Lacer - robust and accurate base quality score recalibration for all organisms
Swaine Chen, Assistant Professor, National University of Singapore
Dramatic advances in next-generation sequencing technology have driven deep insights into the nature and function of genetic variation in all organisms. However, unreliable sequencing base quality scores hinder accurate variant detection. Base quality score recalibration is typically performed to correct this issue. Existing recalibration strategies typically assume that sequencing errors can be reliably identified. However, this assumption generates a logical contradiction to the identification of new variants. This problem can be mitigated to a certain degree by having a good reference sequence or a known variant database, which limits their application to a handful of organisms (primarily humans). To solve this problem, we developed Lacer, and algorithm that recalibrates base quality scores without the assumption of current recalibrators. We demonstrate that Lacer reproduces or improves on the results of the current "gold standard" recalibrator, GATK. Moreover, Lacer can recalibrate data that cannot easily be recalibrated by other methods, such as data sets without a good reference sequence or variant database. In conclusion, we have developed an algorithm that provides effective and accurate recalibration of sequencing data for all organisms.
|
Add to Calendar ▼2014-09-11 00:00:002014-09-12 00:00:00Europe/LondonEnabling Genomic TechnologiesSELECTBIOenquiries@selectbiosciences.com
