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Development of Bioinformatics Pipeline for Analyzing Clinical Pediatric NGS Data.

Crowgey EL, Kolb A, Wu CH - AMIA Jt Summits Transl Sci Proc (2015)

Bottom Line: Using an Illumina exome sequencing dataset generated from pediatric Acute Myeloid Leukemia patients (AML; type FLT3/ITD+) a comprehensive bioinformatics pipeline was developed to aid in a better clinical understanding of the genetic data associated with the clinical phenotype.The pipeline starts with raw next generation sequencing reads and using both publicly available resources and custom scripts, analyzes the genomic data for variants associated with pediatric AML.Furthermore, it compares the somatic mutations at diagnosis with the somatic mutations at relapse and outputs variants and functional annotations that are specific for the relapse state.

View Article: PubMed Central - PubMed

Affiliation: Center for Bioinformatics & Computational Biology, University of Delaware, Newark, DE.

ABSTRACT
Using an Illumina exome sequencing dataset generated from pediatric Acute Myeloid Leukemia patients (AML; type FLT3/ITD+) a comprehensive bioinformatics pipeline was developed to aid in a better clinical understanding of the genetic data associated with the clinical phenotype. The pipeline starts with raw next generation sequencing reads and using both publicly available resources and custom scripts, analyzes the genomic data for variants associated with pediatric AML. By incorporating functional information such as Gene Ontology annotation and protein-protein interactions, the methodology prioritizes genomic variants and returns disease specific results and knowledge maps. Furthermore, it compares the somatic mutations at diagnosis with the somatic mutations at relapse and outputs variants and functional annotations that are specific for the relapse state.

No MeSH data available.


Related in: MedlinePlus

Summary somatic SNP detection
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f2-2091834: Summary somatic SNP detection

Mentions: The pipeline is composed of 3 genomic variant detection algorithms, Pindel, Mutect, and Shimmer, that collectively report single nucleotide polymorphisms (SNP), small insertions and deletions (InDel), and large InDels. When analyzing cancer samples it is important to distinguish, and prioritize, somatic SNPs versus germline SNPs. To aid with validating the pipeline, the somatic SNPs detected were fist compared to the list of verified variants provided by COG (Figure 2). Mutect detected 100% of the verified variants in eight of the twelve samples (diagnosis and relapse), with an average of 93% detection of verified variants. Shimmer detected 100% of the verified variants in five of the twelve samples, with an average of 78% detection of verified variants.


Development of Bioinformatics Pipeline for Analyzing Clinical Pediatric NGS Data.

Crowgey EL, Kolb A, Wu CH - AMIA Jt Summits Transl Sci Proc (2015)

Summary somatic SNP detection
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4525226&req=5

f2-2091834: Summary somatic SNP detection
Mentions: The pipeline is composed of 3 genomic variant detection algorithms, Pindel, Mutect, and Shimmer, that collectively report single nucleotide polymorphisms (SNP), small insertions and deletions (InDel), and large InDels. When analyzing cancer samples it is important to distinguish, and prioritize, somatic SNPs versus germline SNPs. To aid with validating the pipeline, the somatic SNPs detected were fist compared to the list of verified variants provided by COG (Figure 2). Mutect detected 100% of the verified variants in eight of the twelve samples (diagnosis and relapse), with an average of 93% detection of verified variants. Shimmer detected 100% of the verified variants in five of the twelve samples, with an average of 78% detection of verified variants.

Bottom Line: Using an Illumina exome sequencing dataset generated from pediatric Acute Myeloid Leukemia patients (AML; type FLT3/ITD+) a comprehensive bioinformatics pipeline was developed to aid in a better clinical understanding of the genetic data associated with the clinical phenotype.The pipeline starts with raw next generation sequencing reads and using both publicly available resources and custom scripts, analyzes the genomic data for variants associated with pediatric AML.Furthermore, it compares the somatic mutations at diagnosis with the somatic mutations at relapse and outputs variants and functional annotations that are specific for the relapse state.

View Article: PubMed Central - PubMed

Affiliation: Center for Bioinformatics & Computational Biology, University of Delaware, Newark, DE.

ABSTRACT
Using an Illumina exome sequencing dataset generated from pediatric Acute Myeloid Leukemia patients (AML; type FLT3/ITD+) a comprehensive bioinformatics pipeline was developed to aid in a better clinical understanding of the genetic data associated with the clinical phenotype. The pipeline starts with raw next generation sequencing reads and using both publicly available resources and custom scripts, analyzes the genomic data for variants associated with pediatric AML. By incorporating functional information such as Gene Ontology annotation and protein-protein interactions, the methodology prioritizes genomic variants and returns disease specific results and knowledge maps. Furthermore, it compares the somatic mutations at diagnosis with the somatic mutations at relapse and outputs variants and functional annotations that are specific for the relapse state.

No MeSH data available.


Related in: MedlinePlus