• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

临床新一代测序数据库:一种统一管理临床信息和基因变异以加速变异致病性分类的工具。

The Clinical Next-Generation Sequencing Database: A Tool for the Unified Management of Clinical Information and Genetic Variants to Accelerate Variant Pathogenicity Classification.

作者信息

Nishio Shin-Ya, Usami Shin-Ichi

机构信息

Department of Otorhinolaryngology, Shinshu University School of Medicine, Matsumoto City, Japan.

出版信息

Hum Mutat. 2017 Mar;38(3):252-259. doi: 10.1002/humu.23160. Epub 2017 Jan 11.

DOI:10.1002/humu.23160
PMID:28008688
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5324660/
Abstract

Recent advances in next-generation sequencing (NGS) have given rise to new challenges due to the difficulties in variant pathogenicity interpretation and large dataset management, including many kinds of public population databases as well as public or commercial disease-specific databases. Here, we report a new database development tool, named the "Clinical NGS Database," for improving clinical NGS workflow through the unified management of variant information and clinical information. This database software offers a two-feature approach to variant pathogenicity classification. The first of these approaches is a phenotype similarity-based approach. This database allows the easy comparison of the detailed phenotype of each patient with the average phenotype of the same gene mutation at the variant or gene level. It is also possible to browse patients with the same gene mutation quickly. The other approach is a statistical approach to variant pathogenicity classification based on the use of the odds ratio for comparisons between the case and the control for each inheritance mode (families with apparently autosomal dominant inheritance vs. control, and families with apparently autosomal recessive inheritance vs. control). A number of case studies are also presented to illustrate the utility of this database.

摘要

由于在变异致病性解读和大型数据集管理方面存在困难,包括多种公共人群数据库以及公共或商业疾病特异性数据库,新一代测序(NGS)的最新进展带来了新的挑战。在此,我们报告一种名为“临床NGS数据库”的新数据库开发工具,通过对变异信息和临床信息的统一管理来改进临床NGS工作流程。该数据库软件提供了两种变异致病性分类方法。其中第一种方法是基于表型相似性的方法。该数据库允许轻松地将每个患者的详细表型与同一基因突变在变异或基因水平的平均表型进行比较。还能够快速浏览具有相同基因突变的患者。另一种方法是基于使用比值比进行变异致病性分类的统计方法,用于每种遗传模式下病例与对照之间的比较(明显常染色体显性遗传家族与对照,以及明显常染色体隐性遗传家族与对照)。还展示了多个案例研究以说明该数据库的实用性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d46d/5324660/501575bf76ba/HUMU-38-252-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d46d/5324660/bfd45152128e/HUMU-38-252-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d46d/5324660/0b6d42843ed8/HUMU-38-252-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d46d/5324660/501575bf76ba/HUMU-38-252-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d46d/5324660/bfd45152128e/HUMU-38-252-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d46d/5324660/0b6d42843ed8/HUMU-38-252-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d46d/5324660/501575bf76ba/HUMU-38-252-g003.jpg

