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Page 1: Incorporating a collaborative web-based virtual laboratory in an undergraduate bioinformatics course

Article

Incorporating a CollaborativeWeb-Based Virtual Laboratory in anUndergraduate Bioinformatics Course

Received for publication, October 6, 2009, and in revised form, October 29, 2009

DavidWeisman*

Department of Biology, University of Massachusetts Boston, Boston, Massachusetts 02125

Face-to-face bioinformatics courses commonly include a weekly, in-person computer lab to facilitate activelearning, reinforce conceptual material, and teach practical skills. Similarly, fully-online bioinformatics coursesemploy hands-on exercises to achieve these outcomes, although students typically perform this work offsite.Combining a face-to-face lecture course with a web-based virtual laboratory presents new opportunities forcollaborative learning of the conceptual material, and for fostering peer support of technical bioinformaticsquestions. To explore this combination, an in-person lecture-only undergraduate bioinformatics course wasaugmented with a remote web-based laboratory, and tested with a large class. This study hypothesized thatthe collaborative virtual lab would foster active learning and peer support, and tested this hypothesis by con-ducting a student survey near the end of the semester. Respondents broadly reported strong benefits from theonline laboratory, and strong benefits from peer-provided technical support. In comparison with traditional in-person teaching labs, students preferred the virtual lab by a factor of two. Key aspects of the course architec-ture and design are described to encourage further experimentation in teaching collaborative online bioinfor-matics laboratories.

Keywords: Collaborative learning, bioinformatics, molecular evolution, online education, virtual laboratory, studentpeer review, web-enhanced, NCBI.

As bioinformatics has become a standard tool of scientificinquiry, application-oriented bioinformatics courses havebecome routine offerings at the undergraduate level [1–4].These classes typically integrate a face-to-face lecture witha weekly teaching laboratory, following the traditional para-digm used in courses such as cell biology, genetics, and bio-chemistry. As with wet-lab training, the bioinformatics labo-ratory reinforces the conceptual course content and pro-vides a broadly-applicable practical skill set. Commonly intraining labs, the class divides into small groups that performan exercise, implicitly creating an informal collaborativelearning environment.

In distance education bioinformatics courses, the labora-tory paradigm has been moved to an online setting [5–7],taking advantage of web-based learning tools. Whenthoughtfully applied, these technologies can foster a richeducational experience and encourage collaborative learn-ing. However, despite the web-centric nature of bioinfor-matics itself, and despite the wide availability of web-basedlearning environments, face-to-face bioinformatics courseshave retained the traditional on-site teaching laboratory.Combining the strengths of an in-person lecture with aremote web-based laboratory presents a new opportunity toimprove bioinformatics education.

An upper-level undergraduate, face-to-face, lecture-onlybioinformatics course has been taught at the University ofMassachusetts Boston since 2003. As part of a comprehen-sive course redesign, we hypothesized that students wouldbenefit from the addition of a web-enhanced, collaborative,virtual laboratory. To study this hypothesis, the course wasaugmented with a substantive and required online compo-nent, and student opinion data was collected near the end ofthe semester. The experiment had several subcomponents:to test whether collaborative learning was practical in anonline bioinformatics lab, to test whether student groupscould provide peer support for routine questions regardingbioinformatics tools, and to compare the virtual lab with pre-vious experiences in physical labs. This article describes thearchitecture of the course, reports quantitative and qualita-tive student data, and provides suggestions for teaching avirtual laboratory in similar bioinformatics classes.

COURSE OVERVIEW, CONTENT, ANDMETHODOLOGY

The bioinformatics course is offered through the UMassBoston Department of Biology and is targeted primarily toupper-level biology undergraduates. The prerequisites are atwo-semester introductory biology sequence, a one-semes-ter genetics course, general chemistry, and college-levelalgebra. The experimental course ran during Spring 2009with one instructor and no teaching assistants. Of the 50 stu-dents initially enrolled, 48 remained after the add/drop date,and of those, five withdrew. The 43 students who completed

*To whom correspondence should be addressed. E-mail: David.Weisman@ acm.org

DOI 10.1002/bmb.20368 This paper is available on line at http://www.bambed.org4

Q 2010 by The International Union of Biochemistry andMolecular Biology BIOCHEMISTRYANDMOLECULARBIOLOGYEDUCATION

Vol. 38, No. 1, pp. 4–9, 2010

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the course had majors of BS-Biology (n ¼ 37), BS-Biochem-istry (1), MS-Biology (1), BA-Anthropology (1), BA-Psychol-ogy (1), and unknown (2). The class breakdown was eightjuniors, 34 seniors, and one first-year graduate student.

