Abstract
Changes in the surrounding environment are mirrored by changes in the transcript profile of an organism. In the case of a plant pathogen, host colonization would be a challenge that triggers changes in transcript expression patterns. Determining the transcriptional profile could provide valuable clues on how an organism responds to defined stimuli, in this case, how a pathogen colonizes its host. Several robust data analysis methods and pipelines are available that can identify these differentially expressed transcripts. In this chapter we outline the steps and other caveats that are needed to run one such pipeline.
Key words
- RNA-seq
- Transcriptome
- Transcript profile
- Data analysis
- Pipeline
- Differentially expressed genes
- Splice-aware
- HISAT2
- StringTie
- DESeq2
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Acknowledgments
This work is supported by the USDA National Institute of Food and Agriculture, Hatch project FLA-FTL-005926. Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the view of the National Institute of Food and Agriculture (NIFA) or the US Department of Agriculture (USDA).
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Gill, N., Dhillon, B. (2022). RNA-seq Data Analysis for Differential Expression. In: Coleman, J. (eds) Fusarium wilt. Methods in Molecular Biology, vol 2391. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1795-3_4
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DOI: https://doi.org/10.1007/978-1-0716-1795-3_4
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