NGSQC: Cross-platform quality analysis pipeline for deep sequencing data

Manhong Dai, Robert C. Thompson, Christopher Maher, Rafael Contreras-Galindo, Mark H. Kaplan, David M. Markovitz, Gil Omenn, Fan Meng

Research output: Contribution to journalArticlepeer-review

85 Scopus citations


Background: While the accuracy and precision of deep sequencing data is significantly better than those obtained by the earlier generation of hybridization-based high throughput technologies, the digital nature of deep sequencing output often leads to unwarranted confidence in their reliability.Results: The NGSQC (Next Generation Sequencing Quality Control) pipeline provides a set of novel quality control measures for quickly detecting a wide variety of quality issues in deep sequencing data derived from two dimensional surfaces, regardless of the assay technology used. It also enables researchers to determine whether sequencing data related to their most interesting biological discoveries are caused by sequencing quality issues.Conclusions: Next generation sequencing platforms have their own share of quality issues and there can be significant lab-to-lab, batch-to-batch and even within chip/slide variations. NGSQC can help to ensure that biological conclusions, in particular those based on relatively rare sequence alterations, are not caused by low quality sequencing.

Original languageEnglish (US)
Article numberS7
JournalBMC Genomics
Issue numberSUPPL. 4
StatePublished - Dec 2 2010

Bibliographical note

Funding Information:
We want to thank Drs. Robert Lyons and Christine Brennan of the UM Sequencing Core, Joe Washburn of the Cancer Center SOLiD sequencing core and Drs. David Miller and Michael Kabul at Applied Biosystems for their helpful comments and for providing some of the data used in this project. This project is mainly supported by R01CA144043-01 (DMM) and a University of Michigan Center for Computational Medicine and Biology Pilot Project (RCT and FM). MD, RCT and FM are members of the Pritzker Neuropsychiatric Disorders Research Consortium, which is supported by the Pritzker Neuropsychiatric Disorders Research Fund L.L.C. This work is also partly supported by the National Center for Integrated Biomedical Informatics through NIH grant 1U54DA021519-01A1 to the University of Michigan.


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