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Phylogeny estimation (the reconstruction of evolutionary trees) has recently been applied to CRISPR-based cell lineage tracing, allowing the developmental history of an individual tissue or organism to be inferred from a large number of mutated sequences in somatic cells. However, current computational methods are not able to construct phylogenetic trees from extremely large numbers of input sequences. Here, we present a deep distributed computing framework to comprehensively trace accurate large lineages (FRACTAL) that substantially enhances the scalability of current lineage estimation software tools. FRACTAL first reconstructs only an upstream lineage of the input sequences and recursively iterates the same produce for its downstream lineages using independent computing nodes. We demonstrate the utility of FRACTAL by reconstructing lineages from >235 million simulated sequences and from >16 million cells from a simulated experiment with a CRISPR system that accumulates mutations during cell proliferation. We also successfully applied FRACTAL to evolutionary tree reconstructions and to an experiment using error-prone PCR (EP-PCR) for large-scale sequence diversification.

Mapping protein interactions driving cancer Cancer is a genetic disease, and much cancer research is focused on identifying carcinogenic mutations and determining how they relate to disease progression. Three papers demonstrate how mutations are processed through networks of protein interactions to promote cancer (see the Perspective by Cheng and Jackson). Swaney et al . focus on head and neck cancer and identify cancer-enriched interactions, demonstrating how point mutant–dependent interactions of PIK3CA, a kinase frequently mutated in human cancers, are predictive of drug response. Kim et al . focus on breast cancer and identify two proteins functionally connected to the tumor-suppressor gene BRCA1 and two proteins that regulate PIK3CA. Zheng et al . developed a statistical model that identifies protein networks that are under mutation pressure across different cancer types, including a complex bringing together PIK3CA with actomyosin proteins. These papers provide a resource that will be helpful in interpreting cancer genomic data. —VV

NDEx, the Network Data Exchange (https://www.ndexbio.org) is a web-based resource where users can find, store, share and publish network models of any type and size. NDEx is integrated with Cytoscape, the widely used desktop application for network analysis and visualization. NDEx and Cytoscape are the pillars of the Cytoscape Ecosystem, a diverse environment of resources, tools, applications and services for network biology workflows. In this article, we introduce researchers to NDEx and highlight how it can simplify common tasks in network biology workflows as well as streamline publication and access to). Finally, we show how NDEx can be used programmatically via Python with the 'ndex2' client library, and point readers to additional examples for other popular programming languages such as JavaScript and R. © 2021 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Getting started with NDEx Basic Protocol 2: Using NDEx and Cytoscape in a publication-oriented workflow Basic Protocol 3: Manipulating networks in NDEx via Python.

Although artificial neural networks simulate a variety of human functions, their internal structures are hard to interpret. In the life sciences, extensive knowledge of cell biology provides an opportunity to design visible neural networks (VNNs) which couple the model’s inner workings to those of real systems. Here we develop DCell, a VNN embedded in the hierarchical structure of 2526 subsystems comprising a eukaryotic cell (http://d-cell.ucsd.edu/). Trained on several million genotypes, DCell simulates cellular growth nearly as accurately as laboratory observations. During simulation, genotypes induce patterns of subsystem activities, enabling in-silico investigations of the molecular mechanisms underlying genotype-phenotype associations. These mechanisms can be validated and many are unexpected; some are governed by Boolean logic. Cumulatively, 80% of the importance for growth prediction is captured by 484 subsystems (21%), reflecting the emergence of a complex phenotype. DCell provides a foundation for decoding the genetics of disease, drug resistance, and synthetic life.

