Cargando…
Improving protein domain classification for third-generation sequencing reads using deep learning
BACKGROUND: With the development of third-generation sequencing (TGS) technologies, people are able to obtain DNA sequences with lengths from 10s to 100s of kb. These long reads allow protein domain annotation without assembly, thus can produce important insights into the biological functions of the...
Autores principales: | Du, Nan, Shang, Jiayu, Sun, Yanni |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8033682/ https://www.ncbi.nlm.nih.gov/pubmed/33836667 http://dx.doi.org/10.1186/s12864-021-07468-7 |
Ejemplares similares
-
CHEER: HierarCHical taxonomic classification for viral mEtagEnomic data via deep leaRning
por: Shang, Jiayu, et al.
Publicado: (2020) -
HaploDMF: viral haplotype reconstruction from long reads via deep matrix factorization
por: Cai, Dehan, et al.
Publicado: (2022) -
HMM-FRAME: accurate protein domain classification for metagenomic sequences containing frameshift errors
por: Zhang, Yuan, et al.
Publicado: (2011) -
A Sequence-Based Novel Approach for Quality Evaluation of Third-Generation Sequencing Reads
por: Zhang, Wenjing, et al.
Publicado: (2019) -
PhaVIP: Phage VIrion Protein classification based on chaos game representation and Vision Transformer
por: Shang, Jiayu, et al.
Publicado: (2023)