Metascape provides a biologist-oriented resource for the analysis of systems-level datasets — TEI-tagged view

Yingyao ZhoupersName, Bin ZhoupersName, Lars PachepersName, Max ChangpersName, Alireza Hadj KhodabakhshipersName, Olga TanaseichukpersName, Christopher BennerpersName, Sumit K. ChandapersName (2019date). “Metascape provides a biologist-oriented resource for the analysis of systems-level datasets.” Nature Communicationstitle·j.

Object of study — integrated annotation, enrichment, interaction-network, and comparative analysis of single or multiple OMICs gene lists

Research question — how to replace a fragmented, stale, and difficult multi-portal gene-list workflow with an integrated system producing current and interpretable results

Contribution — Metascapeterm, an integrated web portal combining more than 40 knowledgebases, gene annotationterm, membership searchterm, enrichment clusteringterm, interactome analysisterm, and multi-list comparison

Full text — tagged

Metascapeterm provides a biologist-oriented resource for the analysis of systems-level datasets Abstract A critical component in the interpretation of systems-level studiesterm is the inference of enriched biological pathwaysterm and protein complexesterm contained within OMICs datasetsterm. Successful analysis requires the integration of a broad set of current biological databasesterm and the application of a robust analytical pipelineterm to produce readily interpretable results. Metascapeterm is a web-based portalterm designed to provide a comprehensive gene list annotation and analysis resource for experimental biologists. In terms of design features, Metascapeterm combines functional enrichmentterm, interactome analysisterm, gene annotationterm, and membership searchterm to leverage over 40 independent knowledgebases within one integrated portal. Additionally, it facilitates comparative analysesterm of datasets across multiple independent and orthogonal experiments. Metascapeterm provides a significantly simplified user experience through a one-click Express Analysisterm interface to generate interpretable outputs. Taken together, Metascapeterm is an effective and efficient tool for experimental biologists to comprehensively analyze and interpret OMICs-based studies in the big data era.

Introduction It has become a standard practice to employ one or more OMICs assays to deconstruct the molecular mechanisms underlying a biological system. Since each study ultimately results in a list containing dozens or hundreds of gene candidates, it is essential to leverage existing biological knowledge through understanding the representation of known pathways or complexes within these datasets. Providing this molecular context can facilitate the interpretation of systems-level data and enable new discoveries. Common queries include: What pathways or biochemical complexes are enriched? 1 ; What are the functional roles of identified protein complexesterm? 2 ; Which candidate proteins are secreted, contain a transmembrane domain, or are otherwise druggable? 3 ; Or are there any chemical probes available for a rapid candidate validation 4 ? Critically, when multiple gene lists are analyzed, either from common or orthogonal platforms, the identification of consistent underlying pathways or networks can help decipher authentic signals above the experimental noise 5 . Conversely, pathways selectively enriched in specific lists can help elucidate critical molecular features that distinguish experimental conditions. Thus, gene-list analysis web portals have become a necessity for modern biological research. For example, DAVID 6 , a popular web-based annotation tool has been employed by more than 4000 published studies per year since 2014, underscoring the importance of OMICs analysis engines in biological research.

To guide the development of a next-generation tool, we studied 25 existing data analysis portals 7 , 8 , focusing on database updatesterm·var, coverage of analysesterm·var, coherence of interfaces, and interpretability of outputsterm·var. While each platform maintains strong feature sets (Supplementary Data 1 ), multiple portalsterm are required to accomplish a complete systems-level analysis workflow, leading to a fragmented user experienceterm. For example, a user analyzing proteomics data may need to use one tool to convert protein identifiers into gene symbols, a second tool to perform pathway enrichment analysis, a third tool to assess protein interaction networks, followed by other tools to produce high-quality visualizations of the data. Users are not only required to learn the details behind how to use each interface successfully, but they must also learn how to integrate the outputs from multiple types of analyses and file formats in order to generate useful results. For the inexperienced user, this can pose a significant barrier to entry. Thus, it is likely that significant levels of existing biological knowledge are often inadvertently omitted during the analysis of OMICs datasetsterm due to the disintegrated nature of data sources and analysis tools. Ideally, a broad range of biological relationships and classifications can be assessed within one integrated portal.

Additionally, the increased accessibility of systems-level technology platforms has promoted experimental strategies that rely on orthogonal OMICs approachesterm, including transcriptome analysis, genetic screens, and proteomics, to interrogate an experimental system. This approach enables a more comprehensive assessmentterm of the molecular features of a biological process and reduces false positive/negative activitiesterm associated with individual platforms 9 . Thus, meta-analysis of multiple gene liststerm to identify commonly-enriched and selectively-enriched pathways is likely to become an essential component of large-scale data analysis. Methods for the analysis of multiple gene lists continue to be an area of active investigation, which highlights the necessity to develop analytical strategies that extend beyond the consolidation and analysis of individual lists, and implement approaches that will enable emergent insights that cannot be extracted using current approaches 10 , 11 . Our survey found that multi-gene-list meta-analysis is a feature often missing from existing tools (Supplementary Data 1 ).

