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J Data Mining Genomics Proteomics. 2013 Jul 2;4(3). pii: 1000132.

Identification of Insertion Deletion Mutations from Deep Targeted Resequencing.

Author information

1
Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA.
2
Department of Statistics, University of Pennsylvania, Philadelphia, PA, 19104, USA.
3
Stanford Genome Technology Center, Stanford University, Palo Alto, CA, 94306, USA.
4
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.

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.

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