iCTNet2: integrating heterogeneous biological interactions to understand complex traits

Lili Wang; Daniel S. Himmelstein; Adam Santaniello; Mousavi Parvin; Sergio E. Baranzini

doi:10.12688/f1000research.6836.2

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Revised

iCTNet2: integrating heterogeneous biological interactions to understand complex traits

[version 2; peer review: 2 approved]

Lili Wang¹, Daniel S. Himmelstein³, Adam Santaniello², Mousavi Parvin¹, Sergio E. Baranzini^2,3

Lili Wang¹, Daniel S. Himmelstein³, [...] Adam Santaniello², Mousavi Parvin¹, Sergio E. Baranzini^2,3

PUBLISHED 28 Sep 2015

Author details Author details

¹ School of Computing, Queen’s University, Kingston, Ontario, K7L 3N6, Canada
² Department of Neurology, University of California San Francisco, San Francisco, CA, 94158, USA
³ Graduate Program in Biological and Medical Informatics, University of California, San Francisco, San Francisco, CA, 94143-0523, USA

OPEN PEER REVIEW

REVIEWER STATUS

This article is included in the Cytoscape gateway.

Abstract

iCTNet (integrated Complex Traits Networks) version 2 is a Cytoscape app and database that allows researchers to build heterogeneous networks by integrating a variety of biological interactions, thus offering a systems-level view of human complex traits. iCTNet2 is built from a variety of large-scale biological datasets, collected from public repositories to facilitate the building, visualization and analysis of heterogeneous biological networks in a comprehensive fashion via the Cytoscape platform. iCTNet2 is freely available at the Cytoscape app store.

Keywords

big data integration, heterogeneous network, drug re-purposing, disease ontology

Corresponding author: Sergio E. Baranzini

Competing interests: No competing interests were disclosed.

Grant information: This work was supported from grants from the National Multiple sclerosis Society (AN085369) and the National Institutes of Health (R01NS088155) to SEB.
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Copyright: © 2015 Wang L 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: Wang L, Himmelstein DS, Santaniello A et al. iCTNet2: integrating heterogeneous biological interactions to understand complex traits [version 2; peer review: 2 approved]. F1000Research 2015, 4:485 (https://doi.org/10.12688/f1000research.6836.2) First published: 05 Aug 2015, 4:485 (https://doi.org/10.12688/f1000research.6836.1) Latest published: 28 Sep 2015, 4:485 (https://doi.org/10.12688/f1000research.6836.2)

Revised Amendments from Version 1

The manuscript has been streamlined, a clear hypothesis has been laid out and examples of additional functions (e.g. “similarity network”) are being provided
An additional panel was added to Figure 3 (panel C) in which the “similarity network” feature is shown
Figure 1 has been redrawn to satisfy reviewer’s request
Table 1 has been updated to include version and data of access for the resources used in this paper.
The exact number of interactions is now specified for each edge type.
A warning message has been introduced when user tries to download a large network

See the authors' detailed response to the review by Gary D Bader

Introduction

In the past decade, an exponential increase in the amount and variety of publicly available genomic, transcriptomic, proteomic and other ‘omics’ data has occurred, altogether encompassing a wide range of biological interactions. Each dataset captures distinct features of molecular functions involved in complex traits, with the goal of describing and ultimately understanding biological complexity. However, these datasets are mostly used in isolation, and even the integration of any two of them would take a significant effort for the average biological investigator.

Previous work in this area is largely limited to merging data of only two types. Goh et al.¹ built the first “Diseasome”, a bipartite network of diseases and their associated genes. Lage et al.² merged protein-protein interaction networks with disease-gene associations. Similar approaches have been taken to integrate genes (transcripts) with tissue³ and miRNA⁴. More recently, drug-target (and drug-side effects) networks have attracted attention due to the potential of this approach to illuminate on candidates for drug repositioning^5,6. While the integration of heterogeneous biological interactions would be key in fueling practical applications of systems biology from rational drug discovery to disease risk prediction, dedicated approaches and tools to accomplish this task are only starting to emerge.

Heterogeneous data sets can be joined based on common keys (i.e., identifiers or ontology terms), but the integration of large-scale biological interactions is time-consuming, and particularly hampered by the lack of universal identifiers in different repositories. We previously described the integrated Complex Trait Networks (iCTNet) as an attempt to capture multiple biological relationships available in the public domain. In the original version of iCTNet⁷, five types of biological interactions (protein-protein, disease-gene, drug-gene, tissue-gene, disease-tissue) were integrated in a graph fashion, allowing for practical and intuitive integration of those data sources within the Cytoscape 2 environment. We argue that incorporating an expanded roster of popular databases would maximize the utility of this tool in many ways. Such integration of heterogeneous interactions would further accelerate our understanding of complex traits, and ultimately enable development of predictive disease models and facilitate drug discovery and repositioning. In this study, we present iCTNet2, a Cytoscape 3 app and database incorporating nine different types of interactions among six different types of entities: phenotypes, genes (proteins), miRNAs, tissues, drugs, and drug side effects. In addition to increasing the size of the database by a factor of 10, a central and distinctive feature of iCTNet2 is the incorporation of disease and anatomical ontologies as scaffolds onto which the different data types are integrated.

Material and methods

Overview of iCTNet2

iCTNet2 app is an update to the iCTNet plugin for Cytoscape2. The app was developed in Java version 7 for Cytoscape 3. The core of iCTNet2 is the iCTNet2 database, which can be accessed via the iCTNet2 app from Cytoscape⁸, through a user-friendly graphical interface (Figure 1). iCTNet2 app uses the Model-view-controller (MVC) pattern, dividing the app into three parts. The Model objects represent the data structures of a variety of biological entities and interactions. The View objects include three panels, where the user can search and select entities. The Control objects inherit org.cytoscape.work.AbstractTask class, implementing the database connection and the communication between the Model and the View.

Figure 1. iCTNet 2.0 screenshot.

Database

All data resources have been processed and stored in a relational MySQL (http://www.mysql.com) database system. Currently, the iCTNet2 app is the only available access to the iCTNet2 database. The database schema has been designed using MySQL Workbench 5.2 (http://www.mysql.com/products/workbench). All the queries are executed in terms of stored procedures through JDBC API. Once the user clicks the “Load” button, the data is queried and loaded into Cytoscape. The iCTNet2 database collects a variety of large-scale biological datasets from public repositories to facilitate the building, visualization and analysis of heterogeneous biological networks. Additionally, iCTNet2 incorporates the disease ontology (DO)⁹ as the primary vocabulary for cataloguing phenotypes in a tree-like structure. Table 1 lists the publicly available resources used to build the iCTNet2 database.

Table 1. The data resources collected in iCTNet2.

