Tools collaborative environments bioinformatics research

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DeepDyve requires Javascript to function. Please enable Javascript on your browser to continue. Tools and collaborative environments for bioinformatics research Tools and collaborative environments for bioinformatics research Romano, Paolo; Giugno, Rosalba; Pulvirenti, Alfredo Advanced research requires intensive interaction among a multitude of actors, often possessing different expertise and usually working at a distance from each other.

Tools and collaborative environments for bioinformatics research Romano, Paolo ; Giugno, Rosalba ; Pulvirenti, Alfredo. Read Article. In this article, we first present the reasons for an interest of Bioinformatics in this context by also suggesting some research domains that could benefit from collab- orative research.

We then introduce some systems for collaborative document creation, including wiki systems and tools for ontology development, and review some of the most inter- esting biological wikis. We also review the principles of Collaborative Development Environments for software and show some examples in Bioinformatics. Finally, we present the principles and some examples of Learning Management Systems.

In conclusion, we try to devise some of the goals to be achieved in the short term for the exploitation of these technologies. E-mail systems Telecommunication networks are meant to enable soon generated mailing lists, while newsgroups data exchange and collaboration among people. At spawned electronic fora. Synchronous communica- the dawn of the Internet, network tools and appli- tion was introduced with the advent of chat services cations varied widely and did not interoperate.

Tools and instant messaging; an offshoot of these tools was available at that time were merely classified as either the multimedia teleconferencing systems that are network information retrieval NIR or computer- currently in use. Virtual reality was first introduced mediated communication CMC tools. These in allow free access to electronic archives, the latter turn generated mainstream virtual reality environ- were meant to allow network users to communicate ments, such as the second life system.

The Bionet newsgroups Corresponding author. E-mail: paolo. His interests include biological databases, data modelling and integration, automation of retrieval and analysis processes through semantic tools and programming interfaces. Her research interests include data mining and algorithms for bioinformatics.

He has been a visiting researcher at New York University. His research interests include data mining and machine learning, and algorithms for bioinformatics. Published by Oxford University Press. The need CMC systems supporting life sciences research. Websites dedicated to communities ogies, tools and applications available for collabora- of scientists have been launched, and these often tive work, and a discussion of the prospects for their create the grounds for real collaborative research use to support bioinformatics.

Not only are Bioinformatics deals with heterogeneous data, researchers now closely and continuously in touch ranging from structured and unstructured text, nat- via email and instant messaging, but they can also ural and synthetic images, diagrams and schema, and jointly develop software, discuss publication con- including data such as raw sequences, annotated tents, compare development strategies, write docu- genomes, protein structures, expression profiles, ments and build databases and knowledge bases.

Moreover, among researchers. Collaboration allows sharing in- the amount of available information is growing ex- formation or objects that may be stored in web pages ponentially, together with the means to store and or databases. It may be established between two re- analyse it. Data are available online from different searchers peer-to-peer interactions or among repositories with heterogeneous formats, and algo- groups many-to-many interactions , in which case rithms to analyse them are rarely able to inter- it may be implemented by using collaborative sys- communicate and inter-operate.

Communications and collaborations may be Extracting knowledge from biological data has carried out through such technologies such as instant become a very complex task. In addition, expertise messaging, chat, blogs, forums, social networking and skills are now increasingly more specialized and and so on. Since they were meant to There is no shortage of life science projects that allow researchers to implement their systems in a could exploit and benefit from collaboration among shared place, collaboration features were limited.

Social networks, also business owners with possible clients. Researchers, known as online communities, are now very popular too, willing to compare or discuss theories, experi- and widely accessible.

Based on the so-called Web ments or results, have become avid users. Other 2. The collection of tags is field of scientific collaboration. The pictured workflow 1 looks for diseases relevant to a query string. It finds documents related to the words in the query string, proteins from the abstract of the retrieved papers, filter false positive by requiring that they have a valid UniProt ID.

