IMMerge: merging imputation data at scale

SUMMARY: Genomic data are often processed in batches and analyzed together to save time. However, it is challenging to combine multiple large VCFs and properly handle imputation quality and missing variants due to the limitations of available tools. To address these concerns, we developed IMMerge, a...

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Detalles Bibliográficos
Autores principales: Zhu, Wanying, Chen, Hung-Hsin, Petty, Alexander S, Petty, Lauren E, Polikowsky, Hannah G, Gamazon, Eric R, Below, Jennifer E, Highland, Heather M
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Applications Note
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9805583/
https://www.ncbi.nlm.nih.gov/pubmed/36413071
http://dx.doi.org/10.1093/bioinformatics/btac750
Descripción
Sumario:SUMMARY: Genomic data are often processed in batches and analyzed together to save time. However, it is challenging to combine multiple large VCFs and properly handle imputation quality and missing variants due to the limitations of available tools. To address these concerns, we developed IMMerge, a Python-based tool that takes advantage of multiprocessing to reduce running time. For the first time in a publicly available tool, imputation quality scores are correctly combined with Fisher’s z transformation. AVAILABILITY AND IMPLEMENTATION: IMMerge is an open-source project under MIT license. Source code and user manual are available at https://github.com/belowlab/IMMerge.

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