Dataset Information

Comparison of DNA Quantification Methods for Next Generation Sequencing.

ABSTRACT: Next Generation Sequencing (NGS) is a powerful tool that depends on loading a precise amount of DNA onto a flowcell. NGS strategies have expanded our ability to investigate genomic phenomena by referencing mutations in cancer and diseases through large-scale genotyping, developing methods to map rare chromatin interactions (4C; 5C and Hi-C) and identifying chromatin features associated with regulatory elements (ChIP-seq, Bis-Seq, ChiA-PET). While many methods are available for DNA library quantification, there is no unambiguous gold standard. Most techniques use PCR to amplify DNA libraries to obtain sufficient quantities for optical density measurement. However, increased PCR cycles can distort the library's heterogeneity and prevent the detection of rare variants. In this analysis, we compared new digital PCR technologies (droplet digital PCR; ddPCR, ddPCR-Tail) with standard methods for the titration of NGS libraries. DdPCR-Tail is comparable to qPCR and fluorometry (QuBit) and allows sensitive quantification by analysis of barcode repartition after sequencing of multiplexed samples. This study provides a direct comparison between quantification methods throughout a complete sequencing experiment and provides the impetus to use ddPCR-based quantification for improvement of NGS quality.

SUBMITTER: Robin JD

PROVIDER: S-EPMC4822169 | biostudies-literature | 2016 Apr

REPOSITORIES: biostudies-literature

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Comparison of DNA Quantification Methods for Next Generation Sequencing.

Robin Jérôme D JD Ludlow Andrew T AT LaRanger Ryan R Wright Woodring E WE Shay Jerry W JW

Scientific reports 20160406

Next Generation Sequencing (NGS) is a powerful tool that depends on loading a precise amount of DNA onto a flowcell. NGS strategies have expanded our ability to investigate genomic phenomena by referencing mutations in cancer and diseases through large-scale genotyping, developing methods to map rare chromatin interactions (4C; 5C and Hi-C) and identifying chromatin features associated with regulatory elements (ChIP-seq, Bis-Seq, ChiA-PET). While many methods are available for DNA library quanti ...[more]

PMID: 27048884

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Project description:Background/aimsRecently, next-generation sequencing-based technologies have enabled DNA methylation profiling at high resolution and low cost. Methyl-Seq and Reduced Representation Bisulfite Sequencing (RRBS) are two such technologies that interrogate methylation levels at CpG sites throughout the entire human genome. With rapid reduction of sequencing costs, these technologies will enable epigenotyping of large cohorts for phenotypic association studies. Existing quantification methods for sequencing-based methylation profiling are simplistic and do not deal with the noise due to the random sampling nature of sequencing and various experimental artifacts. Therefore, there is a need to investigate the statistical issues related to the quantification of methylation levels for these emerging technologies, with the goal of developing an accurate quantification method.MethodsIn this paper, we propose two methods for Methyl-Seq quantification. The first method, the Maximum Likelihood estimate, is both conceptually intuitive and computationally simple. However, this estimate is biased at extreme methylation levels and does not provide variance estimation. The second method, based on bayesian hierarchical model, allows variance estimation of methylation levels, and provides a flexible framework to adjust technical bias in the sequencing process.ResultsWe compare the previously proposed binary method, the Maximum Likelihood (ML) method, and the bayesian method. In both simulation and real data analysis of Methyl-Seq data, the bayesian method offers the most accurate quantification. The ML method is slightly less accurate than the bayesian method. But both our proposed methods outperform the original binary method in Methyl-Seq. In addition, we applied these quantification methods to simulation data and show that, with sequencing depth above 40-300 (which varies with different tissue samples) per cleavage site, Methyl-Seq offers a comparable quantification consistency as microarrays.

Dataset Information

Comparison of DNA Quantification Methods for Next Generation Sequencing.

Publications

Comparison of DNA Quantification Methods for Next Generation Sequencing.

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