Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, USA.
Methods Mol Biol. 2024;2812:235-242. doi: 10.1007/978-1-0716-3886-6_13.
Identification of somatic indels remains a major challenge in cancer genomic analysis and is rarely attempted for tumor-only RNA-Seq due to the lack of matching normal data and the complexity of read alignment, which involves mapping of both splice junctions and indels. In this chapter, we introduce RNAIndel, a software tool designed for identifying somatic coding indels using tumor-only RNA-Seq. RNAIndel performs indel realignment and employs a machine learning model to estimate the probability of a coding indel being somatic, germline, or artifact. Its high accuracy has been validated in RNA-Seq generated from multiple tumor types.
鉴定体细胞插入缺失仍然是癌症基因组分析的主要挑战，由于缺乏匹配的正常数据和读段比对的复杂性（涉及剪接接头和插入缺失的映射），因此很少针对仅肿瘤 RNA-Seq 尝试进行鉴定。在本章中，我们介绍了 RNAIndel，这是一种用于使用仅肿瘤 RNA-Seq 鉴定体细胞编码插入缺失的软件工具。RNAIndel 执行插入缺失重比对，并使用机器学习模型来估计编码插入缺失是体细胞、种系或伪影的概率。它在来自多种肿瘤类型的 RNA-Seq 中的高准确性已经得到了验证。
