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This article is part of the supplement: The International Conference on Intelligent Biology and Medicine (ICIBM) – Genomics

Open Access Highly Accessed Research

Steps to ensure accuracy in genotype and SNP calling from Illumina sequencing data

Qi Liu12, Yan Guo1, Jiang Li1, Jirong Long3, Bing Zhang124 and Yu Shyr145*

Author Affiliations

1 Center for Quantitative Sciences, Vanderbilt University School of Medicine, Nashville, TN 37232, USA

2 Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA

3 Vanderbilt Epidemiology Center, Vanderbilt University, Nashville, TN 37232, USA

4 Department of Cancer Biology, Vanderbilt University School of Medicine, Nashville, TN 37232, USA

5 Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA

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BMC Genomics 2012, 13(Suppl 8):S8  doi:10.1186/1471-2164-13-S8-S8

Published: 17 December 2012

Abstract

Background

Accurate calling of SNPs and genotypes from next-generation sequencing data is an essential prerequisite for most human genetics studies. A number of computational steps are required or recommended when translating the raw sequencing data into the final calls. However, whether each step does contribute to the performance of variant calling and how it affects the accuracy still remain unclear, making it difficult to select and arrange appropriate steps to derive high quality variants from different sequencing data. In this study, we made a systematic assessment of the relative contribution of each step to the accuracy of variant calling from Illumina DNA sequencing data.

Results

We found that the read preprocessing step did not improve the accuracy of variant calling, contrary to the general expectation. Although trimming off low-quality tails helped align more reads, it introduced lots of false positives. The ability of markup duplication, local realignment and recalibration, to help eliminate false positive variants depended on the sequencing depth. Rearranging these steps did not affect the results. The relative performance of three popular multi-sample SNP callers, SAMtools, GATK, and GlfMultiples, also varied with the sequencing depth.

Conclusions

Our findings clarify the necessity and effectiveness of computational steps for improving the accuracy of SNP and genotype calls from Illumina sequencing data and can serve as a general guideline for choosing SNP calling strategies for data with different coverage.