Lerna: transformer architectures for configuring error correction tools for short- and long-read genome sequencing

BACKGROUND: Sequencing technologies are prone to errors, making error correction (EC) necessary for downstream applications. EC tools need to be manually configured for optimal performance. We find that the optimal parameters (e.g., k-mer size) are both tool- and dataset-dependent. Moreover, evaluat...

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Detalles Bibliográficos
Autores principales: Sharma, Atul, Jain, Pranjal, Mahgoub, Ashraf, Zhou, Zihan, Mahadik, Kanak, Chaterji, Somali
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Methodology Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8734100/
https://www.ncbi.nlm.nih.gov/pubmed/34991450
http://dx.doi.org/10.1186/s12859-021-04547-0

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8734100/
https://www.ncbi.nlm.nih.gov/pubmed/34991450
http://dx.doi.org/10.1186/s12859-021-04547-0

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