Exploring the Consistency of the Quality Scores with Machine Learning for Next-Generation Sequencing Experiments...American Society of Human Genetics Conference 2018
Background. 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 o...
| Published in: | BioMed Research International pp. 1 - 7 |
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| Main Authors: | , |
| Format: | proceedings research tables/charts Journal Article |
| Published: |
Wiley-Blackwell
2/26/2020
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=141933587&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 141933587 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 2/26/2020 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 141933587 141933587 141933587 10.1155/2020/8531502 141933587 ppf: 1 ppct: 6 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Exploring the Consistency of the Quality Scores with Machine Learning for Next-Generation Sequencing Experiments...American Society of Human Genetics Conference 2018 aug: au: Cosgun, Erdal Oh, Min affil: Microsoft Genomics Team, Redmond, WA, USA, 98052 sug: subj: Machine Learning Methods Sequence Analysis Methods Genome, Human Human Congresses and Conferences Algorithms Documentation Decision Trees Neural Networks (Computer) Multiple Linear Regression ab: Background. 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). pubtype: Academic Journal doctype: proceedings research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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