The investigation of gene sets identified in experiments and analyses is a common task for biologists. This includes identification of relevant pathway networks, neighborhoods of protein interactions, and associations with drugs and diseases. However, services that provide these analyses face the following challenges: (1) Curated pathway content tends to lack biological context and lag behind recent findings, as it focuses on consensus biology and is expensive to maintain; (2) User interfaces for network analyses need to be transparent and intuitive, without requiring complex parameter choices; and (3) Users have diverse, changing network analysis needs, requiring an evolving portfolio of methods and data. To address these challenges, we present NDEx Integrated Query (IQuery), a novel and flexible resource for gene set analysis providing enrichment, protein-protein interaction, and protein association analyses based on pathways and networks from NDEx, the Network Data Exchange. IQuery provides researchers with a simple “one click” query interface which runs multiple simultaneous queries without the need to specify the analyses to perform, query parameters, or network data sources. The results are aggregated and presented in a rich, intuitive interface for the user to browse, preview, save, or analyze. For each network, the user can view descriptions, citations, author information and other metadata, and inspect the data associated with each node and edge. The IQuery database is NDEx, a public data commons which is constantly growing via the addition of new public network resources and networks submitted by the authors of publications. The incorporation of published networks in IQuery enables timely coverage of new research as well as clear biological context for each pathway, and fosters cross-discipline insights and collaboration between teams. IQuery is also integrated with Cytoscape, the widely used network visualization and analysis application, enabling query result networks to be seamlessly downloaded to Cytoscape for editing, merging with other networks, annotation with user datasets, or use with any of hundreds of community developed apps. IQuery is built as a modular, web service-oriented platform. The primary IQuery service distributes each query to all component services using a standard API. This enables easy addition of new analyses, including those written by collaborators. This allows IQuery to grow in two ways: Through the growth of the NDEx database, and through the addition of modules providing new gene set analysis tools. In sum, IQuery is a powerful and versatile new network analysis service with collaboration and extensibility at its core, which will allow users to find, access, and analyze networks relevant to their research, and provide opportunities for cross-discipline communication and insight. Citation Format: Dexter R. Pratt, Keiichiro Ono, Sophie Liu, Jing Chen, Christopher Churas. IQuery: A tool aggregating multiple gene set analysis methods, based on networks and pathways in a community-driven public data repository [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3206.

Interpreting cancer genomes requires a broad understanding of the composition and organization of cellular processes under selective pressure for mutations. Here, we integrate systematic screens for protein interaction with tumor genetic analysis to elucidate a multiscale hierarchical map of 378 protein systems recurrently altered in one or more cancer types. Diverse gene mutations converge on commonly mutated systems, from small protein complexes in specific tumor types to large molecular assemblies and organelles disrupted in most cancers. Unexpected findings include collagen structural alterations with significant prognostic value; mutations impacting the desmosome in 65% melanomas; complexes related to splicing and DNA repair; and an expanded actomyosin cluster involving PIK3CA. The map implicates 556 cancer genes, many well supported by functional assays, including canonical genes implicated in new tissues (e.g., BRCA1/2 mutations in bladder cancer). This work explains mutational heterogeneity as a collection of convergence points across scales of cell biology. This abstract is also being presented as Poster B52. Citation Format: Fan Zheng, Keiichiro Ono, Erica Silva, Danielle Swaney, Minkyu Kim, Julia Shangguan, Dexter Pratt, Xiaolin Nan, Nevan Krogan, Trey Ideker. A multiscale map of recurrently mutated systems in cancer [abstract]. In: Proceedings of the AACR Special Conference on the Evolving Landscape of Cancer Modeling; 2020 Mar 2-5; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2020;80(11 Suppl):Abstract nr PR14.

This article is an Editorial and has not been subject to external peer review; International audience; Cytoscape is the premiere platform for interactive analysis, integration and visualization of network data. While Cytoscape itself delivers much basic functionality, it relies on community-written apps to deliver specialized functions and analyses. To date, Cytoscape's CyREST feature has allowed researchers to write workflows that call basic Cytoscape functions, but provides no access to its high value app-based functions. With Cytoscape Automation, workflows can now call apps that have been upgraded to expose their functionality. This article collection is a resource to assist readers in quickly and economically leveraging such apps in reproducible workflows that scale independently to large data sets and production runs.

We have developed a general progressive procedure, Active Interaction Mapping, to guide assembly of the hierarchy of functions encoding any biologicalsystem. Using this process, we assemble an ontology of functions comprising autophagy, a central recycling process implicated in numerous diseases. A first-generation model, built from existing gene networks in Saccharomyces, captures most known autophagy components in broad relation to vesicle transport, cell cycle, and stress response. Systematic analysis identifies synthetic-lethal interactions as most informative for further experiments; consequently, we saturate the model with 156,364 such measurements across autophagy-activating conditions. These targeted interactions provide more information about autophagy than all previous datasets, producing a second-generation ontology of 220 functions. Approximately half are previously unknown; we confirm roles for Gyp1 at the phagophore-assembly site, Atg24 in cargo engulfment, Atg26 in cytoplasm-to-vacuole targeting, and Ssd1, Did4, and others in selective and non-selective autophagy. The procedure and autophagy hierarchy are at http://atgo.ucsd.edu/.