In addition to a combination of diverse knowledgebases and the synthesis of analysis results across related gene lists, we propose that biologist-oriented portals would need to incorporate additional critical features, including future-proofing, usability, and interpretability, to maximize their utility. Specifically, analysis portals must continually be kept up to date, since using outdated databases can severely impact analysis and interpretation of OMICs data. It has been estimated that 67% of ~3900 publications in 2015 missed 74% of potential new biological insightsterm as a consequence of the use of obsolete database contentterm in many analysis portals 7 . This remains a concern to date, as the databases underpinning the interpretation of a majority of published studies are over 2 years old 12 .

Additionally, a well-designed interface that requires a minimal number of user interactionsterm·var (clicks) reduces the barrier to entry for new users, or those with limited computational training. Nevertheless, simplification must be balanced with providing users sufficient information to understand and, if required, adjust statistical and computational criteriaterm·var within the analysis workflow. This feature would provide expanded access to data analysis tools and sources for experimentalists that have limited experience with OMICs analysis, while enabling more advanced users to adjust analytical parameters to customize analyses.

Furthermore, it is essential that analysis results are readily decipherable, highlighting the key, non-redundant results that will be critical for informing future studies. Platforms that go beyond exporting tabular annotation information to produce graphical summaries of resultsterm tend to enhance the global interpretation of complex OMICs datasetsterm. The resulting output analyses should also adopt presentation formats that facilitate data dissemination among scientists.

With these design criteria in mind, we developed Metascapeterm to harness the best practices of OMICs data analyses that have emerged over the last decade into one integrated portal. Here, we describe the features of this platform, which provide enhanced rendition of systems-level data, designed to promote the development of actionable hypotheses. These include data-engineering solutions for over 40 knowledgebases, as well as integrated and simplified workflows for gene annotationterm, membership searchterm, and multi-list comparative analysis. Furthermore, we describe unique graphical outputs that enhance reporting and boost data comprehension. Taken together, Metascapeterm provides an integrated and user-friendly web tool designed to facilitate multi-platform OMICs data analysis and interpretation for the experimental research community.

Results User experience overview To illustrate the features and capabilities of Metascapeterm, we utilized three previously published influenza genetic screens 13 – 15 as examples in this study (see Methods). These studies utilize genome-wide RNAi screening in cells to identify host factors that modulate influenza replication rates. Specifically, single-gene-list analyses were performed with gene targets identified in the Brass et al. study 13 , and multi-gene-list analyses were performed with the combination of Brass et al., Karlas et al 14 ., and Konig et al. 15 target lists. The step-by-step illustration of the Metascapeterm analysis interface for both single- and multiple-list input scenarios are available in Supplementary Figures 1 – 7 . While these examples showcase the analysis of RNAi functional genomics studies, Metascapeterm is equally capable of analyzing lists of genes from multiple assay types, including transcriptomics, epigenetics, proteomics, and others.

The primary Metascapeterm user experience is provided through a one-click analysis interface (Express Analysisterm, Supplementary Fig. 1 ). The user first provides a single or multiple input gene lists, then launches an automated analysis workflow consisting of four major components: identifier c onversion, gene a nnotation, m embership search, and e nrichment analysis (CAME). An analysis report summarizing key results is produced, accompanied by an Excel workbook, a PowerPoint presentation, and a Zip package containing all supporting data files (Fig. 1 ).

Schematic outline of the Metascapeterm analysis workflow. Upon gene list submission, Custom Analysisterm enables users to navigate the analysis workflow and output a report. The Express Analysisterm instead takes a streamlined approach by running the analysis steps with popular default settings, simplifying the user experience The Metascapeterm interface was designed to limit user operations to a minimum number of interactions. Input data is automatically processed to recognize identifier types, such as Entrez Gene ID, gene symbol, RefSeq number, Ensembl ID, etc., without user specification. The file input supports multiple flexible formats (Supplementary Figure 1 ) and can incorporate an optional background gene list to customize the enrichment analysis. Gene identifiers from ten popular model organisms are recognized by Metascapeterm (see Methods), and automatic orthologue mappingterm enables gene lists to be analyzed in the context of species-specific databases across any of these ten organisms. The target analysis species is human by default, since human-centric databases are often the most comprehensive. Importantly, we have observed that 63% of Metascapeterm analyses to date utilize human datasets.

The concept behind Express Analysisterm is to encapsulate the most widely used analysis options and best analysis practices into a streamlined approach. For example, Express Analysisterm automatically includes gene annotationterm metadata that are likely to provide valuable context for candidate interpretation in most cases, such as their brief description, key biological processes involved, protein functions, subcellular locations, and roles in canonical pathways (Supplementary Figure 3 ). It incorporates a core set of default ontologies for enrichment analysis, including GO processes 16 , KEGG pathways 17 , Reactome gene sets 18 , canonical pathways 19 , and CORUM complexes 20 (Supplementary Figure 5 ). Express Analysisterm also automatically sets statistical filters to remove ontology terms and interactome networks that do not meet minimal statistical requirements (Supplementary Figure 5 ). As an alternative to the Express Analysisterm, advanced users can control the four individual steps of the CAME workflow and overwrite default settings through the Custom Analysisterm workflow (Fig. 1 , Supplementary Figures 2 – 6 ).

