Ahmed Zeeshan, Zeeshan Saman, Mendhe Dinesh, Dong XinQi
Institute for Health, Health Care Policy and Aging Research, Rutgers, The State University of New Jersey, New Brunswick, New Jersey, USA.
Department of Medicine, Rutgers Robert Wood Johnson Medical School, Rutgers Biomedical and Health Sciences, New Brunswick, New Jersey, USA.
Clin Transl Med. 2020 Jan;10(1):297-318. doi: 10.1002/ctm2.28.
We are entering the era of personalized medicine in which an individual's genetic makeup will eventually determine how a doctor can tailor his or her therapy. Therefore, it is becoming critical to understand the genetic basis of common diseases, for example, which genes predispose and rare genetic variants contribute to diseases, and so on. Our study focuses on helping researchers, medical practitioners, and pharmacists in having a broad view of genetic variants that may be implicated in the likelihood of developing certain diseases. Our focus here is to create a comprehensive database with mobile access to all available, authentic and actionable genes, SNPs, and classified diseases and drugs collected from different clinical and genomics databases worldwide, including Ensembl, GenCode, ClinVar, GeneCards, DISEASES, HGMD, OMIM, GTR, CNVD, Novoseek, Swiss-Prot, LncRNADisease, Orphanet, GWAS Catalog, SwissVar, COSMIC, WHO, and FDA. We present a new cutting-edge gene-SNP-disease-drug mobile database with a smart phone application, integrating information about classified diseases and related genes, germline and somatic mutations, and drugs. Its database includes over 59 000 protein-coding and noncoding genes; over 67 000 germline SNPs and over a million somatic mutations reported for over 19 000 protein-coding genes located in over 1000 regions, published with over 3000 articles in over 415 journals available at the PUBMED; over 80 000 ICDs; over 123 000 NDCs; and over 100 000 classified gene-SNP-disease associations. We present an application that can provide new insights into the information about genetic basis of human complex diseases and contribute to assimilating genomic with phenotypic data for the availability of gene-based designer drugs, precise targeting of molecular fingerprints for tumor, appropriate drug therapy, predicting individual susceptibility to disease, diagnosis, and treatment of rare illnesses are all a few of the many transformations expected in the decade to come.
我们正在进入个性化医疗时代，在这个时代，个人的基因构成最终将决定医生如何量身定制治疗方案。因此，了解常见疾病的遗传基础变得至关重要，例如，哪些基因易患疾病以及罕见的基因变异如何导致疾病等等。我们的研究致力于帮助研究人员、医生和药剂师全面了解可能与某些疾病发生可能性相关的基因变异。我们在此的重点是创建一个全面的数据库，并通过移动设备访问所有可用的、真实且可操作的基因、单核苷酸多态性（SNP）以及从全球不同临床和基因组数据库收集的分类疾病与药物信息，这些数据库包括Ensembl、GenCode、ClinVar、GeneCards、DISEASES、HGMD、OMIM、GTR、CNVD、Novoseek、Swiss - Prot、LncRNADisease、Orphanet、GWAS Catalog、SwissVar、COSMIC、世界卫生组织（WHO）和美国食品药品监督管理局（FDA）。我们通过一款智能手机应用程序展示了一个全新的前沿基因 - SNP - 疾病 - 药物移动数据库，该数据库整合了有关分类疾病和相关基因、种系和体细胞突变以及药物的数据。其数据库包含超过59000个蛋白质编码基因和非编码基因；超过67000个种系SNP以及针对位于1000多个区域的超过19000个蛋白质编码基因报告的100多万个体细胞突变，这些信息发表在超过415种期刊上的3000多篇文章中，可在PubMed上获取；超过80000个国际疾病分类代码（ICD）；超过123000个国家药品代码（NDC）；以及超过100000个分类的基因 - SNP - 疾病关联。我们展示的应用程序能够为人类复杂疾病的遗传基础信息提供新的见解，并有助于将基因组数据与表型数据相结合，以实现基于基因的定制药物、肿瘤分子指纹的精确靶向、适当的药物治疗、预测个体疾病易感性、疾病诊断以及罕见疾病治疗等目标，这些都是未来十年有望实现的众多转变中的一部分。
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