	Type	Resources	Version/Date	URL
nodes	Phenotype	Disease Ontology	2013-12-12	http://disease-ontology.org/
	Gene	HGNC(including non-coding)	2014-02-05	http://www.genenames.org/
	miRNA	mirCat	2013-11-11	http://www.mirrna.org
	Tissue	BRENDA Tissue Ontology	2013-10-09	http://www.brenda-enzymes.org
	Drug	CTD	2013-12-20	http://ctdbase.org/
	Side effect	Medical Dictionary for Regulatory Activities(MedDRA)	MedDRA 16.1	http://www.meddra.org
	Side effect	UMLS Metathesaurus	2011AB	http://www.nlm.nih.gov/research/umls/
edges	Phenotype-gene	GWAS Catalog	v1.0.1: 2015-07-08	http://www.genome.gov/gwastudies/
	Phenotype-gene	OMIM	2013-11-11	http://www.omim.org/
	Phenotype-gene	CTD	2013-12-20	http://ctdbase.org/
	Phenotype-tissue	Ontology Inference
	Gene-tissue	GNF Gene Atlas	2010-02-01	http://www.gnf.org/
	Drug-phenotype	CTD	2013-12-20	http://ctdbase.org/
	Drug-gene	CTD	2013-12-20	http://ctdbase.org/
	Drug-gene	DrugBank	2012-08-10	http://www.drugbank.ca/
	Drug-side effect	SIDER	SIDER 2: 2012-10-17	http://sideeffects.embl.de/
	Side effect-tissue	Ontology Inference
	Protein-protein	iRefIndex ppiTrim	iRefIndex 12.0	http://irefindex.org http://www.ncbi.nlm.nih.gov/CBBresearch/ Yu/downloads/ppiTrim.html
	miRNA-gene	mirCat		http://www.mirrna.org

Types of nodes

Phenotypes/diseases. In addition to DO, we also included two other disease vocabularies: the Experimental Factor Ontology (EFO) and MEDIC. EFO is an ontology developed by the European Bioinformatics Institute (EBI) with a detailed disease component¹⁰. MEDIC is a list of vocabularies produced by the Comparative Toxicogenomics Database (CTD)¹¹ which incorporates disease terms from the Online Mendelian Inheritance in Man (OMIM)¹² and the U.S. National Library of Medicine’s Medical Subject Headings (MeSH) (http://www.nlm.nih.gov/mesh/). The DO includes OMIM cross-references, thus providing the mapping for our network. DO cross-references were mapped onto OMIM and MeSH to provide mappings to MEDIC. Since the DO did not include direct mappings to the EFO, relevant EFO terms were manually mapped. Our mapping currently only covers the subset of EFO disease terms available in the GWAS catalog as of Dec 2014¹³ (We submitted these 137 mappings to the DO, which now includes them as cross-references). In total, there are 6,338 phenotype records in the iCTNet2 database.

Gene. Gene names are obtained from the HUGO Gene Nomenclature Committee’s list of human genes (HGNC). iCTNet2 only includes currently valid genes, but also incorporates outdated gene symbols and synonyms into an alias table for reference. Non-protein coding genes are included as well. In order to map symbols or identifiers across different data resources, genes are identified using the integer portion of their HGNC IDs¹⁴. The iCTNet database includes 38,079 gene records.

miRNAs. miRNAs and their targets are collected from an online database miRCat (http://www.mirrna.org), which in turn, assembles data from five databases: microRNA.org, miRTarBase, tarbase, microT (v3.0) and miR2Disease.

Tissues. Tissue types were taken from BRENDA tissue ontology¹⁵. We rooted the ontology at ‘whole body’ (BTO:0001489) to exclude the non-animal tissue portions of the ontology.

Drugs. We used the CTD as the primary resource for drugs as references to DrugBank¹⁶ identifiers are provided thus facilitating the mapping between these two resources. Therefore, iCTNet2 contains information of 151,378 drugs in total. However, the function of only 10% of them is currently associated (mapped) to genes. Mapped drugs in iCTNet include over 13,000 curated chemicals and associations with several other major chemical databases. While DrugBank 3.0 contains fewer entries than CTD, it has extensive information on most FDA approved therapeutics.

Side effects. The side effect ontology is retrieved from the Medical Dictionary for Regulatory Activities (MedDRA) (http://www.meddra.org). While providing a high quality and widely adopted vocabulary, the commercial nature of this resource prevents large-scale republication of its terms. Instead, our database reports the Unified Medical Language System (UMLS) (http://www.nlm.nih.gov/research/umls/) concepts for side effects. Since MedDRA is a source vocabulary for the UMLS, the mapping is straightforward and reversible. Nonetheless, upon request we will provide researchers who have a valid MedDRA license with an untranslated version of our database which includes the hierarchical relationships between side effects.

Types of interactions

Phenotype-gene (n=17,778). The phenotype-gene associations are the primary resources to study the genetic factors of complex traits. iCTNet2 merges phenotype-gene associations from three online databases: GWAS Catalog, OMIM and CTD. Only CTD relationships with direct evidence of "marker/mechanism" were included. To convert from SNP to gene associations, we combined overlapping loci for each GWAS Catalog disease as recently described¹⁷. The author reported gene for each loci was selected as the primary association for each disease.

Phenotype-tissue (n=5,377). These edges represent physiopathological information (i.e. which tissues/organs are likely affected by each disease). To identify tissue relationships with diseases and side effects, we used an ontology inference method. Anatomical disease and side effect terms were manually mapped to their affected tissues in the BTO. For example, connective tissue disease (DOID:65) was mapped to connective tissue (BTO:0000421). Affected tissues were propagated to more specific terms, so only high-level DO and MedDRA terms required manual mapping. See Data S1–Data S3 for the complete mappings.

Gene-tissue (n=108,400). iCTNet2 collects an extensive atlas of tissue-specific gene expression from the GNF gene atlas¹⁸. The expression patterns of 79 human tissues are available that can provide important clues about gene functions.

Drug-disease (n=11,701). The drug-disease interactions (indications) are collected from CTD, which in turn, are manually curated from the literature.

Drug-gene (n=3,426). The drug-gene interactions are assembled from CTD and DrugBank, two major databases containing drug information.

Drugs-side effects (n=1,828). The side effects of drugs in humans are an essential source to understand human phenotypes. iCTNet2 collects the information of 888 drugs and 1,450 side effect terms from the side effect resource (SIDER)¹⁹, with available side effect frequency.

Protein-protein interactions (PPI) (n=98,228). PPIs are among the most studied interactions in network biology, although the known interactions may present only one tenth of the entire interactions. PPIs are collected from ppiTrim²⁰, which further curates iRefIndex²¹, a master database consolidating interactions from 15 different sources (including BIND, HPRD, etc).

miRNA-gene (n=2,457). MicroRNAs (miRNAs) are short RNA sequences that regulate the expression of target genes. miRNA-gene interactions are collected from the online database miRCat.

Database

All data resources have been processed and stored in a relational MySQL (http://www.mysql.com) database system. Currently, the iCTNet2 app is the only available access to the iCTNet2 database.

Visualizations

Once installed, iCTNet2 will show up automatically on the left hand side of the Cytoscape window. So through the Cytoscape platform, networks constructed via iCTNet2 can be visualized in different layouts with many visualization features. Cytoscape built-in functions or analysis apps can be easily applied as well.

