.png){kind=icon}
返回
Targeted variant detection using unaligned RNA-Seq reads
.png){kind=icon}
OA
AI
DOI：10.26508/lsa.201900336.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Mutations identified in acute myeloid leukemia patients are useful for prognosis and for selecting targeted therapies. Detection of such mutations using next-generation sequencing data requires a computationally intensive read mapping step followed by several variant calling methods. Targeted mutation identification drastically shifts the usual tradeoff between accuracy and performance by concentrating all computations over a small portion of sequence space. Here, we present km, an efficient approach leveraging k-mer decomposition of reads to identify targeted mutations. Our approach is versatile, as it can detect single-base mutations, several types of insertions and deletions, as well as fusions. We used two independent cohorts (The Cancer Genome Atlas and Leucegene) to show that mutation detection by km is fast, accurate, and mainly limited by sequencing depth. Therefore, km allows the establishment of fast diagnostics from next-generation sequencing data and could be suitable for clinical applications.
Keyword:
ACUTE MYELOID-LEUKEMIA
NPM1 MUTATIONS
ADULT PATIENTS
AML
NUP98-NSD1
IMPACT
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
2.9
论文数:
1.7K
被引数:
4.3K
机构
引用论文
分享收藏Identification of MYC mutations in acute myeloid leukemias with NUP98-NSD1 translocations.png)
LEUKEMIA
LEUKEMIAIF13.4
PREAI
分享收藏Orthopaedic Surgery Transfusion Haemoglobin European Overview: the OSTEO study (extended abstract)
PREAI
分享收藏
查看更多内容