Boyle Evan A, O'Roak Brian J, Martin Beth K, Kumar Akash, Shendure Jay
Department of Genome Sciences, University of Washington, Seattle, WA 98105 and Department of Molecular & Medical Genetics, Oregon Health & Science University, Portland, OR 97239, USA.
Bioinformatics. 2014 Sep 15;30(18):2670-2. doi: 10.1093/bioinformatics/btu353. Epub 2014 May 26.
Molecular inversion probes (MIPs) enable cost-effective multiplex targeted gene resequencing in large cohorts. However, the design of individual MIPs is a critical parameter governing the performance of this technology with respect to capture uniformity and specificity. MIPgen is a user-friendly package that simplifies the process of designing custom MIP assays to arbitrary targets. New logistic and SVM-derived models enable in silico predictions of assay success, and assay redesign exhibits improved coverage uniformity relative to previous methods, which in turn improves the utility of MIPs for cost-effective targeted sequencing for candidate gene validation and for diagnostic sequencing in a clinical setting.
MIPgen is implemented in C++. Source code and accompanying Python scripts are available at http://shendurelab.github.io/MIPGEN/.
分子倒置探针（MIP）可在大型队列中实现经济高效的多重靶向基因重测序。然而，单个MIP的设计是决定该技术在捕获均匀性和特异性方面性能的关键参数。MIPgen是一个用户友好的软件包，可简化针对任意靶标的定制MIP检测的设计过程。新的逻辑回归和支持向量机衍生模型能够在计算机上预测检测成功率，并且与以前的方法相比，检测重新设计显示出更好的覆盖均匀性，这反过来提高了MIP在用于候选基因验证的经济高效靶向测序以及临床环境中的诊断测序方面的实用性。
MIPgen用C++实现。源代码和配套的Python脚本可在http://shendurelab.github.io/MIPGEN/获取。
