Difference between revisions of "Journal:ILAP: A workflow-driven software for experimental protocol development, data acquisition and analysis"

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The development of novel large-scale technologies has considerably changed the way biologists perform experiments. Genome biology experiments do not only generate a wealth of data, but they often rely on sophisticated laboratory protocols comprising hundreds of individual steps. For example, the protocol for chromatin immunoprecipitation on a microarray (Chip-chip) has 90 steps, uses over 30 reagents and 10 different devices.<ref name="AcevedoGen07">{{cite journal |title=Genome-scale ChIP-chip analysis using 10,000 human cells |journal=Biotechniques |author=Acevedo, L.G.; Iniguez, A.L.; Holster, H.L.; Zhang, X.; Green, R.; Farnham, P.J. |volume=43 |issue=6 |pages=791-797 |year=2007 |pmid=18251256 |pmc=PMC2268896}}</ref> Even adopting an established protocol for large-scale studies represents a daunting challenge for the majority of the labs. The development of novel laboratory protocols and/or the optimization of existing ones is still more distressing, since this requires systematic changes of many parameters, conditions, and reagents. Such changes are becoming increasingly difficult to trace using paper lab books. A further complication for most protocols is that many laboratory instruments are used, which generate electronic data stored in an unstructured way at disparate locations. Therefore, protocol data files are seldom or never linked to notes in lab books and can be barely shared within or across labs. Finally, once the experimental large-scale data have been generated, they must be analyzed using various software tools, then stored and made available for other users. Thus, it is apparent that software support for current biological research — be it genomic or performed in a more traditional way — is urgently needed and inevitable.  
The development of novel large-scale technologies has considerably changed the way biologists perform experiments. Genome biology experiments do not only generate a wealth of data, but they often rely on sophisticated laboratory protocols comprising hundreds of individual steps. For example, the protocol for chromatin immunoprecipitation on a microarray (Chip-chip) has 90 steps, uses over 30 reagents and 10 different devices.<ref name="AcevedoGen07">{{cite journal |title=Genome-scale ChIP-chip analysis using 10,000 human cells |journal=Biotechniques |author=Acevedo, L.G.; Iniguez, A.L.; Holster, H.L.; Zhang, X.; Green, R.; Farnham, P.J. |volume=43 |issue=6 |pages=791-797 |year=2007 |pmid=18251256 |pmc=PMC2268896}}</ref> Even adopting an established protocol for large-scale studies represents a daunting challenge for the majority of the labs. The development of novel laboratory protocols and/or the optimization of existing ones is still more distressing, since this requires systematic changes of many parameters, conditions, and reagents. Such changes are becoming increasingly difficult to trace using paper lab books. A further complication for most protocols is that many laboratory instruments are used, which generate electronic data stored in an unstructured way at disparate locations. Therefore, protocol data files are seldom or never linked to notes in lab books and can be barely shared within or across labs. Finally, once the experimental large-scale data have been generated, they must be analyzed using various software tools, then stored and made available for other users. Thus, it is apparent that software support for current biological research — be it genomic or performed in a more traditional way — is urgently needed and inevitable.  


