Design of a Genome Wide siRNA Library Using an Artificial Neural Network  

Oleh:

Huesken, Dieter ; Lange, Joerg ; Mickanin, Craig ; Weiler, Jan ; Asselbergs, Fred ; Warner, Justin ; Meloon, Brian ; Engel, Sharon ; Rosenberg, Avi ; Cohen, Dalia ; Labow, Mark ; Reinhardt, Mischa ; Natt, Francois ; Hall, Jonathan

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: Nature Biotechnology: The Science and Business of Biotechnology vol. 23 no. 8 (Agu. 2005), page 995-1001.
Topik: network; genome-wide; RNA; neural network

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: NN9.4 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

The largest gene knock down experiments performed to date have used multiple short interfering / short hairpin (si/sh) RNAs per gene. To overcome this burden for design of genome wide siRNA library, we used the stugart neural net simulator to train algorithms in 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. BIPREDsi was used in the design of a genome wide siRNA collection with two potent 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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