Abstract
Applying genomics to patient care demands sensitive, unambiguous and rapid characterization of a known set of clinically relevant variants in patients’ samples, an objective substantially different from the standard discovery process, in which every base in every sequenced read must be examined. Further, the approach must be sufficiently robust as to be able to detect multiple and potentially rare variants from heterogeneous samples. To meet this critical objective, we developed a novel variant characterization framework, ClinSeK, which performs targeted analysis of relevant reads from high-throughput sequencing data. ClinSeK is designed for efficient targeted short read alignment and is capable of characterizing a wide spectrum of genetic variants from single nucleotide variation to large-scale genomic rearrangement breakpoints. Applying ClinSeK to over a thousand cancer patients demonstrated substantively better performance, in terms of accuracy, runtime and disk storage, for clinical applications than existing variant discovery tools. ClinSeK is freely available for academic use at http://bioinformatics.mdanderson.org/main/clinsek.Electronic supplementary materialThe online version of this article (doi:10.1186/s13073-015-0155-1) contains supplementary material, which is available to authorized users.
Highlights
A major objective of clinical genomics is to translate the knowledge and technologies that are established in a discovery setting, for example, large-scale cancer genome sequencing, into a clinical setting to benefit individual patients [1]
The development of ClinSeK offers a software-level solution to the ever-increasing demand for efficient and accurate variant characterization in clinical sequencing. It is software designed starting from a set of clinically actionable sites and comprehensively interrogating these sites efficiently without investing computational resource to sites that are of no clear clinical relevance
It is dedicated to characterizing variants in clinical settings where only a limited set of relevant mutations needs to be quickly characterized with the highest possible accuracy
Summary
A major objective of clinical genomics is to translate the knowledge and technologies that are established in a discovery setting, for example, large-scale cancer genome sequencing, into a clinical setting to benefit individual patients [1]. Despite the tremendous progress in discovering mutations in patients, only a small set of variants have been associated with causal clinical evidence and have been regarded as actionable in clinics [2]. Even after accounting for all the mutations reported for the disease up to 2014, the number of mutations is still under 2,000 [4]. In another example, three mutations in HEXA account for over 92% of affected TaySachs patients [5]. The stark contrast between the mutations present and the mutations that physicians could respond to motivates a re-structure of the bioinformatics workflow that concentrates variants that lead to known clinical consequences
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