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The availability of low-cost small-factor sequencers, such as the Illumina MiSeq, MiniSeq, or iSeq, have paved the way for democratizing genomics sequencing, providing researchers in minority universities with access to the technology that was previously only affordable by institutions with large core facilities. However, these instruments are not bundled with software for performing bioinformatics data analysis, and the data analysis can be the main bottleneck for independent laboratories or even small clinical facilities that consider adopting genomic sequencing for medical applications. To address this issue, we have developed miCloud, a bioinformatics platform that enables genomic data analysis through a fully featured data analysis cloud, which seamlessly integrates with genome sequencers over the local network. The miCloud can be easily deployed without any prior bioinformatics expertise on any computing environment, from a laboratory computer workstation to a university computer cluster. Our platform not only provides access to a set of preconfigured RNA-Seq and CHIP-Seq bioinformatics pipelines, but also enables users to develop or install new preconfigured tools from the large selection available on open-source online Docker container repositories. The miCloud built-in analysis pipelines are also integrated with the Visual Omics Explorer framework (Kim et al., 2016), which provides rich interactive visualizations and publication-ready graphics from the next-generation sequencing data. Ultimately, the miCloud demonstrates a bioinformatics approach that can be adopted in the field for standardizing genomic data analysis, similarly to the way molecular biology sample preparation kits have standardized laboratory operations.

Motivation: Given the abundance of genome sequencing and omics data, an opprtunity and challenge in bioinformatics relates to data mining and visualization. The majority of current bioinformatics visualizations are implemented either as multi-tier web server applications that require significant maintenance effort, or as client software that presumes technical expertise for installation. Here we present the Visual Omics Explorer (VOE), a cross-platform data visualization portal that is implemented using only HTML and Javascript code. VOE is a standalone software that can be loaded offline on the web browser from a local copy of the code, or over the internet without any dependency other than distributing the code through a file sharing service. VOE can interactively display genomics, transcriptomics, epigenomics and metagenomics data stored either locally or retrieved from cloud storage services, and runs on both desktop computers and mobile devices., Availability and Implementation: VOE is accessible at http://bcil.github.io/VOE/ CONTACT: agbiotec@gmail.com, Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

Background: A steep drop in the cost of next-generation sequencing during recent years has made the technology affordable to the majority of researchers, but downstream bioinformatic analysis still poses a resource bottleneck for smaller laboratories and institutes that do not have access to substantial computational resources. Sequencing instruments are typically bundled with only the minimal processing and storage capacity required for data capture during sequencing runs. Given the scale of sequence datasets, scientific value cannot be obtained from acquiring a sequencer unless it is accompanied by an equal investment in informatics infrastructure., Results: Cloud BioLinux is a publicly accessible Virtual Machine (VM) that enables scientists to quickly provision on-demand infrastructures for high-performance bioinformatics computing using cloud platforms. Users have instant access to a range of pre-configured command line and graphical software applications, including a full-featured desktop interface, documentation and over 135 bioinformatics packages for applications including sequence alignment, clustering, assembly, display, editing, and phylogeny. Each tool's functionality is fully described in the documentation directly accessible from the graphical interface of the VM. Besides the Amazon EC2 cloud, we have started instances of Cloud BioLinux on a private Eucalyptus cloud installed at the J. Craig Venter Institute, and demonstrated access to the bioinformatic tools interface through a remote connection to EC2 instances from a local desktop computer. Documentation for using Cloud BioLinux on EC2 is available from our project website, while a Eucalyptus cloud image and VirtualBox Appliance is also publicly available for download and use by researchers with access to private clouds., Conclusions: Cloud BioLinux provides a platform for developing bioinformatics infrastructures on the cloud. An automated and configurable process builds Virtual Machines, allowing the development of highly customized versions from a shared code base. This shared community toolkit enables application specific analysis platforms on the cloud by minimizing the effort required to prepare and maintain them.