In recent years, the genome biology community has expended considerable effort to confront the challenges of managing heterogeneous data in a structured and organized way and as a result developed information management systems for both raw and processed data. Laboratory information management systems (LIMS) have been implemented for handling data entry from robotic systems and tracking samples<ref name="PiggeeLIMS08">{{cite journal |title=LIMS and the art of MS proteomics |journal=Analytical Chemistry |author=Piggee, C. |volume=80 |issue=13 |pages=4801-4806 |year=2008 |doi=10.1021/ac0861329 |pmid=18609747}}</ref><ref name="HaquinData08">{{cite journal |title=Data management in structural genomics: an overview |journal=Methods in Molecular Biology |author=Haquin, S.; Oeuillet, E.; Pajon, A.; Harris, M.; Jones, A.T.; van Tilbeurgh, H.; et al. |volume=426 |pages=49-79 |year=2008 |doi=10.1007/978-1-60327-058-8_4 |pmid=18542857}}</ref> as well as data management systems for processed data including microarrays<ref name="MaurerMARS05">{{cite journal |title=MARS: microarray analysis, retrieval, and storage system |journal=BMC Bioinformatics |author=Maurer, M.; Molidor, R.; Sturn, A.; Hartler, J.; Hackl, H.; Stocker, G.; et al. |volume=6 |pages=101 |year=2005 |doi=10.1186/1471-2105-6-101 |pmid=15836795 |pmc=PMC1090551}}</ref><ref name="SaalBio02">{{cite journal |title=BioArray Software Environment (BASE): a platform for comprehensive management and analysis of microarray data |journal=Genome Biology |author=Saal, L.H.; Troein, C.; Vallon-Christersson, J.; Gruvberger, S.; Borg, A.; Peterson, C. |volume=3 |issue=8 |pages=SOFTWARE0003 |year=2002 |doi=10.1186/gb-2002-3-8-software0003 |pmid=12186655 |pmc=PMC139402}}</ref>, proteomics data [6-8], and microscopy data [9]. The latter systems support community standards like FUGE[10,11], MIAME [12], MIAPE [13], or MISFISHIE [14] and have proven invaluable in a state-of-the-art laboratory. In general, these sophisticated systems are able to manage and analyze data generated for only a single type or a limited number of instruments, and were designed for only a specific type of molecule.  
In recent years, the genome biology community has expended considerable effort to confront the challenges of managing heterogeneous data in a structured and organized way and as a result developed information management systems for both raw and processed data. Laboratory information management systems (LIMS) have been implemented for handling data entry from robotic systems and tracking samples<ref name="PiggeeLIMS08">{{cite journal |title=LIMS and the art of MS proteomics |journal=Analytical Chemistry |author=Piggee, C. |volume=80 |issue=13 |pages=4801-4806 |year=2008 |doi=10.1021/ac0861329 |pmid=18609747}}</ref><ref name="HaquinData08">{{cite journal |title=Data management in structural genomics: an overview |journal=Methods in Molecular Biology |author=Haquin, S.; Oeuillet, E.; Pajon, A.; Harris, M.; Jones, A.T.; van Tilbeurgh, H.; et al. |volume=426 |pages=49-79 |year=2008 |doi=10.1007/978-1-60327-058-8_4 |pmid=18542857}}</ref> as well as data management systems for processed data including microarrays<ref name="MaurerMARS05">{{cite journal |title=MARS: microarray analysis, retrieval, and storage system |journal=BMC Bioinformatics |author=Maurer, M.; Molidor, R.; Sturn, A.; Hartler, J.; Hackl, H.; Stocker, G.; et al. |volume=6 |pages=101 |year=2005 |doi=10.1186/1471-2105-6-101 |pmid=15836795 |pmc=PMC1090551}}</ref><ref name="SaalBio02">{{cite journal |title=BioArray Software Environment (BASE): a platform for comprehensive management and analysis of microarray data |journal=Genome Biology |author=Saal, L.H.; Troein, C.; Vallon-Christersson, J.; Gruvberger, S.; Borg, A.; Peterson, C. |volume=3 |issue=8 |pages=SOFTWARE0003 |year=2002 |doi=10.1186/gb-2002-3-8-software0003 |pmid=12186655 |pmc=PMC139402}}</ref>, proteomics data<ref name="HartlerMAS07">{{cite journal |title=MASPECTRAS: a platform for management and analysis of proteomics LC-MS/MS data |journal=BMC Bioinformatics |author=Hartler, J.; Thallinger, G.G.; Stocker, G.; Sturn, A.; Burkard, T.R.; Korner, E.; et al. |volume=8 |pages=197 |year=2007 |doi=10.1186/1471-2105-8-197 |pmid=17567892 |pmc=PMC1906842}}</ref><ref name="CraigOpen04">{{cite journal |title=Open source system for analyzing, validating, and storing protein identification data |journal=Journal of Proteome Research |author=Craig, R.; Cortens, J.P.; Beavis, R.C. |volume=3 |issue=6 |pages=1234-1242 |year=2004 |doi=10.1021/pr049882h |pmid=15595733}}</ref><ref name="RauchComp06">{{cite journal |title=Computational Proteomics Analysis System (CPAS): an extensible, open-source analytic system for evaluating and publishing proteomic data and high throughput biological experiments |journal=Journal of Proteome Research |author=Rauch, A.; Bellew, M.; Eng, J.; Fitzgibbon, M.; Holzman, T.; Hussey, P.; et al. |volume=5 |issue=1 |pages=112-121 |year=2006 |doi=10.1021/pr0503533 |pmid=16396501}}</ref>, and microscopy data.<ref name="MooreOpen08">{{cite journal |title=Open tools for storage and management of quantitative image data |journal=Methods in Cell Biology |author=Moore, J.; Allan, C.; Burel, J.M.; Loranger, B.; MacDonald, D.; Monk, J.; et al. |volume=85 |pages=555-570 |year=2008 |doi=10.1016/S0091-679X(08)85024-8 |pmid=18155479}}</ref> The latter systems support community standards like FUGE<ref name="JonesFuGE06">{{cite journal |title=FuGE: Functional Genomics Experiment Object Model |journal=OMICS |author=Jones, A.R.; Pizarro, A.; Spellman, P.; Miller, M. |volume=10 |issue=2 |pages=179-184 |year=2006 |doi=10.1089/omi.2006.10.179 |pmid=16901224}}</ref><ref name="JonesThe07">{{cite journal |title=The Functional Genomics Experiment model (FuGE): an extensible framework for standards in functional genomics |journal=Nature Biotechnology |author=Jones, A.R.; Miller, M.; Aebersold, R.; Apweiler, R.; Ball, C.A.; Brazma, A.; et al. |volume=25 |issue=10 |pages=1127-1133 |year=2007 |doi=10.1038/nbt1347 |pmid=17921998}}</ref>, MIAME<ref name="BrazmaMin01">{{cite journal |title=Minimum information about a microarray experiment (MIAME)-toward standards for microarray data |journal=Nature Genetics |author=Brazma, A.; Hingamp, P.; Quackenbush, J.; Sherlock, G.; Spellman, P.; Stoeckert, C.; et al. |volume=29 |issue=4 |pages=365-371 |year=2001 |doi=10.1038/ng1201-365 |pmid=11726920}}</ref>, MIAPE<ref name="TaylorThe07">{{cite journal |title=The minimum information about a proteomics experiment (MIAPE) |journal=Nature Biotechnology |author=Taylor, C.F.; Paton, N.W.; Lilley, K.S.; Binz, P.A.; Julian, Jr., R.K.; Jones AR, et al. |volume=25 |issue=8 |pages=887-893 |year=2007 |doi=10.1038/nbt1329 |pmid=17687369}}</ref>, or MISFISHIE<ref name="DeutschMin08">{{cite journal |title=Minimum information specification for in situ hybridization and immunohistochemistry experiments (MISFISHIE) |journal=Nature Biotechnology |author=Deutsch, E.W.; Ball, C.A.; Berman, J.J.; Bova, G.S.; Brazma, A.; Bumgarner, R.E.; et al. |volume=26 |issue=3 |pages=305-312 |year=2008 |doi=10.1038/nbt1391 |pmid=18327244 |pmc=PMC4367930}}</ref> and have proven invaluable in a state-of-the-art laboratory. In general, these sophisticated systems are able to manage and analyze data generated for only a single type or a limited number of instruments, and were designed for only a specific type of molecule.
 