相似文献

1
The Clinical Next-Generation Sequencing Database: A Tool for the Unified Management of Clinical Information and Genetic Variants to Accelerate Variant Pathogenicity Classification.临床新一代测序数据库:一种统一管理临床信息和基因变异以加速变异致病性分类的工具。
Hum Mutat. 2017 Mar;38(3):252-259. doi: 10.1002/humu.23160. Epub 2017 Jan 11.
2
New workflow for classification of genetic variants' pathogenicity applied to hereditary recurrent fevers by the International Study Group for Systemic Autoinflammatory Diseases (INSAID).遗传性复发性发热疾病国际研究组(INSAID)应用于遗传复发性发热疾病的新的遗传变异致病性分类工作流程。
J Med Genet. 2018 Aug;55(8):530-537. doi: 10.1136/jmedgenet-2017-105216. Epub 2018 Mar 29.
3
ClinLabGeneticist: a tool for clinical management of genetic variants from whole exome sequencing in clinical genetic laboratories.临床实验室遗传学家:临床遗传实验室中用于对全外显子测序产生的基因变异进行临床管理的工具。
Genome Med. 2015 Jul 29;7(1):77. doi: 10.1186/s13073-015-0207-6.
4
FMFilter: A fast model based variant filtering tool.FMFilter:一种基于模型的快速变异过滤工具。
J Biomed Inform. 2016 Apr;60:319-27. doi: 10.1016/j.jbi.2016.02.013. Epub 2016 Feb 27.
5
SNooPer: a machine learning-based method for somatic variant identification from low-pass next-generation sequencing.SNooPer:一种基于机器学习从低深度下一代测序中识别体细胞变异的方法。
BMC Genomics. 2016 Nov 14;17(1):912. doi: 10.1186/s12864-016-3281-2.
6
SNVerGUI: a desktop tool for variant analysis of next-generation sequencing data.SNVerGUI:一种用于下一代测序数据分析的桌面工具。
J Med Genet. 2012 Dec;49(12):753-5. doi: 10.1136/jmedgenet-2012-101001. Epub 2012 Sep 28.
7
Genome analysis and knowledge-driven variant interpretation with TGex.基因组分析和基于 TGex 的知识驱动的变异解释。
BMC Med Genomics. 2019 Dec 30;12(1):200. doi: 10.1186/s12920-019-0647-8.
8
iVar, an Interpretation-Oriented Tool to Manage the Update and Revision of Variant Annotation and Classification. iVar,一种面向解释的工具,用于管理变体注释和分类的更新和修订。
Genes (Basel). 2021 Mar 8;12(3):384. doi: 10.3390/genes12030384.
9
Molecular diagnostic workflow, clinical interpretation of sequence variants, and data repository procedures in 140 individuals with familial cerebral cavernous malformations.140 例家族性脑静脉血管畸形患者的分子诊断工作流程、序列变异的临床解读和数据库管理程序。
Hum Mutat. 2019 Nov;40(11):e24-e36. doi: 10.1002/humu.23851.
10
VarElect: the phenotype-based variation prioritizer of the GeneCards Suite.VarElect:基因卡片套件中基于表型的变异优先级排序工具。
BMC Genomics. 2016 Jun 23;17 Suppl 2(Suppl 2):444. doi: 10.1186/s12864-016-2722-2.

引用本文的文献

1
Prevalence and Clinical Characteristics of -Associated Hearing Loss Identified in a Cohort of 7065 Japanese Patients with Hearing Loss.在7065名日本听力损失患者队列中确定的[具体相关因素]相关听力损失的患病率及临床特征 。 需注意,原文中“-Associated”部分内容缺失,我按照通用格式进行了翻译,你可根据实际补充完整准确内容。
Genes (Basel). 2025 Jan 23;16(2):123. doi: 10.3390/genes16020123.
2
Variant Impact Predictor database (VIPdb), version 2: trends from three decades of genetic variant impact predictors.变异影响预测器数据库(VIPdb),版本 2:三十年来遗传变异影响预测器的趋势。
Hum Genomics. 2024 Aug 28;18(1):90. doi: 10.1186/s40246-024-00663-z.
3
Variant Impact Predictor database (VIPdb), version 2: Trends from 25 years of genetic variant impact predictors.