Systems biology requires not only genome-scale data but also methods to integrate these data into interpretable models. Previously, we developed approaches that organize omics data into a structured hierarchy of cellular components and pathways, called a "data-driven ontology." Such hierarchies recapitulate known cellular subsystems and discover new ones. To broadly facilitate this type of modeling, we report the development of a software library called the Data-Driven Ontology Toolkit (DDOT), consisting of a Python package (https://github.com/idekerlab/ddot) to assemble and analyze ontologies and a web application (http://hiview.ucsd.edu) to visualize them. Using DDOT, we programmatically assemble a compendium of ontologies for 652 diseases by integrating gene-disease mappings with a gene similarity network derived from omics data. For example, the ontology for Fanconi anemia describes known and novel disease mechanisms in its hierarchy of 194 genes and 74 subsystems. DDOT provides an easy interface to share ontologies online at the Network Data Exchange.

The Proteomics Standard Initiative Common QUery InterfaCe (PSICQUIC) specification was created by the Human Proteome Organization Proteomics Standards Initiative (HUPO-PSI) to enable computational access to molecular-interaction data resources by means of a standard Web Service and query language. Currently providing >150 million binary interaction evidences from 28 servers globally, the PSICQUIC interface allows the concurrent search of multiple molecular-interaction information resources using a single query. Here, we present an extension of the PSICQUIC specification (version 1.3), which has been released to be compliant with the enhanced standards in molecular interactions. The new release also includes a new reference implementation of the PSICQUIC server available to the data providers. It offers augmented web service capabilities and improves the user experience. PSICQUIC has been running for almost 5 years, with a user base growing from only 4 data providers to 28 (April 2013) allowing access to 151 310 109 binary interactions. The power of this web service is shown in PSICQUIC View web application, an example of how to simultaneously query, browse and download results from the different PSICQUIC servers. This application is free and open to all users with no login requirement (http://www.ebi.ac.uk/Tools/webservices/psicquic/view/main.xhtml).

Cytoscape is open-source software for integration, visualization and analysis of biological networks. It can be extended through Cytoscape plugins, enabling a broad community of scientists to contribute useful features. This growth has occurred organically through the independent efforts of diverse authors, yielding a powerful but heterogeneous set of tools. We present a travel guide to the world of plugins, covering the 152 publicly available plugins for Cytoscape 2.5–2.8. We also describe ongoing efforts to distribute, organize and maintain the quality of the collection.

To derive new insights in diabetic complications, we integrated publicly available human protein-protein interaction (PPI) networks with global metabolic networks using metabolomic data from patients with diabetic nephropathy. We focused on the participating proteins in the network that were computationally predicted to connect the urine metabolites. MDM2 had the highest significant number of PPI connections. As validation, significant downregulation of MDM2 gene expression was found in both glomerular and tubulointerstitial compartments of kidney biopsy tissue from 2 independent cohorts of patients with diabetic nephropathy. In diabetic mice, chemical inhibition of MDM2 with Nutlin-3a led to reduction in the number of podocytes, increased blood urea nitrogen, and increased mortality. Addition of Nutlin-3a decreased WT1+ cells in embryonic kidneys. Both podocyte- and tubule-specific MDM2-knockout mice exhibited severe glomerular and tubular dysfunction, respectively. Interestingly, the only 2 metabolites that were reduced in both podocyte and tubule-specific MDM2-knockout mice were 3-methylcrotonylglycine and uracil, both of which were also reduced in human diabetic kidney disease. Thus, our bioinformatics tool combined with multi-omics studies identified an important functional role for MDM2 in glomeruli and tubules of the diabetic nephropathic kidney and links MDM2 to a reduction in 2 key metabolite biomarkers.