Three strong unifying themes provide a coherent and cen-tral framework of the course, and these themes echo andinterplay constantly throughout the semester. First, studentslearn about mechanisms of molecular evolution, and howselection frequently conserves sequence, structure, andfunction. At this point in their education, students have brieflyencountered these ideas in their introductory biology andgenetics classes, but most have not internalized that molec-ular-level evolution is a pillar of modern biology. In thisregard, the course is effectively a primer in the theory andpractical consequences of molecular evolution. The secondrecurring theme describes the exponentially-growing volumeof biological sequence data, including its curation, storage,and retrieval. The course stresses the enormity and value ofthis resource, and how sequence data provides a direct win-dow into molecular evolution. The final theme is the richnessand practical value of cross-linked data, for example, howhuman diseases and drug targets are found in research liter-ature, how research literature links with gene sequences,how gene sequences link with homologues and proteindomains, and how protein domains link with structural data.Using this conceptual framework, the course covers stand-ard introductory bioinformatics topics including PubMed,Online Mendelian Inheritance in Man (OMIM), GenBank,sequence curation and RefSeq, local alignment andsequence database searching, multiple sequence alignment,substitution matrices, phylogeny, gene feature prediction,protein domain architecture, protein modeling and visualiza-tion, and systems biology. Mathematics and computer sci-ence are not emphasized, although the lecture introducesrudiments of probability, statistical inference, and distancemeasure in high-dimensional space, as well as the notions ofcomputational intractability and heuristic algorithms.

The virtual laboratory was conducted over BlackboardVista (http://www.blackboard.com/). At the beginning of thesemester, the class of 50 divided into 10 lab groups of fivestudents each. A group size of five was chosen to create acritical mass that fosters meaningful collaboration, whileavoiding the diffuse impersonality of a much larger group. Asis common in wet-lab courses, students chose their owngroups and retained their group membership over the se-mester. Because web-enhanced collaborative learning wasnew to most students, the instructor conveyed strongexpectations of active participation throughout the semester.Both in the syllabus and in lecture, students were informedthat 25% of the final course grade represented the qualityand quantity of online collaboration, and a detailed gradingrubric was also provided. Collaborations occurred withinBlackboard Vista threaded group discussions; journal andblog software was not employed.

Students could read and post messages within their spe-cific online group but could not access discussions in othergroups. In addition to their private groups, everyone was alsoenrolled in a single class-wide group that was frequently usedfor clarifying lecture concepts and helping with bioinformaticstools. Instead of privately e-mailing the instructor, studentswere strongly encouraged to use the class-wide group for all

topics of general interest. Because multiple students oftenhave nearly identical questions, this tactic likely reduced thevolume of redundant instructor e-mails and redundant officehour tutoring sessions. A tight feedback loop occurred as theinstructor monitored discussion groups for gaps in compre-hension, and addressed these gaps in the next lecture.

Two major course components were conducted within thevirtual laboratory: weekly laboratory assignments, and sev-eral cycles of peer review of the final project. As in a tradi-tional class, the weekly laboratory assignments were closelycoupled to the lecture topics. Lab cycles ran Friday-to-Fri-day and had the following steps:

1. Each student ran a bioinformatics experiment.Depending on the particular assignment, experimentswere identical for all students, or were varied by the in-structor in specific ways, or were varied based on stu-dents’ final project topics.

2. Each student posted initial findings and interpretedtheir results. Students were required to post by mid-week to facilitate substantive collaboration during theremainder of the week.

3. The group discussed the findings and interpreted theresults.

4. Each student was graded for the experimentalapproach and results, as well as for collaboration inthe online discussion.

The lab exercises were similar to those used for conven-tional bioinformatics homework, although enhanced to facili-tate group discussions. For example, everyone ran a BLASTfor a single assigned gene, but each student chose analtered set of query parameters. Options included searchingby mRNA or amino-acid sequence, changing the substitu-tion matrix, altering gap penalties, specifying a database,and choosing a search algorithm. Students posted theirresults, and collectively interpreted the relationshipsbetween algorithm parameters and BLAST outputs. Stu-dents observed how various BLAST approaches wereappropriate for specific biological investigations.

