ISOWN: Accurate somatic mutation identification in the absence of normal tissue controls

Genome Medicine

Volumn 9, Issue 1, 2017, Pages

  Kalatskaya, Irina ^a   Trinh, Quang M ^a   Spears, Melanie ^a,b   McPherson, John D ^d   Bartlett, John M S ^a,b,c   Stein, Lincoln ^a,b  

^a ONTARIO INSTITUTE FOR CANCER RESEARCH   (Canada)

^b UNIVERSITY OF TORONTO   (Canada)

^c UNIVERSITY OF EDINBURGH   (United Kingdom)

^d UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Matching normal tissue; Next generation sequencing; Somatic mutation; Variant classification

Indexed keywords

ACCURACY; ALGORITHM; ARTICLE; CANCER TISSUE; GENE FREQUENCY; GENETIC VARIATION; MUTATIONAL ANALYSIS; NEXT GENERATION SEQUENCING; PRIORITY JOURNAL; SINGLE NUCLEOTIDE POLYMORPHISM; SOMATIC MUTATION; VALIDATION STUDY; WHOLE EXOME SEQUENCING; DNA MUTATIONAL ANALYSIS; GENETICS; HIGH THROUGHPUT SEQUENCING; HUMAN; MUTATION; NEOPLASM; PROCEDURES; SUPERVISED MACHINE LEARNING;

DNA MUTATIONAL ANALYSIS; HIGH-THROUGHPUT NUCLEOTIDE SEQUENCING; HUMANS; MUTATION; NEOPLASMS; SUPERVISED MACHINE LEARNING;

EID: 85021325251     PISSN: None     EISSN: 1756994X     Source Type: Journal    
DOI: 10.1186/s13073-017-0446-9     Document Type: Article

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