Results

iCTNet 2.0 is an updated, expanded and improved version of the Cytoscape 2.x plugin our group developed⁷. In this new version, developed as a Cytoscape 3.x App, a user can select and download relationships across several biological entities (e.g. diseases, genes, drugs, side effects, etc) to create a heterogeneous network that can be displayed in Cytoscape for further analysis. iCTNet 2.0 can be used to generate new hypothesis about disease relationships, shared pathogenic mechanisms, or prioritize drugs for drug repurposing. In addition, this app can be used to visualize all known information about a particular disease, or process and create publication-ready figures. There are three options to start building networks with iCTNet2. As the metagraph (the graph describing the interactions among the different node types) can be cyclic (Figure 1), we simplified the construction process by enabling the user to select the starting node type as being a disease, gene or drug. Once the starting node type has been selected, the user can choose to add additional features to the network, such as genetic data, interactions among proteins, the drugs that target them and the side effects associated with those drugs. Different types of networks (e.g. disease, gene or drug) offer complementary views from different perspectives. Next, a case study is presented starting with the network from disease nodes as an example.

Global Disease gene network

Starting from any phenotype(s) in the database, users can add gene, drug and tissue directly (if connections among them exist), and secondly add miRNA, side effects and PPIs to further grow the network. As an example, we created three disease (phenotype)-gene networks by selecting all data available in the GWAS Catalogue (threshold p-value 1E^-7), CTD and OMIM databases. The connected component of each network was markedly different in size and topological properties. The GWAS network was comprised of 1547 nodes (82 diseases + 1465 genes) connected through 2010 edges (ratio N/E = 0.77), the CTD network included 5166 nodes (1168 diseases + 3998 genes) and 12657 edges (N/E = 0.41) and the OMIM network was formed by 2265 nodes (699 diseases + 1566 genes) and 2228 edges (N/E = 1.01). Upon layout within Cytoscape (spring embedded) a clearly distinct topology emerged for each network, with the GWAS network displaying a wheel and spoke pattern with most diseases at the center (Figure 2A), and the OMIM network displaying a circular symmetric pattern, with most diseases towards the periphery (Figure 2C). The CTD network displayed a pattern that resembled an aggregate of the other two, an expected outcome given that this database includes information on both common and rare diseases (Figure 2B). The different topology between GWAS and OMIM networks clearly reflects the type of information each database contains. The central disposition of most diseases in the GWAS network (and the larger proportion of genes to diseases) highlights their polygenic nature and reflects the large amount of gene (locus) sharing among common diseases, consistent with our current understanding of their pathogenesis. On the other hand, the peripheral disposition of diseases in the OMIM network is a reflection of the limited genetic sharing characteristic of monogenic diseases, which dominate this database. Consistent with these observations, a network analysis conducted within Cytoscape showed differences between GWAS and OMIM networks in several parameters, including centrality, neighborhood connectivity and shortest path length distributions (Figure 2). By using the “create similarity network” feature (located in the App menu) a user can convert disease-gene networks into disease-disease similarity networks (i.e. from bi-partite to homogeneous), in which two diseases are connected if a user-specified threshold of shared genes is met.

Figure 2. Human disease-gene networks.

Networks were generated using iCTNet 2.0 for diseases represented in the GWAS Catalog (A), the Comparative Toxicogenomics Database (B) and OMIM (C). Note the different topological characteristics (described below each network), particularly between A and C. Topological analysis was performed with Network analysis (a Cytoscape Core app).

The autoimmune disease set (autoimmunome)

We next downloaded the GWAS disease-gene network for 18 common autoimmune diseases (and their first degree protein interactions) (Figure 3A). A clear pattern of gene sharing can be observed (green triangles in the center of the network represent shared genes between at least two diseases), consistent with our understanding on the genetic commonalities among autoimmune diseases. Using standard Cytoscape procedures (i.e. selected node type = genes and then created a new network), we further filtered this network to obtain only the protein interactome associated with more than one autoimmune disease (Figure 3B). A highly connected component (n=98) emerged (N/E = 0.60) with several key genes of known immunological function (e.g. STAT1, STAT3, NFKB1, RELA and MAPK1) at its center. Using the “create similarity network” feature, diseases with more than 2 shared genes were connected in a new graph (Figure 3C). To further explore the biological relevance of these nodes, a gene ontology analysis was performed on this network using the BiNGO App²² and results were displayed as a new network (Figure 3D). Confirming our previous observations, the set of genes associated with multiple autoimmune diseases is highly enriched (as indicated by the orange colored nodes) in immunological processes ranging from levels as general as leukocyte proliferation, and regulation of immune response, to as specific as regulation of MAPKKK and JAK-STAT cascades.

Figure 3. The autoimmune disease network.

(A) Common autoimmune diseases and their associated genes (according to the GWAS catalog) are displayed. (B) Genes associated with multiple autoimmune diseases form a densely connected network at the protein level. (C) Disease similarity network created from (A). Two diseases are connected with more than 2 genes are shared. (D) Gene ontology analysis of the genes in (B) shows over-representation of immune related proteins.

Drug indications for autoimmune diseases

In an attempt to evaluate the current pharmacological landscape in autoimmune disease treatment, we added all drugs known to be used to treat each autoimmune disease in the network according to CTD. As observed for genetic associations, while most treatments are disease-specific, there is substantial sharing of treatment modalities among multiple diseases (Figure 4). This suggests that drug repurposing is a plausible strategy for diseases with shared genetic susceptibility and pathophysiological mechanisms.

Figure 4. Autoimmune disease-drug indications network.

Increased sharing of indications can be readily detected among diseases of similar etiology. Drugs are represented by blue squares, and the opacity of the square is proportional to its degree, thus shared drugs appear darker. Diseases are represented as circles.

Conclusion

The iCTNet2 database and Cytoscape app are a systematically-developed resource and tool for studies requiring integration of multi-domain biological information. iCTNet2 illustrates how powerful the integration of heterogeneous biological interactions can be, through a simple and user-friendly interface. Comprehensive views of a given disease, including its genetic risk, gene expression profile, biological pathways affected, and actual and potential therapeutic options are just a few clicks away. Similarly, global landscapes of entire groups of diseases (i.e. malignancies, autoimmune disorders, etc) and their relevant “data neighbourhoods” can be easily created. Being a Cytoscape app, iCTNet2 also provides flexibility to conduct further analysis on the generated networks for further exploration, such as disease gene prediction, module detection, and topological network analysis.

Software availability

Software access

The App is available via the Cytoscape App Store.

Latest source code

The source code can be accessed at https://github.com/LiliWangQueensu/iCTNet2_v2

Archived source code as at the time of publication

https://zenodo.org/record/21386#.VbIoo_JzbIU

DOI 10.5281/zenodo.21386

Software license

MIT license

Author contributions

LW developed and implemented the app. DSH and AS contributed to develop the database and mapped data types. PM provided supervision and funding. SEB conceived the idea, provided funding and supervision and wrote the manuscript. All authors have agreed to the final content of the manuscript.

Competing interests

No competing interests were disclosed.

Grant information

This work was supported from grants from the National Multiple sclerosis Society (AN085369) and the National Institutes of Health (R01NS088155) to SEB.

