer Expertise Complex network analysis I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (1) Author Response 27 Nov 2024 Christine Brun, TAGC (UMR1090), Aix-Marseille Université, INSERM, Turing Centre for Living Systems, Marseille, 13009, France Dear Referee, We thank you for your positive review of our manuscript and acknowledge your remarks on our work. Please find below our point-by-point response. The introduction section needs to be further strengthened. Since the proposed Clust&See is a Cytoscape for network clustering and analysis of biological data, the authors should provide relevant background and current needs. This will help to better illustrate the significance and applicability of the Cytoscape. The current revised version of the introduction is answering this remark. The introduction has been modified to underline the needs. “Although Cytoscape Apps allowing the identification of clusters such as ClusterMaker 6 , cluster annotations and computation of enrichment statistics such as BinGO 7 , or combinations of those as proposed by Functional Enrichment Collection ( https://apps.cytoscape.org/apps/functionalenrichmentcollection) do exist, those proposing in a single tool the identification of clusters and their annotation with custom annotation files are seldom. We therefore felt there was room for a tool such as Clust&See3.0, that performs all in a single workflow.” In the methods section, for the use of Clust&See3, the authors should provide a general process. Particularly, the differences between Clust&See3 and Clust&See should be emphasized. The Method section is following the general process: 1- Decompose a network, 2- Cluster Visualization and exploration, 3- Importing nodes annotations, 4- Annotation rules, statistical enrichments, 5- Cluster and annotation analyses. While the two first steps were already possible with the first version of Clust&See, the step 3 to 5 are specific to Cluts&See3.0. Clust&See3.0 provides the handling of the annotations. In fact, Clust&See is included within Clust&See3.0 but has been re-coded to fulfill the Cytoscape3 format. 3. The novel version of cluster&see3.0 allows users to annotate and analyze nodes and clusters, which is indeed very favorable. However, in the paper, the authors should focus on the advantages and implications of doing so. As explained in the manuscript, the advantage of annotating clusters with Clust&See3.0 is the possibility of using custom annotations, allowing the analysis to exactly fit the will and the question of the user. Then, “For each annotation list, the user can perform a statistical analysis of the clusters and the annotation terms, with different goals. At first, a global analysis of the annotations of the partition can be performed. “…”This first type of analysis allows getting a global view of the annotation distribution and to quickly identify clusters that are enriched for annotation terms of interest.” 4. In the conclusion section, the authors should briefly discuss the limitations and future improvements of Clust&See3.0. The limitations of Clust&See3.0 could reside in the performances of the clustering algorithms. However, all our partitioning/clustering algorithms have been assessed previously by us and others. For instance, the performances of the 3 algorithms (i.e. FT, TFit, OCG) have been recognized to outperform 4 other algorithms on different biological datasets by measuring 5 performance indices (1). The comparison of OCG performances with other algorithms has also been performed by us, in the original publication (2) and by others (3). 1. Sharma P, Ahmed HA, Roy S, Bhattacharyya DK. Unsupervised methods for finding protein complexes from PPI networks. Netw Model Anal Health Inform Bioinforma. 2 juin 2015;4(1):8. 2. Becker E, Robisson B, Chapple CE, Guénoche A, Brun C. Multifunctional proteins revealed by overlapping clustering in protein interaction network. Bioinformatics. 1 janv 2012;28(1):84‑90. 3. Ding Z, Zhang X, Sun D, Luo B. Overlapping Community Detection based on Network Decomposition. Sci Rep. 12 avr 2016;6(1):24115. Is the rationale for developing the new software tool clearly explained? Yes Is the description of the software tool technically sound? No Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others? No As stated in the manuscript, the code is available on github and zenodo : https://github.com/fafa13/ClustnSee-3 , https://doi.org/10.5281/zenodo.13220735 Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool? No Are the conclusions about the tool and its performance adequately supported by the findings presented in the article? No On the behalf of all authors, Christine Brun View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern Hu L. Peer Review Report For: Clust&See3.0 : clustering, module exploration and annotation [version 2; peer review: 2 approved, 1 approved with reservations] . F1000Research 2024, 13 :994 ( https://doi.org/10.5256/f1000research.167505.r320337) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-994/v1#referee-response-320337 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2024 Xiang J. