Nature Biotechnology
23, 995 - 1001 (2005)
Published online: 17 July 2005; | doi:10.1038/nbt1118
There are Corrigenda (October 2005) and (August 2006) associated with this Letter. Please see the PDF for details.
Design of a genome-wide siRNA library using an artificial neural networkDieter Huesken1, 6, Joerg Lange1, 6, Craig Mickanin2, Jan Weiler1, Fred Asselbergs1, Justin Warner2, Brian Meloon3, 5, Sharon Engel4, Avi Rosenberg4, Dalia Cohen2, Mark Labow2, Mischa Reinhardt1, François Natt1
& Jonathan Hall11
Novartis Institutes for BioMedical Research, Genome and Proteome Sciences, CH-4002
Basel, Switzerland. 2
Novartis Institutes for BioMedical Research, Inc., Genome and Proteome Sciences, 250 Massachusetts Avenue, Cambridge, Massachusetts
02139, USA. 3
Compugen USA, Inc., 7 Centre Drive Suite 9, Jamesburg, New Jersey
08831, USA. 4
Computational Life Sciences R&D, Compugen Ltd., 72 Pinchas Rosen St., Tel Aviv
69512, Israel. 5
Present address: Campbell & Company, Inc., 210 West Pennsylvania Ave., Suite 770, Towson, Maryland
21204, USA. 6
These authors contributed equally.
Correspondence should be addressed to Jonathan Hall jonathan.hall@Novartis.com The largest gene knock-down experiments performed to date have used multiple short interfering/short hairpin (si/sh)RNAs per gene1,
2,
3. To overcome this burden for design of a genome-wide siRNA library, we used the Stuttgart Neural Net Simulator to train algorithms on a data set of 2,182 randomly selected siRNAs targeted to 34 mRNA species, assayed through a high-throughput fluorescent reporter gene system. The algorithm, (BIOPREDsi), reliably predicted activity of 249 siRNAs of an independent test set (Pearson coefficient r = 0.66) and siRNAs targeting endogenous genes at mRNA and protein levels. Neural networks trained on a complementary 21-nucleotide (nt) guide sequence were superior to those trained on a 19-nt sequence. BIOPREDsi was used in the design of a genome-wide siRNA collection with two potent siRNAs per gene. When this collection of 50,000 siRNAs was used to identify genes involved in the cellular response to hypoxia, two of the most potent hits were the key hypoxia transcription factors HIF1A and ARNT.
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