Genomics pipelines to investigate susceptibility in whole genome and exome sequenced data for variant discovery, annotation, prediction and genotyping.
Genomics pipelines to investigate susceptibility in whole genome and exome sequenced data for variant discovery, annotation, prediction and genotyping.
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作者：
Ahmed Z;Renart EG;Zeeshan S
在过去的几十年里，基因组学正在引领我们走向大胆的未来，并改变了我们对进行生物医学研究、研究疾病和理解人类社会多样性的看法。全基因组和外显子组测序（WGS/WES）是目前用于检测具有临床意义的遗传变异的两种最流行的下一代测序（NGS）方法。研究WGS/WES数据的变异发现和基因分型是基于不同数据分析应用的联系。虽然已经开发了一些生物信息学应用程序，其中许多是免费提供和出版的。及时发现和解释遗传变异仍然是诊断实验室和临床医生的挑战性任务。在这项研究中，我们有兴趣了解，评估和报告的解决方案，可用于处理不同长度和类型的NGS数据的变体，等位基因和单倍型的识别的当前状态。在范围内，我们查阅了过去10年发表的高质量同行评审文献。我们专注于独立和网络化的生物信息学应用程序，旨在有效处理WGS和WES数据，并支持基因变异发现，注释，预测和解释的下游分析。我们在本手稿中讨论了我们的发现，包括但不限于操作集、工作流程、数据处理、涉及的工具、技术和算法以及评估应用的局限性。
Over the last few decades, genomics is leading toward audacious future, and has been changing our views about conducting biomedical research, studying diseases, and understanding diversity in our society across the human species. The whole genome and exome sequencing (WGS/WES) are two of the most popular next-generation sequencing (NGS) methodologies that are currently being used to detect genetic variations of clinical significance. Investigating WGS/WES data for the variant discovery and genotyping is based on the nexus of different data analytic applications. Although several bioinformatics applications have been developed, and many of those are freely available and published. Timely finding and interpreting genetic variants are still challenging tasks among diagnostic laboratories and clinicians. In this study, we are interested in understanding, evaluating, and reporting the current state of solutions available to process the NGS data of variable lengths and types for the identification of variants, alleles, and haplotypes. Residing within the scope, we consulted high quality peer reviewed literature published in last 10 years. We were focused on the standalone and networked bioinformatics applications proposed to efficiently process WGS and WES data, and support downstream analysis for gene-variant discovery, annotation, prediction, and interpretation. We have discussed our findings in this manuscript, which include but not are limited to the set of operations, workflow, data handling, involved tools, technologies and algorithms and limitations of the assessed applications.
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影响因子：
4.3
作者：
Bragg LM;Stone G;Butler MK;Hugenholtz P;Tyson GW
通讯作者：
Tyson GW
影响因子：
10.6
作者：
Ahmed, Zeeshan;Zeeshan, Saman;Liang, Bruce T.
通讯作者：
Liang, Bruce T.
影响因子：
3.9
作者：
Chao, Elizabeth C.;Velasquez, Jonathan L.;Lipkin, Steven M.
通讯作者：
Lipkin, Steven M.
影响因子：
7
作者：
Brouard, Jean-Simon;Schenkel, Flavio;Bissonnette, Nathalie
通讯作者：
Bissonnette, Nathalie
DOI：
10.1016/0165-022x(86)90038-2
发表时间：
1986-12-01
影响因子：
--
作者：
ANSORGE, W;SPROAT, BS;SCHWAGER, C
通讯作者：
SCHWAGER, C