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 23 Oct 2024 | for Version 1 Ju Xiang , Central South University, Changsha, Hunan, China 0 Views copyright © 2024 Xiang J. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The authors reported Clust&See3.0, a new version of Cytoscape app “Clust&See” for the identification, visualization and manipulation of network clusters. It provided functionalities allowing custom annotations and computation of statistical enrichments. It may be a useful tool of network cluster analyses. --It would be better to provide introduction for other tools of the same type in section introduction. --It is suggested to provide a table for comparing the different versions of Clust&See and other tools of the same type. --It is helpful for users to choose and use tools if the complexity of time and space for the algorithms in the tool can be provided. --Furthermore, it would be even better if comparisons with other tools could be provided. Is the rationale for developing the new software tool clearly explained? Yes Is the description of the software tool technically sound? Yes Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others? Partly Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool? Yes Are the conclusions about the tool and its performance adequately supported by the findings presented in the article? Yes Competing Interests No competing interests were disclosed. Reviewer Expertise Bioinformatics, Data mining, ComputationalBiology I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (1) Author Response 14 Nov 2024 Christine Brun, TAGC (UMR1090), Aix-Marseille Université, INSERM, Turing Centre for Living Systems, Marseille, 13009, France Dear Referee, We thank you for your positive review on our manuscript and acknowledge your remarks on our work. Please find below our point-by-point response. The authors reported Clust&See3.0, a new version of Cytoscape app “Clust&See” for the identification, visualization and manipulation of network clusters. It provided functionalities allowing custom annotations and computation of statistical enrichments. It may be a useful tool of network cluster analyses. We thank the reviewer to recognize the utility of our work. --It would be better to provide introduction for other tools of the same type in section introduction. Cytoscape Apps proposing in a single tool the identification of clusters AND their annotation with custom annotation files are seldom. We add this information in the introduction section in a new paragraph. “Although Cytoscape Apps allowing the identification of clusters such as ClusterMaker 6 , cluster annotations and computation of enrichment statistics such as BinGO 7 , or combinations of those as proposed by Functional Enrichment Collection ( https://apps.cytoscape.org/apps/functionalenrichmentcollection) do exist, those proposing in a single tool the identification of clusters and their annotation with custom annotation files are seldom. We therefore felt there was room for a tool such as Clust&See3.0, that performs all in a single workflow.” --It is suggested to provide a table for comparing the different versions of Clust&See and other tools of the same type. The previous Clust&See version is completely included in the new one. The differences between the two App versions lie in the statistical enrichment functionalities newly implemented in Clust&See3. Regarding other tools of the same type, the comparison is difficult for several reasons. As explain now in the text, other tools propose either to compute clusters, or to annotate them (one by one). Then collections such as Functional Enrichment Collection proposes to install several of these individual tools at once, but the user must perform the transfer of the results of the first tool to the second, etc., whereas Clust&See3.0 provided everything at once. Finally, CyCommunityDetection detect communities and annotate all clusters but not with custom annotations. In addition, it runs on a remote sever whereas Clust&See3.0 is locally installed, thus avoiding issues due to server accessibility or network connection. --It is helpful for users to choose and use tools if the complexity of time and space for the algorithms in the tool can be provided. OCG runs in O(n 3 ) with default parameters. The two others run faster. In an independent evaluation (9), we can read “OCG is an elite algorithm of high time efficiency…” --Furthermore, it would be even better if comparisons with other tools could be provided. As explained previously, a formal comparison is difficult, mainly because the purpose of the tools being different, the functionalities are different. However, all our partitioning/clustering algorithms have been assessed previously by us and others. For instance, the performances of the 3 algorithms (i.e. FT, TFit, OCG) have been recognized to outperform 4 other algorithms on different biological datasets by measuring 5 performance indices (1). The comparison of OCG performances with other algorithms has also been performed by us, in the original publication (2) and by others (3). 1. Sharma P, Ahmed HA, Roy S, Bhattacharyya DK. Unsupervised methods for finding protein complexes from PPI networks. Netw Model Anal Health Inform Bioinforma. 2 juin 2015;4(1):8. 2. Becker E, Robisson B, Chapple CE, Guénoche A, Brun C. Multifunctional proteins revealed by overlapping clustering in protein interaction network. Bioinformatics. 1 janv 2012;28(1):84‑90. 3. Ding Z, Zhang X, Sun D, Luo B. Overlapping Community Detection based on Network Decomposition. Sci Rep. 12 avr 2016;6(1):24115. On behalf of the authors, Christine Brun View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern Xiang J. Peer Review Report For: Clust&See3.0 : clustering, module exploration and annotation [version 2; peer review: 2 approved, 1 approved with reservations] . F1000Research 2024, 13 :994 ( https://doi.org/10.5256/f1000research.167505.r329845) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-994/v1#referee-response-329845 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2024 Ali A. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 10 Sep 2024 | for Version 1 Anooja Ali , REVA University, Bengaluru, Karnataka, India 0 Views copyright © 2024 Ali A. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The authors propose Clust&See3.0, an improved version of Cytoscape, for visualizing and manipulating the bioinformatic network clusters. It has additional functionalities , when compared to Cytoscape like custom annotations of nodes. It provided more statistical analysis for cluster evaluation. How does the user interface of Clust&See3.0 compare to previous versions in terms of usability and accessibility for biologists unfamiliar with network analysis? What are the key characteristics of clusters identified in large protein-protein interaction networks using Clust&See3.0? How do these characteristics correlate with biological functions? How does the annotation and enrichment analysis provided by Clust&See3.0 contribute to the accuracy of protein function predictions in complex biological networks? I hope the user feedback on Clust&See3.0's functionalities inform future updates and enhancements to the software. The citations refer to the aligners for creating a huge protein network based on functional similarities among proteins. The next citation uses cytoscape tool with KEGG pathway analysis and GO annotation tool for generating biclusters. It deals with the generation of gene ontology clusters for each category of MF, BP and CC from biclusters. Is the rationale for developing the new software tool clearly explained? Yes Is the description of the software tool technically sound? Yes Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others? Yes Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool? Yes Are the conclusions about the tool and its performance adequately supported by the findings presented in the article? Yes References 1. Ali A, Ajil A, Meenakshi Sundaram A, Joseph N: Detection of Gene Ontology Clusters Using Biclustering Algorithms. SN Computer Science . 2023; 4 (3). Publisher Full Text 2. H V, Ramachandra Anooja, Ali P S, Ambili S, et al.: An Optimization on Bicluster Algorithm for Gene Expression Data. IEEE explore . 2023. Publisher Full Text 3. A, Ali H. V. Ramachandra, Sundaram A. Ajil, Ramakrishnan: Pareto Optimization Technique for Protein Motif Detection in Genomic Data Set. Springer link . 