A method for finding variants from targeted sequencing panels

Abstract

Provided herein is a method for identifying a sequence variant in an enriched sample. In certain embodiments, the method may comprise: (a) obtaining: (i) a plurality of sequence reads from a sample that has been enriched for a genomic region and (ii) a reference sequence for the genomic region; (b) assembling the sequence reads to obtain a plurality of discrete sequence assemblies that correspond to potential variants; (c) determining which of the potential variants are true and which are artifacts by examining the sequence reads that make up each of the discrete sequence assemblies; (d) optionally determining whether each of the true potential variants contains a mutation that is known to be associated with the reference sequence; and (e) outputting a report indicating whether the sample comprises a sequence variant.

Description

[0001] cross reference [0002] This application claims the benefit of US Provisional Patent Application No. 61 / 859,625, filed July 29, 2013, which is hereby incorporated by reference in its entirety. Background technique [0003] A comprehensive account of mutations is indispensable for understanding, diagnosing and treating many diseases, including cancer. A number of methods have been proposed for finding mutations in sequencing data, which usually consist of statistically assessing the presence of variant bases compared to a reference. However, pinpointing mutations in situations where they are only found in a small fraction of reads remains a challenge. Delineating such mutations is important, especially in cancer. Such mutations are important not only for low-tumor-content samples, but also for capturing micro-tumor subclones in order to understand tumor heterogeneity and thus the underlying causes of relapse and therapy resistance. [0004] Therefore, it is attractiv...

AI Technical Summary

Problems solved by technology

Statistically, while sampling such a large sample space, rare events occur, and it is difficult to distinguish low-frequency calls from sequencing errors

The presence of other artifacts of amplification and capture further complicates matters

In genomic regions where complex events and insertions ( insert enter- lack missing), the reference sequence does not accurately represent the variation distribution, which leads to further pseudobiological

Many existing solutions attempt to address these issues by using multiple independent methods, but in the current literature there is no solution that can reliably call these mutations

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