Deep learning for predicting 16S rRNA gene copy number.

Miao, Jiazheng; Chen, Tianlai; Misir, Mustafa; Lin, Yajuan

Miao, Jiazheng; Chen, Tianlai; Misir, Mustafa; Lin, Yajuan.

Afiliación

Miao J; Division of Applied and Natural Sciences, Duke Kunshan University, Suzhou, China.
Chen T; Department of Biomedical Informatics, Harvard Medical School, Boston, USA.
Misir M; Division of Applied and Natural Sciences, Duke Kunshan University, Suzhou, China.
Lin Y; Department of Biomedical Engineering, Duke University, Durham, USA.

Sci Rep ; 14(1): 14282, 2024 06 20.

Article en En | MEDLINE | ID: mdl-38902329

ABSTRACT

ABSTRACT

Culture-independent 16S rRNA gene metabarcoding is a commonly used method for microbiome profiling. To achieve more quantitative cell fraction estimates, it is important to account for the 16S rRNA gene copy number (hereafter 16S GCN) of different community members. Currently, there are several bioinformatic tools available to estimate the 16S GCN values, either based on taxonomy assignment or phylogeny. Here we present a novel approach ANNA16, Artificial Neural Network Approximator for 16S rRNA gene copy number, a deep learning-based method that estimates the 16S GCN values directly from the 16S gene sequence strings. Based on 27,579 16S rRNA gene sequences and gene copy number data from the rrnDB database, we show that ANNA16 outperforms the commonly used 16S GCN prediction algorithms. Interestingly, Shapley Additive exPlanations (SHAP) shows that ANNA16 can identify unexpected informative positions in 16S rRNA gene sequences without any prior phylogenetic knowledge, which suggests potential applications beyond 16S GCN prediction.

Asunto(s)

Aprendizaje Profundo; Dosificación de Gen; Filogenia; ARN Ribosómico 16S; ARN Ribosómico 16S/genética; Biología Computacional/métodos; Algoritmos; Microbiota/genética; Redes Neurales de la Computación

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Filogenia / ARN Ribosómico 16S / Dosificación de Gen / Aprendizaje Profundo Idioma: En Revista: Sci Rep Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido

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