Cargando…
Exploring the Consistency of the Quality Scores with Machine Learning for Next-Generation Sequencing Experiments
BACKGROUND: Next-generation sequencing enables massively parallel processing, allowing lower cost than the other sequencing technologies. In the subsequent analysis with the NGS data, one of the major concerns is the reliability of variant calls. Although researchers can utilize raw quality scores o...
Autores principales: | Cosgun, Erdal, Oh, Min |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7061114/ https://www.ncbi.nlm.nih.gov/pubmed/32219145 http://dx.doi.org/10.1155/2020/8531502 |
Ejemplares similares
-
The Quality Sequencing Minimum (QSM): providing comprehensive, consistent, transparent next generation sequencing data quality assurance
por: Mahamdallie, Shazia, et al.
Publicado: (2018) -
seqQscorer: automated quality control of next-generation sequencing data using machine learning
por: Albrecht, Steffen, et al.
Publicado: (2021) -
Compression of next-generation sequencing quality scores using memetic algorithm
por: Zhou, Jiarui, et al.
Publicado: (2014) -
Comparing nominal and real quality scores on next-generation sequencing genotype calls
por: Stram, Alexander H
Publicado: (2011) -
Next Generation Sequencing and Machine Learning Technologies Are Painting the Epigenetic Portrait of Glioblastoma
por: Jovčevska, Ivana
Publicado: (2020)