SequencErr: measuring and suppressing sequencer errors in next-generation sequencing data

SequencErr: measuring and suppressing sequencer errors in next-generation sequencing data

Abstract Background There is currently no method to precisely measure the errors that occur in the sequencing instrument/sequencer, which is critical for next-generation sequencing applications aimed at discovering the genetic makeup of heterogeneous cellular populations. Results We propose a novel...

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Bibliographic Details
Main Authors: Eric M. Davis, Yu Sun, Yanling Liu, Pandurang Kolekar, Ying Shao, Karol Szlachta, Heather L. Mulder, Dongren Ren, Stephen V. Rice, Zhaoming Wang, Joy Nakitandwe, Alexander M. Gout, Bridget Shaner, Salina Hall, Leslie L. Robison, Stanley Pounds, Jeffery M. Klco, John Easton, Xiaotu Ma
Format: Article
Language:English
Published: BMC 2021-01-01
Series:Genome Biology
Subjects:
Sequencer/instrument error
Error suppression
DNA sequencing
Online Access:https://doi.org/10.1186/s13059-020-02254-2

Internet

https://doi.org/10.1186/s13059-020-02254-2

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