Estimating Phred scores of Illumina base calls by logistic regression and sparse modeling
Abstract 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 ba...
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| Format: | Article |
| Language: | English |
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BMC
2017-07-01
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| Series: | BMC Bioinformatics |
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| Online Access: | http://link.springer.com/article/10.1186/s12859-017-1743-4 |
| _version_ | 1818361398458056704 |
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| author | Sheng Zhang Bo Wang Lin Wan Lei M. Li |
| author_facet | Sheng Zhang Bo Wang Lin Wan Lei M. Li |
| author_sort | Sheng Zhang |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-12-13T21:16:03Z |
| format | Article |
| id | doaj.art-f24ba62b8014458399026b7dac0c1927 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-13T21:16:03Z |
| publishDate | 2017-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-f24ba62b8014458399026b7dac0c19272022-12-21T23:31:14ZengBMCBMC Bioinformatics1471-21052017-07-0118111410.1186/s12859-017-1743-4Estimating Phred scores of Illumina base calls by logistic regression and sparse modelingSheng Zhang0Bo Wang1Lin Wan2Lei M. Li3National Center of Mathematics and Interdisciplinary Sciences, Academy of Mathematics and Systems Science, Chinese Academy of SciencesNational Center of Mathematics and Interdisciplinary Sciences, Academy of Mathematics and Systems Science, Chinese Academy of SciencesNational Center of Mathematics and Interdisciplinary Sciences, Academy of Mathematics and Systems Science, Chinese Academy of SciencesNational Center of Mathematics and Interdisciplinary Sciences, Academy of Mathematics and Systems Science, Chinese Academy of SciencesAbstract 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.http://link.springer.com/article/10.1186/s12859-017-1743-4Base-callingLogistic regressionQuality scoreL 1 regularizationAICBIC |
| spellingShingle | Sheng Zhang Bo Wang Lin Wan Lei M. Li Estimating Phred scores of Illumina base calls by logistic regression and sparse modeling BMC Bioinformatics Base-calling Logistic regression Quality score L 1 regularization AIC BIC |
| title | Estimating Phred scores of Illumina base calls by logistic regression and sparse modeling |
| title_full | Estimating Phred scores of Illumina base calls by logistic regression and sparse modeling |
| title_fullStr | Estimating Phred scores of Illumina base calls by logistic regression and sparse modeling |
| title_full_unstemmed | Estimating Phred scores of Illumina base calls by logistic regression and sparse modeling |
| title_short | Estimating Phred scores of Illumina base calls by logistic regression and sparse modeling |
| title_sort | estimating phred scores of illumina base calls by logistic regression and sparse modeling |
| topic | Base-calling Logistic regression Quality score L 1 regularization AIC BIC |
| url | http://link.springer.com/article/10.1186/s12859-017-1743-4 |
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