Short IN/DEL Prediction by Large deviation Inference and Non-linear True frequency Estimation by Recursion (SPLINTER)

Washington University in St. Louis
posted on 01/07/2010

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Pooled-DNA sequencing strategies enable fast, accurate, and cost-effect detection of rare variants in large cohorts but current approaches are unable to identify short insertions and deletions (IN/DELs), despite their pivotal role in important disease phenotypes (for example breast and ovarian cancers).

A new algorithm called SPLINTER (Short IN/DEL Prediction by Large deviation Inference and Non-linear True frequency Estimation by Recursion) was developed, which detects and quantifies short IN/DELs as well as single nucleotide substitutions in pooled-DNA samples. SPLINTER can accurately detect rare variants in large pools, providing a novel and sensitive method that will allow for significant progress in the discovery of novel disease-causing rare variants from DNA pools of affected individuals, such as germ-line mutation discovery for breast and ovarian cancer patients.

Detect and quantify short IN/DELs as well as single nucleotide substitutions in pooled-DNA samples.