GCRTcall: a transformer based basecaller for nanopore RNA sequencing enhanced by gated convolution and relative position embedding via joint loss training.

Autor:	Li, Qingwen, Sun, Chen, Wang, Daqian, Lou, Jizhong
Předmět:	RNA sequencing TRANSFORMER models DNA sequencing TRANSCRIPTOMES RNA
Zdroj:	Frontiers in Genetics; 2024, p1-8, 8p
Abstrakt:	Nanopore sequencing, renowned for its ability to sequence DNA and RNA directly with read lengths extending to several hundred kilobases or even megabases, holds significant promise in fields like transcriptomics and other omics studies. Despite its potential, the technology's limited accuracy in base identification has restricted its widespread application. Although many algorithms have been developed to improve DNA decoding, advancements in RNA sequencing remain limited. Addressing this challenge, we introduce GCRTcall, a novel approach integrating Transformer architecture with gated convolutional networks and relative positional encoding for RNA sequencing signal decoding. Our evaluation demonstrates that GCRTcall achieves state-of-the-art performance in RNA basecalling. [ABSTRACT FROM AUTHOR]
Databáze:	Complementary Index
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