Evaluating the performance of low-frequency variant calling tools for the detection of variants from short-read deep sequencing data.

Xiang, Xudong; Lu, Bowen; Song, Dongyang; Li, Jie; Shu, Kunxian; Pu, Dan

Xiang, Xudong; Lu, Bowen; Song, Dongyang; Li, Jie; Shu, Kunxian; Pu, Dan.

Afiliación

Xiang X; Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China.
Lu B; Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China.
Song D; Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China.
Li J; Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China.
Shu K; Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China. shukx@cqupt.edu.cn.
Pu D; Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China. pudan@cqupt.edu.cn.

Sci Rep ; 13(1): 20444, 2023 11 22.

Article en En | MEDLINE | ID: mdl-37993475

Asunto(s)

Investigación Biomédica; Polimorfismo de Nucleótido Simple; Algoritmos; Frecuencia de los Genes; Secuenciación de Nucleótidos de Alto Rendimiento/métodos

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Polimorfismo de Nucleótido Simple / Investigación Biomédica Idioma: En Revista: Sci Rep Año: 2023 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido

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