Kovaka Sam, Hook Paul W, Jenike Katharine M, Shivakumar Vikram, Morina Luke B, Razaghi Roham, Timp Winston, Schatz Michael C
Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.
Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
Nat Methods. 2025 Apr;22(4):681-691. doi: 10.1038/s41592-025-02631-4. Epub 2025 Mar 28.
Nanopore signal analysis enables detection of nucleotide modifications from native DNA and RNA sequencing, providing both accurate genetic or transcriptomic and epigenetic information without additional library preparation. At present, only a limited set of modifications can be directly basecalled (for example, 5-methylcytosine), while most others require exploratory methods that often begin with alignment of nanopore signal to a nucleotide reference. We present Uncalled4, a toolkit for nanopore signal alignment, analysis and visualization. Uncalled4 features an efficient banded signal alignment algorithm, BAM signal alignment file format, statistics for comparing signal alignment methods and a reproducible de novo training method for k-mer-based pore models, revealing potential errors in Oxford Nanopore Technologies' state-of-the-art DNA model. We apply Uncalled4 to RNA 6-methyladenine (m6A) detection in seven human cell lines, identifying 26% more modifications than Nanopolish using m6Anet, including in several genes where m6A has known implications in cancer. Uncalled4 is available open source at github.com/skovaka/uncalled4 .
纳米孔信号分析能够从天然DNA和RNA测序中检测核苷酸修饰,无需额外的文库制备即可提供准确的遗传或转录组以及表观遗传信息。目前,只有有限的一组修饰可以直接进行碱基识别(例如,5-甲基胞嘧啶),而大多数其他修饰则需要探索性方法,这些方法通常从将纳米孔信号与核苷酸参考序列比对开始。我们展示了Uncalled4,这是一个用于纳米孔信号比对、分析和可视化的工具包。Uncalled4具有高效的带状信号比对算法、BAM信号比对文件格式、用于比较信号比对方法的统计数据以及基于k-mer的孔模型的可重复从头训练方法,揭示了牛津纳米孔技术公司最先进的DNA模型中的潜在错误。我们将Uncalled4应用于七种人类细胞系中的RNA 6-甲基腺嘌呤(m6A)检测,与使用m6Anet的Nanopolish相比,发现的修饰多26%,包括在几个m6A对癌症有已知影响的基因中。Uncalled4可在github.com/skovaka/uncalled4上开源获取。