I confirm that the funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Supplementary data availability

Supplementary Data S1.

Mapping file between Genomics Institute of the Novartis Research Foundation (GNF) and Brenda Tissue Ontology (BTO).

Click here to access the data.

Supplementary Data S2.

Mapping file between Brenda Tissue Ontology (BTO) and the Human disease ontology (DO).

Click here to access the data.

Supplementary Data S3.

Mapping file between Brenda Tissue Ontology (BTO) and the Medical Dictionary for Regulatory Activities (MedRA).

Click here to access the data.

Faculty Opinions recommended

References

1. Goh KI, Cusick ME, Valle D, et al.: The human disease network. Proc Natl Acad Sci U S A. 2007; 104(21): 8685–90. PubMed Abstract | Publisher Full Text | Free Full Text
2. Lage K, Hansen NT, Karlberg EO, et al.: A large-scale analysis of tissue-specific pathology and gene expression of human disease genes and complexes. Proc Natl Acad Sci U S A. 2008; 105(52): 20870–5. PubMed Abstract | Publisher Full Text | Free Full Text
3. Guan Y, Gorenshteyn D, Burmeister M, et al.: Tissue-specific functional networks for prioritizing phenotype and disease genes. PLoS Comput Biol. 2012; 8(9): e1002694. PubMed Abstract | Publisher Full Text | Free Full Text
4. Kozomara A, Griffiths-Jones S: miRBase: annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Res. 2014; 42(Database issue): D68–73. PubMed Abstract | Publisher Full Text | Free Full Text
5. Yamanishi Y, Kotera M, Moriya Y, et al.: DINIES: drug-target interaction network inference engine based on supervised analysis. Nucleic Acids Res. 2014; 42(Web Server issue): W39–45. PubMed Abstract | Publisher Full Text | Free Full Text
6. Schadt EE, Friend SH, Shaywitz DA: A network view of disease and compound screening. Nat Rev Drug Discov. 2009; 8(4): 286–95. PubMed Abstract | Publisher Full Text
7. Wang L, Khankhanian P, Baranzini SE, et al.: iCTNet: a Cytoscape plugin to produce and analyze integrative complex traits networks. BMC Bioinformatics. 2011; 12: 380. PubMed Abstract | Publisher Full Text | Free Full Text
8. Shannon P, Markiel A, Ozier O, et al.: Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003; 13(11): 2498–504. PubMed Abstract | Publisher Full Text | Free Full Text
9. Schriml LM, Arze C, Nadendla S, et al.: Disease Ontology: a backbone for disease semantic integration. Nucleic Acids Res. 2012; 40(Database issue): D940–6. PubMed Abstract | Publisher Full Text | Free Full Text
10. Malone J, Holloway E, Adamusiak T, et al.: Modeling sample variables with an Experimental Factor Ontology. Bioinformatics. 2010; 26(8): 1112–8. PubMed Abstract | Publisher Full Text | Free Full Text
11. Davis AP, Murphy CG, Johnson R, et al.: The Comparative Toxicogenomics Database: update 2013. Nucleic Acids Res. 2013; 41(Database issue): D1104–14. PubMed Abstract | Publisher Full Text | Free Full Text
12. Hamosh A, Scott AF, Amberger JS, et al.: Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res. 2005; 33(Database issue): D514–7. PubMed Abstract | Publisher Full Text | Free Full Text
13. Welter D, MacArthur J, Morales J, et al.: The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Res. 2014; 42(Database issue): D1001–6. PubMed Abstract | Publisher Full Text | Free Full Text
14. HGCN Hugo Gene Nomenclature Committee. Reference Source
15. Gremse M, Chang A, Schomburg I, et al.: The BRENDA Tissue Ontology (BTO): the first all-integrating ontology of all organisms for enzyme sources. Nucleic Acids Res. 2011; 39(Database issue): D507–13. PubMed Abstract | Publisher Full Text | Free Full Text
16. Knox C, Law V, Jewison T, et al.: DrugBank 3.0: a comprehensive resource for ‘omics’ research on drugs. Nucleic Acids Res. 2011; 39(Database issue): D1035–41. PubMed Abstract | Publisher Full Text | Free Full Text
17. Himmelstein DS: Extracting disease-gene associations from the GWAS Catalog. In ThinkLab. 2015. Publisher Full Text
18. Su AI, Wiltshire T, Batalov S, et al.: A gene atlas of the mouse and human protein-encoding transcriptomes. Proc Natl Acad Sci U S A. 2004; 101(16): 6062–7. PubMed Abstract | Publisher Full Text | Free Full Text
19. Kuhn M, Campillos M, Letunic I, et al.: A side effect resource to capture phenotypic effects of drugs. Mol Syst Biol. 2010; 6(1): 343. PubMed Abstract | Publisher Full Text | Free Full Text
20. Stojmirovic A, Yu YK: ppiTrim: constructing non-redundant and up-to-date interactomes. Database (Oxford). 2011; 2011: bar036. PubMed Abstract | Publisher Full Text | Free Full Text
21. Razick S, Magklaras G, Donaldson IM: iRefIndex: a consolidated protein interaction database with provenance. BMC Bioinformatics. 2008; 9: 405. PubMed Abstract | Publisher Full Text | Free Full Text
22. Maere S, Heymans K, Kuiper M: BiNGO: a Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks. Bioinformatics. 2005; 21(16): 3448–9. PubMed Abstract | Publisher Full Text

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Version 2

VERSION 2 PUBLISHED 05 Aug 2015

Author details Author details

Competing interests

No competing interests were disclosed.

Grant information

This work was supported from grants from the National Multiple sclerosis Society (AN085369) and the National Institutes of Health (R01NS088155) to SEB.
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Article Versions (2)

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Published: 28 Sep 2015, 4:485

https://doi.org/10.12688/f1000research.6836.2

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Published: 05 Aug 2015, 4:485

https://doi.org/10.12688/f1000research.6836.1

© 2015 Wang L 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.

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Wang L, Himmelstein DS, Santaniello A et al. iCTNet2: integrating heterogeneous biological interactions to understand complex traits [version 2; peer review: 2 approved]. F1000Research 2015, 4:485 (https://doi.org/10.12688/f1000research.6836.2)

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Reviewer Report 29 Dec 2015

Gary D Bader, Department of Computer Science, University of Toronto, Toronto, ON, Canada

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https://doi.org/10.5256/f1000research.7667.r10539

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Reviewer Report 02 Sep 2015

Gary D Bader, Department of Computer Science, University of Toronto, Toronto, ON, Canada

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https://doi.org/10.5256/f1000research.7350.r9868

The authors describe a Cytoscape app that provides the entry point to the iCTNet resource, containing networks connecting a variety of concepts, including genes, drugs, side effects, tissues, miRNAs and phenotypes. This resource is very useful for users wishing to start with one of these information types and navigate to others e.g. to find all genes and drugs involved in a disease of interest. In general the app works very smoothly. My comments relate mainly to missing details and text to be clarified, as detailed below.

