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115384 |
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|a dc
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|a Puccinelli, Robert
|e author
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|a Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
|e contributor
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|a Massachusetts Institute of Technology. Department of Mathematics
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|a Orenstein, Yaron
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|a Berger Leighton, Bonnie
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|a Kim, Ryan
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|a Fordyce, Polly
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|a Orenstein, Yaron
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|a Berger Leighton, Bonnie
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|a Optimized Sequence Library Design for Efficient In Vitro Interaction Mapping
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|b Elsevier,
|c 2018-05-16T13:17:47Z.
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|z Get fulltext
|u http://hdl.handle.net/1721.1/115384
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|a Sequence libraries that cover all k-mers enable universal, unbiased measurements of binding to both oligonucleotides and peptides. While the number of k-mers grows exponentially in k, space on all experimental platforms is limited. Here, we shrink k-mer library sizes by using joker characters, which represent all characters in the alphabet simultaneously. We present the JokerCAKE (joker covering all k-mers) algorithm for generating a short sequence such that each k-mer appears at least p times with at most one joker character per k-mer. By running our algorithm on a range of parameters and alphabets, we show that JokerCAKE produces near-optimal sequences. Moreover, through comparison with data from hundreds of DNA-protein binding experiments and with new experimental results for both standard and JokerCAKE libraries, we establish that accurate binding scores can be inferred for high-affinity k-mers using JokerCAKE libraries. JokerCAKE libraries allow researchers to search a significantly larger sequence space using the same number of experimental measurements and at the same cost. We present a new compact sequence design that covers all k-mers utilizing joker characters and develop an efficient algorithm to generate such designs. We show through simulations and experimental validation that these sequence designs are useful for identifying high-affinity binding sites at significantly reduced cost and space. Keywords: sequence libraries; microarray design; de Bruijn graph
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|a National Institutes of Health (U.S.) (Grant R01GM081871)
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|a Article
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| 773 |
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|t Cell Systems
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