Identifier conversion analysis The first step of the Metascapeterm analysis workflow is gene identifier conversion (Supplementary Figure 2 ). Metascapeterm automatically recognizes the popular gene identifier types described above, as well as primary locus names from various model organism databases, such as SGD 21 , FlyBase 22 , WormBase 23 , etc. Source identifiers are mapped onto a unique Entrez Gene ID listterm prior to analyses, as a significant number of bioinformatics knowledgebases underlying Metascapeterm rely on Entrez Gene IDs as their primary keys. Deprecated Entrez Gene IDs are also recognized and replaced by successor IDs when encountered.

Many gene annotationterm, pathway, and protein interaction databases are primarily compiled for human genes/proteins. For instance, the size of the mouse interactome encompasses only ~6% of the available human interactome 24 , even though many of these interactions are likely conserved across species. Therefore, it can be beneficial to cast gene candidates obtained in model organisms into their human orthologs prior to analysis. Ortholog mappingterm is a built-in featureterm·mth and can be triggered by explicitly choosing the target analysis species (Supplementary Fig. 1 ), which will instruct subsequent analysis steps to use resources for that particular species.

Gene annotationterm Detailed annotations for genes provide biological context to serve as important selection criteria when choosing candidates for follow-up studies. Metascapeterm integrates annotation information from over 40 knowledgebases (Supplementary Figure 8 , Supplementary Data 2 ), encompassing gene descriptions, gene summaries, disease implications, genomic variants, subcellular localizations, tissue expression, the availability of chemical probes, etc. Express Analysisterm delivers nine descriptive fields by default, however, as illustrated in Supplementary Figure 3 , the Custom Analysisterm feature allows the user to append up to 47 columns of metadata to a gene annotationterm spreadsheet.

To assimilate this level of information using current tools, the experimentalist is required to first identify the appropriate knowledgebases that contain the required information. Since many of these portals do not incorporate gene list upload features for batch annotation, users must, in certain cases, search individual gene candidates one at a time. For example, it becomes an onerous undertaking to identify all extracellular proteins through UniProt 25 , or all chemical probes through DrugBank 26 , using a list with hundreds of gene candidates. Thus, Metascapeterm’s automated gene annotationterm feature provides users with detailed annotation information not otherwise easily accessible, aiding in data interpretation of large-scale datasets and providing prioritization criteria for candidate genes.

Membership searchterm The membership searchterm feature in the Custom Analysisterm workflow allows users to apply specific query keywords, such as “infection” or “kinase”, against knowledgebase category term names and description fields, then review and select those matched terms that most accurately describe the process of interest (Supplementary Figure 4 ). Metascapeterm will then dynamically construct a column of binary flags indicating whether each gene in the input gene list is a member of the matched ontology terms. This allows the user to identify genes with specific functions or features, and reduces the reliance on existing hierarchical ontologies during data interpretation. For example, Metascapeterm offers two approaches to identify transmembrane proteins candidates. First, there are pre-integrated transmembrane predictions provided by Ensembl 27 or UniProt that are extractable through the gene annotationterm feature as described above. An alternative is to use the Membership tools to search the keyword “transmembrane” within the GO Cellular Component catalog to identify member genes. Although these two approaches are complementary in this specific example, membership searchterm becomes critical when metadata is not readily available through pre-integrated data sources. For instance, highlighting known infection-related genes for an influenza study cannot be accomplished via standard gene annotationterm. Despite the fact that this knowledge is embedded in the ontology databases, it has not been explicitly compiled in most annotation resources and, thus, is not readily accessible to experimentalists. The binary result data from membership searchesterm can also be visualized within nested pie charts (Supplementary Figure 9 ).

Enrichment clusteringterm Enrichment analysis comprises the core of most existing gene annotationterm portals. During enrichment analysis, the input gene list is compared to thousands of gene sets defined by their involvement in specific biological processes, protein localization, enzymatic function, pathway membership, or other features. Gene sets whose members are significantly overrepresented in the input gene list are reported to users to serve as putative biological insights into their study. A neglected problem in most current analyses is that redundancies in descriptors and ontologies can often complicate interpretation of the output. For example, ontology terms found in GO form a hierarchical structure of increasing granularity, making the terms inherently redundant. Terms across different ontology sources, such as GO, KEGG, and MSigDB 19 , etc. can be closely related as well. As a result, functional enrichmentterm analysis can identify overlapping or related terms, making it difficult to extract non-redundant and representative processes to report in the analysis output. This problem is demonstrated in examples from other analysis portals using the Brass list (Fig. 2a, b ), which report the enrichment of dozens of highly-related categories. During post-processing of data generated through the Metascapeterm analysis, Kappa similaritiesterm 28 among all pairs of enriched termsterm are computed and used to first hierarchically cluster terms into a tree then cast subtrees into clusters of similar terms (see Methods). By absorbing most redundancies into representative clusters, enrichment clusteringterm eliminates confounding data interpretation issues that can arise from the reporting of multiple ontologies.