Major points:
P5 “Next, a case study is presented starting with the network from disease nodes as an example.” It would be useful to describe the full workflow, including the scientific question, rationale and end goal.

There is a “Create similarity network” feature present in the App menu, but this is not described in the manuscript. It would be useful to add a section describing the feature and a use case.

If the user loads too much data, the app will take a long time to respond and the process can’t easily be canceled. The user should be warned in the manuscript or via the app that large queries may take a long time.

Search starting points can be gene, disease or drug. Why can’t users search by other starting points e.g. tissue?

The last update date of the ICT database and the date and version used for each resource should be clearly communicated to the user e.g. via the manuscript, app and/or ICT website.

Minor points:
Page 2: clarify “stacked onto”.

P2: “ids” -> identifiers

P3: “The CTD (12)” – what does ‘(12)’ mean?

P3 – drugs paragraph. This section is a bit unclear. CTD provides references to drugbank identifiers? Should it be that CTD provides references to drugbank records? How is the ‘function’ of drugs defined – is this just the drug target? Drugbank contains fewer entries compared to what?

P3 – “phenotype-gene” section. “To convert from SNP to gene associations, we combined overlapping loci for each GWAS Catalog disease17.” How were the loci combined?

“The mode author reported gene for each loci was selected as primary.” – what is a mode?

Page 3 and 4 – in the “Types of interactions” section, all sub-sections should include the number of interactions e.g. how many gene-tissue interactions are there?

P5 – what is a metagraph?

P5 – “spike” -> “spoke”?

P5 – “Using standard Cytoscape procedures, we further filtered this network to obtain only the protein interactome associated with more than one autoimmune disease (Figure 3B)” – the Cytoscape procedures should be detailed to make it easier for users to replicate the results in the manuscript.

Figure 1 – the tissues circle of nodes is covered by edges from other circles – can it be moved out a bit to show how it connects to other circles?

Competing Interests: I am a PI on the Cytoscape project, thus benefit somewhat when new apps are published.

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.

CITE

Report a concern

Author Response 28 Sep 2015

Sergio Baranzini, Department of Neurology, University of California San Francisco, San Francisco, 94158, USA

28 Sep 2015

Author Response

Thank you for your comments!
Please see a point-by point answer below

Major points:
P5 “Next, a case study is presented starting with the network from disease nodes as an example.” It would ... Continue reading Thank you for your comments!
Please see a point-by point answer below

Major points:
P5 “Next, a case study is presented starting with the network from disease nodes as an example.” It would be useful to describe the full workflow, including the scientific question, rationale and end goal.

This has been added to the revised manuscript

There is a “Create similarity network” feature present in the App menu, but this is not described in the manuscript. It would be useful to add a section describing the feature and a use case.

We have expanded the manuscript to describe this feature in detail and have modified Figure 3 to include an example of this feature.

If the user loads too much data, the app will take a long time to respond and the process can’t easily be canceled. The user should be warned in the manuscript or via the app that large queries may take a long time.

We have introduced a warning message for large networks. Specifically, the message will be shown if:
(1) the size of query diseases > 50 and PPI depth>0; or the size of
query diseases > 100;
(2) the size of query genes >500 and PPI depth >0; or the size of query
genes > 1000;
(3) the size of query drugs > 100 and PPI depth >0; or the size of query
drugs > 200;

Search starting points can be gene, disease or drug. Why can’t users search by other starting points e.g. tissue?

Technically, this should be possible. however, with the three provided starting points, all other searches are technically possible using basic Cytoscape functions.

The last update date of the ICT database and the date and version used for each resource should be clearly communicated to the user e.g. via the manuscript, app and/or ICT website.

Table 1 has been updated.

Minor points:
Page 2: clarify “stacked onto”.

done

P2: “ids” -> identifiers

done

P3: “The CTD (12)” – what does ‘(12)’ mean?

removed (12)

P3 – drugs paragraph. This section is a bit unclear. CTD provides references to drugbank identifiers? Should it be that CTD provides references to drugbank records? How is the ‘function’ of drugs defined – is this just the drug target? Drugbank contains fewer entries compared to what?

This paragraph has been re-written

P3 – “phenotype-gene” section. “To convert from SNP to gene associations, we combined overlapping loci for each GWAS Catalog disease17.” How were the loci combined?

We have provided a reference detailing how this was done. Basically, we proceeded as follows:
Lead-SNPs were assigned windows—regions wherein the causal SNPs are assumed to lie—retrieved from the DAPPLE server. Windows were calculated for each lead-SNP by finding the furthest upstream and downstream SNPs where r2 > 0.5 and extending outwards to the next recombination hotspot. Associations were ordered by confidence, sorting on following criteria: high/low confidence, p-value (low to high), and recency. In order of confidence, associations were overlapped by their windows into disease-specific loci. By organizing associations into loci, associations from multiple studies tagging the same underlying signal were condensed.

“The mode author reported gene for each loci was selected as primary.” – what is a mode?

Corrected

Page 3 and 4 – in the “Types of interactions” section, all sub-sections should include the number of interactions e.g. how many gene-tissue interactions are there?

The numbers of interactions are now specified.

P5 – what is a metagraph?

We refer to a metagraph as the graph describing the interactions among the different node types.

P5 – “spike” -> “spoke”?

done

P5 – “Using standard Cytoscape procedures, we further filtered this network to obtain only the protein interactome associated with more than one autoimmune disease (Figure 3B)” – the Cytoscape procedures should be detailed to make it easier for users to replicate the results in the manuscript.

Done

Figure 1 – the tissues circle of nodes is covered by edges from other circles – can it be moved out a bit to show how it connects to other circles?

Done
Thank you for your comments!
Please see a point-by point answer below

Major points:
P5 “Next, a case study is presented starting with the network from disease nodes as an example.” It would be useful to describe the full workflow, including the scientific question, rationale and end goal.

This has been added to the revised manuscript

There is a “Create similarity network” feature present in the App menu, but this is not described in the manuscript. It would be useful to add a section describing the feature and a use case.

We have expanded the manuscript to describe this feature in detail and have modified Figure 3 to include an example of this feature.

If the user loads too much data, the app will take a long time to respond and the process can’t easily be canceled. The user should be warned in the manuscript or via the app that large queries may take a long time.

We have introduced a warning message for large networks. Specifically, the message will be shown if:
(1) the size of query diseases > 50 and PPI depth>0; or the size of
query diseases > 100;
(2) the size of query genes >500 and PPI depth >0; or the size of query
genes > 1000;
(3) the size of query drugs > 100 and PPI depth >0; or the size of query
drugs > 200;

Search starting points can be gene, disease or drug. Why can’t users search by other starting points e.g. tissue?

Technically, this should be possible. however, with the three provided starting points, all other searches are technically possible using basic Cytoscape functions.

The last update date of the ICT database and the date and version used for each resource should be clearly communicated to the user e.g. via the manuscript, app and/or ICT website.

Table 1 has been updated.

