370
V. Mittard-Runte et al.
are often sufficient. Normalization of microarray data however is crucial. Initially,
it is preferable to use well-established and well-understood statistical methods (e.g.
t-tests, ANOVA) to identify genes with significantly different patterns of expression, while critically assessing the merits of novel tools for the given application. In
almost every case, several competing methods should be tested.
For small laboratories and occasional use, stand-alone software with low administrative requirements should be adopted initially. For large labs and international
collaborations, database based systems may be used, providing collaborative functions such as data sharing. To reduce administrative effort, a central installation of
such a database and analysis software can be provided. Due to its high flexibility,
the use of the R and BioConductor environment should be considered, at least as a
complementary tool.
Before publication, experimental data and annotations should be submitted to one
of the public repositories. For high-volume submissions, an automated submission
based on MAGE-TAB should be considered.
Despite the recent success of microarrays, we expect sequencing based methods
will become increasingly popular and efficient in the mid-term. For classical geneexpression studies, sequencing methods and especially shotgun-transcriptomics
are likely to surpass microarrays in terms of cost efficiency and precision of
measurement in the coming years.
Acknowledgments We are grateful to the CeBiTec at Bielefeld University, the BMBF
Competence Network GenoMik-Plus (grant 0313805A), the International NRW Graduate School
in Bioinformatics and Genome Research, the EU FP6 Network of Excellence Marine Genomics
Europe (contract No. COGE-CT-2004-505403) and Nestlé Research Center for financial support
of our work. Special thanks to our native speaker Sita Lange, the chapter would not have been
the same without her efforts. The authors would also like to thank Guy Cochrane, Naryttza Diaz,
Michele Magrane, Nicky Mulder, Kai Runte and Rafael Szczepanowski, who read sections of the
chapter and provided valuable comments. Many thanks to our present and former colleagues from
the Junior Group Computational Genomics for their patience during the writing.
References
Adams CP, Kron SJ, Mosaic Technologies USA (1997) Method for performing amplification of
nucleic acid with two primers bound to a single solid support. US Patent 5,641,658.
Alexandersson M, Cawley S, Pachter L (2003) SLAM: cross-species gene finding and alignment
with a generalized pair hidden Markov model. Genome Res 13(3):496–502
Allen JE, Salzberg SL (2005) JIGSAW: integration of multiple sources of evidence for gene
prediction. Bioinformatics 21(18):3596–3603
Allison DB, Cui X, Page GP et al (2006) Microarray data analysis: from disarray to consolidation
and consensus. Nat Rev Genet 7(1):55–65
Altschul SF, Gish W, Miller W et al (1990) Basic local alignment search tool. J Mol Biol
215(3):403–410
Ashburner M, Ball CA, Blake JA et al (2000) Gene ontology: tool for the unification of biology.
The Gene Ontology Consortium. Nat Genet 25(1):25–29
Aziz RK, Bartels D, Best AA et al (2008) The RAST Server: rapid annotations using subsystems
technology. BMC Genomics 9:75
Badger JH, Olsen GJ (1999) CRITICA: coding region identification tool invoking comparative
analysis. Mol Biol Evol 16(4):512–524
V. Mittard-Runte et al.
are often sufficient. Normalization of microarray data however is crucial. Initially,
it is preferable to use well-established and well-understood statistical methods (e.g.
t-tests, ANOVA) to identify genes with significantly different patterns of expression, while critically assessing the merits of novel tools for the given application. In
almost every case, several competing methods should be tested.
For small laboratories and occasional use, stand-alone software with low administrative requirements should be adopted initially. For large labs and international
collaborations, database based systems may be used, providing collaborative functions such as data sharing. To reduce administrative effort, a central installation of
such a database and analysis software can be provided. Due to its high flexibility,
the use of the R and BioConductor environment should be considered, at least as a
complementary tool.
Before publication, experimental data and annotations should be submitted to one
of the public repositories. For high-volume submissions, an automated submission
based on MAGE-TAB should be considered.
Despite the recent success of microarrays, we expect sequencing based methods
will become increasingly popular and efficient in the mid-term. For classical geneexpression studies, sequencing methods and especially shotgun-transcriptomics
are likely to surpass microarrays in terms of cost efficiency and precision of
measurement in the coming years.
Acknowledgments We are grateful to the CeBiTec at Bielefeld University, the BMBF
Competence Network GenoMik-Plus (grant 0313805A), the International NRW Graduate School
in Bioinformatics and Genome Research, the EU FP6 Network of Excellence Marine Genomics
Europe (contract No. COGE-CT-2004-505403) and Nestlé Research Center for financial support
of our work. Special thanks to our native speaker Sita Lange, the chapter would not have been
the same without her efforts. The authors would also like to thank Guy Cochrane, Naryttza Diaz,
Michele Magrane, Nicky Mulder, Kai Runte and Rafael Szczepanowski, who read sections of the
chapter and provided valuable comments. Many thanks to our present and former colleagues from
the Junior Group Computational Genomics for their patience during the writing.
References
Adams CP, Kron SJ, Mosaic Technologies USA (1997) Method for performing amplification of
nucleic acid with two primers bound to a single solid support. US Patent 5,641,658.
Alexandersson M, Cawley S, Pachter L (2003) SLAM: cross-species gene finding and alignment
with a generalized pair hidden Markov model. Genome Res 13(3):496–502
Allen JE, Salzberg SL (2005) JIGSAW: integration of multiple sources of evidence for gene
prediction. Bioinformatics 21(18):3596–3603
Allison DB, Cui X, Page GP et al (2006) Microarray data analysis: from disarray to consolidation
and consensus. Nat Rev Genet 7(1):55–65
Altschul SF, Gish W, Miller W et al (1990) Basic local alignment search tool. J Mol Biol
215(3):403–410
Ashburner M, Ball CA, Blake JA et al (2000) Gene ontology: tool for the unification of biology.
The Gene Ontology Consortium. Nat Genet 25(1):25–29
Aziz RK, Bartels D, Best AA et al (2008) The RAST Server: rapid annotations using subsystems
technology. BMC Genomics 9:75
Badger JH, Olsen GJ (1999) CRITICA: coding region identification tool invoking comparative
analysis. Mol Biol Evol 16(4):512–524