Visualizations of functional enrichmentterm and interactome analysisterm results. a Screenshot of a portion of the directed acyclic graph rendered by Babelomicsterm 53 based on enriched GO terms. b Screenshot of a portion of the gene-term association matrix rendered by g:profile 42 , where enriched GO terms are organized hierarchically. c Screenshot of a portion of the tabular display of enriched termsterm rendered by DAVID based on all GO terms, KEGG pathways, Reactome, and CORUM. The terms related to “viral gene expression” were missed in the visualizations generated by Babelomicsterm and g:profile, but were identified by DAVID (in cluster 8 with ranks around 20). d Metascapeterm bar graph for viewing top non-redundant enrichment clusters, one per cluster, using a discrete color scale to represent statistical significance. e Metascapeterm enrichment networkterm visualization showing the intra-cluster and inter-cluster similarities of enriched termsterm, up to ten terms per cluster. Cluster annotations are shown in color code. f A complex interactome network generated by g:profile with the Brass gene list. g Metascapeterm visualization of the interactome network formed by all 121 gene candidates from the Brass list, where the MCODEterm complexes are colored according to their identities. h Seven MCODEterm complexes automatically identified in Metascapeterm, colored by their identities. Their functional labels are generated based on the top-three functional enriched termsterm, if available In comparison with Fig. 2a–c, d shows the Metascapeterm enrichment analysis results of the Brass list, where bars are discretely colored to encode p -valuesterm·var of increasing statistical significance. The bar graph representation nevertheless does not capture inter-cluster similarities and intra-cluster redundancies. To address this, we have implemented a visualization approach called enrichment networksterm. As shown in Fig. 2e , enrichment networksterm are created by representing each enriched termterm as a node and connecting pairs of nodes with Kappa similaritiesterm above 0.3, forming a network that can be depicted using Cytoscapeterm 29 . Nodes can be colored to reflect either their statistical p -valuesterm·var or cluster memberships. Redundant terms within a cluster naturally drive the formation of tight local complexes due to their high intra-cluster similarities, while clusters are occasionally bridged through terms with similarities reflecting the relatedness of two separate processes.

Although Metascapeterm introduces the above features to facilitate the interpretation of enrichment analysis results, it is not designed to supersede existing functional enrichment analysis portalsterm. For users solely conducting enrichment analysis for a single gene-list, some existing tools, such as Enrichr 30 and ToppFunc 31 , contain additional gene signature collections and they remain a powerful approach to enrichment analysis.

Protein network analysis Analyzing gene lists in the context of protein interactions can help illuminate biochemical complexes or signal transduction components that govern biological outputs 32 . However, a typical gene list of a few hundred human proteins can form highly complicated network layouts, making interpretation difficult. This is readily exemplified by the network output of other analysis portals using the Brass list (Fig. 2f ). These are colloquially referred to as hairball networksterm. Although such large networksterm are generally statistically significant, they do not yield directly actionable insights unless the experimentalist has specific expectations of the data. To infer more biologically interpretable results, Metascapeterm applies a mature complex identification algorithm called MCODEterm 33 to automatically extract protein complexesterm embedded in the large networkterm. Taking advantage of Metascapeterm’s functional enrichmentterm analysis capability, the three most significantly enriched ontology terms are combined to annotate putative biological roles for each MCODEterm complex. As shown in the example, Metascapeterm results are relatively simple to visualize and interpret (Fig. 2g, h ).

Multi-gene-list meta-analysis The proliferation and general adaptation of orthogonal OMICs platforms, including proteomics, genomics, functional genetic screens, and metabolomics, has made the meta-analysis of multiple gene liststerm a critical and largely unaddressed need. Routine comparative approaches include the use of Venn diagramsterm to identify hits that are common or unique to certain gene lists. We, and others, have previously reported that an overlap between OMICs datasetsterm is more readily apparent at the level of pathways or protein complexesterm 9 , 34 , 35 , which can be considerably more challenging to implement without extensive computational expertise.

Metascapeterm has been designed to allow cross comparison of an arbitrary number of gene lists across both gene identities and ontologies. Specifically, upon the submission of multiple gene lists, all candidates are merged into one list, while encoding the original lists as additional binary membership columns (Supplementary Fig. 10 ). This pivoted layout greatly simplifies the representation of analysis results, as both gene annotationterm and membership searchterm can be applied to the combined list the same way as in a single gene list input. Using a single output spreadsheet to incorporate evidence collected from each gene list thus enables efficient global gene filtering and prioritization. The overlap among the gene lists is visualized using a Circos plotterm 36 (Supplementary Fig. 11 ), which is a more intuitive and scalable representation compared to a Venn diagramterm.

