Abstract
Transcriptome sequencing provides quick, direct access to the mRNA. With this information, one can design primers for PCR of thousands of different genes, SNP markers, probes for microarrays and qPCR, or just use the sequence data itself in comparative studies. Transcriptome sequencing, while getting cheaper, is still an expensive endeavor, with an examination of data quality and its assembly infrequently performed in depth. Here, we outline many of the important issues we think need consideration when starting a transcriptome sequencing project. We also walk the reader through a detailed analysis of an example transcriptome dataset, highlighting the importance of both within-dataset analysis and comparative inferences. Our hope is that with greater attention focused upon assessing assembly performance, advances in transcriptome assembly will increase as prices continue to drop and new technologies, such as Illumina sequencing, start to be used.
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Acknowledgments
The authors would like to thank many of our colleagues over the past couple of years who have shared their experiences with us. CWW would additionally like to thank W. Stephan, R. Butlin, E. Randi, and D. Tautz for invitations to speak at, and learn from, various Next Generation Sequencing workshops over the past year. CWW would also like to thank Jim Marden for his initial experience with 454 sequencing, as it was he who decided that 454 sequencing could be used for the transcriptome. Support for this work comes from the Max Planck Gesellschaft and Finnish Academy Grant 131155.
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Wheat, C.W., Vogel, H. (2012). Transcriptome Sequencing Goals, Assembly, and Assessment. In: Orgogozo, V., Rockman, M. (eds) Molecular Methods for Evolutionary Genetics. Methods in Molecular Biology, vol 772. Humana Press. https://doi.org/10.1007/978-1-61779-228-1_7
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DOI: https://doi.org/10.1007/978-1-61779-228-1_7
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