RNA-seq: impact of RNA degradation on transcript quantification
Background The use of low quality RNA samples in whole-genome gene expression profiling remains controversial. It is unclear if transcript degradation in low quality RNA samples occurs uniformly, in which case the effects of degradation can be corrected via data normalization, or whether different...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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BioMed Central
2015
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| Online Access: | http://hdl.handle.net/1721.1/97557 https://orcid.org/0000-0002-7995-9948 |
| _version_ | 1811081367981654016 |
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| author | Gallego Romero, Irene Tung, Jenny Gilad, Yoav Pai, Athma A. |
| author2 | Massachusetts Institute of Technology. Department of Biology |
| author_facet | Massachusetts Institute of Technology. Department of Biology Gallego Romero, Irene Tung, Jenny Gilad, Yoav Pai, Athma A. |
| author_sort | Gallego Romero, Irene |
| collection | MIT |
| description | Background
The use of low quality RNA samples in whole-genome gene expression profiling remains controversial. It is unclear if transcript degradation in low quality RNA samples occurs uniformly, in which case the effects of degradation can be corrected via data normalization, or whether different transcripts are degraded at different rates, potentially biasing measurements of expression levels. This concern has rendered the use of low quality RNA samples in whole-genome expression profiling problematic. Yet, low quality samples (for example, samples collected in the course of fieldwork) are at times the sole means of addressing specific questions.
Results
We sought to quantify the impact of variation in RNA quality on estimates of gene expression levels based on RNA-seq data. To do so, we collected expression data from tissue samples that were allowed to decay for varying amounts of time prior to RNA extraction. The RNA samples we collected spanned the entire range of RNA Integrity Number (RIN) values (a metric commonly used to assess RNA quality). We observed widespread effects of RNA quality on measurements of gene expression levels, as well as a slight but significant loss of library complexity in more degraded samples.
Conclusions
While standard normalizations failed to account for the effects of degradation, we found that by explicitly controlling for the effects of RIN using a linear model framework we can correct for the majority of these effects. We conclude that in instances in which RIN and the effect of interest are not associated, this approach can help recover biologically meaningful signals in data from degraded RNA samples. |
| first_indexed | 2024-09-23T11:45:34Z |
| format | Article |
| id | mit-1721.1/97557 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T11:45:34Z |
| publishDate | 2015 |
| publisher | BioMed Central |
| record_format | dspace |
| spelling | mit-1721.1/975572022-09-27T21:41:41Z RNA-seq: impact of RNA degradation on transcript quantification Gallego Romero, Irene Tung, Jenny Gilad, Yoav Pai, Athma A. Massachusetts Institute of Technology. Department of Biology Pai, Athma A. Background The use of low quality RNA samples in whole-genome gene expression profiling remains controversial. It is unclear if transcript degradation in low quality RNA samples occurs uniformly, in which case the effects of degradation can be corrected via data normalization, or whether different transcripts are degraded at different rates, potentially biasing measurements of expression levels. This concern has rendered the use of low quality RNA samples in whole-genome expression profiling problematic. Yet, low quality samples (for example, samples collected in the course of fieldwork) are at times the sole means of addressing specific questions. Results We sought to quantify the impact of variation in RNA quality on estimates of gene expression levels based on RNA-seq data. To do so, we collected expression data from tissue samples that were allowed to decay for varying amounts of time prior to RNA extraction. The RNA samples we collected spanned the entire range of RNA Integrity Number (RIN) values (a metric commonly used to assess RNA quality). We observed widespread effects of RNA quality on measurements of gene expression levels, as well as a slight but significant loss of library complexity in more degraded samples. Conclusions While standard normalizations failed to account for the effects of degradation, we found that by explicitly controlling for the effects of RIN using a linear model framework we can correct for the majority of these effects. We conclude that in instances in which RIN and the effect of interest are not associated, this approach can help recover biologically meaningful signals in data from degraded RNA samples. American Heart Association (Predoctoral Fellowship) 2015-06-29T16:37:10Z 2015-06-29T16:37:10Z 2014-05 2014-05 2015-06-29T08:37:57Z Article http://purl.org/eprint/type/JournalArticle 1741-7007 http://hdl.handle.net/1721.1/97557 Gallego Romero, Irene, Athma A Pai, Jenny Tung, and Yoav Gilad. “RNA-Seq: Impact of RNA Degradation on Transcript Quantification.” BMC Biology 12, no. 1 (2014): 42. https://orcid.org/0000-0002-7995-9948 en http://dx.doi.org/10.1186/1741-7007-12-42 BMC Biology Gallego Romero et al.; licensee BioMed Central Ltd. application/pdf BioMed Central |
| spellingShingle | Gallego Romero, Irene Tung, Jenny Gilad, Yoav Pai, Athma A. RNA-seq: impact of RNA degradation on transcript quantification |
| title | RNA-seq: impact of RNA degradation on transcript quantification |
| title_full | RNA-seq: impact of RNA degradation on transcript quantification |
| title_fullStr | RNA-seq: impact of RNA degradation on transcript quantification |
| title_full_unstemmed | RNA-seq: impact of RNA degradation on transcript quantification |
| title_short | RNA-seq: impact of RNA degradation on transcript quantification |
| title_sort | rna seq impact of rna degradation on transcript quantification |
| url | http://hdl.handle.net/1721.1/97557 https://orcid.org/0000-0002-7995-9948 |
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