To facilitate the understanding of pathways (and pathway clusters) that are shared between, or selectively ascribed to, specific gene lists, additional visualizations were developed for Metascapeterm. First, Metascapeterm depicts top enriched clusters and their enrichment patterns across multiple gene lists as a clustered heatmap (Fig. 3a ). The heatmap is complemented by an enrichment networkterm where each network node is represented by a pie chart, where the sector size is proportional to the number of genes originated from each gene list (Fig. 3b ). This representation is designed to be a biologically intuitive illustration of selective or shared pathway clusters. Importantly, this approach can also be applied to visualize protein–protein interaction networks to enable the elucidation of common/selective complex components enriched across OMICs datasetsterm (Fig. 3c , Supplementary Fig. 12 ).

Visualizations of meta-analysis results based on multiple gene lists. a Heatmap showing the top enrichment clusters, one row per cluster, using a discrete color scale to represent statistical significance. Gray color indicates a lack of significance. The category GO:0016032 (viral process) is common to all studies, while GO:0046777 (protein autophosphorylation) is enriched exclusively in a single study, and is therefore likely a process associated with one particular experimental system. b Enrichment networkterm visualization for results from the three gene lists, where nodes are represented by pie charts indicating their associations with each input study. Cluster labels were added manually. Color code represents the identities of gene lists. The network shows that processes such as viral gene expression, nucleocytoplasmic transport, and cellular response to external stimuli are generally shared among all three lists. RNA metabolism is shared between the Brass and Karlas lists; cellular development processes are mostly shared between the Karlas and Konig lists. c Selected MCODEterm components identified from the combined list of 541 genes, where each node represents a protein with a pie chart encoding its origin. The complex related to viral process is shared among all three lists, while the complex related to the MAP kinase cascade is specific to the Konig list. Supplementary Figure 12 presents all MCODEterm components identified Maintaining updated data sources Metascapeterm integrates over 40 knowledgebases (Supplementary Fig. 8 ) to support comprehensive analyses over ten highly studied organisms. Over time, some underlying data sources may adopt new access mechanisms, rename file packages, or be impacted by modifications to cross-referenced databases. Even if each data source is 99% robust, a system relying on 40 independent sources can only be 67% reliable. As the result of this compound effect, unexpected changesterm are encountered during data synchronization processes at frequencies significant enough to hinder attempts to automate content updates, which eventually results in outdated portalsterm. DAVID 6 , a heavily utilized annotation tool accounting for over 80% of all functional enrichmentterm portal usage, was not updated between 2010 and 2016, and then again not since 2016 12 . Our latest analyses on DAVID indicated 4–10% of the input gene candidates (varied by identifier types) are not recognized. Further analysis of the human GO archive data indicated 44% of annotation records have been added or modified since its last update. This is in agreement with a semantic similarity measure suggesting that 20% of genes change their functional identity over a two-year period 37 . Along with other outdated portalsterm, it was estimated that thousands of studies were affected, and the potential impact on follow-up studies could last for years to come 7 .

Metascapeterm has adopted a novel two-phase approach to overcome this critical issue and ensure monthly updates (Fig. 4 ). In the first phase, individual data sources are automatically crawled, wrangled, and assembled according to a predefined topological order of dependency, where gene identifier resources are processed before dependent annotation resources. When significant changespersName or unexpected errors occur while processing a data source, a database’s last known compatible copy from a previous update is reused, and an internal alert is issued to Metascapeterm quality control (QC) developers (see below). Using this strategy, conflicts in any given database component will not stop the timely completion of the Metascapeterm update cycle. It is important to note that more subtle inconsistencies in data content can be harder to detect and may not trigger these exceptions. For example, the accidental deletion of records for a certain model organism in NCBI 38 may go undetected. The Metascapeterm pipeline automatically carries out a detailed comparison between the current database and the new updated database, summarizing count differences into a bar graph web report, indicating the number of data records that are added or removed as illustrated in Fig. 4 . In the second phase, a Metascapeterm QC member inspects the graphical report for suspicious changespersName in record counts. If no concerning issues are identified, the release candidate is approved, and the database updateterm·var goes to production. Alerts received during the whole process are manually examined and addressed in parallel. Taken together, this workflow ensures that Metascapeterm users are not adversely impacted by outdated data sources.

Schematic diagram of the semi-automatic data synchronization workflow Usage and citation analysis Metascapeterm has been in development since 2014, and a beta version of the software was initially released in late 2015. The portal usage data, as well as feedback from users, have been decisive in correcting or refining underlying concepts that drove the architectural design and features of Metascapeterm. Some usage statistics are particularly enlightening. Among all recently recorded analysis sessions, ~78% users chose Express Analysisterm over Custom Analysisterm, implying that Express Analysisterm successfully captures a set of core algorithms and settings suitable for the majority of analysis needs. Six percent of analyses contained more than one gene list, indicating a demand for meta-analysis in the current research landscape. Data collected on input identifier typesterm·var indicates gene symbol and synonyms are the most widely used (77%), followed by Ensembl identifiers (11%), Entrez Gene ID (4%), and RefSeq (3%). The remaining IDs are mostly UniProt IDs and model-organism-specific IDs. Microarray IDs are hardly used (<0.2%), likely reflecting the shift of the gene expression technology platform from microarrays to next-generation sequencing. About 63% of analyses were performed using human identifiers, followed by 31% using mouse. Other model organisms such as D. rerio, R. norvegicus, A. thalianai , etc. accounted for the remainder. Interestingly, 3% usage took advantage of the ortholog mappingterm feature to mostly cast mouse/rat gene lists into human context for analysis. These data validate a need to support multiple gene/protein identifiers and multiple model organisms.