Minor points:
Page 2: clarify “stacked onto”.

done

P2: “ids” -> identifiers

done

P3: “The CTD (12)” – what does ‘(12)’ mean?

removed (12)

P3 – drugs paragraph. This section is a bit unclear. CTD provides references to drugbank identifiers? Should it be that CTD provides references to drugbank records? How is the ‘function’ of drugs defined – is this just the drug target? Drugbank contains fewer entries compared to what?

This paragraph has been re-written

P3 – “phenotype-gene” section. “To convert from SNP to gene associations, we combined overlapping loci for each GWAS Catalog disease17.” How were the loci combined?

We have provided a reference detailing how this was done. Basically, we proceeded as follows:
Lead-SNPs were assigned windows—regions wherein the causal SNPs are assumed to lie—retrieved from the DAPPLE server. Windows were calculated for each lead-SNP by finding the furthest upstream and downstream SNPs where r2 > 0.5 and extending outwards to the next recombination hotspot. Associations were ordered by confidence, sorting on following criteria: high/low confidence, p-value (low to high), and recency. In order of confidence, associations were overlapped by their windows into disease-specific loci. By organizing associations into loci, associations from multiple studies tagging the same underlying signal were condensed.

“The mode author reported gene for each loci was selected as primary.” – what is a mode?

Corrected

Page 3 and 4 – in the “Types of interactions” section, all sub-sections should include the number of interactions e.g. how many gene-tissue interactions are there?

The numbers of interactions are now specified.

P5 – what is a metagraph?

We refer to a metagraph as the graph describing the interactions among the different node types.

P5 – “spike” -> “spoke”?

done

P5 – “Using standard Cytoscape procedures, we further filtered this network to obtain only the protein interactome associated with more than one autoimmune disease (Figure 3B)” – the Cytoscape procedures should be detailed to make it easier for users to replicate the results in the manuscript.

Done

Figure 1 – the tissues circle of nodes is covered by edges from other circles – can it be moved out a bit to show how it connects to other circles?

Done
Competing Interests: I am the senior author of this manuscript. Close
Report a concern
Respond or Comment

COMMENTS ON THIS REPORT

Author Response 28 Sep 2015

Sergio Baranzini, Department of Neurology, University of California San Francisco, San Francisco, 94158, USA

28 Sep 2015

Author Response

Thank you for your comments!
Please see a point-by point answer below

Major points:
P5 “Next, a case study is presented starting with the network from disease nodes as an example.” It would ... Continue reading Thank you for your comments!
Please see a point-by point answer below

Major points:
P5 “Next, a case study is presented starting with the network from disease nodes as an example.” It would be useful to describe the full workflow, including the scientific question, rationale and end goal.

This has been added to the revised manuscript

There is a “Create similarity network” feature present in the App menu, but this is not described in the manuscript. It would be useful to add a section describing the feature and a use case.

We have expanded the manuscript to describe this feature in detail and have modified Figure 3 to include an example of this feature.

If the user loads too much data, the app will take a long time to respond and the process can’t easily be canceled. The user should be warned in the manuscript or via the app that large queries may take a long time.

We have introduced a warning message for large networks. Specifically, the message will be shown if:
(1) the size of query diseases > 50 and PPI depth>0; or the size of
query diseases > 100;
(2) the size of query genes >500 and PPI depth >0; or the size of query
genes > 1000;
(3) the size of query drugs > 100 and PPI depth >0; or the size of query
drugs > 200;

Search starting points can be gene, disease or drug. Why can’t users search by other starting points e.g. tissue?

Technically, this should be possible. however, with the three provided starting points, all other searches are technically possible using basic Cytoscape functions.

The last update date of the ICT database and the date and version used for each resource should be clearly communicated to the user e.g. via the manuscript, app and/or ICT website.

Table 1 has been updated.

Minor points:
Page 2: clarify “stacked onto”.

done

P2: “ids” -> identifiers

done

P3: “The CTD (12)” – what does ‘(12)’ mean?

removed (12)

P3 – drugs paragraph. This section is a bit unclear. CTD provides references to drugbank identifiers? Should it be that CTD provides references to drugbank records? How is the ‘function’ of drugs defined – is this just the drug target? Drugbank contains fewer entries compared to what?

This paragraph has been re-written

P3 – “phenotype-gene” section. “To convert from SNP to gene associations, we combined overlapping loci for each GWAS Catalog disease17.” How were the loci combined?

We have provided a reference detailing how this was done. Basically, we proceeded as follows:
Lead-SNPs were assigned windows—regions wherein the causal SNPs are assumed to lie—retrieved from the DAPPLE server. Windows were calculated for each lead-SNP by finding the furthest upstream and downstream SNPs where r2 > 0.5 and extending outwards to the next recombination hotspot. Associations were ordered by confidence, sorting on following criteria: high/low confidence, p-value (low to high), and recency. In order of confidence, associations were overlapped by their windows into disease-specific loci. By organizing associations into loci, associations from multiple studies tagging the same underlying signal were condensed.

“The mode author reported gene for each loci was selected as primary.” – what is a mode?

Corrected

Page 3 and 4 – in the “Types of interactions” section, all sub-sections should include the number of interactions e.g. how many gene-tissue interactions are there?

The numbers of interactions are now specified.

P5 – what is a metagraph?

We refer to a metagraph as the graph describing the interactions among the different node types.

P5 – “spike” -> “spoke”?

done

P5 – “Using standard Cytoscape procedures, we further filtered this network to obtain only the protein interactome associated with more than one autoimmune disease (Figure 3B)” – the Cytoscape procedures should be detailed to make it easier for users to replicate the results in the manuscript.

Done

Figure 1 – the tissues circle of nodes is covered by edges from other circles – can it be moved out a bit to show how it connects to other circles?

Done
Thank you for your comments!
Please see a point-by point answer below

Major points:
P5 “Next, a case study is presented starting with the network from disease nodes as an example.” It would be useful to describe the full workflow, including the scientific question, rationale and end goal.

This has been added to the revised manuscript

There is a “Create similarity network” feature present in the App menu, but this is not described in the manuscript. It would be useful to add a section describing the feature and a use case.

We have expanded the manuscript to describe this feature in detail and have modified Figure 3 to include an example of this feature.

If the user loads too much data, the app will take a long time to respond and the process can’t easily be canceled. The user should be warned in the manuscript or via the app that large queries may take a long time.

We have introduced a warning message for large networks. Specifically, the message will be shown if:
(1) the size of query diseases > 50 and PPI depth>0; or the size of
query diseases > 100;
(2) the size of query genes >500 and PPI depth >0; or the size of query
genes > 1000;
(3) the size of query drugs > 100 and PPI depth >0; or the size of query
drugs > 200;

Search starting points can be gene, disease or drug. Why can’t users search by other starting points e.g. tissue?

Technically, this should be possible. however, with the three provided starting points, all other searches are technically possible using basic Cytoscape functions.

The last update date of the ICT database and the date and version used for each resource should be clearly communicated to the user e.g. via the manuscript, app and/or ICT website.

Table 1 has been updated.

