MiST: A new approach to variant detection in deep sequencing datasets
Autor: | Valentina Di Pierro, Anitha D. Jayaprakash, Ravi Sachidanandam, Ian Weisberger, Ajish George, Jaehee V. Shim, Bruce D. Gelb, Hardik Shah, Sai Lakshmi Subramanian |
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Rok vydání: | 2013 |
Předmět: |
Cancer genome sequencing
Genomics Hybrid genome assembly Computational biology Biology Polymorphism Single Nucleotide Deep sequencing 03 medical and health sciences symbols.namesake 0302 clinical medicine otorhinolaryngologic diseases Genetics Humans RNA Messenger Exome sequencing 030304 developmental biology Sanger sequencing 0303 health sciences Massive parallel sequencing Shotgun sequencing Genetic Variation High-Throughput Nucleotide Sequencing symbols Methods Online Sequence Alignment Algorithms 030217 neurology & neurosurgery |
Zdroj: | Nucleic Acids Research |
ISSN: | 1362-4962 0305-1048 |
Popis: | MiST is a novel approach to variant calling from deep sequencing data, using the inverted mapping approach developed for Geoseq. Reads that can map to a targeted exonic region are identified using exact matches to tiles from the region. The reads are then aligned to the targets to discover variants. MiST carefully handles paralogous reads that map ambiguously to the genome and clonal reads arising from PCR bias, which are the two major sources of errors in variant calling. The reduced computational complexity of mapping selected reads to targeted regions of the genome improves speed, specificity and sensitivity of variant detection. Compared with variant calls from the GATK platform, MiST showed better concordance with SNPs from dbSNP and genotypes determined by an exonic-SNP array. Variant calls made only by MiST confirm at a high rate (>90%) by Sanger sequencing. Thus, MiST is a valuable alternative tool to analyse variants in deep sequencing data. |
Databáze: | OpenAIRE |
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