 
 
Hartler J, Thallinger GG, Stocker G, Sturn A, Burkard TR, Korner E, et al.: MASPECTRAS: a platform for management and analysis of proteomics LC-MS/MS data. BMC Bioinformatics 2007, 8:197. PubMed Abstract | BioMed Central Full Text | PubMed Central Full Text OpenURL
 
Craig R, Cortens JP, Beavis RC: Open source system for analyzing, validating, and storing protein identification data. J Proteome Res 2004, 3:1234-1242. PubMed Abstract | Publisher Full Text OpenURL
 
Rauch A, Bellew M, Eng J, Fitzgibbon M, Holzman T, Hussey P, et al.: Computational Proteomics Analysis System (CPAS): an extensible, open-source analytic system for evaluating and publishing proteomic data and high throughput biological experiments. J Proteome Res 2006, 5:112-121. PubMed Abstract | Publisher Full Text OpenURL
 
Moore J, Allan C, Burel JM, Loranger B, MacDonald D, Monk J, et al.: Open tools for storage and management of quantitative image data. Methods Cell Biol 2008, 85:555-570. PubMed Abstract | Publisher Full Text OpenURL
 
Jones AR, Pizarro A, Spellman P, Miller M: FuGE: Functional Genomics Experiment Object Model. OMICS 2006, 10:179-184. PubMed Abstract | Publisher Full Text OpenURL
 
Jones AR, Miller M, Aebersold R, Apweiler R, Ball CA, Brazma A, et al.: The Functional Genomics Experiment model (FuGE): an extensible framework for standards in functional genomics. Nat Biotechnol 2007, 25:1127-1133. PubMed Abstract | Publisher Full Text OpenURL
 
Brazma A, Hingamp P, Quackenbush J, Sherlock G, Spellman P, Stoeckert C, et al.: Minimum information about a microarray experiment (MIAME)-toward standards for microarray data. Nat Genet 2001, 29:365-371. PubMed Abstract | Publisher Full Text OpenURL
 
Taylor CF, Paton NW, Lilley KS, Binz PA, Julian RK Jr, Jones AR, et al.: The minimum information about a proteomics experiment (MIAPE). Nat Biotechnol 2007, 25:887-893. PubMed Abstract | Publisher Full Text OpenURL
 