Finally, it links proteins to diseases contained in the OMIM database highlighted in the red box. A user must register 2 and he can then create or join some groups 3. The system keeps trace of his friends and workflows 3 and personal information 5. A web naviga- tor can search for workflows, users and groups by inserting key words 6.

Researchers may also reuse parts of that allows the sharing of web-based metadata. Figure 2 shows the interface marks that can be associated with a web page or to of myExperiment. Once a user retrieves the document, the munity of registered users. Participants may use, attached annotations are also loaded and the user modify and re-upload any existing workflow. They obtains the opinion of peers about it. Workflows are protected by copyright, beset with several critical issues.

Beyond the possible so that rights of users who contributed to their re- uncontrolled spread of incorrect information and the lease are guaranteed. Some of the specific aims More precisely, a user needs to identify himself in of wikis for biology biological wikis include collab- each network in which he participates, and commu- orative efforts for the development and sharing of nities may rarely merge [5]. Moreover, people do not knowledge, and the creation and annotation of data- have any control on their own personal data e.

Indeed, valuable expertise on and interests in services with the same digital identity. These services, special topics are usually distributed and are rarely however, must allow and implement the OpenID concentrated in a unique site or research group. Such distributed networks can help enhance and with some common members, researchers are extend database curation beyond what it is usually migrating to decentralized web models [7], where possible because of limited numbers of dedicated users may select a trusted server as a repository for staff.

Although the contents of works only. Such models [8,9] make use of tools the database are collaboratively annotated, the allowing the standardization of formats, such as underlying database is left unchanged. The open edition model of many wiki systems, e. Also, two of the best-known tools enabling Internet special features are needed in order to accommodate users to share and collectively edit documents.

In addition, numerous users may simul- data types, such as images, plots and diagrams, must taneously edit documents. Windows Live Office is be taken into account and properly managed. A tight integration with the MS Some wiki systems devoted to biological research Office software suite is available, so that files may have already been developed, many of which were easily be downloaded, edited and re-uploaded.

Here, we introduce some tool able to stimulate users to collaboratively con- biological wikis that try to respond with above issues. Wikipedia aimed at re-organizing, extending and The variety of advantages that wiki systems offer completing its articles related to human genes. Wikipedia is indeed very popular and its articles Table 1: Results of on-line searches of gene symbols often appear among first Google search results.

By taking into account that about was found in the first page for all 16 gene symbols. A similar result was achieved by using Bing, although in this case links to Gene Wiki did not one-third of human genes are currently represented appear in the first result page for four symbols.

A similar test was carried out with the Bing search engine. In this case, we searched 11 symbols that returned hits to Wikipedia, with the same percentage as Google. In Ref. Authors introduce a pyramid framework, which recognizes five levels of coordination support and three critical crosscutting tools categories artefacts management, task management and communication. Tools that are located higher in the pyramid layer provide more sophisticated automated support, thereby reducing the user effort required in collaborating.

These are categorized in a practical manner as version control systems that allow users to share artefacts, web accessible trackers able to manage issues such as tickets or bugs, remote building tools, modellers allowing the creation of formal artefacts including UML, knowledge centres that permit users to share knowledge through the web, and communication tools which support remote interactions.

A taxonomy of collaboration tools and a list of some representative systems with web site addresses [adapted from Ref. Those categories are then plugged into the more general Collaborative Development Environment CDE that yields a workspace composed of a set of standardized tools suitable for global software development teams.

Improvements of awareness in distributed software, mainly based on Web 2. Jazz supports the tagging of development tasks by user-defined keywords. In Refs [ 31 , 32 ], mining algorithms, such as the HITS algorithm [ 33 ] for recommendation, are applied among software project entities.

The tool translates technical dependences among components into social dependences among developers and graphically describes the dependence information the general architecture of a CDE Figure 3.

