MOTIVATION: Next-generation sequencing (NGS) technologies have become much more efficient, allowing whole human genomes to be sequenced faster and cheaper than ever before. However, processing the raw sequence reads associated with NGS technologies requires care and sophistication in order to draw compelling inferences about phenotypic consequences of variation in human genomes. It has been shown that different approaches to variant calling from NGS data can lead to different conclusions. Ensuring appropriate accuracy and quality in variant calling can come at a computational cost. RESULTS: We describe our experience implementing and evaluating a group-based approach to calling variants on large numbers of whole human genomes. We explore the influence of many factors that may impact the accuracy and efficiency of group-based variant calling, including group size, the biogeographical backgrounds of the individuals who have been sequenced, and the computing environment used. We make efficient use of the Gordon supercomputer cluster at the San Diego Supercomputer Center by incorporating job-packing and parallelization considerations into our workflow while calling variants on 437 whole human genomes generated as part of large association study. CONCLUSIONS: We ultimately find that our workflow resulted in high-quality variant calls in a computationally efficient manner. We argue that studies like ours should motivate further investigations combining hardware-oriented advances in computing systems with algorithmic developments to tackle emerging 'big data' problems in biomedical research brought on by the expansion of NGS technologies.

This is a project for developing a distributed job execution environment for highly iterative jobs. An iterative job is one where the same binary code is run hundreds of times with incremental changes in the input values for each run. An execution environment is a set of resources on a computing platform that can be made available to run the job and hold the output until it is collected. The goal is to design a complete, object-oriented scheduling system that will run a variety of jobs with minimal changes. Areas of code that are unique to one specific type of job are decoupled from the rest. The system allows for fine-grained job control, timely status notification and dynamic registration and deregistration of execution platforms depending on resources available. Several objected-oriented technologies are employed: Java, CORBA, UML, and software design patterns. The environment has been tested using a CFD code, INS2D.