Deutsch EW, Ball CA, Berman JJ, Bova GS, Brazma A, Bumgarner RE, et al.: Minimum information specification for in situ hybridization and immunohistochemistry experiments (MISFISHIE). Nat Biotechnol 2008, 26:305-312. PubMed Abstract | Publisher Full Text OpenURL


==References==
==References==

Revision as of 21:49, 18 August 2015

Full article title iLAP: a workflow-driven software for experimental protocol development, data acquisition and analysis
Journal BMC Bioinformatics
Author(s) Stocker, Gernot; Fischer, Maria; Rieder, Dietmar; Bindea, Gabriela; Kainz, Simon; Oberstolz, Michael; McNally, James G.; Trajanoski, Zlatko
Author affiliation(s) Institute for Genomics and Bioinformatics, Graz University of Technology; National Cancer Institute, National Institutes of Health
Primary contact Email: zlatko.trajanoski@tugraz.at
Year published 2009
Volume and issue 10
Page(s) 390
DOI 10.1186/1471-2105-10-390
ISSN 1471-2105
Distribution license Creative Commons Attribution 2.0
Website http://www.biomedcentral.com/1471-2105/10/390
This article should not be considered complete until this message box has been removed. This is a work in progress.

Abstract

Background

In recent years, the genome biology community has expended considerable effort to confront the challenges of managing heterogeneous data in a structured and organized way and developed laboratory information management systems (LIMS) for both raw and processed data. On the other hand, electronic notebooks were developed to record and manage scientific data, and facilitate data-sharing. Software which enables both, management of large datasets and digital recording of laboratory procedures would serve a real need in laboratories using medium and high-throughput techniques.

Results

We have developed iLAP (Laboratory data management, Analysis, and Protocol development), a workflow-driven information management system specifically designed to create and manage experimental protocols, and to analyze and share laboratory data. The system combines experimental protocol development, wizard-based data acquisition, and high-throughput data analysis into a single, integrated system. We demonstrate the power and the flexibility of the platform using a microscopy case study based on a combinatorial multiple fluorescence in situ hybridization (m-FISH) protocol and 3D-image reconstruction. iLAP is freely available under the open source license AGPL from http://genome.tugraz.at/iLAP/. (Webcite)

Conclusion

iLAP is a flexible and versatile information management system, which has the potential to close the gap between electronic notebooks and LIMS and can therefore be of great value for a broad scientific community.

Background

The development of novel large-scale technologies has considerably changed the way biologists perform experiments. Genome biology experiments do not only generate a wealth of data, but they often rely on sophisticated laboratory protocols comprising hundreds of individual steps. For example, the protocol for chromatin immunoprecipitation on a microarray (Chip-chip) has 90 steps, uses over 30 reagents and 10 different devices.[1] Even adopting an established protocol for large-scale studies represents a daunting challenge for the majority of the labs. The development of novel laboratory protocols and/or the optimization of existing ones is still more distressing, since this requires systematic changes of many parameters, conditions, and reagents. Such changes are becoming increasingly difficult to trace using paper lab books. A further complication for most protocols is that many laboratory instruments are used, which generate electronic data stored in an unstructured way at disparate locations. Therefore, protocol data files are seldom or never linked to notes in lab books and can be barely shared within or across labs. Finally, once the experimental large-scale data have been generated, they must be analyzed using various software tools, then stored and made available for other users. Thus, it is apparent that software support for current biological research — be it genomic or performed in a more traditional way — is urgently needed and inevitable.

In recent years, the genome biology community has expended considerable effort to confront the challenges of managing heterogeneous data in a structured and organized way and as a result developed information management systems for both raw and processed data. Laboratory information management systems (LIMS) have been implemented for handling data entry from robotic systems and tracking samples[2][3] as well as data management systems for processed data including microarrays[4][5], proteomics data[6][7][8], and microscopy data.[9] The latter systems support community standards like FUGE[10][11], MIAME[12], MIAPE[13], or MISFISHIE[14] and have proven invaluable in a state-of-the-art laboratory. In general, these sophisticated systems are able to manage and analyze data generated for only a single type or a limited number of instruments, and were designed for only a specific type of molecule.

