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Design of a genome-wide siRNA library using an artificial neural network

A Corrigendum to this article was published on 01 August 2006

A Corrigendum to this article was published on 01 October 2005

Abstract

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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Figure 1: Thirty seven siRNAs were designed and tested against three human targets (for siRNA sequences, see Supplementary Table 1).
Figure 2: Management of data during the training and testing of artificial neural networks (ANN).
Figure 3: Performance of the algorithm with respect to both ranking 36 siRNAs of various potencies and selecting potent siRNAs.

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Acknowledgements

We thank Erik Bury for preliminary work on development of the algorithm. We also thank B. Csordas, V. Drephal, A. Garnier, S. Gfeller, D. Kirk, B. Pak, R. Theurillat, R. Widmer, S. Zhao and W. Zuercher for excellent technical support. We thank J. Mestan and F. Hofmann for E2 antibodies. We thank P. Weiss for helpful discussions and J. Hunziker and R. Haener for carefully reading the manuscript.

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Correspondence to Jonathan Hall.

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Competing interests

D.H., J.L., C.M., J.W., F.A., J.W., M.L., M.R., D.C., F.N. and J.H. are employees of Novartis. A.R., S.E. and B.M. are current or former employees of Compugen.

Supplementary information

Supplementary Fig. 1

Dual reporter assay used to generate a data set for training of neural networks. (PDF 473 kb)

Supplementary Table 1

siRNAs targeted to endogenous genes and used in experiments displayed in figures 1a-i and figure 3a-c. (PDF 48 kb)

Supplementary Table 2

cDNA insert sequences used for siRNA design. (PDF 131 kb)

Supplementary Table 3

siRNA sequences and their normalized inhibitory activities used for training or testing of ANNs. (PDF 618 kb)

Supplementary Table 4

Single nucleotide (nt) motifs overrepresented with significance lower than 5% in the 200 most potent and 200 least potent siRNAs were taken from Supplementary Table 3. (PDF 56 kb)

Supplementary Table 5

siRNAs targeted to endogenous genes and used in experiments displayed in figures 3d-k. (PDF 51 kb)

Supplementary Table 6

Q-PCR primer sequences. (PDF 41 kb)

Supplementary Table 7

ABI Assays-on-demand. (PDF 43 kb)

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Huesken, D., Lange, J., Mickanin, C. et al. Design of a genome-wide siRNA library using an artificial neural network. Nat Biotechnol 23, 995–1001 (2005). https://doi.org/10.1038/nbt1118

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