Sequencing and disease variation detection tools and techniques

A. P DeLuca; A. H Wagner; K. R Taylor; B Faga; D Thole; V. C Sheffield; E. M Stone; T. L Casavant; T. E Scheetz; T. A Braun

doi:10.1109/AICCSA.2011.6126607

Conference proceeding

Sequencing and disease variation detection tools and techniques

A. P DeLuca, A. H Wagner, K. R Taylor, B Faga, D Thole, V. C Sheffield, E. M Stone, T. L Casavant, T. E Scheetz and T. A Braun

2011 9th IEEE/ACS International Conference on Computer Systems and Applications (AICCSA), pp.80-83

12/2011

DOI: 10.1109/AICCSA.2011.6126607

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Abstract

Next-generation sequencing technology provides greater sequencing capacity to identify disease-causing variations. Currently there are many alignment tools and variation detection techniques, multiple SNP data sets used to filter out benign polymorphisms with the intent of enriching for disease-causing variations, and many tools and websites for annotating variations and performing pathogenicity prediction of variations. There is no consensus or standard with regards to which tools and corresponding parameter settings for each tool is appropriate for the different application of next-generation sequencing technologies for disease-causing variation identification. We have implemented an annotation pipeline that can easily use the output from different alignment tools (BLAT, BWA, BFAST, Bowtie) typically used to align sequence from next-generation sequencing and Sanger sequencing experiments. The Automated Sequence Analysis Pipeline (ASAP) reports variations using the HGVS standardized nomenclature[1] to facilitate comparison across tools and publications. To date, we have annotated the sequence from 13 exomes in an effort to identify disease-causing mutations.

Bioinformatics

Genomics

Pipelines

Humans

Next generation networking

Diseases

Details

Title: Subtitle: Sequencing and disease variation detection tools and techniques
Creators: A. P DeLuca - Center for Bioinf. & Comput. Biol., Univ. of Iowa, Iowa City, IA, USA
A. H Wagner - Center for Bioinf. & Comput. Biol., Univ. of Iowa, Iowa City, IA, USA
K. R Taylor - Center for Bioinf. & Comput. Biol., Univ. of Iowa, Iowa City, IA, USA
B Faga - Center for Bioinf. & Comput. Biol., Univ. of Iowa, Iowa City, IA, USA
D Thole - Center for Bioinf. & Comput. Biol., Univ. of Iowa, Iowa City, IA, USA
V. C Sheffield - Howard Hughes Med. Inst., Iowa City, IA, USA
E. M Stone - Howard Hughes Med. Inst., Iowa City, IA, USA
T. L Casavant - Center for Bioinf. & Comput. Biol., Univ. of Iowa, Iowa City, IA, USA
T. E Scheetz - Center for Bioinf. & Comput. Biol., Univ. of Iowa, Iowa City, IA, USA
T. A Braun - Center for Bioinf. & Comput. Biol., Univ. of Iowa, Iowa City, IA, USA
Resource Type: Conference proceeding
Publication Details: 2011 9th IEEE/ACS International Conference on Computer Systems and Applications (AICCSA), pp.80-83
Publisher: IEEE
DOI: 10.1109/AICCSA.2011.6126607
ISSN: 2161-5322
eISSN: 2161-5330
Language: English
Date published: 12/2011
Academic Unit: Electrical and Computer Engineering; Roy J. Carver Department of Biomedical Engineering; Business Analytics; Ophthalmology and Visual Sciences; Center for Bioinformatics and Computational Biology; Iowa Neuroscience Institute; Stead Family Department of Pediatrics; Medical Genetics and Genomics
Record Identifier: 9984070289302771

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