At the time of writing, our citation survey using Google Scholar identified 62% of the citations have adopted Metascapeterm graphical outputs directly in publications (Supplementary Data 5 ), underscoring the utility of tools that generate high-quality data visualizations (Fig. 5a ). The top three most used visualizations are the enrichment bar graph, enrichment networkterm, and enrichment heatmapterm, highlighting the utility of the tool for functional enrichmentterm analyses (Fig. 5b ). In addition to graphical outputs, Metascapeterm analysis generates two Excel spreadsheets providing gene annotationterm and functional enrichmentterm results (Supplementary Data 3 , 4 , Supplementary Figure 7 ). These data outputs have been directly used in over 16% of publications as supplementary materials to support analysis conclusions (Supplementary Data 5 ). Since Circos plotsterm and enrichment heatmapsterm are two visualization tools specifically designed for multiple gene lists only, and a bar graph is only used for a single gene list, we estimated that 70% of the published studies studied a single gene list and 30% studied multiple gene lists. This finding, together with our web portal usage data, highlight the increasing usage of multi-OMICs approaches in current studies, and suggest that meta-analysis has become an important tool for the analysis of OMICs datasetsterm.

Citation statistics illustrating the use of Metascapeterm visualizations in publications. a 62% of publications adopted visualizations prepared by Metascapeterm. b Visualization types sorted according to their popularity, where the enrichment bar graph, network, and heatmap are frequently used to summarize functional enrichmentterm outputs Discussion The systematic categorization of genes by their functions by the Gene Ontology Consortium (GO) has initiated the widespread practice of functional enrichmentterm analysis; many portals have been developed over the years and have led to the continual refinement of gene list analysis algorithms 39 – 41 . Metascapeterm was not developed to replace existing specialized toolsterm, but to provide a pipeline to assimilate many of the powerful features of these analysis platforms to enable integrative, end-to-end analysis of OMICs-level datasets.

Our study of 25 existing analysis portals found that the underlying databases were often out-of-date (Supplementary Data 1 ). Although portals such as g:profile 42 , PANTHER 43 , TopFun 31 , InterMine 44 , GoEast 45 , and GeneTrail2 46 provide up-to-date knowledgebases, a significant portion of portals (60%) relied on knowledgebases older than one year (Supplementary Data 1 ). Due to the inclusion of over 40 unique data sources in Metascapeterm, a fully automated synchronization pipeline becomes unstable due to changespersName in the underlying resources. This motivated us to develop a workflow that combines an automated robot crawling phaseterm with a supervised human review phaseterm (Fig. 4 ). This data-engineering solution has demonstrated success through our monthly data refresh cycles, providing a viable data synchronization model for complex portals facing similar challenges.

In addition to regular database updatesterm·var, gene annotationterm and membership searchterm are two features that are not readily addressed by existing portals (Supplementary Data 1 ). Although the term gene annotationterm is occasionally used by existing portals as a synonym for enrichment analysis 44 , 47 , 48 , here we consider gene annotationterm as a method to extract a diverse set of metadata, such as identifying extracellular proteins or associated chemical probes, for hundreds to thousands of gene candidates at once. Membership searchterm is a closely related yet complementary service that dynamically highlights genes known to be involved in specific biology, such as host-pathogen interactions. Typically, retrieving these metadata requires querying specific portals, often one gene at a time, and compiling outputs into a centralized format for analysis. Metascapeterm enables the batch extraction of contents from over 40 databases in an extremely efficient manner (Supplementary Figures 3 – 4 ).

Analyzing multiple gene lists simultaneously through robust meta-analysis is another unmet need in the current OMICs data analysis landscape (Supplementary Data 1 ). Evaluated portals provide no, or only limited, support for this activity. For example, GoEast 45 and agriGO 49 provide some meta-analysis functions using a two-step process, where users analyze gene lists individually first, then reupload their enrichment results for cross comparison. However, GoEast analyzes a maximum of three lists and agriGO only supports organisms of agricultural relevance. GeneCodis 50 supports a maximum of two lists. GoMiner 51 can compute enrichment terms for multiple lists in parallel, however, the separate outputs are not further integrated and cross compared. g:Cocoa, a component in g:profile, compiles a tabular output conceptually similar to Metascapeterm’s enrichment heatmapterm. However, it currently lacks an enrichment clusteringterm feature and does not support interactome meta-analysis (Fig. 2b ). Metascapeterm’s architecture is designed to support a seamless user experience towards the analysis and visualization of an arbitrary number of gene lists, only limited by available computing time and memory spaceterm·var.

