|BURKART-WACO, DIANA - University Of California|
|TSAI, HELEN - University Of California|
|NGO, KATHIE - University Of California|
|HENRY, ISABELLE - University Of California|
|COMAI, LUCA - University Of California|
Submitted to: Book Chapter
Publication Type: Book / Chapter
Publication Acceptance Date: 3/16/2016
Publication Date: N/A
Interpretive Summary: Targeting of Induced Local Lesions in Genomes (TILLING) is a reverse genetics method that combines generation of chemically-induced mutants and high throughput mutation discovery platforms. This book chapter describes a detailed protocol for the generation and sequencing of pooled mutant DNAs amplified from specific gene targets. A brief description of a computational analysis pipeline for calling real mutations versus changes resulting from sequencing-based errors is also provided. The example presented involves rice although the method has been successfully applied to several species.
Technical Abstract: Advances in DNA sequencing (i.e., next-generation sequencing, NGS) have greatly increased the power and efficiency of detecting rare mutations in large mutant populations. Targeting Induced Local Lesions in Genomes (TILLING) is a reverse genetics approach for identifying gene mutations resulting from chemical mutagenesis. In traditional TILLING, mutation discovery is accomplished through mismatch cleavage of mutant and wild-type DNA heteroduplexes using endonucleases. This is followed by Sanger sequencing to determine the specific sequence changes. TILLING by sequencing (TBS) uses NGS to facilitate the concurrent detection and sequence characterization of mutations, which allows researchers to prioritize mutants for further analyses. NGS increases the sensitivity of mutation detection and thus improves screening efficiency by allowing the pooling of more DNAs. Here we describe a protocol for TBS using rice as an example. First, target genes are amplified from mutant population of interest. Then, amplicons are combined in overlapping pool designs to maximize throughput and increase likelihood of mutation detection during sequencing. Once sequence data is obtained, mutations are called using statistical approaches that weigh likelihood of rare mutations versus the probability of sequencing error.