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Background A variety of protocols exist for producing whole genome run-on transcription datasets. However, little is known about how differences between these protocols affect the signal within the resulting libraries. Results Using run-on transcription datasets generated from the same biological system, we show that a variety of GRO- and PRO-seq preparation methods leave identifiable signatures within each library. Specifically we show that the library preparation method results in differences in quality control metrics, as well as differences in the signal distribution at the 5 ′ end of transcribed regions. These shifts lead to disparities in eRNA identification, but do not impact analyses aimed at inferring the key regulators involved in changes to transcription. Conclusions Run-on sequencing protocol variations result in technical signatures that can be used to identify both the enrichment and library preparation method of a particular data set. These technical signatures are batch effects that limit detailed comparisons of pausing ratios and eRNAs identified across protocols. However, these batch effects have only limited impact on our ability to infer which regulators underlie the observed transcriptional changes.
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Simulation with RADinitio improves RADseq experimental design and sheds light on sources of missing data
Abstract Restriction‐site associated DNA sequencing (RADseq) has become a powerful and versatile tool in modern population genomics, enabling large‐scale evolutionary and genomic analyses in otherwise inaccessible biological systems. With its widespread use, different variants on the protocol have been developed to suit specific experimental needs. Researchers face the challenge of choosing the optimal molecular and sequencing protocols for their reduced representation experimental design, an often‐complicated process. Strategic errors can lead to biased data generation that has reduced power to answer biological questions. Here, we present RADinitio, simulation software for the selection and optimization of RADseq experiments via the generation of sequencing data that behave similarly to empirical sources. RADinitio provides an evolutionary simulation of populations, implementation of various RADseq protocols with customizable parameters, and thorough assessment of missing data. We test the efficacy of the software using different RAD protocols across several organisms, highlighting the importance of protocol selection on the magnitude and quality of data acquired. Additionally, we test the effects of RAD library preparation and sequencing on allelic dropout, observing that library preparation and sequencing often contributes more to missing alleles than population‐level variation.
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- Award ID(s):
- 1645087
- PAR ID:
- 10453430
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Molecular Ecology Resources
- Volume:
- 21
- Issue:
- 2
- ISSN:
- 1755-098X
- Page Range / eLocation ID:
- p. 363-378
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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