Determining the accuracy of next generation sequencing based copy number variation analysis in Hereditary Breast and Ovarian Cancer

多重连接依赖探针扩增 拷贝数变化 多路复用 医学 乳腺癌 计算生物学 肿瘤科 生物 遗传学 生物信息学 癌症 基因 基因组 外显子
作者
Nihat Buğra Ağaoğlu,Busra Unal,Özlem Akgün Doğan,Payam Zolfagharian,Pari Sharifli,Aylin Karakurt,Burak Can Senay,Tugba Kizilboga,Jale Yıldız,Gizem Dinler Doğanay,Levent Doğanay
出处
期刊:Expert Review of Molecular Diagnostics [Informa]
卷期号:22 (2): 239-246 被引量:3
标识
DOI:10.1080/14737159.2022.2048373
摘要

Background Copy number variations (CNVs) are commonly associated with malignancies, including hereditary breast and ovarian cancers. Next generation sequencing (NGS) provides solutions for CNV detection in a single run. This study aimed to compare the accuracy of CNV detection by NGS analyzing tool against Multiplex Ligation Dependent Probe Amplification (MLPA).Research design and methods In total, 1276 cases were studied by targeted NGS panels and 691 cases (61 calls in 58 NGS-CNV positive and 633 NGS-CNV negative cases) were validated by MLPA.Results Twenty-eight (46%) NGS-CNV positive calls were consistent, whereas 33 (54%) calls showed discordance with MLPA. Two cases were detected as SNV by the NGS and CNV by the MLPA analysis. In total, 2% of the cases showed an MLPA confirmed CNV region in BRCA1/2. The results of this study showed that despite the high false positive call rate of the NGS-CNV algorithm, there were no false negative calls. The cases that were determined to be negative by the NGS and positive by the MLPA were actually carrying SNVs that were located on the MLPA probe binding sites.Conclusion The diagnostic performance of NGS-CNV analysis is promising; however, the need for confirmation by different methods remains.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
1秒前
闪闪明轩完成签到,获得积分10
1秒前
科研通AI6.1应助bin采纳,获得10
1秒前
1秒前
万能图书馆应助正直听白采纳,获得10
1秒前
Jiaocm完成签到,获得积分10
2秒前
量子星尘发布了新的文献求助10
2秒前
2秒前
梅子酒完成签到,获得积分10
3秒前
S1mon发布了新的文献求助10
3秒前
Lucas应助大橘子采纳,获得10
3秒前
少不入川完成签到,获得积分10
3秒前
丹丹完成签到,获得积分20
3秒前
虚幻的楼房完成签到 ,获得积分10
3秒前
blue完成签到,获得积分10
4秒前
大模型应助wjj采纳,获得10
4秒前
Yi发布了新的文献求助10
4秒前
4秒前
歪梨小菲发布了新的文献求助10
5秒前
5秒前
hujieshi发布了新的文献求助10
5秒前
彭于晏应助无心的思山采纳,获得10
6秒前
noob发布了新的文献求助10
6秒前
7秒前
7秒前
隐形曼青应助优秀乐松采纳,获得10
7秒前
XRT发布了新的文献求助10
7秒前
sxmt123456789发布了新的文献求助20
8秒前
8秒前
科研通AI6.1应助Stephanie采纳,获得10
8秒前
酷波er应助科研红绿灯采纳,获得10
8秒前
Huang发布了新的文献求助10
8秒前
8秒前
9秒前
安详的甜瓜完成签到,获得积分10
9秒前
9秒前
长命百岁发布了新的文献求助20
9秒前
9秒前
柍踏发布了新的文献求助10
10秒前
何必呢发布了新的文献求助10
11秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Encyclopedia of Forensic and Legal Medicine Third Edition 5000
Introduction to strong mixing conditions volume 1-3 5000
Agyptische Geschichte der 21.30. Dynastie 3000
Aerospace Engineering Education During the First Century of Flight 2000
„Semitische Wissenschaften“? 1510
从k到英国情人 1500
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 计算机科学 有机化学 物理 生物化学 纳米技术 复合材料 内科学 化学工程 人工智能 催化作用 遗传学 数学 基因 量子力学 物理化学
热门帖子
关注 科研通微信公众号,转发送积分 5769365
求助须知:如何正确求助?哪些是违规求助? 5579538
关于积分的说明 15421436
捐赠科研通 4903042
什么是DOI,文献DOI怎么找? 2638103
邀请新用户注册赠送积分活动 1586002
关于科研通互助平台的介绍 1541075