ISAAC - InterSpecies Analysing Application using Containers
© Baier and Schultz; licensee BioMed Central Ltd. 2014
Received: 26 July 2013
Accepted: 10 January 2014
Published: 15 January 2014
Information about genes, transcripts and proteins is spread over a wide variety of databases. Different tools have been developed using these databases to identify biological signals in gene lists from large scale analysis. Mostly, they search for enrichments of specific features. But, these tools do not allow an explorative walk through different views and to change the gene lists according to newly upcoming stories.
To fill this niche, we have developed ISAAC, the InterSpecies Analysing Application using Containers. The central idea of this web based tool is to enable the analysis of sets of genes, transcripts and proteins under different biological viewpoints and to interactively modify these sets at any point of the analysis. Detailed history and snapshot information allows tracing each action. Furthermore, one can easily switch back to previous states and perform new analyses. Currently, sets can be viewed in the context of genomes, protein functions, protein interactions, pathways, regulation, diseases and drugs. Additionally, users can switch between species with an automatic, orthology based translation of existing gene sets. As todays research usually is performed in larger teams and consortia, ISAAC provides group based functionalities. Here, sets as well as results of analyses can be exchanged between members of groups.
ISAAC fills the gap between primary databases and tools for the analysis of large gene lists. With its highly modular, JavaEE based design, the implementation of new modules is straight forward. Furthermore, ISAAC comes with an extensive web-based administration interface including tools for the integration of third party data. Thus, a local installation is easily feasible. In summary, ISAAC is tailor made for highly explorative interactive analyses of gene, transcript and protein sets in a collaborative environment.
Over the last 10 to 15 years, biology has changed into a ‘more precise and quantitative science’ . New high throughput technologies generate data covering different aspects of molecules in an ever increasing pace. As a result, we are now drowning in data when looking for biological stories. Accordingly, bioinformatics methods and databases to deal with this flood of information have been developed. Whereas in the beginning these computational tools were available mainly to bioinformaticians, many tools and databases are nowadays accessible via the web and can be interrogated also by non-computational trained biologists. But, there are still some challenges to cope with when trying to find biological meaning within this flood of data. First, different types of data are distributed over a wide variety of databases and web-based resources. For example a biologist will have to go to Ensembl  or the UCSC genome browser  when searching for genomic information. Next she might look up functional information in the GeneOntology  (which generously has been integrated in a multitude of other tools and databases). If especially interested in disease genes, OMIM  and DrugBank  might be useful resources. Next, to identify functionally related genes, databases like KEGG , STRING , or in more specific cases mirRBase  might be questioned. The challenge of distributed data has been addressed by different higher level tools. These focus mainly on the evaluation of larger datasets generated by high throughput methods and the more or less automated annotation and statistical evaluation of these gene sets. An outstanding example is DAVID [10, 11]. It provides a variety of functional annotation tools (like gene enrichment analysis, pathway mapping), gene accession conversion, a genome browser and a stateful web service . Gene lists can be uploaded in different identifier formats and sub lists can be created during the enrichment analysis. Furthermore, the lists can be renamed, removed, combined and downloaded. Related functional profiling tools are GEPAT , Onto-Express , Onto-Tools , FACT  BABELOMICS , FatiGO + , GeneTrail , g:Profiler , VisANT , Reactome , MAPPFinder , GFINDer , GOLEM . Provides a good overview on enrichment tools . The Ingenuity System  is a commercial software that is widely used to analyze and model complex biological and chemical systems. Finally, Cytoscape  is a generic tool for network analysis and visualization whose network information can be associated with gene expression data.
As mentioned above, the main goal of these tools is the statistical evaluation and functional characterization of given, mostly large, gene sets. Thus it is in the nature of these tools, that the user is not allowed to interactively change the gene lists within one analysis. For their application, this makes perfect sense, as these tools provide a reproducible annotation pipeline. But, there is a different type of user who might be more interested in the explorative analysis of smaller gene sets. She might start with a few genes, analyze them under one aspect and find other genes of interest. Now she might want to extend the gene sets and analyze the new list under a different aspect. WebGestalt [29, 30] did a first step into this direction. Here, different sets could be merged, but the manual addition of genes is not possible. However, in the current online version of WebGestalt these set operations are missing. Complementary, WhichGenes  enables generating gene sets based on various sources and to combine these sets. Thus, sets of genes involved in glycolysis and encoded on a specific chromosome can be generated. Still, it does not allow viewing one gene or gene set under different biological aspects or performing analyses on sets. Thus, we wanted to create a tool which integrates the main idea of enrichment tools, namely to analyze gene lists under a wide variety of functional aspects, with the ability to manually add and delete sets of interesting genes to enable explorative analyses. As the amount and detail of functional information differs widely between different species, we also wanted to enable cross species analysis. We have implemented these ideas in the Web based tool ISAAC (http://isaac.bioapps.biozentrum.uni-wuerzburg.de), an acronym for ‘InterSpecies Analysing Application using Containers’.