Summary: We developed cyNeo4j, a Cytoscape App to link Cytoscape and Neo4j databases to utilize the performance and storage capacities Neo4j offers. We implemented a Neo4j NetworkAnalyzer, ForceAtlas2 layout and Cypher component to demonstrate the possibilities a distributed setup of Cytoscape and Neo4j have. Availability and implementation: The app is available from the Cytoscape App Store at http://apps.cytoscape.org/apps/cyneo4j, the Neo4j plugins at www.github.com/gsummer/cyneo4j-parent and the community and commercial editions of Neo4j can be found at http://www.neo4j.com. Contact: georg.summer@gmail.com

Cancer is governed by modular systems of genes, the composition and organization of which remains poorly understood. Here, we integrate physical and functional networks from a wide range of molecular studies to assemble a comprehensive multi-scale map of human cancer cell biology. This map consists of a hierarchical catalog of protein complexes, signaling pathways and inter-pathway crosstalk implicated in cancer, and it suggests many uncharacterized functional modules as intriguing hypotheses for further validation. Analysis of the pattern of somatic mutations in The Cancer Genome Atlas (TCGA) reveals that these mutations target systems of varying scales above the level of individual genes. The map also provides a platform to integrate and interpret new 'omics data; we integrate new protein-protein interactions identified using AP-MS in multiple breast cancer cell lines, revealing how different functional modules are rewired in cancer cells. A general model browsing tool has been created to visualize and navigate these hierarchical cancer maps. This multi-scale mapping approach elucidates the molecular heterogeneity of cancer, connects tumor genotypes to phenotypes and, ultimately, enables a platform for cancer precision medicine. Citation Format: Fan Zheng, Michael K. Yu, Minkyu Kim, Keiichiro Ono, Mitchell Flagg, Jason F. Kreisberg, Nevan Krogan, Trey Ideker. Multi-scale mapping of the physical and functional architecture of the cancer cell [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 1317.

We describe a case of CEOT of the mandible, which underwent malignant transformation and developed metastatic tumors of the lung after repeated local recurrence. The primary tumor revealed typical histological features of benign CEOT showing sheets of polyhedral epithelial cells associated with abundant eosinophilic amyloid-like materials. On the other hand, the locally recurrent tumors had malignant features, such as increased nuclear pleomorphism with frequent mitotic figures and vascular invasion of tumor cells, as well as increased proliferative activity assessed by immunostaining for Ki-67. Chemotherapy was carried out against the pulmonary metastatic lesions, which showed a drastic response after 3 courses of intravenous administration of cisplatin (CDDP). To date, a total of 6 courses of CDDP and 6 courses of nedaplatin (CDGP) have been done, and the remaining pulmonary tumors have been dormant. This suggests that platinum derivatives could be a chemotherapeutic agent of choice against this rare tumor.

SummaryNetworks are a powerful and flexible methodology for expressing biological knowledge for computation and communication. Network-encoded information can include systematic screens for molecular interactions, biological relationships curated from literature, and outputs from analyses of Big Data. NDEx, the Network Data Exchange (www.ndexbio.org), is an online commons where scientists can upload, share, and publicly distribute networks. Networks in NDEx receive globally unique accession IDs and can be stored for private use, shared in pre-publication collaboration, or released for public access. Standard and novel data formats are accommodated in a flexible storage model. Organizations can use NDEx as a distribution channel for networks they generate or curate. Developers of bioinformatic applications can store and query NDEx networks via a common programmatic interface. NDEx helps expand the role of networks in scientific discourse and facilitates the integration of networks as data in publications. It is a step toward an ecosystem in which networks bearing data, hypotheses, and findings flow easily between scientists.

A case of peripheral odontogenic fibroma occurring in the posterior region of the maxilla in a 7-year-old girl is presented. The patient was referred to our hospital because of a painless swelling in the buccal gingiva at 6 1. Physical examination revealed a localized elastic-hard tumor, with no radiological abnormalities in the adjacent alveolar bone. A biopsy specimen showed proliferation of fibroblastic cells in the submucosal layer. With the patient under general anesthesia, the tumor was resected subperiosteally, and the underlying bone surface was smoothened with a bur. Histologically, the lesion was composed of cellular fibrous connective tissue with scattered regions of cellular strands interwoven with less cellular areas and islands of odontogenic epithelium. The final diagnosis was peripheral odontogenic fibroma. Specific staining demonstrated that the tumor stroma contained large amounts of oxytalan fibers and tenascin. A positive reaction for tenascin was found to be a valuable pathohistological marker that could distinguish between peripheral odontogenic fibroma and calcifying fibrous epulis.As of about 9 years after the operation, there has been no evidence of recurrence.

