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FORGe: prioritizing variants for graph genomes

By Jacob Pritt, Nae-Chyun Chen, Ben Langmead

Posted 30 Apr 2018
bioRxiv DOI: 10.1101/311720 (published DOI: 10.1186/s13059-018-1595-x)

There is growing interest in using genetic variants to augment the reference genome into a "graph genome" to improve read alignment accuracy and reduce allelic bias. While adding a variant has the positive effect of removing an undesirable alignment-score penalty, it also increases both the ambiguity of the reference genome and the cost of storing and querying the genome index. We introduce methods and a software tool called FORGe for modeling these effects and prioritizing variants accordingly. We show that FORGe enables a range of advantageous and measurable trade-offs between accuracy and computational overhead.

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