Estimating Phred scores of Illumina base calls by logistic regression and sparse modeling

doi:10.1186/s12859-017-1743-4

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	Estimating Phred scores of Illumina base calls by logistic regression and sparse modeling
	Zhang, Sheng 1,2; Wang, Bo 1,2; Wan, Lin1,2 ; Li, Lei M.1,2
	2017-07-11
发表期刊	BMC BIOINFORMATICS
ISSN	1471-2105
卷号	18 页码:14
摘要	Background: Phred quality scores are essential for downstream DNA analysis such as SNP detection and DNA assembly. Thus a valid model to define them is indispensable for any base-calling software. Recently, we developed the base-caller 3Dec for Illumina sequencing platforms, which reduces base-calling errors by 44-69% compared to the existing ones. However, the model to predict its quality scores has not been fully investigated yet. Results: In this study, we used logistic regression models to evaluate quality scores from predictive features, which include different aspects of the sequencing signals as well as local DNA contents. Sparse models were further obtained by three methods: the backward deletion with either AIC or BIC and the L-1 regularization learning method. The L-1-regularized one was then compared with the Illumina scoring method. Conclusions: The L-1-regularized logistic regression improves the empirical discrimination power by as large as 14 and 25% respectively for two kinds of preprocessed sequencing signals, compared to the Illumina scoring method. Namely, the L-1 method identifies more base calls of high fidelity. Computationally, the L-1 method can handle large dataset and is efficient enough for daily sequencing. Meanwhile, the logistic model resulted from BIC is more interpretable. The modeling suggested that the most prominent quenching pattern in the current chemistry of Illumina occurred at the dinucleotide "GT". Besides, nucleotides were more likely to be miscalled as the previous bases if the preceding ones were not "G". It suggested that the phasing effect of bases after "G" was somewhat different from those after other nucleotide types.
关键词	Base-calling Logistic regression Quality score L-1 regularization AIC BIC Empirical discrimination power
DOI	10.1186/s12859-017-1743-4
语种	英语
资助项目	National Natural Science Foundation of China[91130008] ; National Natural Science Foundation of China[91530105] ; Chinese Academy of Sciences[XDB13040600] ; National Center for Mathematics and Interdisciplinary Sciences of the CAS ; Key Laboratory of Systems and Control of the CAS ; CAS ; NSFC[11571349] ; NSFC[11201460] ; Youth Innovation Promotion Association of the CAS
WOS研究方向	Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Mathematical & Computational Biology
WOS类目	Biochemical Research Methods ; Biotechnology & Applied Microbiology ; Mathematical & Computational Biology
WOS记录号	WOS:000406607100004
出版者	BIOMED CENTRAL LTD
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/375
专题	系统科学研究所
通讯作者	Li, Lei M.
作者单位	1.Chinese Acad Sci, Acad Math & Syst Sci, Natl Ctr Math & Interdisciplinary Sci, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Sheng,Wang, Bo,Wan, Lin,et al. Estimating Phred scores of Illumina base calls by logistic regression and sparse modeling[J]. BMC BIOINFORMATICS,2017,18:14.
APA	Zhang, Sheng,Wang, Bo,Wan, Lin,&Li, Lei M..(2017).Estimating Phred scores of Illumina base calls by logistic regression and sparse modeling.BMC BIOINFORMATICS,18,14.
MLA	Zhang, Sheng,et al."Estimating Phred scores of Illumina base calls by logistic regression and sparse modeling".BMC BIOINFORMATICS 18(2017):14.