Department of Medicine, Division of Cardiovascular Medicine
Computational Biology | Genomics | Nucleic Acids, Nucleotides, and Nucleosides
Small RNA-seq is increasingly being used for profiling of small RNAs. Quantitative characteristics of long RNA-seq have been extensively described, but small RNA-seq involves fundamentally different methods for library preparation, with distinct protocols and technical variations that have not been fully and systematically studied. We report here the results of a study using common references (synthetic RNA pools of defined composition, as well as plasma-derived RNA) to evaluate the accuracy, reproducibility and bias of small RNA-seq library preparation for five distinct protocols and across nine different laboratories. We observed protocol-specific and sequence-specific bias, which was ameliorated using adapters for ligation with randomized end-nucleotides, and computational correction factors. Despite this technical bias, relative quantification using small RNA-seq was remarkably accurate and reproducible, even across multiple laboratories using different methods. These results provide strong evidence for the feasibility of reproducible cross-laboratory small RNA-seq studies, even those involving analysis of data generated using different protocols.
RNA-seq, small RNAs, microRNAs, exRNAs, accuracy, bias, reproducibility, genomics
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The copyright holder for this preprint (which was not peer-reviewed) is the author/funder. It is made available under a CC-BY-NC-ND 4.0 International license.
DOI of Published Version
bioRxiv 113050; doi: https://doi.org/10.1101/113050. Link to preprint on bioRxiv service.
Giraldez MD, Tanriverdi K, Freedman JE, Tewari M. (2017). Accuracy, Reproducibility And Bias Of Next Generation Sequencing For Quantitative Small RNA Profiling: A Multiple Protocol Study Across Multiple Laboratories [preprint]. UMass Chan Medical School Faculty Publications. https://doi.org/10.1101/113050. Retrieved from https://escholarship.umassmed.edu/faculty_pubs/1562
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This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.