SPLINTER
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Application data |
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| Biological application domain(s) | Indel detection, SNP detection |
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| Principal bioinformatics method(s) | |
| Created at | Washington University in St. Louis |
| Maintained? | Yes |
| Software features | finds rare indels in pooled samples, error model profiler for sequencing library |
| Programming language(s) | C, C++ |
| Licence | Commercial, Freeware |
Summary: Identification of indel variants in pooled DNA with spike-in controls
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Description
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.
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