- Open Access
InteGO2: a web tool for measuring and visualizing gene semantic similarities using Gene Ontology
- Jiajie Peng†1, 4,
- Hongxiang Li†2,
- Yongzhuang Liu2,
- Liran Juan3,
- Qinghua Jiang3,
- Yadong Wang2Email author and
- Jin Chen4, 5Email author
© The Author(s) 2016
- Published: 31 August 2016
The Erratum to this article has been published in BMC Genomics 2017 18:262
The Gene Ontology (GO) has been used in high-throughput omics research as a major bioinformatics resource. The hierarchical structure of GO provides users a convenient platform for biological information abstraction and hypothesis testing. Computational methods have been developed to identify functionally similar genes. However, none of the existing measurements take into account all the rich information in GO. Similarly, using these existing methods, web-based applications have been constructed to compute gene functional similarities, and to provide pure text-based outputs. Without a graphical visualization interface, it is difficult for result interpretation.
We present InteGO2, a web tool that allows researchers to calculate the GO-based gene semantic similarities using seven widely used GO-based similarity measurements. Also, we provide an integrative measurement that synergistically integrates all the individual measurements to improve the overall performance. Using HTML5 and cytoscape.js, we provide a graphical interface in InteGO2 to visualize the resulting gene functional association networks.
InteGO2 is an easy-to-use HTML5 based web tool. With it, researchers can measure gene or gene product functional similarity conveniently, and visualize the network of functional interactions in a graphical interface. InteGO2 can be accessed via http://mlg.hit.edu.cn:8089/.
- Gene Ontology
- Semantic similarity
- Web tool
The hierarchical structure and the detailed gene annotation of Gene Ontology (GO) provide biologists a convenient tool to identify enriched gene sets in high-throughput omics-based experiments. In GO, the ontology terms represent biological knowledge and describe functions for genes and gene products. GO consists of three categories, i.e. molecular function (MF), biological process (BP) and cellular component (CC). GO provides rich information as an integrated resource and is convenient to study gene functional similarity [1, 2]. With GO, biologists can quickly test their biological hypotheses and design new experiments .
Various computational tools have been developed to identify functionally similar genes or gene products by comparing the annotated GO terms. According to the types of information in GO they use, these methods have been divided into three categories: 1) edge-based measurements, 2) node-based measurements, and 3) hybrid measurements [3, 4]. In the first category, tools are fully dependent on the structure of GO, so that these tools simply treat the terms at the same topological level equally . In the second category, tools consider both the gene annotation and the common ancestors of the target terms. But they neglect the complex topology of GO [6, 7]. In the third category, tools focus on the topological property of the GO structure but neglect the gene annotations .
Since none of the existing GO-based gene function similarity measurements can consider all the information in GO (i.e., hierarchical structure, gene annotation, all common ancestors, most informative common parent, etc.), we recently proposed two integrative measurements successively to unite the advantage of the existing measures [9, 10]. Our measurements automatically select and integrate seed measurements with a meta-heuristic search based method. In the following text, we briefly introduce our measurements; please refer to the algorithmic details at . Our algorithm has three steps. First, given a background gene set which includes a lot of genes, all their ranked similarity values are pre-calculated with all the selected GO-based semantic similarity measurements (called seed measurements). Second, for every gene pair in user’s input, the most appropriate seed measurements are selected with a grouping method. Finally, we develop a meta-heuristic search model and estimate its parameters by maximizing the distances between distinct EC groups which are manually curated. The algorithm has been tested on MF category, BP category, and protein sequence data. The experimental results indicate that our integrative measurement performs significantly better than the existing measurements.
Various web-based applications have been developed to calculate gene functional similarities based on Gene Ontology. The web-based approach is favorable since users do not need to install tools and maintain the GO data on their computers. The existing web-based GO applications include GossToWeb , ProteInOn , FunSimMat  and G-SESAM . While choosing the best measurement for a specific gene set is critical, none of the aforementioned web-based applications provide a solution. On top of it, most of these tools use the pure text-based format as output. Simply listing gene-to-gene similarity values in a big table neglects the fact that such data visualization is far beyond the direct perception of the human eyes. Biologists face challenges to effectively reduce vast and diverse data into representations that can be interpreted in a biological context. Moreover, there is currently no tools that allow researchers to wander around gene-to-gene associations and make discoveries by following intuition or simple serendipity.
InteGO2 is an integrative solution toward automatically choosing and weighing gene functional similarity measurements for the user provided gene set.
InteGO2 has an easy-to-use HTML5 based web interface. It can effectively visualize the network of genes based on their functional similarities.
InteGO2 is available for 98 species and supports 24 kinds of popular Gene ID types.
InteGO2 provides a convenient way to calculate and visualize the functional association between genes based on GO. The user guide of InteGO2 is included in Additional file 1. There are two main operations to use InteGO2: 1) to submit a gene list and specify parameters, and 2) to visualize and download the gene functional similarities.
List of available organisms in InteGO2. Annotated entity count field represents the number of annotated entity. Annotation count field represents the total number of annotations in the annotation file. (Noted that this table may be changed, since the annotation file is updated with the official Gene Ontology website automatically. This table was generated at Feb. 6th, 2015)
Canis lupus familiaris
Pseudomonas aeruginosa PAO1
In the second category, a user can choose a similarity measurement and a GO category. A recent measurement  and six widely-used similarity measurements [6–8, 16–18] are available to choose. Also, we provide an integrative measurement of all the aforementioned approaches . The description of these measurements is in subsection 2.4.
In the third category, a user can leave an email address and the name of the experiment, so that notification will be sent to the user when the job is done. Once all the information is submitted, we validate it for error checking. The validation process checks the format of input genes or gene pairs and all the user specified parameters. The user is notified immediately if any error is found. After that, we calculate the gene-to-gene similarities using the user specified measurement and construct a functional association network.