Novel efficient synthesis of several enantio-pure 2-alkylated 1α,25-dihydroxy-19-norvitamin D 3 analogues through radical introduction of 2-alkyl chain and C 5 -C 6 position coupling using Julia-type olefination as key steps was established starting from commercially available (-)-quinic acid as a chiral pool.

The Network-extracted Ontology (NeXO) is a gene ontology inferred directly from large-scale molecular networks. While most ontologies are constructed through manual expert curation, NeXO uses a principled computational approach which integrates evidence from hundreds of thousands of individual gene and protein interactions to construct a global hierarchy of cellular components and processes. Here, we describe the development of the NeXO Web platform (http://www.nexontology.org)-an online database and graphical user interface for visualizing, browsing and performing term enrichment analysis using NeXO and the gene ontology. The platform applies state-of-the-art web technology and visualization techniques to provide an intuitive framework for investigating biological machinery captured by both data-driven and manually curated ontologies.

34歳女性の左側口蓋粘膜に発症した腺様嚢胞癌を経験した。その初回来院時は, 他院処置後の当該部歯原性炎症の処置の依頼であって, その処置を遂行することによって, 症状は軽快治癒した。口蓋部にはび慢性腫脹が残されたが, これに対する次回の当科での検索処置まで6年余が経過し, 本腫瘍の治療に当たって大きな困難が経験された。本例は, 2回目の初診後, 生検確定診と共に肺転移巣の存在が疑われ, 腺様嚢胞癌T2N0M1 (T2bN0M1, 岡部提案) の診断が確定された。その主治療は初め3者併用療法によったが不充分で, 上顎左半側広範切除・D-P皮弁再建を行った。本研究では, 患者のQOLを考慮する立場から, 原発巣に対する治療方針・切除範囲, 胸部肺転移巣の処置などにおいて, 患者と主治医の立揚から多くを思考した。特に, 腺様嚢胞癌のもつ生物学的特性と対比して, 患者のQOLを考え, informed consentに格段の考慮をなすべき諸課題を見いだした。主治療の結果, 菲薄化した上赤唇左側を過剰となった皮膚組織を利用してcomposite skin graftを行い, 患者の機能的並びに美容的障害を回復した。これは, 患者の退院後のQOLに大きな役割を果たしたものである。当報告症例は現在, 腫瘍治療手術を開始してから, 3年6か月余を経過し, 家庭・社会生活に復帰している。

Background BioHackathon 2010 was the third in a series of meetings hosted by the Database Center for Life Sciences (DBCLS) in Tokyo, Japan. The overall goal of the BioHackathon series is to improve the quality and accessibility of life science research data on the Web by bringing together representatives from public databases, analytical tool providers, and cyber-infrastructure researchers to jointly tackle important challenges in the area of in silico biological research. Results The theme of BioHackathon 2010 was the 'Semantic Web', and all attendees gathered with the shared goal of producing Semantic Web data from their respective resources, and/or consuming or interacting those data using their tools and interfaces. We discussed on topics including guidelines for designing semantic data and interoperability of resources. We consequently developed tools and clients for analysis and visualization. Conclusion We provide a meeting report from BioHackathon 2010, in which we describe the discussions, decisions, and breakthroughs made as we moved towards compliance with Semantic Web technologies - from source provider, through middleware, to the end-consumer.

To the Editor.-- Author Manuscript.-- et al., This study was supported by the European Commission under the Serving Life-science Information for the Next Generation contract 226073; Proteomics Standards Initiative and International Molecular Exchange contract FP7-HEALTH-2007-223411; Apoptosis Systems Biology Applied to Cancer and AIDS contract FP7-HEALTH-2007-200767; Experimental Network for Functional Integration contract LSHG-CT-2005-518254; German National Genome Research Network; German Research Foundation contract KFO 129/1-2; US National Institutes of Health grant R01GM071909; the Italian Association for Cancer Research; a Wellcome Trust Strategic Award to the European Molecular Biology Laboratory–European Bioinformatics Institute for Chemogenomics Databases; Grand Challenges in Global Health Research, the Canadian Institutes of Health Research, Foundation for the National Institutes of Health and Genome British Columbia; and a German Research Foundation–funded Cluster of Excellence for Multimodal Computing and Interaction.

