Paper Info
Title
Predicting success of oligomerized pool engineering (OPEN) for zinc finger target site sequences.
Abstract
BACKGROUND: Precise and efficient methods for gene targeting are critical for detailed functional analysis of genomes and regulatory networks and for potentially improving the efficacy and safety of gene therapies. Oligomerized Pool ENgineering (OPEN) is a recently developed method for engineering C2H2 zinc finger proteins (ZFPs) designed to bind specific DNA sequences with high affinity and specificity in vivo. Because generation of ZFPs using OPEN requires considerable effort, a computational method for identifying the sites in any given gene that are most likely to be successfully targeted by this method is desirable. RESULTS: Analysis of the base composition of experimentally validated ZFP target sites identified important constraints on the DNA sequence space that can be effectively targeted using OPEN. Using alternate encodings to represent ZFP target sites, we implemented Naïve Bayes and Support Vector Machine classifiers capable of distinguishing "active" targets, i.e., ZFP binding sites that can be targeted with a high rate of success, from those that are "inactive" or poor targets for ZFPs generated using current OPEN technologies. When evaluated using leave-one-out cross-validation on a dataset of 135 experimentally validated ZFP target sites, the best Naïve Bayes classifier, designated ZiFOpT, achieved overall accuracy of 87% and specificity+ of 90%, with an ROC AUC of 0.89. When challenged with a completely independent test set of 140 newly validated ZFP target sites, ZiFOpT performance was comparable in terms of overall accuracy (88%) and specificity+ (92%), but with reduced ROC AUC (0.77). Users can rank potentially active ZFP target sites using a confidence score derived from the posterior probability returned by ZiFOpT. CONCLUSION: ZiFOpT, a machine learning classifier trained to identify DNA sequences amenable for targeting by OPEN-generated zinc finger arrays, can guide users to target sites that are most likely to function successfully in vivo, substantially reducing the experimental effort required. ZiFOpT is freely available and incorporated in the Zinc Finger Targeter web server (http://bindr.gdcb.iastate.edu/ZiFiT).
Year
DOI
Venue
2010
10.1186/1471-2105-11-543
BMC Bioinformatics
Keywords
DocType
Volume
zinc fingers,leave one out cross validation,binding site,zinc finger,algorithms,binding sites,machine learning,gene targeting,bioinformatics,artificial intelligence,microarrays,posterior probability,zinc finger protein,functional analysis,dna binding proteins,support vector machine,dna sequence,protein engineering,bayes classifier
Journal
11
Issue
ISSN
Citations 
1
1471-2105
9
PageRank 
References 
Authors
0.35
11
13
Name
Order
Citations
PageRank
Jeffry Sander1718.89
Deepak Reyon2191.81
Morgan Maeder3284.39
Jonathan E Foley4131.58
Stacey Thibodeau-Beganny5182.93
Xiaohong Li690.35
Maureen R. Regan790.35
Elizabeth Dahlborg890.35
Mathew J. Goodwin990.35
Fengli Fu10183.26
Daniel F Voytas11619.41
J Keith Joung12316.08
Drena Dobbs1342335.43