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giab003.pdf (1.65 MB) an interactive catalogue of analysis methods for long-read sequencing data

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posted on 2021-03-30, 00:55 authored by SL Amarasinghe, ME Ritchie, Quentin GouilQuentin Gouil
© The Author(s) 2021. Published by Oxford University Press GigaScience. BACKGROUND: The data produced by long-read third-generation sequencers have unique characteristics compared to short-read sequencing data, often requiring tailored analysis tools for tasks ranging from quality control to downstream processing. The rapid growth in software that addresses these challenges for different genomics applications is difficult to keep track of, which makes it hard for users to choose the most appropriate tool for their analysis goal and for developers to identify areas of need and existing solutions to benchmark against. FINDINGS: We describe the implementation of, an open-source database that organizes the rapidly expanding collection of long-read data analysis tools and allows its exploration through interactive browsing and filtering. The current database release contains 478 tools across 32 categories. Most tools are developed in Python, and the most frequent analysis tasks include base calling, de novo assembly, error correction, quality checking/filtering, and isoform detection, while long-read single-cell data analysis and transcriptomics are areas with the fewest tools available. CONCLUSION: Continued growth in the application of long-read sequencing in genomics research positions the database as an essential resource that allows researchers to keep abreast of both established and emerging software to help guide the selection of the most relevant tool for their analysis needs.


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Oxford University Press (OUP)



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