Department of Computer Science, Colorado State University, 1873 Campus Delivery, Fort Collins, CO, 80523-1873, USA.
Department of Biology, Colorado State University, 1878 Campus Delivery, Fort Collins, CO, 80523-1878, USA.
BMC Bioinformatics. 2022 Apr 20;23(1):142. doi: 10.1186/s12859-022-04686-y.
Despite recent progress in basecalling of Oxford nanopore DNA sequencing data, its wide adoption is still being hampered by its relatively low accuracy compared to short read technologies. Furthermore, very little of the recent research was focused on basecalling of RNA data, which has different characteristics than its DNA counterpart.
We fill this gap by benchmarking a fully convolutional deep learning basecalling architecture with improved performance compared to Oxford nanopore's RNA basecallers.
The source code for our basecaller is available at: https://github.com/biodlab/RODAN .
尽管牛津纳米孔 DNA 测序数据的碱基调用技术最近取得了进展，但与短读长技术相比，其相对较低的准确性仍阻碍了其广泛应用。此外，最近的研究很少关注 RNA 数据的碱基调用，因为 RNA 数据与 DNA 数据有不同的特点。
我们通过基准测试一个完全卷积深度学习碱基调用架构来填补这一空白，该架构的性能优于牛津纳米孔的 RNA 碱基调用器。
我们的碱基调用器的源代码可在以下网址获得：https://github.com/biodlab/RODAN 。
