Work

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

Year: 2024

Type: article

Abstract: 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.

Source: Frontiers in Genetics

DOI: 10.3389/fgene.2024.1443532

Authors: Qingwen Li, Chen Sun, Daqian Wang, Jizhong Lou

Institutions: Chinese Academy of Sciences, Institute of Biophysics, University of Chinese Academy of Sciences

Language: English

Topic: Genomics and Phylogenetic Studies

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: gold

Estimated APC paid: $3,295

Funder: Chinese Academy of Sciences