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dc.contributor.authorFu, Yu
dc.contributor.authorBeane, Timothy J.
dc.contributor.authorZamore, Phillip D.
dc.contributor.authorWeng, Zhiping
dc.date2022-08-11T08:08:23.000
dc.date.accessioned2022-08-23T15:53:03Z
dc.date.available2022-08-23T15:53:03Z
dc.date.issued2018-01-22
dc.date.submitted2018-06-06
dc.identifier.citation<p>bioRxiv 251892; doi: https://doi.org/10.1101/251892. <a href="https://doi.org/10.1101/251892" target="_blank">Link to preprint on bioRxiv service.</a></p>
dc.identifier.doi10.1101/251892
dc.identifier.urihttp://hdl.handle.net/20.500.14038/29275
dc.description.abstractRNA-seq and small RNA-seq are powerful, quantitative tools to study gene regulation and function. Common high-throughput sequencing methods rely on polymerase chain reaction (PCR) to expand the starting material, but not every molecule amplifies equally, causing some to be overrepresented. Unique molecular identifiers (UMIs) can be used to distinguish undesirable PCR duplicates derived from a single molecule and identical but biologically meaningful reads from different molecules. We have incorporated UMIs into RNA-seq and small RNA-seq protocols and developed tools to analyze the resulting data. Our UMIs contain stretches of random nucleotides whose lengths sufficiently capture diverse molecule species in both RNA-seq and small RNA-seq libraries generated from mouse testis. Our approach yields high-quality data while allowing unique tagging of all molecules in high-depth libraries. Using simulated and real datasets, we demonstrate that our methods increase the reproducibility of RNA-seq and small RNA-seq data. Notably, we find that the amount of starting material and sequencing depth, but not the number of PCR cycles, determine PCR duplicate frequency. Finally, we show that computational removal of PCR duplicates based only on their mapping coordinates introduces substantial bias into data analysis.
dc.language.isoen_US
dc.relation<p>Now published in BMC Genomics, doi: <a href="https://doi.org/10.1186/s12864-018-4933-1" target="_blank" title="View published article">10.1186/s12864-018-4933-1</a></p>
dc.rightsThe copyright holder for this preprint (which was not peer-reviewed) is the author/funder.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.
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectpolymerase chain reaction
dc.subjectRNA-seq
dc.subjectUnique molecular identifiers
dc.subjectBioinformatics
dc.subjectComputational Biology
dc.subjectMolecular Biology
dc.titleElimination of PCR duplicates in RNA-seq and small RNA-seq using unique molecular identifiers [preprint]
dc.typePreprint
dc.source.journaltitlebioRxiv
dc.identifier.legacyfulltexthttps://escholarship.umassmed.edu/cgi/viewcontent.cgi?article=2511&amp;context=faculty_pubs&amp;unstamped=1
dc.identifier.legacycoverpagehttps://escholarship.umassmed.edu/faculty_pubs/1504
dc.identifier.contextkey12265168
refterms.dateFOA2022-08-23T15:53:03Z
html.description.abstract<p>RNA-seq and small RNA-seq are powerful, quantitative tools to study gene regulation and function. Common high-throughput sequencing methods rely on polymerase chain reaction (PCR) to expand the starting material, but not every molecule amplifies equally, causing some to be overrepresented. Unique molecular identifiers (UMIs) can be used to distinguish undesirable PCR duplicates derived from a single molecule and identical but biologically meaningful reads from different molecules. We have incorporated UMIs into RNA-seq and small RNA-seq protocols and developed tools to analyze the resulting data. Our UMIs contain stretches of random nucleotides whose lengths sufficiently capture diverse molecule species in both RNA-seq and small RNA-seq libraries generated from mouse testis. Our approach yields high-quality data while allowing unique tagging of all molecules in high-depth libraries. Using simulated and real datasets, we demonstrate that our methods increase the reproducibility of RNA-seq and small RNA-seq data. Notably, we find that the amount of starting material and sequencing depth, but not the number of PCR cycles, determine PCR duplicate frequency. Finally, we show that computational removal of PCR duplicates based only on their mapping coordinates introduces substantial bias into data analysis.</p>
dc.identifier.submissionpathfaculty_pubs/1504
dc.contributor.departmentDepartment of Biochemistry and Molecular Pharmacology
dc.contributor.departmentRNA Therapeutics Institute
dc.contributor.departmentProgram in Bioinformatics and Integrative Biology


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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.
Except where otherwise noted, this item's license is described as 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.