Exploring the Consistency of the Quality Scores with Machine Learning for Next-Generation Sequencing Experiments

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dc.contributor.authorCosgun, Erdalen
dc.contributor.authorOh, Minen
dc.contributor.departmentComputer Scienceen
dc.date.accessioned2020-05-14T13:08:27Zen
dc.date.available2020-05-14T13:08:27Zen
dc.date.issued2020-02-26en
dc.description.abstractBackground. Next-generation sequencing enables massively parallel processing, allowing lower cost than the other sequencing technologies. In the subsequent analysis with the NGS data, one of the major concerns is the reliability of variant calls. Although researchers can utilize raw quality scores of variant calling, they are forced to start the further analysis without any preevaluation of the quality scores. Method. We presented a machine learning approach for estimating quality scores of variant calls derived from BWA+GATK. We analyzed correlations between the quality score and these annotations, specifying informative annotations which were used as features to predict variant quality scores. To test the predictive models, we simulated 24 paired-end Illumina sequencing reads with 30x coverage base. Also, twenty-four human genome sequencing reads resulting from Illumina paired-end sequencing with at least 30x coverage were secured from the Sequence Read Archive. Results. Using BWA+GATK, VCFs were derived from simulated and real sequencing reads. We observed that the prediction models learned by RFR outperformed other algorithms in both simulated and real data. The quality scores of variant calls were highly predictable from informative features of GATK Annotation Modules in the simulated human genome VCF data (R2: 96.7%, 94.4%, and 89.8% for RFR, MLR, and NNR, respectively). The robustness of the proposed data-driven models was consistently maintained in the real human genome VCF data (R2: 97.8% and 96.5% for RFR and MLR, respectively).en
dc.description.notesThis study was funded by Microsoft Genomics team, One Microsoft Way, 98052, Redmond, WA, USA.en
dc.description.sponsorshipMicrosoft Genomics team, One Microsoft Way, Redmond, WA, USAen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1155/2020/8531502en
dc.identifier.eissn2314-6141en
dc.identifier.issn2314-6133en
dc.identifier.other8531502en
dc.identifier.pmid32219145en
dc.identifier.urihttp://hdl.handle.net/10919/98262en
dc.identifier.volume2020en
dc.language.isoenen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.titleExploring the Consistency of the Quality Scores with Machine Learning for Next-Generation Sequencing Experimentsen
dc.title.serialBiomed Research Internationalen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.dcmitypeStillImageen

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