title: Identification of Insertion Deletion Mutations from Deep Targeted Resequencing.
abstract: Taking advantage of the deep targeted sequencing capabilities of next generation sequencers, we have developed a novel two step insertion deletion (indel) detection algorithm (IDA) that can determine indels from single read sequences with high computational efficiency and sensitivity when indels are fractionally less compared to wild type reference sequence. First, it identifies candidate indel positions utilizing specific sequence alignment artifacts produced by rapid alignment programs. Second, it confirms the location of the candidate indel by using the Smith-Waterman (SW) algorithm on a restricted subset of Sequence reads. We demonstrate that IDA is applicable to indels of varying sizes from deep targeted sequencing data at low fractions where the indel is diluted by wild type sequence. Our algorithm is useful in detecting indel variants present at variable allelic frequencies such as may occur in heterozygotes and mixed normal-tumor tissue.
authors: Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA.
authors: Georges
authors: G
authors: Natsoulis
authors: Department of Statistics, University of Pennsylvania, Philadelphia, PA, 19104, USA.
authors: Nancy
authors: N
authors: Zhang
authors: Stanford Genome Technology Center, Stanford University, Palo Alto, CA, 94306, USA.
authors: Katrina
authors: K
authors: Welch
authors: Stanford Genome Technology Center, Stanford University, Palo Alto, CA, 94306, USA.
authors: John
authors: J
authors: Bell
authors: Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA ; Stanford Genome Technology Center, Stanford University, Palo Alto, CA, 94306, USA.
authors: Hanlee P
authors: HP
authors: Ji