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Kosuri, S.

Publications and source records attributed to Kosuri, S..

20 records · Page 2Linked to original sources

Highly parallel genome variant engineering with CRISPR/Cas9 in eukaryotic cells

Direct measurement of functional effects of DNA sequence variants throughout a genome is a major challenge. We developed a method that uses CRISPR/Cas9 to engineer many specific variants of interest in parallel in the budding yeast Saccharomyces cerevisiae, and to screen them for functional effects. We used the method to examine the functional consequences of premature termination codons (PTCs) at different locations within all annotated essential genes in yeast. We found that most PTCs were highly deleterious unless they occurred close to the C-terminal end and did not interrupt an annotated protein domain. Surprisingly, we discovered that some putatively essential genes are dispensable, while others have large dispensable regions. This approach can be used to profile the effects of large classes of variants in a high-throughput manner.

genomics↗

A systematic comparison of error correction enzymes by next-generation sequencing

Gene synthesis, the process of assembling gene-length fragments from shorter groups of oligonucleotides (oligos), is becoming an increasingly important tool in molecular and synthetic biology. The length, quality, and cost of gene synthesis is limited by errors produced during oligo synthesis and subsequent assembly. Enzymatic error correction methods are cost-effective means to ameliorate errors in gene synthesis. Previous analyses of these methods relied on cloning and Sanger sequencing to evaluate their efficiencies, limiting quantitative assessment and throughput. Here we develop a method to quantify errors in synthetic DNA by next-generation sequencing. We analyzed errors in a model gene assembly and systematically compared six different error correction enzymes across 11 conditions. We find that ErrASE and T7 Endonuclease I are the most effective at decreasing average error rates (up to 5.8-fold relative to the input), whereas MutS is the best for increasing the number of perfect assemblies (up to 25.2-fold). We are able to quantify differential specificities such as ErrASE preferentially corrects C/G [->] G/C transversions whereas T7 Endonuclease I preferentially corrects A/T [->] T/A transversions. More generally, this experimental and computational pipeline is a fast, scalable, and extensible way to analyze errors in gene assemblies, to profile error correction methods, and to benchmark DNA synthesis methods.

synthetic biology↗