bioRxivSystems BiologyPreprint
Defining protein variant functions using high-complexity mutagenesis libraries and enhanced mutant detection software ASMv1.0
Xiaoping YangiD, Andrew Lee HongiD, Ted Sharpe, Andrew O. GiacomelliiD, Robert E. LintneriD, Douglas Alan, Thomas Green, Tikvah K. HayesiD, Federica PiccioniiD, Briana Fritchman, Hinako KawabeiD, Edith Sawyer, Luke Sprenkle, Benjamin P. LeeiD, Nicole S. PerskyiD, Adam BrowniD, Heidi E. GreulichiD, Andrew J. AguirreiD, Matthew M. MeyersoniD, William C. HahniD, Cory M. JohannesseniD, David E. RootiD
Abstract
Pooled variant expression libraries can test the phenotypes of thousands of variants of a gene in a single multiplexed experiment. In a library encoding all single-amino-acid substitutions of a protein, each variant differs from its reference only at a single codon-position located anywhere along the coding sequence. Consequently, accurately identifying these variants by sequencing is a major technical challenge. A popular but expensive brute-force approach is to divide the pool of variants into multiple smaller sub-libraries that each contains variants of a small region and that must each be constructed and screened individually, but that can then be PCR-amplified and fully sequenced with a single read to allow direct readout of variant abundance. Here we present an approach to screen very large variant libraries with mutations spanning a wide region in a single pool, including library design criteria and mutant-detection algorithms that permit reliable calling and counting of variants from large-scale sequencing data.
- assay: proteomics
- research focus: variant calling
- research focus: benchmarking
- research focus: software / tool
- approach: statistical model
- approach: machine learning (classical)
- Citations
- 12
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