Bioinformatics

Next-generation sequencing technologies have decentralized sequence acquisition, increasing the demand for new bioinformatics tools that are easy to use,

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portable across multiple platforms, and scalable for high-throughput applications. Cloud computing platforms provide on-demand access to computing infrastructure over the Internet and can be used in combination with custom built virtual machines to distribute pre-packaged with pre-configured software.

A new desktop application for push-button automated sequence analysis that can utilize cloud computing resources has been now developed, called as Cloud Virtual Resource, CloVR. CloVR is implemented as a single portable virtual machine (VM) that provides several automated analysis pipelines for microbial genomics, including 16S, whole genome and metagenome sequence analysis.

CloVR executes on a desktop (or laptop) computer, providing the ability to run analysis pipelines

on local resources. CloVR is invoked using one of two supported VM players, VMware and Virtual-Box; at least one of which is freely available on all major desktop platforms: Windows, Unix/Linux, and Mac OS. On a local computer, CloVR utilizes local disk storage and compute resources, as supported by the VM player, including multi-core CPUs if available. To access data stored on the local computer, users can copy files into a “shared folder” that is accessible on both the VM and the local desktop and uses available hard drive space on the computer. Once inside the shared folder, CloVR can read this data for processing. Similarly, CloVR writes output data to this shared folder, making the pipeline output available on the desktop. This shared folder feature is supported by both VMware and VirtualBox.

The CloVR utilizes local computer resources and requires minimal installation, addressing key challenges in deploying bioinformatics workflows. In addition CloVR supports use of remote cloud computing resources to improve performance for large-scale sequence processing. The CloVR VM and associated architecture lowers the barrier of entry for utilizing complex analysis protocols on both local single- and multi-core computers and cloud systems for high throughput data processing.

Authors: Samuel V Angiuoli, Malcolm Matalka, Aaron Gussman, Kevin Galens, Mahesh Vangala, David R Riley, Cesar Arze1, James R White, Owen White and W Florian Fricke

Link: http://www.biomedcentral.com/1471-2105/12/356