The integration of Circos plotsterm overcomes the limitation of Venn diagramsterm to graphically visualize the overlaps among many lists (Supplementary Figure 11 ), and enrichment heatmapsterm (Fig. 3a ) are applicable to the number of lists encountered in practice (Fig. 3a ). Metascapeterm also builds on previous implementations of enrichment networkterm maps 52 by depicting enriched termsterm as pie charts, which more readily indicate common and selective pathways enriched across multiple gene lists (Fig. 3b ).

While interactome analysisterm is implemented in Babelomicsterm 53 , Intermine 44 , and ConsensusPathDB 54 , Metascapeterm’s interactome analysisterm is distinguished by two notable features. First, in addition to the BioGrid knowledgebase, Metascapeterm integrates the more recent human interactome datasetsterm InWeb_IM 55 and OmniPath 56 , resulting in a tripling of human interactome coverageterm (Supplementary Figure 13 ). Second, Metascapeterm automatically extracts protein complexesterm and annotates their respective functions (Fig. 2h , Supplementary Figure 12 ). Compared to existing visualization approaches (Fig. 2f ), Metascapeterm is designed to highlight dense interactome neighborhoods and facilitate the interpretation of interactome data in a biological context; it further integrates with Cytoscapeterm 29 to leverage its rich set of visualization and interactive features.

Critically, Metascapeterm was designed to provide more interpretable and actionable results for experimentalists. The redundancy among ontology terms presents a significant obstacle in result interpretation. The primary strategy adopted by existing portals, as seen in g:Profilerterm 42 , WebGestalt 8 , Babelomicsterm 53 , and others, is to utilize directed acyclic graph (DAG) or tree structures embedded within ontologies such as GO 16 (examples in Fig. 2a, b ). WebGestalt provides an option only using a subset such as GOSlim 16 to reduce redundancy. However, these strategies are not applicable to reduce redundancies across different ontology sources. Metascape and DAVIDterm 6 , by contrast, adopt a dynamic clustering solution that avoids the above-mentioned shortcomings. Metascapeterm further simplifies the result presentation via bar graph (Fig. 2d ) and heatmap (Fig. 3a ) visualizations, compared to DAVID’s tabular presentation (Fig. 2c ). Although p -valueterm is used as the default representation metric for ranking enriched termsterm, it nevertheless has its limitations. Users may need to consider using alternative metrics, such as the multi-test corrected q -valueterm or the biological context-sensitive enrichment factor. All popular enrichment metrics, including detailed gene counts, are computed and made available for offline analyses. Metascapeterm further enhances the interpretability of analysis results through advanced graphical and spreadsheet outputs, a concise PowerPoint presentation (Supplementary Figure 7 ), and a journal-style web-based Analysis Report containing additional method descriptions and references (Supplementary Figure 7 ). Offline analysis is facilitated by a Zip export containing third-party compatible data files, such as the Cytoscapeterm 29 and JTreeView 57 formats.

In conclusion, Metascapeterm has been designed for experimentalists (bench biologists) to apply powerful computational analysis pipelines to analyze and interpret large-scale datasets. To ensure that its content is kept current, a new knowledgebase synchronization pipeline was engineered. To address gaps in current analysis tools, Metascapeterm introduces a workflow integrating gene annotationterm, membership analysis, and multi-gene-list meta-analysis capabilities. Its rich set of analysis tools are accessible through a convenient one-click Express Analysisterm interface and results are communicated via an article-like analysis report. We expect that the wider adoption of Metascapeterm will significantly enhance biological interpretationterm of OMICs studies by enabling experimentalists to directly analyze their data to identify novel therapeutic targets, mechanisms of action, or molecular insight into disease. Metascapeterm is an open-access resource available at http://metascapeterm.org .

Methods Architecture and data source Metascapeterm is a web resourceterm compatible with all major browsers. The backend knowledgebaseterm is a relational databaseterm managed by MySQL. The front-end user interface is implemented by Angular JavaScript framework to provide a rich interactive user experience. The server-side computational logic is mostly implemented in Python and leverages several packages provided by the community ( https://pypi.org ). Each analysis request is associated with a randomly-generated unique session ID (Supplementary Figure 2 ), which can be used to access analysis results interactively through the web or to download all result data in a Zip package for offline use, before the data is removed from the server (Supplementary Figure 7 ).

The rich analyses of Metascapeterm sit on top of over 40 bioinformatics knowledgebases maintained by the scientific community (Supplementary Figure 8 , Supplementary Data 2 ). A data synchronization pipeline, as described in Fig. 4 , is applied monthly to process data into an Entrez Gene ID-centric relational data format. Metascapeterm currently supports ten organisms: H. sapiens , M. musculus , R. norvegicus , D. rerio , D. melanogaster , C. elegans , S. cerevisiae , A. thaliana , S. Pombe , and P. falciparum .

