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Development of Bioinformatics Pipeline for Analyzing Clinical Pediatric NGS Data
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 wi...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Medical Informatics Association
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4525226/ https://www.ncbi.nlm.nih.gov/pubmed/26306272 |
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author | Crowgey, Erin L. Kolb, Anders Wu, Cathy H. |
author_facet | Crowgey, Erin L. Kolb, Anders Wu, Cathy H. |
author_sort | Crowgey, Erin L. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-4525226 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | American Medical Informatics Association |
record_format | MEDLINE/PubMed |
spelling | pubmed-45252262015-08-24 Development of Bioinformatics Pipeline for Analyzing Clinical Pediatric NGS Data Crowgey, Erin L. Kolb, Anders Wu, Cathy H. AMIA Jt Summits Transl Sci Proc Articles 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. American Medical Informatics Association 2015-03-23 /pmc/articles/PMC4525226/ /pubmed/26306272 Text en ©2015 AMIA - All rights reserved. This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose |
spellingShingle | Articles Crowgey, Erin L. Kolb, Anders Wu, Cathy H. Development of Bioinformatics Pipeline for Analyzing Clinical Pediatric NGS Data |
title | Development of Bioinformatics Pipeline for Analyzing Clinical Pediatric NGS Data |
title_full | Development of Bioinformatics Pipeline for Analyzing Clinical Pediatric NGS Data |
title_fullStr | Development of Bioinformatics Pipeline for Analyzing Clinical Pediatric NGS Data |
title_full_unstemmed | Development of Bioinformatics Pipeline for Analyzing Clinical Pediatric NGS Data |
title_short | Development of Bioinformatics Pipeline for Analyzing Clinical Pediatric NGS Data |
title_sort | development of bioinformatics pipeline for analyzing clinical pediatric ngs data |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4525226/ https://www.ncbi.nlm.nih.gov/pubmed/26306272 |
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