MIPgen: optimized modeling and design of molecular inversion probes for targeted resequencing - PubMed (original) (raw)
MIPgen: optimized modeling and design of molecular inversion probes for targeted resequencing
Evan A Boyle et al. Bioinformatics. 2014.
Abstract
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.
Availability and implementation: MIPgen is implemented in C++. Source code and accompanying Python scripts are available at http://shendurelab.github.io/MIPGEN/.
© The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
Figures
Fig. 1.
Model scores predict MIP performance. Both logistic and SVR modeling capture most of the variation in MIP performance. SVR scoring displays slightly greater power to discriminate adequately performing MIPs from poorly performing MIPs, as demonstrated by the higher AUC for the ROC curve conditioned on whether an MIP attained at least 10% of the median number of reads per MIP (upper left panel). Additionally, redesigning MIPs to the locus with MIPgen slightly increases the fraction of MIPs attaining levels of coverage at or below the level of average MIP coverage across sets (set to 1.0 in the upper right panel). Also shown are scatterplots of MIP scores versus realized read depth in the redesigned MIP set (lower panels)
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