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ORIGINAL RESEARCH article
Front. Genet.
Sec. Computational Genomics
Volume 15 - 2024 |
doi: 10.3389/fgene.2024.1443532
GCRTcall: a Transformer based basecaller for nanopore RNA sequencing enhanced by gated convolution and relative position embedding via joint loss training
Provisionally accepted- 1 Institute of Biophysics, Chinese Academy of Sciences (CAS), Beijing, China
- 2 Beijing Polyseq Biotech Co. Ltd., Beijing, China
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.
Keywords: Basecaller, Nanopore RNA sequencing, transformer, Gated convolution, Relative position embedding
Received: 04 Jun 2024; Accepted: 04 Nov 2024.
Copyright: © 2024 Li, Sun, Wang and Lou. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Jizhong Lou, Institute of Biophysics, Chinese Academy of Sciences (CAS), Beijing, China
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