Minimizing Reference Bias with an Impute-First Approach
Kavya Vaddadi, Taher Mun, Ben Langmead
The paper introduces an impute-first alignment framework that improves variant calling accuracy and efficiency by using a personalized diploid reference.
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Johns Hopkins UniversityORCID 0000-0002-3588-0883Unclaimed ORCID profile · follow to get notified of new preprints
6 papers
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Kavya Vaddadi, Taher Mun, Ben Langmead
The paper introduces an impute-first alignment framework that improves variant calling accuracy and efficiency by using a personalized diploid reference.
Taher Mun, Kavya Vaddadi, Ben Langmead
The paper introduces rowbowt, a tool for pangenomic genotyping using a marker array to improve accuracy and efficiency over existing methods.
Taher Mun, Nae-Chyun Chen, Ben Langmead
LevioSAM lifts SAM/BAM alignments between references using VCF files, achieving faster performance with multi-threading.
Nae-Chyun Chen, Brad Solomon, Taher Mun…·Ben Langmead
The paper introduces a reference flow alignment method that reduces reference bias using multiple population reference genomes, achieving similar accuracy to graph aligners with lower memory usage and faster speed.
Alan Kuhnle, Taher Mun, Christina A. Boucher…·Giovanni Manzini
The paper introduces an efficient method for constructing a complete index for pan-genomics read alignment, improving memory and time efficiency over existing methods.
Christina A. Boucher, Travis Gagie, Alan Kuhnle…·Taher Mun
The paper introduces a preprocessing algorithm called prefix-free parsing to efficiently build compressed Burrows-Wheeler Transform indexes for large genomic datasets.