Object oriented strategy
A version control system manages the sets in a tree structure that allows biologists to keep track of different versions. Furthermore, for each action a history is logged enabling the tracing of changes of sets. In ISAAC context a set configuration managed by the version control system is called snapshot and a container is a collection of snapshots. Each snapshot belongs to exactly one container and all snapshots in a version tree belong to the same container. At any time, a user can go back to an older snapshot and use it as the starting point for a new analysis by generating a new child snapshot. Thus, a tree like structure of analyses can be generated. Special properties can be defined in a container, such as a description, color and comments.
This core system can now be used from different modules, which mainly perform analyses on sets, modify sets with the given methods and visualize sets and results of analyses. Thus, complex biological analyses covering different biological aspects are broken down into independent, interchangeable modules. The resulting non-linear application flow supports the biologist in searching for biological stories in their data.
Java EE technology
ISAAC is implemented in Java EE 6 (Java Platform, Enterprise Edition) and uses the web component JSF 2.0 (Java Server Faces) to generate dynamic web pages with Ajax support. This technology substantially simplifies the development of an application, since it creates standardized, reusable modular components and enables the tier to handle many aspects of programming automatically like persistence, messaging and security. Hence, a Java application server is required to run ISAAC. ISAAC was developed and tested using JBoss application server. The strict client/server architecture allows multiple frontend clients to be developed and integrated in a standard and easy way, since the process logics are performed on the server. In ISAAC, there is no time out for a client web session. As long the web page is open, its session is held on the web server.
As aforementioned each module contains everything necessary to perform the desired functionality and, therefore, information has to be imported from other sources. However, this information is not tied to specific sources. Each module provides well-defined interfaces and any source fulfilling their requirements can be used. The development of new modules and even web services is therefore straightforward.
Module processes requiring large computer resources are started in background, which avoids blocking the clients till the processes are finished. As soon as a process is finished, the owner is notified within the web interface or, if desired, also via E-Mail. The processes’ results are held in the private pools and can be recovered as needed.
Team work capabilities
Today, many research groups are embedded in larger teams. Frequently, different groups work on related aspects of a biological phenomenon using different model species. Therefore, ISSAC as a multi-user system supports teamwork. Each user owns private pools of (i) containers with sets of genes, transcripts and proteins and (ii) results of analysis. Furthermore, pools shared within a group of users can be created. Access to the pools comes in two authorization levels: (i) in the normal level a user is only allowed to read the pool data and (i) in the coordinator level a user is also allowed to update pools and to add/remove users from the group. Special groups can be defined, which allow all users to access their pools in a normal level. In addition to sharing containers, coordinators can also place processes in group pools enabling access to all group members.
Results and discussion
Currently implemented modules
Search for genes, transcripts and proteins and add them to sets. Features are visualized.
Analyze protein interactions within a set. Identify interacting proteins and add them to sets.
Analyze genes in sets in a metabolic and pathways context. Add further genes of a pathway to sets.
Functional characterization of sets. Extend sets based on function.
Reveal microRNA based regulation of genes in sets. Search for genes regulated by specific microRNAs.
Identify mendelian disease genes in sets. Search for genes associated with a mendelian disease.
Identify drug targets in sets. Search for genes affected by a drug.
Orthology based translation of sets to other species.
Share sets between users and within groups.
In a cell, no protein works on its own. Thus, to understand the function of a single or sets of proteins, one always has to consider their interaction partners. To allow analyzing sets in this context, we implemented the protein interaction module. Starting with direct interactions, it can be extended to show higher level interactions. Although data from any protein interaction database can be integrated, we currently imported data from STRING . Thus, interactions are annotated on the gene level, although in the cell proteins are interacting. As ISAAC ensures consistent sets with gene and transcript automatically added, this distinction is hidden from the user. In the graph, single genes of interest or all genes can be added to a snapshot. Again, STRING data can be imported via the administration interface.
The protein interaction module enables viewing sets in the context of networks. Still, the type of interaction is not detailed out. The biological pathway module allows the graphical visualization of enzymes belonging to a snapshots of a species in their pathway context . In a pathway diagram the EC-numbers are highlighted in three different ways: (i) the enzymatic function is covered by the selected snapshots, (ii) the species contains an enzyme with the EC classification but it is not part of the snapshots and (iii) no protein with the EC number is annotated in the species. As usual, genes coding for a given enzymatic function or the whole pathway can be added to snapshots. Furthermore, an enzyme can be selected going to the protein’s view of the genome module.
