TY  - GEN
AB  - Genomic structural variations are an important class of genetic variants with a wide va- riety of functional impacts. The detection of structural variations using high-throughput short-read sequencing data is a difficult problem, and published algorithms do not pro- vide the sensitivity and specificity required in research and clinical settings. Meanwhile, high-throughput sequencing is rapidly generating ever-larger data sets, necessitating the development of algorithms that can provide results rapidly and scale to use cloud and cluster infrastructures. MapReduce and Hadoop are becoming a standard for managing the distributed processing of large data sets, but existing structural variation detection approaches are difficult to translate into the MapReduce framework. We have formulated a general framework for structural variation detection in MapReduce, and implemented a software package called Cloudbreak, which detects genomic deletions and insertions with very high accuracy compared to existing popular tools.
AD  - Oregon Health and Science University
AU  - Whelan, Christopher
DA  - 2014
DO  - 10.6083/M4DJ5CZ2
DO  - DOI
ED  - Sönmez, Kemal
ED  - Advisor
ID  - 2707
KW  - Computational Biology
KW  - Machine Learning
KW  - Artificial Intelligence
KW  - Genomics
KW  - Genomic Structural Variation
L1  - https://digitalcollections.ohsu.edu/record/2707/files/3482_etd.pdf
L2  - https://digitalcollections.ohsu.edu/record/2707/files/3482_etd.pdf
L4  - https://digitalcollections.ohsu.edu/record/2707/files/3482_etd.pdf
LK  - https://digitalcollections.ohsu.edu/record/2707/files/3482_etd.pdf
N2  - Genomic structural variations are an important class of genetic variants with a wide va- riety of functional impacts. The detection of structural variations using high-throughput short-read sequencing data is a difficult problem, and published algorithms do not pro- vide the sensitivity and specificity required in research and clinical settings. Meanwhile, high-throughput sequencing is rapidly generating ever-larger data sets, necessitating the development of algorithms that can provide results rapidly and scale to use cloud and cluster infrastructures. MapReduce and Hadoop are becoming a standard for managing the distributed processing of large data sets, but existing structural variation detection approaches are difficult to translate into the MapReduce framework. We have formulated a general framework for structural variation detection in MapReduce, and implemented a software package called Cloudbreak, which detects genomic deletions and insertions with very high accuracy compared to existing popular tools.
PY  - 2014
T1  - Detecting and analyzing genomic structural variation using distributed computing
TI  - Detecting and analyzing genomic structural variation using distributed computing
UR  - https://digitalcollections.ohsu.edu/record/2707/files/3482_etd.pdf
Y1  - 2014
ER  -