RNA Preparation and RNA-Seq Bioinformatics for Comparative Transcriptomics
- PMID: 37642840
- DOI: 10.1007/978-1-0716-3385-4_6
RNA Preparation and RNA-Seq Bioinformatics for Comparative Transcriptomics
Abstract
The principal transcriptome analysis is the determination of differentially expressed genes across experimental conditions. For this, the next-generation sequencing of RNA (RNA-seq) has several advantages over other techniques, such as the capability of detecting all the transcripts in one assay over RT-qPCR, such as its higher accuracy and broader dynamic range over microarrays or the ability to detect novel transcripts, including non-coding RNA molecules, at nucleotide-level resolution over both techniques. Despite these advantages, many microbiology laboratories have not yet applied RNA-seq analyses to their investigations. The high cost of the equipment for next-generation sequencing is no longer an issue since this intermediate part of the analysis can be provided by commercial or central services. Here, we detail a protocol for the first part of the analysis, the RNA extraction and an introductory protocol to the bioinformatics analysis of the sequencing data to generate the differential expression results.
Keywords: Bioconductor; Differential expression; RNA extraction; RNA-seq; Transcriptomics.
© 2023. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.
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References
-
- Garalde DR, Snell EA, Jachimowicz D et al (2018) Highly parallel direct RNA sequencing on an array of nanopores. Nat Methods 15:201–206. https://doi.org/10.1038/nmeth.4577 - DOI - PubMed
-
- Hrdlickova R, Toloue M, Tian B (2017) RNA-seq methods for transcriptome analysis. Wiley Interdiscip Rev RNA 8:1364. https://doi.org/10.1002/wrna.1364 - DOI
-
- Shi H, Zhou Y, Jia E et al (2021) Bias in RNA-seq library preparation: current challenges and solutions. Biomed Res Int 2021:6647597. https://doi.org/10.1155/2021/6647597 - DOI - PubMed - PMC
-
- Yoder-Himes DR, Chain PSG, Zhu Y et al (2009) Mapping the Burkholderia cenocepacia niche response via high-throughput sequencing. Proc Natl Acad Sci U S A 106:3976–3981. https://doi.org/10.1073/pnas.0813403106 - DOI - PubMed - PMC
-
- Valentin-Hansen P, Eriksen M, Udesen C (2004) The bacterial Sm-like protein Hfq: a key player in RNA transactions. Mol Microbiol 51:1525–1533. https://doi.org/10.1111/j.1365-2958.2003.03935.x - DOI - PubMed
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