Minor points:
Page 2: clarify “stacked onto”.

done

P2: “ids” -> identifiers

done

P3: “The CTD (12)” – what does ‘(12)’ mean?

removed (12)

P3 – drugs paragraph. This section is a bit unclear. CTD provides references to drugbank identifiers? Should it be that CTD provides references to drugbank records? How is the ‘function’ of drugs defined – is this just the drug target? Drugbank contains fewer entries compared to what?

This paragraph has been re-written

P3 – “phenotype-gene” section. “To convert from SNP to gene associations, we combined overlapping loci for each GWAS Catalog disease17.” How were the loci combined?

We have provided a reference detailing how this was done. Basically, we proceeded as follows:
Lead-SNPs were assigned windows—regions wherein the causal SNPs are assumed to lie—retrieved from the DAPPLE server. Windows were calculated for each lead-SNP by finding the furthest upstream and downstream SNPs where r2 > 0.5 and extending outwards to the next recombination hotspot. Associations were ordered by confidence, sorting on following criteria: high/low confidence, p-value (low to high), and recency. In order of confidence, associations were overlapped by their windows into disease-specific loci. By organizing associations into loci, associations from multiple studies tagging the same underlying signal were condensed.

“The mode author reported gene for each loci was selected as primary.” – what is a mode?

Corrected

Page 3 and 4 – in the “Types of interactions” section, all sub-sections should include the number of interactions e.g. how many gene-tissue interactions are there?

The numbers of interactions are now specified.

P5 – what is a metagraph?

We refer to a metagraph as the graph describing the interactions among the different node types.

P5 – “spike” -> “spoke”?

done

P5 – “Using standard Cytoscape procedures, we further filtered this network to obtain only the protein interactome associated with more than one autoimmune disease (Figure 3B)” – the Cytoscape procedures should be detailed to make it easier for users to replicate the results in the manuscript.

Done

Figure 1 – the tissues circle of nodes is covered by edges from other circles – can it be moved out a bit to show how it connects to other circles?

Done
Competing Interests: I am the senior author of this manuscript. Close
Report a concern

Views

Reviewer Report 24 Aug 2015

Amitabh Sharma, Department of Medicine, Harvard Medical Center, Boston, MA, USA

Approved

https://doi.org/10.5256/f1000research.7350.r9869

Baranzini et al. updated the iCTNet database from the data collected from public repositories to facilitate the building, visualization and analysis of heterogeneous biological networks in a comprehensive fashion via the Cytoscape platform. I like the manuscript and source developed by Baranzini group. The resource update is important and timely, and is well-done and clearly described. It is freely available and provides a good resource for the community to understand the connections between different omics or the big data in disease medicine.

A few minor comments would improve the manuscript:

Add some text in the conclusion about how version 2 is better than version 1.
The Phentoypes/diseases vocabulary sources do not overlap much, did this result in a lot of data loss while integrating?
Add some description regarding the edges in the Figure 2 legend. What are different node colors? GWAS network is much sparse because of the incompleteness of the interactome and also we have literature bias for the OMIM data.
In figure 3, is the network PPI only or aggregated network of all sources? Also, Figure 3C should include only those terms that are below specific thresholds, like p<0.05.

Overall, an excellent work.

Competing Interests: No competing interests were disclosed.

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.

CITE

Report a concern

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Version 2

VERSION 2 PUBLISHED 05 Aug 2015

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Amitabh Sharma, Harvard Medical Center, Boston, USA
Gary D Bader, University of Toronto, Toronto, Canada

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Reviewer Report

11 Views

29 Dec 2015 | for Version 2

Gary D Bader, Department of Computer Science, University of Toronto, Toronto, ON, Canada

11 Views Cite this report Responses(0)

Approved

The authors have addressed all the concerns raised in the last review. The manuscript reads much more smoothly and clearly now.

Competing Interests

None in addition to those disclosed in the first report.

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.

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Reviewer Report

31 Views

02 Sep 2015 | for Version 1

Gary D Bader, Department of Computer Science, University of Toronto, Toronto, ON, Canada

31 Views Cite this report Responses(1)

Approved With Reservations

Competing Interests

I am a PI on the Cytoscape project, thus benefit somewhat when new apps are published.

Respond to this report

Responses (1)

Author Response

28 Sep 2015

Sergio Baranzini, Department of Neurology, University of California San Francisco, San Francisco, 94158, USA

Thank you for your comments!
Please see a point-by point answer below

Major points:
P5 “Next, a case study is presented starting with the network from disease nodes as an example.” It would be useful to describe the full workflow, including the scientific question, rationale and end goal.

This has been added to the revised manuscript

There is a “Create similarity network” feature present in the App menu, but this is not described in the manuscript. It would be useful to add a section describing the feature and a use case.

We have expanded the manuscript to describe this feature in detail and have modified Figure 3 to include an example of this feature.

If the user loads too much data, the app will take a long time to respond and the process can’t easily be canceled. The user should be warned in the manuscript or via the app that large queries may take a long time.

We have introduced a warning message for large networks. Specifically, the message will be shown if:
(1) the size of query diseases > 50 and PPI depth>0; or the size of
query diseases > 100;
(2) the size of query genes >500 and PPI depth >0; or the size of query
genes > 1000;
(3) the size of query drugs > 100 and PPI depth >0; or the size of query
drugs > 200;

Search starting points can be gene, disease or drug. Why can’t users search by other starting points e.g. tissue?

Technically, this should be possible. however, with the three provided starting points, all other searches are technically possible using basic Cytoscape functions.

The last update date of the ICT database and the date and version used for each resource should be clearly communicated to the user e.g. via the manuscript, app and/or ICT website.

Table 1 has been updated.

Minor points:
Page 2: clarify “stacked onto”.

done

P2: “ids” -> identifiers

done

P3: “The CTD (12)” – what does ‘(12)’ mean?

removed (12)

P3 – drugs paragraph. This section is a bit unclear. CTD provides references to drugbank identifiers? Should it be that CTD provides references to drugbank records? How is the ‘function’ of drugs defined – is this just the drug target? Drugbank contains fewer entries compared to what?

This paragraph has been re-written

P3 – “phenotype-gene” section. “To convert from SNP to gene associations, we combined overlapping loci for each GWAS Catalog disease17.” How were the loci combined?

We have provided a reference detailing how this was done. Basically, we proceeded as follows:
Lead-SNPs were assigned windows—regions wherein the causal SNPs are assumed to lie—retrieved from the DAPPLE server. Windows were calculated for each lead-SNP by finding the furthest upstream and downstream SNPs where r2 > 0.5 and extending outwards to the next recombination hotspot. Associations were ordered by confidence, sorting on following criteria: high/low confidence, p-value (low to high), and recency. In order of confidence, associations were overlapped by their windows into disease-specific loci. By organizing associations into loci, associations from multiple studies tagging the same underlying signal were condensed.

“The mode author reported gene for each loci was selected as primary.” – what is a mode?

Corrected

Page 3 and 4 – in the “Types of interactions” section, all sub-sections should include the number of interactions e.g. how many gene-tissue interactions are there?

The numbers of interactions are now specified.

P5 – what is a metagraph?

We refer to a metagraph as the graph describing the interactions among the different node types.