Web services have become a key technology for bioinformatics, since life science databases are globally decentralized and the exponential increase in the amount of available data demands for efficient systems without the need to transfer entire databases for every step of an analysis. However, various incompatibilities among database resources and analysis services make it difficult to connect and integrate these into interoperable workflows. To resolve this situation, we invited domain specialists from web service providers, client software developers, Open Bio* projects, the BioMoby project and researchers of emerging areas where a standard exchange data format is not well established, for an intensive collaboration entitled the BioHackathon 2008. The meeting was hosted by the Database Center for Life Science (DBCLS) and Computational Biology Research Center (CBRC) and was held in Tokyo from February 11th to 15th, 2008. In this report we highlight the work accomplished and the common issues arisen from this event, including the standardization of data exchange formats and services in the emerging fields of glycoinformatics, biological interaction networks, text mining, and phyloinformatics. In addition, common shared object development based on BioSQL, as well as technical challenges in large data management, asynchronous services, and security are discussed. Consequently, we improved interoperability of web services in several fields, however, further cooperation among major database centers and continued collaborative efforts between service providers and software developers are still necessary for an effective advance in bioinformatics web service technologies.

小児の下顎骨に発生したエナメル上皮腫の5症例について報告した。性別は男児2例, 女児3例であった。年齢は10歳から14歳にわたり, 平均12.6歳であった。X線所見では3例が単房性を示し, 2例が多房性の嚢胞様透過像を示した。5例中4例に対し治療を行い, 開窓療法により, 臨床的ならびにX線学的に病変の縮小を認めた。その後, 下顎骨の縮小手術を実施し, 満足し得る成績を得た。病理組織所見では全例が叢状型のエナメル上皮腫であった。小児のエナメル上皮腫の治療においては顎骨の拡大切除は行い難い。嚢胞性のエナメル上皮腫に対しては開窓療法が効果的であった。加えて臨床的ならびに文献的考察を行い, 小児のエナメル上皮腫について考察した。

Primary cilia are evolutionarily conserved cellular organelles that organize diverse signalling pathways. Defects in the formation or function of primary cilia are associated with a spectrum of human diseases and developmental abnormalities. Genetic screens in model organisms have discovered core machineries of cilium assembly and maintenance. However, regulatory molecules that coordinate the biogenesis of primary cilia with other cellular processes, including cytoskeletal organization, vesicle trafficking and cell-cell adhesion, remain to be identified. Here we report the results of a functional genomic screen using RNA interference (RNAi) to identify human genes involved in ciliogenesis control. The screen identified 36 positive and 13 negative ciliogenesis modulators, which include molecules involved in actin dynamics and vesicle trafficking. Further investigation demonstrated that blocking actin assembly facilitates ciliogenesis by stabilizing the pericentrosomal preciliary compartment (PPC), a previously uncharacterized compact vesiculotubular structure storing transmembrane proteins destined for cilia during the early phase of ciliogenesis. The PPC was labelled by recycling endosome markers. Moreover, knockdown of modulators that are involved in the endocytic recycling pathway affected the formation of the PPC as well as ciliogenesis. Our results uncover a critical regulatory step that couples actin dynamics and endocytic recycling with ciliogenesis, and also provides potential target molecules for future study.

Cytoscape is a free software package for visualizing, modeling and analyzing molecular and genetic interaction networks. This protocol explains how to use Cytoscape to analyze the results of mRNA expression profiling, and other functional genomics and proteomics experiments, in the context of an interaction network obtained for genes of interest. Five major steps are described: (i) obtaining a gene or protein network, (ii) displaying the network using layout algorithms, (iii) integrating with gene expression and other functional attributes, (iv) identifying putative complexes and functional modules and (v) identifying enriched Gene Ontology annotations in the network. These steps provide a broad sample of the types of analyses performed by Cytoscape.