Integrated Development Environments IDEs are equipped with a set of integrated tools allowing awareness and interaction among users communities. Many CDEs are used to build bioinformatics software. Although we are only at the beginning of such development software in the field of bioinformatics, several successful initiatives are already present. Bioconductor [ 35 ] implements many tools for the analysis of high-throughput genomic data on top of R programming language.

It is open source and open development. It has two releases per year, more than packages and an active user community. Cytoscape [ 36 ] is a bioinformatics tool for the visualization and analysis of biological networks.

The Core is extensible through a plug-in architecture, allowing rapid development of additional computational analyses and features. Extensions can be found in Refs [ 38 , 39 ].

Confucius [ 40 ], previously named Co-Taverna [ 41 ], allows the collaborative composition of scientific workflows. It is based on an ontology of scientific collaboration based on a set of primitives and patterns.

Collaboration protocols are then applied to support effective concurrency control in the process of collaborative workflow composition. Biocep-R [ 42 ] is an open source for the virtualization of scientific computing environments SCEs such as R and Scilab. It allows the collaborative analysis of computation tools running on the Cloud. In the connected era, human knowledge is growing exponentially. This results in the paradox that the more we have to learn, the less time we have to learn it.

We are thus faced with the challenge keep pace with everything we must know, when we must know it [ 43 ]. One strategy relies on capturing knowledge so that it can be instantaneously accessed and shared. The technological revolution underpinned by a strong pedagogical theory, based on constructivism, connection and separations concepts, allows us to reach such a target.

According to the theory of constructivism [ 44 ], interaction of human experiences and ideas generates knowledge: we learn from the environment and from each other. The implications in e-learning are remarkable. Commonly, groups rank what is knowledge and at the same time determine what is not considered knowledge at all.

Constructivism derives from a more general concept called social constructionism [ 45 ], which is based on the idea that the best way for people to learn is being involved in a social process of constructing knowledge for others. The process of negotiating semantics and utilizing shared artefacts is a process of constructing knowledge too.

This results in the fact that learning is something we do mainly in groups. Thus, learning can be viewed as a process of negotiating meaning in a culture of shared artefacts and symbols [ 45 , 46 ].

Moreover, concepts such as connections and separations reveal that the sharing of information among communities stimulates the behaviour of a single user. However, the single user should carefully retain his individualism and his own ideas. In the field of bioinformatics, preliminary studies in small communities have shown the effectiveness of such an approach, compared to traditional methods, in the cooperative learning of students of biochemistry classes [ 47 ].

Those outcomes were subsequently confirmed by a combination of a standard bioinformatics course with a web-based virtual laboratory aimed at stimulating collaboration and peer support on technical questions [ 48 ].

Collaboration may be across classrooms, communities and countries and may make use of tools such as blogs, sharing of videos and so on. These also guarantee peer-to-peer communication, which is at the heart of a collaborative learning process Figure 1. However, important to the success of collaborations, in terms of quality and duration over time, is the environment, which needs to be flexible, easy to use and adaptable to suit the needs of members.

The LMS approach, which is increasingly used for university courses, particularly for small groups [ 47 ], is able to assist students by guaranteeing a variety of learning outcomes, including working collaboratively with others, taking responsibility for their own learning and deepening their understanding of course contents.

Moodle and Drupal [ 49—51 ] are two successful examples of LMSs other more general purpose software packages are available at wordpress. Moodle stands for modular object-oriented dynamic learning environment, but used as a verb it denotes a process of enjoyable tinkering that often leads to increased knowledge, insight and creativity. This fits both the philosophy underpinning Moodle's development and the way it is used to teach and learn.

Its main goal is to create rich interactions between teachers and learners. Its main features are: store, communicate, evaluate and collaborate. Users can.

Users may act as administrators, teachers, students, parents and guests. Students may share notes, see and debate on line the correction and grading of their homework and watch lessons. Teachers may collect all their lessons, grades and corrected assignments in one place, cumulate scores, disciplinary actions and notes, and learn from the feedback and interactions with and among their students.

