Recent advances in next-generation sequencing technologies has led to a proliferation of genomic data in biomedical research. Analyzing and understanding this vast amount of genomic data is a time consuming and computationally intensive process, which can be even more challenging with limited computing resources. While some bioinformatics and biomedical applications are non-modular and non-trivial to partition, others are granular and can be split into many smaller independent tasks. These smaller tasks can be distributed to multiple computing resources in parallel. High-throughput computing (HTC) reduces the computation time significantly of workflows composed of independent tasks by distributing the tasks to numerous resources. The Open Science Grid (OSG) provides a highly-efficient and user-friendly HTC service to accomplish this. OSG is a global consortium of geographically distributed academic institutions, national laboratories, and other community groups that share cyberinfrastructure resources and collaborate on scientific projects. OSG provides software and services to its users for free. OSG is widely popular in scientific fields such as physics, chemistry, and astronomy. In recent years, biological and medical sciences are increasingly utilizing this platform as well. During the past 12 months, more than 30 projects in the fields of bioinformatics, biological and medical sciences have been deployed on OSG. These projects had utilized more than 64 million CPU hours to perform analysis, which would have taken over 7,324 years on a standard computer. With OSG, researchers are able to examine a wide range of genomic data and extract novel and insightful discoveries while obeying time constraints.  

The goal of this workshop is to bring together scientists in the fields of bioinformatics, biomedical sciences and high-throughput computing to discuss the challenges of omics applications and the benefits of using distributed computing for divisible workloads. With this workshop, we aim to encourage and motivate researchers to develop efficient scientific workflows, analysis pipelines and algorithms that can utilize OSG.