P5 – “spike” -> “spoke”?

done

P5 – “Using standard Cytoscape procedures, we further filtered this network to obtain only the protein interactome associated with more than one autoimmune disease (Figure 3B)” – the Cytoscape procedures should be detailed to make it easier for users to replicate the results in the manuscript.

Done

Figure 1 – the tissues circle of nodes is covered by edges from other circles – can it be moved out a bit to show how it connects to other circles?

Done

View more View less

Competing Interests

I am the senior author of this manuscript.

Back to all reports

Reviewer Report

22 Views

24 Aug 2015 | for Version 1

Amitabh Sharma, Department of Medicine, Harvard Medical Center, Boston, MA, USA

22 Views Cite this report Responses(0)

Approved

Add some text in the conclusion about how version 2 is better than version 1.
The Phentoypes/diseases vocabulary sources do not overlap much, did this result in a lot of data loss while integrating?
Add some description regarding the edges in the Figure 2 legend. What are different node colors? GWAS network is much sparse because of the incompleteness of the interactome and also we have literature bias for the OMIM data.
In figure 3, is the network PPI only or aggregated network of all sources? Also, Figure 3C should include only those terms that are below specific thresholds, like p<0.05.

Overall, an excellent work.

Competing Interests

No competing interests were disclosed.

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.

Respond to this report

Responses (0)

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

[1] 1. Goh KI, Cusick ME, Valle D, et al.: The human disease network. Proc Natl Acad Sci U S A. 2007; 104(21): 8685–90. PubMed Abstract | Publisher Full Text | Free Full Text

[2] 2. Lage K, Hansen NT, Karlberg EO, et al.: A large-scale analysis of tissue-specific pathology and gene expression of human disease genes and complexes. Proc Natl Acad Sci U S A. 2008; 105(52): 20870–5. PubMed Abstract | Publisher Full Text | Free Full Text

[3] 3. Guan Y, Gorenshteyn D, Burmeister M, et al.: Tissue-specific functional networks for prioritizing phenotype and disease genes. PLoS Comput Biol. 2012; 8(9): e1002694. PubMed Abstract | Publisher Full Text | Free Full Text

[4] 4. Kozomara A, Griffiths-Jones S: miRBase: annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Res. 2014; 42(Database issue): D68–73. PubMed Abstract | Publisher Full Text | Free Full Text

[5] 5. Yamanishi Y, Kotera M, Moriya Y, et al.: DINIES: drug-target interaction network inference engine based on supervised analysis. Nucleic Acids Res. 2014; 42(Web Server issue): W39–45. PubMed Abstract | Publisher Full Text | Free Full Text

[6] 6. Schadt EE, Friend SH, Shaywitz DA: A network view of disease and compound screening. Nat Rev Drug Discov. 2009; 8(4): 286–95. PubMed Abstract | Publisher Full Text

[7] 7. Wang L, Khankhanian P, Baranzini SE, et al.: iCTNet: a Cytoscape plugin to produce and analyze integrative complex traits networks. BMC Bioinformatics. 2011; 12: 380. PubMed Abstract | Publisher Full Text | Free Full Text

[8] 8. Shannon P, Markiel A, Ozier O, et al.: Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003; 13(11): 2498–504. PubMed Abstract | Publisher Full Text | Free Full Text

[9] 9. Schriml LM, Arze C, Nadendla S, et al.: Disease Ontology: a backbone for disease semantic integration. Nucleic Acids Res. 2012; 40(Database issue): D940–6. PubMed Abstract | Publisher Full Text | Free Full Text

[10] 10. Malone J, Holloway E, Adamusiak T, et al.: Modeling sample variables with an Experimental Factor Ontology. Bioinformatics. 2010; 26(8): 1112–8. PubMed Abstract | Publisher Full Text | Free Full Text

[11] 11. Davis AP, Murphy CG, Johnson R, et al.: The Comparative Toxicogenomics Database: update 2013. Nucleic Acids Res. 2013; 41(Database issue): D1104–14. PubMed Abstract | Publisher Full Text | Free Full Text

[12] 12. Hamosh A, Scott AF, Amberger JS, et al.: Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res. 2005; 33(Database issue): D514–7. PubMed Abstract | Publisher Full Text | Free Full Text

[13] 13. Welter D, MacArthur J, Morales J, et al.: The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Res. 2014; 42(Database issue): D1001–6. PubMed Abstract | Publisher Full Text | Free Full Text

[14] 14. HGCN Hugo Gene Nomenclature Committee. Reference Source

[15] 15. Gremse M, Chang A, Schomburg I, et al.: The BRENDA Tissue Ontology (BTO): the first all-integrating ontology of all organisms for enzyme sources. Nucleic Acids Res. 2011; 39(Database issue): D507–13. PubMed Abstract | Publisher Full Text | Free Full Text

[16] 16. Knox C, Law V, Jewison T, et al.: DrugBank 3.0: a comprehensive resource for ‘omics’ research on drugs. Nucleic Acids Res. 2011; 39(Database issue): D1035–41. PubMed Abstract | Publisher Full Text | Free Full Text

[17] 17. Himmelstein DS: Extracting disease-gene associations from the GWAS Catalog. In ThinkLab. 2015. Publisher Full Text

[18] 18. Su AI, Wiltshire T, Batalov S, et al.: A gene atlas of the mouse and human protein-encoding transcriptomes. Proc Natl Acad Sci U S A. 2004; 101(16): 6062–7. PubMed Abstract | Publisher Full Text | Free Full Text

[19] 19. Kuhn M, Campillos M, Letunic I, et al.: A side effect resource to capture phenotypic effects of drugs. Mol Syst Biol. 2010; 6(1): 343. PubMed Abstract | Publisher Full Text | Free Full Text

[20] 20. Stojmirovic A, Yu YK: ppiTrim: constructing non-redundant and up-to-date interactomes. Database (Oxford). 2011; 2011: bar036. PubMed Abstract | Publisher Full Text | Free Full Text

[21] 21. Razick S, Magklaras G, Donaldson IM: iRefIndex: a consolidated protein interaction database with provenance. BMC Bioinformatics. 2008; 9: 405. PubMed Abstract | Publisher Full Text | Free Full Text

[22] 22. Maere S, Heymans K, Kuiper M: BiNGO: a Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks. Bioinformatics. 2005; 21(16): 3448–9. PubMed Abstract | Publisher Full Text

iCTNet2: integrating heterogeneous biological interactions to understand complex traits

Abstract

Keywords

Revised Amendments from Version 1

Introduction

Material and methods

Overview of iCTNet2

Figure 1. iCTNet 2.0 screenshot.

Database

Table 1. The data resources collected in iCTNet2.

Types of nodes

Types of interactions

Database

Visualizations

Results

Global Disease gene network

Figure 2. Human disease-gene networks.

The autoimmune disease set (autoimmunome)

Figure 3. The autoimmune disease network.

Drug indications for autoimmune diseases

Figure 4. Autoimmune disease-drug indications network.

Conclusion

Software availability

Software access

Latest source code

Archived source code as at the time of publication

Software license

Author contributions

Competing interests

Grant information

Supplementary data availability

References

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