Drupal is not a traditional LMS, but contains viable modules that can manage the learning process [ 52 ]. Everything a user creates in Drupal is a node, which is a piece of content of the web site. Drupal is also flexible: when creating a web site, one can choose from among several different content structures. One of the many uses of Drupal is the creation of a collaborative book in which chapters, sections and subsections may be managed as pages.

A group of users may work together in writing, modifying and organizing pages. Examples of Drupal's use come from Economist. Due to the boom of heterogeneous e-learning systems, rules to ensure compatibility standardization are needed. Technologies and applications for collaborative research and development, including those supporting document creation, software development and education and training, are evolving intensively. These new tools are often based on the principles of social networks and thus introduce into a researcher's daily activities continuous interaction with peers through large communities of users.

Although the fall-out of these collaborative environments in bioinformatics research is still limited to a few, but enlightening, cases, there are clear prospects for their utilization in the short- to mid-term. These include the creation of coherent and comprehensive knowledge bases supported by highly qualified experts, the development of modular and interoperable software based on common data models and structures, the carrying out of standardized, public, comprehensive online courses aimed at shared education and training in bioinformatics given by the most distinguished scientists and professors.

Before these goals may be reached, however, a number of issues must be faced and solved. Assessing and ensuring a digital identity is still difficult, if not impossible. Instead, it should be granted in order to guarantee privacy and to prevent impostors.

User names and passwords alone cannot authenticate the identity of researchers, who should be urged to adopt unique open identities for their participation in collaborative activities. Authentication of researchers is indeed essential: knowing who is who prevents fraud, assigns rights on functions, actions and documents, and attributes the origin of annotations, comments and information.

Also, knowing who actually did what, that is disambiguating authorship, is needed in order to assign credits to users for their contributions. This can be extremely relevant to stimulate the broadest and most qualified participation in collaborative efforts. Development of modular open source tools is still far from being satisfactory. Additional common data models and structures are needed so that software tools may be developed and updated faster and easily reused.

Semantic Wiki systems could provide the grounds for the construction of a shared knowledge base. A survey of existing systems, and of current developments, would be useful in order to identify possible synergies and acknowledge the best efforts achieved by relevant communities, as well as to ensure a coherent set of interoperable biological wikis and to support the majority of biological databases.

For this to happen, a major effort is needed. Interested communities should meet and discuss possible collaborations, interactions and convergence on common technologies and tools. Public courses on tools and technologies for collaborative work in support of bioinformatics should be designed, implemented and promoted. At present, biological research projects may greatly benefit from a broad collaboration of scientists, from different domains and with different expertise and skills.

Researchers are now closely connected through networks in which they can develop software, discuss publication content, compare research strategies, write documents and collectively build data and knowledge bases. The adoption of Web 2. Authors wish to thank Tom Wiley for his precious support in the preparation of the final version of the article.

His interests include biological databases, data modelling and integration, automation of retrieval and analysis processes through semantic tools and programming interfaces. Her research interests include data mining and algorithms for bioinformatics. He has been a visiting researcher at New York University. His research interests include data mining and machine learning, and algorithms for bioinformatics.

Read article at publisher's site DOI : Iran J Parasitol , 16 2 , 01 Apr Clin Exp Vaccine Res , 10 1 , 31 Jan Clin Exp Vaccine Res , 9 2 , 31 Jul Galen Med J , 9:e, 20 Jul Front Cell Infect Microbiol , , 08 May To arrive at the top five similar articles we use a word-weighted algorithm to compare words from the Title and Abstract of each citation. Free to read. Genome Biol , 5 10 :R80, 15 Sep Bioinformatics , 26 17 , 30 Jun Cited by: 3 articles PMID: J Nurs Educ , 54 6 , 01 Jun Cited by: 2 articles PMID: Brief Bioinform , 9 1 , 05 Dec Drupal is not a traditional LMS, but contains viable modules that can manage the learning process [ 52 ].