The virtual laboratory experiments were performed usingstandard web-based bioinformatics tools and databases. Thebulk of the work was performed at NCBI, and students werealso encouraged to find and explore other tools. Purchase ofbioinformatics software or services was not required. To intro-duce practical use of the tools, in-class demonstrations pro-vided a general overview of the experiment workflow; how-ever, detailed, cookbook-style instructions were intentionallynot provided. Instead, students were taught to become self-sufficient in locating and reading bioinformatic tool documen-tation. In addition, students were strongly encouraged to asktheir online peers for help when encountering mechanicalproblems. This peer-support practice was designed toincrease active learning, foster collaboration, and reduce thetechnical support burden of the laboratory instructor.

For the final project, students produced a substantive writ-ten report that described bioinformatic analyses of a gene.Students performed research and wrote their reports individ-ually, and each was also required to provide constructivefeedback to the peers in their group. The project began earlyin the semester, giving students an opportunity to experiencea broad-themed bioinformatic study that closely related to

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the lectures and labs. As a first step, after learning about

PubMed, review papers, OMIM, NCBI Bookshelf, and Gen-

Bank, each student found an interesting gene to study. Many

chose genes related to human diseases, often from an inter-

est in a medical career or from firsthand knowledge of

affected individuals. Several weekly lab assignments directly

supported the final project by requiring specific analyses of

the student’s chosen gene. Overall, the final project was

designed to maximize student engagement and learningthroughout the semester, to connect the major themes andunits of the course, to develop critical thinking and scientificwriting skills, and to assess individual performance.

Milestones were required at specific dates, and are shownas weeks from the beginning of the semester:

• Week 4: Gene and topic choice, GenBank or Refseqaccession numbers for gene and protein, and briefreview of literature with citations

• Week 11: Paper outline• Week 13: Paper draft• Week 14: Near-final version• Week 15: Final version to be graded

Following each checkpoint, students were required toread and provide constructive feedback on their peers’ post-ings. This peer review process had several goals: to exposestudents to a collaborative review practice common in aca-demia and industry; to raise the group’s overall performancelevel; to prevent last minute surprises over project scope andexpectations; and to cross-pollinate ideas within the group.

RESULTS

Survey Results

To quantitatively test the hypothesis that the virtual labora-tory was beneficial to learning bioinformatics, students werepolled two weeks before the end of the semester. Figure 1describes the resulting data. The first group of questionsevaluates the benefit of collaborative learning in the virtuallaboratory. Questions A and B measure benefits of askingand answering online questions, respectively, and QuestionC measures the benefits from passive observation. On thesepoints, the data broadly support the hypothesis that collabo-ration in the virtual lab was beneficial. Question D examinedwhether students found their peers to be helpful in a techni-cal support role; the large majority of students found benefitfrom this service.

Question E asked whether students preferred a traditionallab experience to the online lab. Based on the course pre-requisites, students had previously completed at least fourtraditional wet-lab courses. With that background, approxi-mately two-thirds preferred the virtual lab, and of those, thelarge majority strongly preferred the virtual lab. The responseto Question F supports the hypothesis that homework con-ducted within the virtual lab contributed towards learning thecourse conceptual material.

As bioinformatics is an interdisciplinary field and can betaught from multiple perspectives, and given that this courseintentionally emphasized a biological perspective, QuestionG tests whether that emphasis successfully integrated bioin-formatics with core biological principles. To examine whetherthis emphasis corresponded with students’ interests, Ques-

tion H asked whether more mathematics and computer sci-ence should be taught. Responses to these queries indicatethat the course achieved its goals of presenting bioinfor-matics within a biological framework, and that this particularemphasis meshed with student interests.

Examples of Student Collaboration

To illustrate representative examples of collaborativelearning in the virtual laboratory, Table I provides excerptsfrom several discussions.

