A clinically validated diagnostic second-generation sequencing assay for detection of hereditary BRCA1 and BRCA2 mutations.

Individuals who inherit mutations in BRCA1 or BRCA2 are predisposed to breast and ovarian cancers. However, identifying mutations in these large genes by conventional dideoxy sequencing in a clinical testing laboratory is both time consuming and costly, and similar challenges exist for other large genes, or sets of genes, with relevance in the clinical setting. Second-generation sequencing technologies have the potential to improve the efficiency and throughput of clinical diagnostic sequencing, once clinically validated methods become available. We have developed a method for detection of variants based on automated small-amplicon PCR followed by sample pooling and sequencing with a second-generation instrument. To demonstrate the suitability of this method for clinical diagnostic sequencing, we analyzed the coding exons and the intron-exon boundaries of BRCA1 and BRCA2 in 91 hereditary breast cancer patient samples. Our method generated high-quality sequence coverage across all targeted regions, with median coverage greater than 4000-fold for each sample in pools of 24. Sensitive and specific automated variant detection, without false-positive or false-negative results, was accomplished with a standard software pipeline using bwa for sequence alignment and samtools for variant detection. We experimentally derived a minimum threshold of 100-fold sequence depth for confident variant detection. The results demonstrate that this method is suitable for sensitive, automatable, high-throughput sequence variant detection in the clinical laboratory.

Software for automated analysis of DNA fingerprinting gels.

Here we describe software tools for the automated detection of DNA restriction fragments resolved on agarose fingerprinting gels. We present a mathematical model for the location and shape of the restriction fragments as a function of fragment size, with model parameters determined empirically from "marker" lanes containing molecular size standards. Automated identification of restriction fragments involves several steps, including: image preprocessing, to put the data in a form consistent with a linear model; marker lane analysis, for determination of the model parameters; and data lane analysis, a procedure for detecting restriction fragment multiplets while simultaneously determining the amplitude curve that describes restriction fragment amplitude as a function of mobility. In validation experiments conducted on fingerprinted and sequenced Bacterial Artificial Chromosome (BAC) clones, sensitivity and specificity of restriction fragment identification exceeded 96% on restriction fragments ranging in size from 600 base pairs (bp) to 30,000 bp. The integrated suite of software tools, written in MATLAB and collectively called BandLeader, is in use at the BC Cancer Agency Genome Sciences Centre (GSC) and the Washington University Genome Sequencing Center, and has been provided to the Wellcome Trust Sanger Institute and the Whitehead Institute. Employed in a production mode at the GSC, BandLeader has been used to perform automated restriction fragment identification for more than 850,000 BAC clones for mouse, rat, bovine, and poplar fingerprint mapping projects.