本文引用的文献

1
Human genetic variation database, a reference database of genetic variations in the Japanese population.人类遗传变异数据库,一个关于日本人群体遗传变异的参考数据库。
J Hum Genet. 2016 Jun;61(6):547-53. doi: 10.1038/jhg.2016.12. Epub 2016 Feb 25.
2
Rare variant discovery by deep whole-genome sequencing of 1,070 Japanese individuals.通过对1070名日本个体进行全基因组深度测序发现罕见变异。
Nat Commun. 2015 Aug 21;6:8018. doi: 10.1038/ncomms9018.
3
Deafness gene variations in a 1120 nonsyndromic hearing loss cohort: molecular epidemiology and deafness mutation spectrum of patients in Japan.
变异影响预测数据库(VIPdb),版本2:25年基因变异影响预测的趋势
bioRxiv. 2024 Jun 28:2024.06.25.600283. doi: 10.1101/2024.06.25.600283.
4
The prevalence and clinical features of MYO7A-related hearing loss including DFNA11, DFNB2 and USH1B.MYO7A 相关听力损失(包括 DFNA11、DFNB2 和 USH1B)的患病率和临床特征。
Sci Rep. 2024 Apr 9;14(1):8326. doi: 10.1038/s41598-024-57415-1.
5
Frequency of the STRC-CATSPER2 deletion in STRC-associated hearing loss patients.STR-C 相关听力损失患者中 STRC-CATSPER2 缺失的频率。
Sci Rep. 2022 Jan 12;12(1):634. doi: 10.1038/s41598-021-04688-5.
6
Variants in CDH23 cause a broad spectrum of hearing loss: from non-syndromic to syndromic hearing loss as well as from congenital to age-related hearing loss.CDH23 基因突变可导致广泛的听力损失:从非综合征型到综合征型听力损失,以及从先天性到与年龄相关的听力损失。
Hum Genet. 2022 Apr;141(3-4):903-914. doi: 10.1007/s00439-022-02431-2. Epub 2022 Jan 12.
7
Genetic background in late-onset sensorineural hearing loss patients.遗传性听力损失患者的发病背景。
J Hum Genet. 2022 Apr;67(4):223-230. doi: 10.1038/s10038-021-00990-2. Epub 2021 Nov 26.
8
The genetic etiology of hearing loss in Japan revealed by the social health insurance-based genetic testing of 10K patients.日本通过对 10000 名患者的社会健康保险为基础的基因检测揭示的听力损失的遗传病因。
Hum Genet. 2022 Apr;141(3-4):665-681. doi: 10.1007/s00439-021-02371-3. Epub 2021 Oct 1.
9
Prevalence and clinical features of autosomal dominant and recessive TMC1-associated hearing loss.常染色体显性遗传和隐性 TMC1 相关听力损失的患病率和临床特征。
Hum Genet. 2022 Apr;141(3-4):929-937. doi: 10.1007/s00439-021-02364-2. Epub 2021 Sep 14.
10
A comparative analysis of genetic hearing loss phenotypes in European/American and Japanese populations.欧洲/美洲和日本人群中遗传性听力损失表型的比较分析。
Hum Genet. 2020 Oct;139(10):1315-1323. doi: 10.1007/s00439-020-02174-y. Epub 2020 May 7.
1120例非综合征性听力损失队列中的耳聋基因变异:日本患者的分子流行病学及耳聋突变谱
Ann Otol Rhinol Laryngol. 2015 May;124 Suppl 1:49S-60S. doi: 10.1177/0003489415575059. Epub 2015 Mar 18.
4
Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology.序列变异解读的标准与指南:美国医学遗传学与基因组学学会和分子病理学协会的联合共识推荐
Genet Med. 2015 May;17(5):405-24. doi: 10.1038/gim.2015.30. Epub 2015 Mar 5.
5
In silico prediction of splice-altering single nucleotide variants in the human genome.人类基因组中剪接改变单核苷酸变异的计算机模拟预测
Nucleic Acids Res. 2014 Dec 16;42(22):13534-44. doi: 10.1093/nar/gku1206.
6
Utilizing ethnic-specific differences in minor allele frequency to recategorize reported pathogenic deafness variants.利用次要等位基因频率的种族特异性差异对已报道的致病性耳聋变异进行重新分类。
Am J Hum Genet. 2014 Oct 2;95(4):445-53. doi: 10.1016/j.ajhg.2014.09.001. Epub 2014 Sep 25.
7
When a "disease-causing mutation" is not a pathogenic variant.当“致病突变”并非致病变异时。
Clin Chem. 2014 May;60(5):711-3. doi: 10.1373/clinchem.2013.215947. Epub 2013 Dec 20.
8
ClinVar: public archive of relationships among sequence variation and human phenotype.ClinVar:序列变异与人类表型之间关系的公共档案。
Nucleic Acids Res. 2014 Jan;42(Database issue):D980-5. doi: 10.1093/nar/gkt1113. Epub 2013 Nov 14.
9
Molecular genetic testing and the future of clinical genomics.分子遗传学检测与临床基因组学的未来。
Nat Rev Genet. 2013 Jun;14(6):415-26. doi: 10.1038/nrg3493.
10
Analysis of 6,515 exomes reveals the recent origin of most human protein-coding variants.对 6515 个外显子组的分析揭示了大多数人类蛋白质编码变异的近期起源。
Nature. 2013 Jan 10;493(7431):216-20. doi: 10.1038/nature11690. Epub 2012 Nov 28.