DISCUSSION

The quantitative data in Fig. 1A–1C show that a large ma-jority of students found the online collaborative lab environ-ment benefited their learning. Similarly, Question D foundthat peer-support was valuable in resolving problems withbioinformatics tools. As this study does not include a controlgroup measuring the results of an in-person lab, Question Easks students to compare the virtual lab to their previousexperiences in physical labs. The strongly positive responsesuggests that students were enthusiastic about the virtuallab, and suggests that they found it more beneficial than atraditional lab. Further supporting this hypothesis, end-of-se-mester student evaluation forms reported that the virtual labwas highly educational (data not shown). Other factors, how-ever, may have contributed to the students’ preference, forexample, having greater flexibility with their weekly sched-ules.

Several themes emerge from the discussions excerpted inTable I. First, students successfully provided their own tech-nical support over routine mechanical issues such as inputformat problems and navigating GenBank. These postingsare consistent with the survey data in Fig. 1D, as well as therather low volume of technical questions directly addressedto the instructor (data not shown). Second, student collabo-ration explored the challenging conceptual material of thecourse, in this example, the Ka/Ks selective pressure ratio, aswell as variable evolution rates within a gene. These discus-sions frequently integrated core biological concepts withpractical bioinformatics, which was a key learning goal of thecourse. Third, the examples show that students put genuineeffort into helping their peers and cross-pollinating ideas,which likely raised the overall quality of work. From thesepassages, it is clear that students offered ideas from theirown projects, thereby demonstrating that they could applyacquired knowledge in new contexts.

Finally, from the last example in Table I, having a friendlypeer support network was invaluable for students who werestuck. In a traditional setting, panicked students can consultwith the instructor, although reticence due to embarrassmentcan cause unnecessarily poor grades or course failures. Incontrast, in the virtual lab setting, students routinelyapproached their peers for help.

Collaborative learning within an online laboratory was newto most students in the class. All had previously collabo-rated, albeit informally, in their traditional lab courses, andmany had worked on group projects in other classes. Still,online collaborative learning was unfamiliar territory andneeded instructor coaching, as well as concrete require-ments specified in the syllabus. Over the semester, activity

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became more automatic as students developed workingrelationships and experienced the benefits of learning withtheir peers. The most substantive and beneficial online dis-cussions occurred towards the end of the semester, whenstudents provided feedback on the final projects. This upturnmay have been partly due to increased stress of loomingdeadlines, the flexible nature of the research project, and therelative inexperience in writing substantive scientific reports.Throughout the semester, online behavior was consistently

altruistic and positive, and appeared to elevate the groupperformance level.

Minimizing the instructor burden of providing remote tech-nical support impacted several structural aspects of thiscourse. Students own a highly heterogeneous mix of com-puters, which necessitated the use of web-based tools forall assignments, and ruled out the practicality of installingand running software locally. As an informal experiment, stu-dents were requested to install the molecular visualization

FIG. 1.Results of student survey data taken two weeks before the final class. The term homework refers to the virtual lab.

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tool Cn3D; and, even with such a widely-deployed program,several reported problems. Along similar lines, Holtzclawet al. [8] remark, not surprisingly, that installing, maintaining,and troubleshooting bioinformatics software on multiplecomputers places a substantive burden on instructors.

To further minimize the technical support burden and max-imize self-sufficiency, students performedmost assignmentsat NCBI. Because documentation of NCBI resources iswidely and freely available, and because NCBI pages com-monly have ‘help’ icons, students were empowered to inves-tigate details of tool use. Additionally, because NCBI webservices are generally mature, students typically receivemeaningful diagnostic messages rather than cryptic soft-ware errors; and, because the tools have been heavily exer-cised by the biology community, students are relativelyunlikely to encounter server crashes or browser dependen-cies. At the same time, the individual NCBI tools such asPubMed, BLAST, GenBank, and CDD reflect their independ-ent evolutionary development, rather than being compo-nents of a highly unified user-interface. As a consequence ofthis inconsistency, students must learn multiple usage para-digms. In contrast, environments such as the Next Genera-tion Biology Workbench [9] present an integrated suite oftools, and adopting such an environment would presumablyreduce the level of student problems. However, field usageof integrated environments is not particularly widespread,and, therefore, students are relatively unlikely to encounterthese suites elsewhere. There is distinct value in knowinghow to use ubiquitous tools, even if learning those toolsrequires some effort and support.