2023. Publisher Full Text Competing Interests No competing interests were disclosed. Reviewer Expertise Bioinformatics, Deep learning , Computer Vision, Data mining, Computational Biology I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (1) Author Response 14 Nov 2024 Christine Brun, TAGC (UMR1090), Aix-Marseille Université, INSERM, Turing Centre for Living Systems, Marseille, 13009, France Dear Referee, We thank you for your positive review on our manuscript and acknowledge your remarks on our work. Please find below our point-by-point response. The authors propose Clust&See3.0, an improved version of Cytoscape, for visualizing and manipulating the bioinformatic network clusters. It has additional functionalities , when compared to Cytoscape like custom annotations of nodes. It provided more statistical analysis for cluster evaluation. - How does the user interface of Clust&See3.0 compare to previous versions in terms of usability and accessibility for biologists unfamiliar with network analysis? Author response: The previous version of Clust&See had been developed as an app for Cytoscape2.8, which had a completely different architecture and is almost not used anymore. The new Clust&See3.0 has been completely re-developed to comply with Cytoscape3 architecture. Clust&See3.0 therefore benefits in term of usability and accessibility for biologists from the Cytoscape3 features. Note that Cytoscape3 is far more intuitive than Cytoscape2. In addition, to increase the usability of Clust&See3.0 user interface compared to the previous version, we have significantly improved the interconnection of graphical elements and their responsiveness to user actions, so that the user's experience can be enhanced. - What are the key characteristics of clusters identified in large protein-protein interaction networks using Clust&See3.0? How do these characteristics correlate with biological functions? Author response: Both versions of Clust&See propose the same 3 algorithms to identify clusters: FT, TFit and OCG. Most of the clusters found by these algorithms display a biological homogeneity, i.e. they contain proteins involved in the same pathway, the same biological process. This is illustrated by our own network biology analyses in different contexts (1–7) as well as by other authors: the algorithms provided by Clust&See have been used in more than 40 analyses (49 citations in Google Scholar since its first publication (8)). - How does the annotation and enrichment analysis provided by Clust&See3.0 contribute to the accuracy of protein function predictions in complex biological networks? Author response: The advantage of Clust&See3 is twofold. First, any annotation file can be loaded in Clust&See3, allowing the user to highlight clusters of interest at a glance. Second, according to the goal of the analysis, the user can choose between (i) the accuracy of the annotation using the hypergeometric law (e.g. to highlight clusters containing nodes annotated with rare terms) and (ii) the homogeneity of the annotation with the majority rule (e.g. to highlight clusters containing nodes annotated with the same terms). 1. Kim DK, Weller B, Lin CW, Sheykhkarimli D, Knapp JJ, Dugied G, et al. A proteome-scale map of the SARS-CoV-2-human contactome. Nat Biotechnol. janv 2023;41(1):140‑9. 2. Katsogiannou M, Andrieu C, Baylot V, Baudot A, Dusetti NJ, Gayet O, et al. The functional landscape of Hsp27 reveals new cellular processes such as DNA repair and alternative splicing and proposes novel anticancer targets. Mol Cell Proteomics. déc 2014;13(12):3585‑601. 3. Zanzoni A, Spinelli L, Braham S, Brun C. Perturbed human sub-networks by Fusobacterium nucleatum candidate virulence proteins. Microbiome. 10 août 2017;5(1):89. 4. Zanzoni A, Spinelli L, Ribeiro DM, Tartaglia GG, Brun C. Post-transcriptional regulatory patterns revealed by protein-RNA interactions. Sci Rep. 13 mars 2019;9(1):4302. 5. Zanzoni A, Brun C. Integration of quantitative proteomics data and interaction networks: Identification of dysregulated cellular functions during cancer progression. Methods. 2016;93:103‑9. 6. Ribeiro DM, Zanzoni A, Cipriano A, Delli Ponti R, Spinelli L, Ballarino M, et al. Protein complex scaffolding predicted as a prevalent function of long non-coding RNAs. Nucleic Acids Res. 25 janv 2018;46(2):917‑28. 7. Ribeiro DM, Prod’homme A, Teixeira A, Zanzoni A, Brun C. The role of 3’UTR-protein complexes in the regulation of protein multifunctionality and subcellular localization. Nucleic Acids Res. 9 juill 2020;48(12):6491‑502. 8. Spinelli L, Gambette P, Chapple CE, Robisson B, Baudot A, Garreta H, et al. Clust&See: a Cytoscape plugin for the identification, visualization and manipulation of network clusters. BioSystems. août 2013;113(2):91‑5. On behalf of the authors, Christine Brun View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern Ali A. Peer Review Report For: Clust&See3.0 : clustering, module exploration and annotation [version 2; peer review: 2 approved, 1 approved with reservations] . F1000Research 2024, 13 :994 ( https://doi.org/10.5256/f1000research.167505.r320335) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-994/v1#referee-response-320335 Alongside their report, reviewers assign a status to the article: Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. 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