Functional enrichmentterm analysis Metascapeterm utilizes the well-adopted hypergeometric testterm 58 and Benjamini-Hochberg p -value correction algorithmterm 59 to identify all ontology terms that contain a statistically greater number of genes in common with an input list than expected by chance. By default, Metascapeterm pathway enrichment analysis makes use of Gene Ontology 16 , KEGG 17 , Reactome 18 , MSigDB 19 , etc. Distinguishing it from many existing portals, Metascapeterm automatically clusters enriched termsterm into non-redundant groupsterm, where it implements similar logic as found in DAVID 6 . Briefly, pairwise similarities between any two enriched termsterm are computed based on a Kappa-test score 28 . The similarity matrix is then hierarchically clustered and a 0.3 similarity threshold is applied to trim the resultant tree into separate clusters. Metascapeterm chooses the most significant (lowest p -valueterm) term within each cluster (Supplementary Data 4 ) to represent the cluster in bar graph and heatmap representations. The analysis provides other popular enrichment metrics in addition to p -valuesterm·var.

Interactome analysisterm Metascapeterm utilizes physical protein–protein interactions captured in BioGrid 24 as the main data source. In addition, it integrates more recent human interactome datasetsterm including InWeb_IM 55 and OmniPath 56 to provide additional interactome coverage (Supplementary Figure 13 ). Given a list of proteins, it first automatically extracts a protein interaction network formed by these candidates. Then for each connected network component, it iteratively applies the MCODEterm algorithm 33 , with modifications for performance improvements, to identify densely-connected complexes. For each complex, it further applies function enrichment analysis and uses the top three enriched termsterm for the annotation of its biological roles (Fig. 2h , Supplementary Figure 12 ). All network visualizations are generated with Cytoscapeterm 29 and exported in multiple formats.

Datasets Previously published host factors that regulate influenza replication were used as example gene lists in this study. The 121 host factors identified by Brass et al. 13 were used as a single gene list input for all portals studied in Supplementary Data 1 and Fig. 2 . To demonstrate meta-analysis capabilities, three influenza gene lists were used as the input, including 121 host factors from Brass et al. 13 , 168 from Karlas et al. 14 , and 294 from Konig et al. 15 . All gene lists were downloaded from the IAV database 9 .

User interface Supplementary Figures 1 – 7 describe the Metascapeterm user interface step-by-step for gene-list analyses using the above-mentioned datasets. Screenshots specific to the single gene list analysis are outlined in orange; those specific to the multiple gene list meta-analysis are outlined in green; common screenshot components are outlined in gray. During the Express Analysisterm, the submission of gene list(s) (Supplementary Figure 1 ) directly leads to an analysis report (Supplementary Figure 7 ) without the need to step through the intermediate operations (Supplementary Figures 2 – 6 ). Conversely, the detailed analysis interface (Supplementary Figures 2 – 6 ) is only accessible through Custom Analysisterm.

Usage statistics To avoid statistics bias due to new users, who tested and learned Metascapeterm using either example gene lists provided on the portal site or users’ own lists, all analysis sessions associated with an exact same input gene-list group were considered duplicates, regardless of users’ IP address and the time of submission. With such duplicates being removed first, statistics on web site analysis pattern were compiled based on over 70,000 most recent sessions by the time of writing. The relative percentage usage of Metascapeterm visualizations and spreadsheets are derived based on a total of 241 publications (2016-current), implying a maximum standard deviation of 3% in the estimations.

Supplementary information Supplementary Information Description of Additional Supplementary Files Supplementary Data 1 Supplementary Data 2 Supplementary Data 3 Supplementary Data 4 Supplementary Data 5 Acknowledgements M.C., L.P., C.B. and S.C. acknowledge support by NIH grants U19 AI106754, U19 AI135972, and R01 DA033773.

Author contributions Y.Z., B.Z., M.C., L.P., A.H.K, O.T. implemented the Metascapeterm web portal; Y.Z. and S.K.C. managed project; Y.Z. and B.Z. analyzed usage data; Y.Z., C.B. and S.K.C. wrote the manuscript.

Data availability Gene lists referenced in this study are available in Supplementary Data 3 .

Code availability Custom code used in this study is available from the corresponding authors upon reasonable request.

Competing interests The authors declare no competing interests.

Footnotes Contributor Information Yingyao ZhoupersName, Email: yzhou@gnf.org.

Sumit K. ChandapersName, Email: schanda@sbpdiscovery.org Supplementary information Supplementary Information accompanies this paper at 10.1038/s41467-019-09234-6.

References Associated Data Supplementary Materials Supplementary Information Description of Additional Supplementary Files Supplementary Data 1 Supplementary Data 2 Supplementary Data 3 Supplementary Data 4 Supplementary Data 5 Data Availability Statement Gene lists referenced in this study are available in Supplementary Data 3 .

Custom code used in this study is available from the corresponding authors upon reasonable request.