No abstract

Transposable elements (TEs) are dynamic components of genomes that often vary in copy number among members of the same species. With the advent of next-generation sequencing TE insertion-site polymorphism can be examined at an unprecedented level of detail when combined with easy-to-use bioinformatics software. Here we report a new tool, RelocaTE, that rapidly identifies specific TE insertions that are either polymorphic or shared between a reference and unassembled next-generation sequencing reads. Furthermore, a novel companion tool, CharacTErizer, exploits the depth of coverage to classify genotypes of nonreference insertions as homozygous, heterozygous or, when analyzing an active TE family, as rare somatic insertion or excision events. It does this by comparing the numbers of RelocaTE aligned reads to reads that map to the same genomic position without the TE. Although RelocaTE and CharacTErizer can be used for any TE, they were developed to analyze the very active mPing element which is undergoing massive amplification in specific strains of Oryza sativa (rice). Three individuals of one of these strains, A123, were resequenced and analyzed for mPing insertion site polymorphisms. The majority of mPing insertions found (~97%) are not present in the reference, and two siblings from a self-crossed of this strain were found to share only ~90% of their insertions. Private insertions are primarily heterozygous but include both homozygous and predicted somatic insertions. The reliability of the predicted genotypes was validated by polymerase chain reaction.

Purpose To compare screening magnetic resonance (MR) imaging performance in the Breast Cancer Surveillance Consortium (BCSC) with Breast Imaging Reporting and Data System (BI-RADS) benchmarks. Materials and Methods This study was approved by the institutional review board and compliant with HIPAA and included BCSC screening MR examinations collected between 2005 and 2013 from 5343 women (8387 MR examinations) linked to regional Surveillance, Epidemiology, and End Results program registries, state tumor registries, and pathologic information databases that identified breast cancer cases and tumor characteristics. Clinical, demographic, and imaging characteristics were assessed. Performance measures were calculated according to BI-RADS fifth edition and included cancer detection rate (CDR), positive predictive value of biopsy recommendation (PPV), sensitivity, and specificity. Results The median patient age was 52 years; 52% of MR examinations were performed in women with a first-degree family history of breast cancer, 46% in women with a personal history of breast cancer, and 15% in women with both risk factors. Screening MR imaging depicted 146 cancers, and 35 interval cancers were identified (181 total-54 in situ, 125 invasive, and two status unknown). The CDR was 17 per 1000 screening examinations (95% confidence interval [CI]: 15, 20 per 1000 screening examinations; BI-RADS benchmark, 20-30 per 1000 screening examinations). PPV was 19% (95% CI: 16%, 22%; benchmark, 15%). Sensitivity was 81% (95% CI: 75%, 86%; benchmark, >80%), and specificity was 83% (95% CI: 82%, 84%; benchmark, 85%-90%). The median tumor size of invasive cancers was 10 mm; 88% were node negative. Conclusion The interpretative performance of screening MR imaging in the BCSC meets most BI-RADS benchmarks and approaches benchmark levels for remaining measures. Clinical practice performance data can inform ongoing benchmark development and help identify areas for quality improvement. RSNA, 2017.