Biocomputing

The Facility

The CGRB Biocomputing and Bioinformatics facility provides wide-ranging resources, expertise and support for computational needs of the molecular biosciences community at OSU. The facility offers a robust computing environment for high-level computational biology and a versatile intellectual resource for interdisciplinary collaboration.

Since 2001, the CGRB has focused investments towards a scalable and distributed infrastructure capable of supporting highly parallel computing. Based extensively on open-source technologies and the Linux operating system, the infrastructure includes servers that host websites, handle much of the molecular sequence database search load (BLAST, FASTA, HMMER), and function as the SQL database server on which much of our custom-designed software relies. To manage the ever-increasing amount of data generated by new technologies like the Illumina DNA sequencer, the infrastructure also includes nearly 100TB of shared disk space for storage and backup purposes. The CGRB leads a significant software development effort that allows researchers across campus to tap into the power of biocomputing.

The infrastructure also features "Genome", a 330-node Beowulf-style, Linux-based compute-farm. Nodes in Genome range from single-processor to multi-processor multi-core machines with over 32GB of RAM. The CGRB encourages faculty to contribute to the infrastructure by purchasing server-class machines and nodes in Genome. Faculty have priority access to their own machines, which also become integrated into the overall computational infrastrucuture. This agglomerative strategy allows bioscientists to accomplish computationally-intensive research with only moderate hardware investments.

In addition to developing a scaleable hardware infrastructure, the CGRB has grown significantly as an intellectual resource for computational biology at OSU. This represents a major attraction for new research faculty recruited to OSU, and offers a vehicle through which faculty can development their own tools and resources for computational biology at OSU.

CGRB Biocomputing Group

Scott Givan, Assistant Professor in the Department of Botany and Plant Pathology, serves as Bioinformatics Coordinator for the CGRB. Dr. Givan has extensive expertise in development of bioinformatics applications and databases.

Chris Sullivan serves as the CGRB systems administrator and has extensive expertise in all aspects of hardware and infrastructure design and maintenance. Chris also develops software and applications.

Steve Drake develops and maintains the CGRB Core Laboratory computational environment, which enables Core Lab and researches to place orders, access data and monitor progress with jobs. With his experience in various programming languages, Steve will increasingly be involved with biocomputing projects in the future.

Publications

The quality of the CGRB Computational Group is reflected in several recent high-profile publications resulting from collaborations, including:

• Giovannoni et al. (2005) Genome streamlining in a cosmopolitan oceanic bacterium. Science 309, 1242-1245.
• Allen et al. (2005) microRNA-directed phasing during trans-acting siRNA biogenesis in plants. Cell 121, 207-221.
• Tilton et al. (2006). Toxicogenomic Profiling of the Hepatic Tumor Promoters Indole-3-Carbinol, 17{beta}-estradiol and {beta}-naphthoflavone in Rainbow Trout. Toxicological Sciences 90, 61-72.
• Kasschau et al. (2007). Genome-Wide Profiling and Analysis of Arabidopsis siRNAs. PLoS Biology 5, e57.
• Fahlgren et al. (2007).  High-Throughput Sequencing of Arabidopsis microRNAs: Evidence for Frequent Birth and Death of MIRNA Genes.  PLoS ONE. 2, e219.
• Howell et al. (2007).  Genome-Wide Analysis of the RNA-DEPENDENT RNA POLYMERASE6/DICER-LIKE4 Pathway in Arabidopsis Reveals Dependency on miRNA- and tasiRNA-Directed Targeting.  The Plant Cell 19, 926-942.
• Backman et al. (2008). Update of ASRP: The Arabidopsis Small RNA Project Database. Nucleic Acids Research 36(Database issue):D982-5.
• Michael et al. (2008). Network discovery pipeline elucidates conserved time of day specific cis-regulatory modules. PLoS Genetics 4(2): e14. doi:10.1371/journal.pgen.0040014