Note that all the submitted jobs are maintained on the backend server by a job scheduler. Once a job is finished, its job id will be sent to the user who submitted the job, if the user’s email address is provided. If a user does not leave the email address, the user should keep the submission webpage unclosed, so that the experimental results can be displayed on the same webpage. The experimental results will be kept on the back end server for at least two weeks. In addition we also keep the detailed information of the calculation process, such as the number of genes in the input list that cannot be measured because of lack of GO annotations.
The layouts supported in the visualization interface. The six layouts supported in the visualization interface of InteGO2
The concentric layout positions nodes in concentric circles.
Users could select this layout to put the graph in the middle
of the explorer.
The breadth-first layout puts nodes in a hierarchy, based
on a breadth-first traversal of the graph. The hierarchical
structure of the gene functional association network is
shown in this layout.
The circle layout puts nodes in a circle. From a circle layout,
the user could easily find the nodes with high degree and
The cose (Compound Spring Embedder) layout uses a
force-directed simulation to lay out compound graphs.
This layout helps the users to find the density region of the
The cola layout uses a force-directed physics simulation
with several sophisticated constraints.
The grid layout puts nodes in a well-spaced grid.
The gene information panels (Fig. 2 d,e,f) show the recently selected genes, current gene, and the neighbors of the recently selected gene respectively. By clicking a gene ID, a user can fetch more detailed information about the gene from NCBI (www.ncbi.nlm.nih.gov/gene) and the GO term information from Amigo (amigo.geneontology.org).
An illustrative example of using InteGO2
We use the sample gene list in InteGO2 website as the example to demonstrate how to use I n t e G O2. First, we set the parameters in Fig. 1 a as follows: the organism is Homo sapiens, the type of input is “gene list”, and the gene list is the sample gene list provided by the website in the UniProtKB AC/ID format. Second, in Fig. 1 b we select “Integrative Approach (InteGO2)” to be the GO similarity measurement and Molecular Function to be the GO category used in the measurement. The parameters in Fig. 1 c are optional, but we still enter an email address and provide the experiment name. Finally, we click the “submission” button.
GO-based semantic similarity measures
Eight GO-based semantic similarity measures are available in our web tool I n t e G O2. In this subsection, we will introduce the eight measurements briefly.
1) Integrative approach (InteGO2)
InteGO2 is an integrative measure of computing similarity. It automatically selects appropriate seed measures and then integrates them using a meta-heuristic search method . I n t e G O2 has three steps. First, calculate all the similarity scores using all the candidate measures and rank them, resulting in a ranked matrix M r . Second, a grouping process is applied on M r to identify the common features of all measures, with which we define seed measures for each gene pair, saved in S can . Third, integrate all the measures in S can with an addition model, in which the weight of each component is estimated by applying a learning process on a training set. Experimental results using ECs and pathways show that InteGO2 performs better than the existing measures. It also indicates that InteGO2 is robust against the unavailability of candidate measures. It is noted that an algorithm called InteGO was proposed in the previous work to unify different measures , which can be considered as a simplified case of InteGO2. The new functional association maps generated based on the gene-to-gene similarities based on InteGO2, together with the existing biological networks, may provide more biological insights into gene function and regulation.
2) Information content-based (Resnik)
3) Normalized information content-based (Schlicker)
4) Topology information based (Wang)
where P a and P b represent the sets of all the ancestors of t a and t b respectively.
5) Union information-based (simUI)
6) Graph information content (simGIC)
Combining simUI and Resnik measure, simGIC sums information content (IC) of the terms, not just count the terms .
7) Term overlap (TO)
8) Hybrid relative specificity similarity (HRSS)
where root represents the root term of GO; MICA represents the most informative common ancestor of t a and t b ; M I L a and M I L b are the most informative child leaf of t a and t b respectively; d i s t(x,y) represents the distance from x to y in GO; I C(x) represents the information content (IC) of x.
The Gene Ontology (GO) is a widely used bioinformatics resource. Various methods and web tools have been proposed to compute gene functional similarities based on GO. However, these tools only provide text file or web page includes similarity scores as final output for users, ignoring the appropriate visualization interface for result interpretation.
In this paper, we developed an easy-to-use web tool, named InteGO2, which allows users to conveniently measure gene functional similarity with eight different measures and visualize the resulting gene functional association networks with a web interface. InteGO2 supports up to 98 different of gene IDs belonging to 24 species. The GO data used in InteGO2 tool could be updated automatically to keep consistent with the most recent data from the official website of GO. In summary, InteGO2 is an easy-to-use web tool for researchers to measure and visulize GO-based gene functional similarities.
This project has been funded by the U.S. Department of Energy, grant no. DE-FG02-91ER20021 to J.C; the National High Technology Research and Development Program of China grant (no. 2012AA020404 and 2012AA02A602) and the National Natural Science Foundation of China grant (no. 61173085) to Y.W.
This article has been published as part of BMC Genomics Volume 17 Supplement 5, 2016. Selected articles from the 11th International Symposium on Bioinformatics Research and Applications (ISBRA ’15): genomics. The full contents of the supplement are available online https://0-bmcgenomics-biomedcentral-com.brum.beds.ac.uk/articles/supplements/volume-17-supplement-5.
The publication costs for this article were funded by the corresponding author’s institution.
Availability of data and materials
All data sets are available at http://mlg.hit.edu.cn:8089/.
JC and YW designed the web tool framework; JP and HL implemented the web tool; JC, JP and HL wrote this manuscript; YL and LJ helped design the visualization interface; QJ helped design the input interface. All authors read and approved the final manuscript.
The authors declare that there are no competing interests.
Consent for publication
Ethics approval and consent to participate
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