References

  1. ↑ Acevedo, L.G.; Iniguez, A.L.; Holster, H.L.; Zhang, X.; Green, R.; Farnham, P.J. (2007). "Genome-scale ChIP-chip analysis using 10,000 human cells". Biotechniques 43 (6): 791-797. PMC PMC2268896. PMID 18251256. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2268896. 
  2. ↑ Piggee, C. (2008). "LIMS and the art of MS proteomics". Analytical Chemistry 80 (13): 4801-4806. doi:10.1021/ac0861329. PMID 18609747. 
  3. ↑ Haquin, S.; Oeuillet, E.; Pajon, A.; Harris, M.; Jones, A.T.; van Tilbeurgh, H.; et al. (2008). "Data management in structural genomics: an overview". Methods in Molecular Biology 426: 49-79. doi:10.1007/978-1-60327-058-8_4. PMID 18542857. 
  4. ↑ Maurer, M.; Molidor, R.; Sturn, A.; Hartler, J.; Hackl, H.; Stocker, G.; et al. (2005). "MARS: microarray analysis, retrieval, and storage system". BMC Bioinformatics 6: 101. doi:10.1186/1471-2105-6-101. PMC PMC1090551. PMID 15836795. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1090551. 
  5. ↑ Saal, L.H.; Troein, C.; Vallon-Christersson, J.; Gruvberger, S.; Borg, A.; Peterson, C. (2002). "BioArray Software Environment (BASE): a platform for comprehensive management and analysis of microarray data". Genome Biology 3 (8): SOFTWARE0003. doi:10.1186/gb-2002-3-8-software0003. PMC PMC139402. PMID 12186655. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC139402. 
  6. ↑ Hartler, J.; Thallinger, G.G.; Stocker, G.; Sturn, A.; Burkard, T.R.; Korner, E.; et al. (2007). "MASPECTRAS: a platform for management and analysis of proteomics LC-MS/MS data". BMC Bioinformatics 8: 197. doi:10.1186/1471-2105-8-197. PMC PMC1906842. PMID 17567892. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1906842. 
  7. ↑ Craig, R.; Cortens, J.P.; Beavis, R.C. (2004). "Open source system for analyzing, validating, and storing protein identification data". Journal of Proteome Research 3 (6): 1234-1242. doi:10.1021/pr049882h. PMID 15595733. 
  8. ↑ Rauch, A.; Bellew, M.; Eng, J.; Fitzgibbon, M.; Holzman, T.; Hussey, P.; et al. (2006). "Computational Proteomics Analysis System (CPAS): an extensible, open-source analytic system for evaluating and publishing proteomic data and high throughput biological experiments". Journal of Proteome Research 5 (1): 112-121. doi:10.1021/pr0503533. PMID 16396501. 
  9. ↑ Moore, J.; Allan, C.; Burel, J.M.; Loranger, B.; MacDonald, D.; Monk, J.; et al. (2008). "Open tools for storage and management of quantitative image data". Methods in Cell Biology 85: 555-570. doi:10.1016/S0091-679X(08)85024-8. PMID 18155479. 
  10. ↑ Jones, A.R.; Pizarro, A.; Spellman, P.; Miller, M. (2006). "FuGE: Functional Genomics Experiment Object Model". OMICS 10 (2): 179-184. doi:10.1089/omi.2006.10.179. PMID 16901224. 
  11. ↑ Jones, A.R.; Miller, M.; Aebersold, R.; Apweiler, R.; Ball, C.A.; Brazma, A.; et al. (2007). "The Functional Genomics Experiment model (FuGE): an extensible framework for standards in functional genomics". Nature Biotechnology 25 (10): 1127-1133. doi:10.1038/nbt1347. PMID 17921998. 
  12. ↑ Brazma, A.; Hingamp, P.; Quackenbush, J.; Sherlock, G.; Spellman, P.; Stoeckert, C.; et al. (2001). "Minimum information about a microarray experiment (MIAME)-toward standards for microarray data". Nature Genetics 29 (4): 365-371. doi:10.1038/ng1201-365. PMID 11726920. 
  13. ↑ Taylor, C.F.; Paton, N.W.; Lilley, K.S.; Binz, P.A.; Julian, Jr., R.K.; Jones AR, et al. (2007). "The minimum information about a proteomics experiment (MIAPE)". Nature Biotechnology 25 (8): 887-893. doi:10.1038/nbt1329. PMID 17687369. 
  14. ↑ Deutsch, E.W.; Ball, C.A.; Berman, J.J.; Bova, G.S.; Brazma, A.; Bumgarner, R.E.; et al. (2008). "Minimum information specification for in situ hybridization and immunohistochemistry experiments (MISFISHIE)". Nature Biotechnology 26 (3): 305-312. doi:10.1038/nbt1391. PMC PMC4367930. PMID 18327244. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4367930. 

Notes

This presentation is faithful to the original, with only a few minor changes to presentation. In most of the article's references DOIs and PubMed IDs were not given; they've been added to make the references more useful.

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