In addition, the strategic choice to avoid cookbook-styleassignments likely increased student demand for support.Multiple compelling arguments drove this choice. Mostimportantly, students who become highly dependent on

detailed instructions are likely to have difficulties approach-ing new tools, have problems when tools undergo user inter-face changes, and have little practice diagnosing routineerrors. Locating and understanding documentation areessential bioinformatics skills, and an introductory courseshould provide ample opportunities to develop those skills.Such an approach is broadly consistent with learningthrough guided inquiry, and has been considered by otherbioinformatics instructors [2, 3, 10, 11]. A second argumentagainst producing detailed cookbooks is that the constantevolution of bioinformatic software makes these documentsquickly obsolete, thereby requiring a large and open-endedmaintenance effort by the instructor. Finally, a rigid set ofinstructions often becomes invalid when working with newdata that causes the tools to respond differently. This brittle-ness would create new support problems, particularly as thestudents investigate unique genes for their final projects.

Having students choose the gene for their final projectbrought several benefits. First, it required that studentssearch and explore research literature, a fundamental bioin-formatic skill that is ideally developed in an upper-level biol-ogy course such as this. Many had never substantivelyexplored PubMed before, andmany were unaware of the ex-istence or value of review papers. A second benefit ofchoosing their own gene was that students became quiteengaged, curious, and motivated about their projects.Others have recommended that assignments follow theresearch interests of the professor as a means of optimizinginstructor time [12]. While enticing, this idea was not adoptedhere, as fostering student engagement was of paramountimportance. A final benefit of student choice came from thediversity of topics. Some chose heavily-researched genes,for example, p53, while others found barely-studied subjectssuch as the wheat protein alpha-gliadin involved in human

TABLE IExcerpts from collaboration in the online virtual lab

� Sorry about your TCOFFEE problems. . . are you using all the same type of sequence data. . .i.e. all protein sequences or all nucleotidesequences? I ran into a couple problems when I hadmixed formats.

� It looks like you have the right amino acid sequence for Question #7. The protein accession number (NP_000185) indicates that you are inRefSeq–two letter, underscore, numbers. I found the protein accession number by first searching the nucleotide db for ‘‘Lesch-Nyhan’’ andchose the entry Homo sapiens which contained the DNA accession number (NC_000023, again RefSeq format), clicked on this, choseGENBANKwhich led to the protein accession number–look for /protein-id¼. Hope this helps.

� That’s amazing that so many different mutations have been associated with the disease ? I’m not sure what to think about that, manymutations are allowed, but many are also pathogenic . . . It might not make sense if point mutations are a very rarely associated with Tay-Sachs, but it might be interesting to calculate the Ka/Ks ratio for your gene. I downloaded a program called KaKs calculator (http://evolution.genomics.org.cn/software.htm) - neat thing about it is it computes KaKs using several different models at once, counts thenumber of synomous and non-synonmous mutation sites and the number of synomous,non-synomous mutations but the bad thing is thatI wasn’t able to find any instructions on how to use the thing and its pretty picky about the format of the sequences it takes. There are alsosome KaKs tools online (http://services.cbu.uib.no/tools/kaks), but I haven’t really tried them out. Is the gene under positive, neutral,negative selection? Are the point mutations associated with changes in the amino acid sequence? Do the pathogenic mutations tend to atspecific places? For example my protein has a repeated segment at the N-terminus which binds with copper ion mutations (both pointmutations and insertion/deletions) near this location are associated with the disease.

� Also you could use some of the other queries to identify regions of the protein which are particularly mutable or . . .?What protein domainsare located in this gene? Are the mutations localized in a particular domain? Is there a domain which is generally associated with breakingdown lipids or asked another way is there a class of proteins which is associated with breaking down lipids? Is there a particular domain/orset of domains common to them? Interesting that the HEXB is similar identity/structure(?) to HEXA, are mutations within HEXB alsoassociated with Tay-Sachs?

� one thing I noticed that you did not mentioned about the structure of your gene. . . you can use tool like CND3 or Rasmol to see thestructure and what kind of protein product your gene have. you can also include some of the catagories like mechanism and pathway ofthe gene in your paper. i think, using this kind of things will help to creat better research paper. let me know if I can help with anything

� so truth be told, i had absolutely no clue how to even start this paper. that was until i read all of your papers and sawwhat you guys did, whatworked, what didnt. What was the same in your strategies and where you differed. ANd from here, i can actually finally start this dauntingtask, and put all my data together instead of just having scambles of random files onmy computer. my full 1st draft should be done bytuesday and from there on i figure i can just post it to another discussion and have you guys just help me out somemore from there.

