DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Sunhee | ko |
dc.contributor.author | Lee, Young-suk | ko |
dc.contributor.author | Lee, Chang-Yong | ko |
dc.date.accessioned | 2023-08-31T23:00:14Z | - |
dc.date.available | 2023-08-31T23:00:14Z | - |
dc.date.created | 2023-02-24 | - |
dc.date.issued | 2022-12 | - |
dc.identifier.citation | 2022 IEEE International Conference on Big Data, Big Data 2022, pp.6696 - 6698 | - |
dc.identifier.uri | http://hdl.handle.net/10203/312090 | - |
dc.description.abstract | In this study, we examined the impact of the variant database in recalibration and developed a database-generation model that gathers potential candidates directly from resequencing genome data. Based on human genome data, we optimize the hyper-parameters in the model and evaluate the performance improvements both in terms of recalibration and variant calling. To test whether our pseudo-database approach is applicable to species other than human, we constructed pseudo-databases for sheep, rice, and chickpea, and compared its performance with dbSNP. Consistently, we find that our pseudo-database provides improved recalibration and error rates. More importantly, the use of pseudo-databases led to the identification of additional genetic variants. Therefore, the reanalysis with our pseudo-databases approach effectively recalibrates the base quality scores and consequently uncovers hidden genetic variations in published resequencing data. | - |
dc.language | English | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.title | Computational method of database construction for genetic variant calling | - |
dc.type | Conference | - |
dc.identifier.scopusid | 2-s2.0-85147957857 | - |
dc.type.rims | CONF | - |
dc.citation.beginningpage | 6696 | - |
dc.citation.endingpage | 6698 | - |
dc.citation.publicationname | 2022 IEEE International Conference on Big Data, Big Data 2022 | - |
dc.identifier.conferencecountry | JA | - |
dc.identifier.conferencelocation | Osaka | - |
dc.identifier.doi | 10.1109/BigData55660.2022.10020263 | - |
dc.contributor.localauthor | Lee, Young-suk | - |
dc.contributor.nonIdAuthor | Kim, Sunhee | - |
dc.contributor.nonIdAuthor | Lee, Chang-Yong | - |
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