TY  - JOUR
A1  - Jun, Goo
A1  - Wing, Mary Kate
A1  - Abecasis, Gonçalo R
A1  - Kang, Hyun Min
T1  - An efficient and scalable analysis framework for variant extraction and refinement from population scale DNA sequence data
Y1  - 2015/04/16 
JF  - Genome Research 
JO  - Genome Research 
DO  - 10.1101/gr.176552.114 
SP  - gr.176552.114 
UR  - http://genome.cshlp.org/content/early/2015/04/14/gr.176552.114.abstract 
N2  - The analysis of next-generation sequencing data is computationally and statistically challenging because of massive data volumes and imperfect data quality. We present GotCloud, a pipeline for efficiently detecting and genotyping high-quality variants from large-scale sequencing data. GotCloud automates sequence alignment, sample-level quality control, variant calling, filtering of likely artifacts using machine learning techniques, and genotype refinement using haplotype information. The pipeline can process thousands of samples in parallel and requires less computational resources than current alternatives. Experiments with whole genome and exome targeted sequence data generated by the 1000 Genomes Project show that the pipeline provides effective filtering against false positive variants and high power to detect true variants. Our pipeline has already contributed to variant detection and genotyping in several large-scale sequencing projects, including the 1000 Genomes Project and the NHLBI Exome Sequencing Project.  We hope it will now prove useful to many medical sequencing studies. 
ER  -