Internal and external normalization of nascent RNA sequencing run-on experiments
Abstract In experiments with significant perturbations to transcription, nascent RNA sequencing protocols are dependent on external spike-ins for reliable normalization. Unlike in RNA-seq, these spike-ins are not standardized and, in many cases, depend on a run-on reaction that is assumed to have co...
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| Format: | Article |
| Language: | English |
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BMC
2024-01-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12859-023-05607-3 |
| _version_ | 1797355473555423232 |
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| author | Zachary L. Maas Robin D. Dowell |
| author_facet | Zachary L. Maas Robin D. Dowell |
| author_sort | Zachary L. Maas |
| collection | DOAJ |
| description | Abstract In experiments with significant perturbations to transcription, nascent RNA sequencing protocols are dependent on external spike-ins for reliable normalization. Unlike in RNA-seq, these spike-ins are not standardized and, in many cases, depend on a run-on reaction that is assumed to have constant efficiency across samples. To assess the validity of this assumption, we analyze a large number of published nascent RNA spike-ins to quantify their variability across existing normalization methods. Furthermore, we develop a new biologically-informed Bayesian model to estimate the error in spike-in based normalization estimates, which we term Virtual Spike-In (VSI). We apply this method both to published external spike-ins as well as using reads at the $$3^\prime$$ 3 ′ end of long genes, building on prior work from Mahat (Mol Cell 62(1):63–78, 2016. https://doi.org/10.1016/j.molcel.2016.02.025 ) and Vihervaara (Nat Commun 8(1):255, 2017. https://doi.org/10.1038/s41467-017-00151-0 ). We find that spike-ins in existing nascent RNA experiments are typically under sequenced, with high variability between samples. Furthermore, we show that these high variability estimates can have significant downstream effects on analysis, complicating biological interpretations of results. |
| first_indexed | 2024-03-08T14:12:38Z |
| format | Article |
| id | doaj.art-e33bb6c2e04c4f18b3815360846f29eb |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-03-08T14:12:38Z |
| publishDate | 2024-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-e33bb6c2e04c4f18b3815360846f29eb2024-01-14T12:38:49ZengBMCBMC Bioinformatics1471-21052024-01-0125111510.1186/s12859-023-05607-3Internal and external normalization of nascent RNA sequencing run-on experimentsZachary L. Maas0Robin D. Dowell1Department of Computer Science, University of ColoradoDepartment of Computer Science, University of ColoradoAbstract In experiments with significant perturbations to transcription, nascent RNA sequencing protocols are dependent on external spike-ins for reliable normalization. Unlike in RNA-seq, these spike-ins are not standardized and, in many cases, depend on a run-on reaction that is assumed to have constant efficiency across samples. To assess the validity of this assumption, we analyze a large number of published nascent RNA spike-ins to quantify their variability across existing normalization methods. Furthermore, we develop a new biologically-informed Bayesian model to estimate the error in spike-in based normalization estimates, which we term Virtual Spike-In (VSI). We apply this method both to published external spike-ins as well as using reads at the $$3^\prime$$ 3 ′ end of long genes, building on prior work from Mahat (Mol Cell 62(1):63–78, 2016. https://doi.org/10.1016/j.molcel.2016.02.025 ) and Vihervaara (Nat Commun 8(1):255, 2017. https://doi.org/10.1038/s41467-017-00151-0 ). We find that spike-ins in existing nascent RNA experiments are typically under sequenced, with high variability between samples. Furthermore, we show that these high variability estimates can have significant downstream effects on analysis, complicating biological interpretations of results.https://doi.org/10.1186/s12859-023-05607-3Nascent RNA sequencingNormalizationBayesian |
| spellingShingle | Zachary L. Maas Robin D. Dowell Internal and external normalization of nascent RNA sequencing run-on experiments BMC Bioinformatics Nascent RNA sequencing Normalization Bayesian |
| title | Internal and external normalization of nascent RNA sequencing run-on experiments |
| title_full | Internal and external normalization of nascent RNA sequencing run-on experiments |
| title_fullStr | Internal and external normalization of nascent RNA sequencing run-on experiments |
| title_full_unstemmed | Internal and external normalization of nascent RNA sequencing run-on experiments |
| title_short | Internal and external normalization of nascent RNA sequencing run-on experiments |
| title_sort | internal and external normalization of nascent rna sequencing run on experiments |
| topic | Nascent RNA sequencing Normalization Bayesian |
| url | https://doi.org/10.1186/s12859-023-05607-3 |
| work_keys_str_mv | AT zacharylmaas internalandexternalnormalizationofnascentrnasequencingrunonexperiments AT robinddowell internalandexternalnormalizationofnascentrnasequencingrunonexperiments |