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Shedding light: The importance of reverse transcription efficiency standards in data interpretation
The RNA-to-cDNA conversion step in transcriptomics experiments is widely recognised as inefficient and variable, casting doubt on the ability to do quantitative transcriptomics analyses. Multiple studies have focused on ways to optimise this process, resulting in contradictory recommendations. Here...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6374950/ https://www.ncbi.nlm.nih.gov/pubmed/30805297 http://dx.doi.org/10.1016/j.bdq.2018.12.002 |
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author | Schwaber, Jessica Andersen, Stacey Nielsen, Lars |
author_facet | Schwaber, Jessica Andersen, Stacey Nielsen, Lars |
author_sort | Schwaber, Jessica |
collection | PubMed |
description | The RNA-to-cDNA conversion step in transcriptomics experiments is widely recognised as inefficient and variable, casting doubt on the ability to do quantitative transcriptomics analyses. Multiple studies have focused on ways to optimise this process, resulting in contradictory recommendations. Here we explore the problem of reverse transcription efficiency using digital PCR and the RT method’s impact on subsequent data analysis. Using synthetic RNA standards, an example experiment is presented, outlining a method to (1) determine relevant efficiency and variability values and then to (2) incorporate this information into downstream analyses as a way to improve the accuracy of quantitative transcriptomics experiments. |
format | Online Article Text |
id | pubmed-6374950 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-63749502019-02-25 Shedding light: The importance of reverse transcription efficiency standards in data interpretation Schwaber, Jessica Andersen, Stacey Nielsen, Lars Biomol Detect Quantif Original Research Article The RNA-to-cDNA conversion step in transcriptomics experiments is widely recognised as inefficient and variable, casting doubt on the ability to do quantitative transcriptomics analyses. Multiple studies have focused on ways to optimise this process, resulting in contradictory recommendations. Here we explore the problem of reverse transcription efficiency using digital PCR and the RT method’s impact on subsequent data analysis. Using synthetic RNA standards, an example experiment is presented, outlining a method to (1) determine relevant efficiency and variability values and then to (2) incorporate this information into downstream analyses as a way to improve the accuracy of quantitative transcriptomics experiments. Elsevier 2019-02-12 /pmc/articles/PMC6374950/ /pubmed/30805297 http://dx.doi.org/10.1016/j.bdq.2018.12.002 Text en © 2019 The Authors http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Original Research Article Schwaber, Jessica Andersen, Stacey Nielsen, Lars Shedding light: The importance of reverse transcription efficiency standards in data interpretation |
title | Shedding light: The importance of reverse transcription efficiency standards in data interpretation |
title_full | Shedding light: The importance of reverse transcription efficiency standards in data interpretation |
title_fullStr | Shedding light: The importance of reverse transcription efficiency standards in data interpretation |
title_full_unstemmed | Shedding light: The importance of reverse transcription efficiency standards in data interpretation |
title_short | Shedding light: The importance of reverse transcription efficiency standards in data interpretation |
title_sort | shedding light: the importance of reverse transcription efficiency standards in data interpretation |
topic | Original Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6374950/ https://www.ncbi.nlm.nih.gov/pubmed/30805297 http://dx.doi.org/10.1016/j.bdq.2018.12.002 |
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