Note: Comments were not edited for spelling or grammar.

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celiac disease. Both endpoints of that spectrum provide richopportunities for exploration, yet require different bioinfor-matic approaches. This diversity was a good lesson thatreflects real-world bioinformatics research.

A review of the literature found no reports of a face-to-facebioinformatics course with a web-enhanced collaborativevirtual laboratory. However, distance education bioinfor-matics courses have employed online discussion forumsand can be compared with the present work. Lim et al. [5]describe the graduate-level S*STAR project, which deliveredlectures by video with synchronized slides, and constructeddiscussion forums. That study surveyed students on theeffectiveness of the discussion forums and reported a wide,approximately Gaussian distribution of scores, with the aver-age level between ineffective and very effective. ThisS*STAR survey data appears to contrast with the resultshere (Fig. 1A–1E), in which the large majority of studentsreport strong benefits from the online laboratory. Lim et al.speculate that the wide distribution may have been causedby differences in learning styles and differences in comfortwith online forums. The contrast between those results andthe data reported here suggest other possible causes, forexample, that preexisting relationships between groupmem-bers, as were common in this course, improve the outcomeof online collaboration. Another possible difference is thatthis course frequently encouraged and formally required col-laboration, thereby fostering a beneficial atmosphere.

Tolvanen and Vihinen [7] describe a fully online bioinfor-matics course that made a tutor available for online discus-sions, but did not create a larger collaboration group. Theirsurvey data reported high satisfaction with the virtual natureof the course but did not specifically assay the online tutorcollaboration. The authors discuss the workload for thiscourse; in addition to the online teaching, each tutor has anaverage of 15 contacts per student over a semester. In plan-ning the course described here, it was clear that with 50 stu-dents, one instructor, and no teaching assistants, a largeamount of peer support would be absolutely necessary aswell as beneficial for the students. That realization was a keymotivation for establishing online collaborative behavior as afoundation of this course.

Honts has reported success in introducing elementarycomputer programming in lab-based bioinformatics courses[3]; however, despite its potential value to bench scientists,that activity was considered impractical here. Studentsencountering programming for the first time typically requirea high level of tutoring and support that is well beyond thevolume a single instructor can provide to a large class. Thisproblem becomes more acute when the class worksremotely on heterogeneous hardware and operating systemplatforms. Additionally, as this bioinformatics course inten-tionally emphasizes biology over computer science, a sub-stantive programming component would entail an opportu-

nity cost of reducing the biological content. Finally, most stu-dents were broadly uninterested in programming (Fig. 1H),and while student opinion can not govern a curriculum, thenegative effects of disinterest would be exacerbated by thelack of adequate support and tutoring.

Taken together, the quantitative and qualitative datareported in this study support the hypothesis that a virtuallaboratory is beneficial to undergraduate bioinformatics stu-dents. Importantly, the data also support that web-enhancedcollaborative learning is both practical and beneficial in aface-to-face bioinformatics course. To study the hypothesisfurther, it would be appropriate to perform a controlledexperiment, randomly placing students in an in-person lab ora virtual lab, and assessing learning outcomes. The initialdata are certainly encouraging, and it is hoped that this reportstimulates further discussion and experiments in this area.

Supplementary Materials – Additional materials describing thiscourse are available from the author.

Acknowledgments— I wish to thank BrianWhite and James Starkfor critically reviewing this manuscript and providing valuable rec-ommendations. In addition, I am grateful to the University of Massa-chusetts Boston Department of Biology for fostering an environ-ment that encourages creativity and research in instructionaldesign.

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[9] R. Rifaieh, R. Unwin, J. Carver, M. A. Miller, in S. C. Boulakia, V. Tannen,Ed. (2007) Data Integration in the Life Sciences (4th International Work-shop, DILS 2007, Philadelphia, PA, USA, 2007 Proceedings) volume4544: Lecture Notes in Computer Science, Springer, Berlin/Heidelberg.pp. 48–58.

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