Everything a user creates in Drupal is a node, which is a piece of content of the web site. Drupal is also flexible: when creating a web site, one can choose from among several different content structures.

One of the many uses of Drupal is the creation of a collaborative book in which chapters, sections and subsections may be managed as pages. A group of users may work together in writing, modifying and organizing pages. Examples of Drupal's use come from Economist.

Due to the boom of heterogeneous e-learning systems, rules to ensure compatibility standardization are needed. Technologies and applications for collaborative research and development, including those supporting document creation, software development and education and training, are evolving intensively. These new tools are often based on the principles of social networks and thus introduce into a researcher's daily activities continuous interaction with peers through large communities of users.

Although the fall-out of these collaborative environments in bioinformatics research is still limited to a few, but enlightening, cases, there are clear prospects for their utilization in the short- to mid-term. These include the creation of coherent and comprehensive knowledge bases supported by highly qualified experts, the development of modular and interoperable software based on common data models and structures, the carrying out of standardized, public, comprehensive online courses aimed at shared education and training in bioinformatics given by the most distinguished scientists and professors.

Before these goals may be reached, however, a number of issues must be faced and solved. Assessing and ensuring a digital identity is still difficult, if not impossible. Instead, it should be granted in order to guarantee privacy and to prevent impostors. User names and passwords alone cannot authenticate the identity of researchers, who should be urged to adopt unique open identities for their participation in collaborative activities. Authentication of researchers is indeed essential: knowing who is who prevents fraud, assigns rights on functions, actions and documents, and attributes the origin of annotations, comments and information.

Also, knowing who actually did what, that is disambiguating authorship, is needed in order to assign credits to users for their contributions. This can be extremely relevant to stimulate the broadest and most qualified participation in collaborative efforts. Development of modular open source tools is still far from being satisfactory.

Additional common data models and structures are needed so that software tools may be developed and updated faster and easily reused. Semantic Wiki systems could provide the grounds for the construction of a shared knowledge base. A survey of existing systems, and of current developments, would be useful in order to identify possible synergies and acknowledge the best efforts achieved by relevant communities, as well as to ensure a coherent set of interoperable biological wikis and to support the majority of biological databases.

For this to happen, a major effort is needed. Interested communities should meet and discuss possible collaborations, interactions and convergence on common technologies and tools. Public courses on tools and technologies for collaborative work in support of bioinformatics should be designed, implemented and promoted. At present, biological research projects may greatly benefit from a broad collaboration of scientists, from different domains and with different expertise and skills.

Researchers are now closely connected through networks in which they can develop software, discuss publication content, compare research strategies, write documents and collectively build data and knowledge bases. The adoption of Web 2. Authors wish to thank Tom Wiley for his precious support in the preparation of the final version of the article.

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Close mobile search navigation Article Navigation. Volume Article Contents Abstract. Tools and collaborative environments for bioinformatics research. Paolo Romano , Paolo Romano. Oxford Academic. Rosalba Giugno. Alfredo Pulvirenti. Revision received:.

Select Format Select format. Permissions Icon Permissions. Abstract Advanced research requires intensive interaction among a multitude of actors, often possessing different expertise and usually working at a distance from each other. Figure Open in new tab Download slide. Gene Symbol. Rank size.

Rank growth. Open in new tab. Table 2: A taxonomy of collaboration tools and a list of some representative systems with web site addresses [adapted from Ref. My experiment: a repository and social network for the sharing of bioinformatics workflows. Google Scholar Crossref. Search ADS. The CKC Challenge: exploring tools for collaborative knowledge construction. On the use of visualization to support awareness of human activities in software development: a survey and a framework.

Workspace awareness in real-time distributed groupware: framework, widgets, and evaluation. Challenges and improvements in distributed software development: a systematic review. Advances in Software Engineering. Creating an Infrastructure for Ubiquitous Awareness. Extended version in Journal of the ACM ; 46 — Bioconductor: open software development for computational biology and bioinformatics.