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Iadarola, B.

Publications and source records attributed to Iadarola, B..

2 recordsLinked to original sources

Enhanced targeted resequencing by optimizing the combination of enrichment technology and DNA fragment length

Whole-exome sequencing (WES) enrichment platforms are usually evaluated by measuring the depth of coverage at target regions. However, variants called in WES are reported in the variant call format (VCF) file, which is filtered by minimum site coverage and mapping quality. Therefore, genotypability (base calling calculated by combining depth of coverage with the confidence of read alignment) should be considered as a more informative parameter to assess the performance of WES. We found that the mapping quality of reads aligned to difficult target regions was improved by increasing the DNA fragment length well above the average exon size. We tested three different DNA fragment lengths using four major commercial WES platforms and found that longer DNA fragments achieved a higher percentage of callable bases in the target regions and thus improved the genotypability of many genes, including several associated with clinical phenotypes. DNA fragment size also affected the uniformity of coverage, which in turn influences genotypability, indicating that different platforms are optimized for different DNA fragment lengths. Finally, we found that although the depth of coverage continued to increase in line with the sequencing depth (overall number of reads), base calling reached saturation at a depth of coverage that depended on the enrichment platform and DNA fragment length. This confirmed that genotypability provides better estimates for the optimal sequencing depth of each fragment size/enrichment platform combination.

genomics↗

Tracking Subclonal Mutation Frequencies Throughout Lymphomagenesis Identifies Cancer Drivers in Mouse Models of Lymphoma.

Determining whether recurrent but rare cancer mutations are bona fide driver mutations remains a bottleneck in cancer research. Here we present the most comprehensive analysis of retrovirus driven lymphomagenesis produced to date, sequencing 700,000 mutations from >500 malignancies collected at time points throughout tumor development. This enabled identification of positively selected events, and the first demonstration of negative selection of mutations that may be deleterious to tumor development indicating novel avenues for therapy. Customized sequencing and bioinformatics methodologies were developed to quantify subclonal mutations in both premalignant and malignant tissue, greatly expanding the statistical power for identifying driver mutations and yielding a high-resolution, genome wide map of the selective forces surrounding cancer gene loci. Screening two BCL2 transgenic models confirms known drivers of human B-cell non-Hodgkin lymphoma, and implicates novel candidates including modifiers of immunosurveillance such as co-stimulatory molecules and MHC loci. Correlating mutations with genotypic and phenotypic features also gives robust identification of known cancer genes independently of local variance in mutation density. An online resource http://mulv.lms.mrc.ac.uk allows customized queries of the entire dataset.

cancer biology↗