Abstract: There are many situations where oligonucleotides that efficiently bind a target DNA or RNA are desired. These oligonucleotides can be used for a variety of purposes, including antisense, diagnostics, and array generation. While researchers have worked for many years to identify algorithms and methods for predicting the oligonucleotides that will bind the target with the highest efficiency, better prediction methods are needed. Disclosed are methods, articles, machines, and compositions that aid in identifying oligonucleotides and sets of oligonucleotides that will efficiently bind a target nucleic acid molecule. Also disclosed are optimized sets of oligonucleotides that bind HIV-1 genomic RNA or DNA, such as the GAG RNA, and methods of using them.

Abstract: An artificial neural network system for analyzing sequence motif content for prediction of antisense oligonucleotide-target activity is disclosed. The system was developed for high specificity predictions, with cross-validation used to rigorously test against the database that was used in the development of the system. The system is able to choose effective oligonucleotides leading to >75% reduction in RNA target expression with >55% accuracy. This is in contrast to <10% success rate for trial-and-error oligonucleotide selection. Thus, the program provides a five-fold reduction in the number of oligonucleotides to be screened in vivo to find effective targets.

Abstract: Methods are disclosed for selecting oligonucleotide probes with high target affinity. These methods include combining several types of thermodynamic criteria or nucleotide composition criteria for the selection of efficient oligonucleotide probes. The development of these methods was based on statistical analysis of datasets of hybridization or antisense experiments. The methods described here demonstrated variable efficiency in practice, however any or all of them can be used depending on the task of probe design.