21041413
This reference describes SPLINTER.
| PMID | PMID 21041413 |
|---|---|
| Title | High-throughput discovery of rare insertions and deletions in large cohorts. |
| Year | 2010 |
| Journal | Genome Research |
| Author | Vallania FL, Druley TE, Ramos E, Wang J, Borecki I, Province M, Mitra RD. |
| Volume | |
| Start page |
Error: No contents found at URL http://www.ebi.ac.uk/europepmc/webservices/rest/MED/21041413/citations/4000.
According to Europe PubMed Central, this reference has Error: no local variable "citations" was set. " Error: no local variable "citations" was set. " is not a number. citations.
For reference, you can check Google Scholar, which lacks an API because Google ...
Error: Invalid JSON. According to Almetric, this reference has an Altmetric score of Error: no local variable "altscore" was set. " Error: no local variable "altscore" was set. " is not a number..
Full text description
Pooled-DNA sequencing strategies enable fast, accurate, and cost-effect detection of rare variants, but current approaches are not able to accurately identify short insertions and deletions (indels), despite their pivotal role in genetic disease. Furthermore, the sensitivity and specificity of these methods depend on arbitrary, user-selected significance thresholds, whose optimal values change from experiment to experiment. Here, we present a combined experimental and computational strategy that combines a synthetically engineered DNA library inserted in each run and a new computational approach named SPLINTER that detects and quantifies short indels and substitutions in large pools. SPLINTER integrates information from the synthetic library to select the optimal significance thresholds for every experiment. We show that SPLINTER detects indels (up to 4 bp) and substitutions in large pools with high sensitivity and specificity, accurately quantifies variant frequency (r = 0.999), and compares favorably with existing algorithms for the analysis of pooled sequencing data. We applied our approach to analyze a cohort of 1152 individuals, identifying 48 variants and validating 14 of 14 (100%) predictions by individual genotyping. Thus, our strategy provides a novel and sensitive method that will speed the discovery of novel disease-causing rare variants.