To enable a fast functional characterization of genes, transcripts and proteins in sets, we implemented the GO term enrichment module. Based on GeneOntology , the biological processes, cellular components and molecular functions of proteins/genes of snapshots can be analyzed and displayed. To improve the tree based presentation of the directed acyclic GO graph, nodes with more than one parent are replicated and only sub trees with at least one match are shown. For each node the following information is given: (i) the GO description, (ii) the number of proteins in the selected snapshot(s) belonging to this node, (iii) the total number of proteins in the genome belonging to this node, (iv) the number of proteins of the selected snapshot(s) belonging to the sub tree rooted in this node, (v) the total number of proteins in the selected snapshot and (vi) the p-value of this node (parent–child-union approach of the hypergeometric distribution [36, 37]). Proteins belonging to a node or its sub tree can be added to snapshots. On the other direction, for each selected protein a list of GO identifiers is given. A protein can be selected going to the protein’s view of the genome module. Furthermore, a GO identifier can be selected which highlights the paths to the root (a node can coexist more than once in the tree). All GO data can be imported via the web based administration interface.
To get insights about possible regulatory mechanisms of genes in a snapshot, the microRNA module was implemented. It supports the search for microRNAs and lists genes regulated by the specified microRNA, which can be added to a snapshot. Complementary, all microRNAs regulating genes in selected snapshots can be listed. Information about microRNA was imported from TarBase .
Finally, we enable to search for genes associated with a disease and genes which are known drug targets in the disease module and the drug module, respectively. Again, a user can start with a disease or a drug, get information about involved genes and add them to the snapshot. Alternatively she can list all diseases associated with genes in snapshots and drugs affecting these genes. The disease module supports external links to the OMIM database  and the drug module to DrugBank . Tools are provided to import OMIM information from the Ensembl (BioMart) database and drugs from DrugBank.
One of the main ideas behind the development of ISAAC was to carry out analyses across species boundaries. This is enabled by adding orthology information. Here, the user can switch between different species and the actual snapshot is ‘translated’ to the new species. For administration, an interface was implemented to import orthologous data from TSV files created by e.g. BioMart .
Different aspects of genes/proteins
ISAAC enables non-computer trained researchers to explore gene lists under different biological aspects. From a user’s point of view, the main difference to other related projects is that the gene lists can be changed interactively at any point of an analysis. Obviously this inherently carries the danger of losing track about how a set was generated. We therefore implemented an integrated version and logging system which supports the users on the persistence, administration and tracking of these sets. Together with the snapshot function, every part of the analysis can be traced and become a starting point for new analyses. Thus, ISAAC indeed enables the explorative mining for genes of interest. As new genomes are sequenced with an increasing pace, analyses crossing the species border become of increasing importance. As ISAAC includes information about orthologous relationships between genes, users can switch between species, automatically ‘translating’ gene sets from one species to another. Finally, ISAAC is not focused on single users. Instead, it offers options to share sets and even results of analyses between users and teams. Thus, not only a single user can look at a problem from different biological views, she can also let other researchers look at her genes to get an external view. Thereby, ISAAC supports multi team collaborative efforts getting ever more prominent in biological research.
From a programmer’s point of view, ISAAC is based on an object oriented approach contrasting more workflow oriented programs, which are usually procedural. Sets of proteins, transcripts and genes with a well-defined structure together with comparison and operation methods build the core of this tool. Using this core, different modules can be implemented covering different biological aspects. The object oriented strategy and its modularity make this straightforward. Especially when performing highly explorative analyses, a user will need some breaks to e.g. gather further information. Therefore, there is no time out for a client web session. As long the web page is open, its session is held on the web server. To enable the integration of further modules, the source code is freely available from our web page.
Together with the web client, we developed an administration interface. Here, not only users and groups can be managed. More importantly, integration of third party data needed by a module can be carried out via the administration interface. This allows for example the straightforward addition of further genomes, as scripts which directly can insert Ensembl genomes are implemented and can be administrated via the web interface.
In summary, with its focus on small but highly explorative analyses ISAAC closes the gap between databases covering only on one or a few aspects of genes and proteins on the one hand and automated analysis tools which do not allow for interactive modifications of gene lists on the other.
Availability and requirements
Project name: ISAAC.
Project home page: http://isaac.bioapps.biozentrum.uni-wuerzburg.de.
Operating systems: Platform independent, tested on linux.
Web browser: Tested with Mozilla Firefox 16.0.2 and Internet Explorer 10.
Programming language: Java ≥ 1.7.
Other requirements: Java application server (Java EE 6 and JSF 2.0).
License: Free for academic users under the GNU Lesser General Public License (LGPL).
HB was funded by DFG Grants SCHU2352/2-1 and the DFG Priority Programme SPP 1464: ‘Principles and evolution of actin nucleator complexes’. This publication was funded by the German Research Foundation (DFG) and the University of Wuerzburg in the funding programme Open Access Publishing.
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