Expertise and hardware for systems biology analyses that distill large-scale regulation data to specific pathways leading to prediction of molecular manipulations to alter cell and tissue function.

NextGen Sequencing

The facility provides sequencing DNA and RNA services for varied applications. The instrumentation includes an Illumina NextSeq 500 Next Generation sequencer, an Illumina Neoprep system for automated library preparation, an Agilent genetic analyzer for quality control, a QIAgility pipetting robot and a BluePippin automated nucleic acid selection system for applications benefiting from targeted size sequencing. For more information go to

Location: SDSU
Contact: Jose Gonzalez

High Performance Computing

HPC Systems include 3 clusters 1) Bigjack IBM iDataPlex Linux cluster 71 nodes with 12 processor cores (852 cores) and 48 GB RAM/node.  Nine nodes have dual Nvidia Tesla M2090 graphic processing units (GPUs). The nodes have Mellanox InfiniBand low-latency interconnect, two nodes have host bus adaptors enabling presentation to SAN Block storage which is presented to nodes through Ethernet layer NFS file system to all nodes. Jobs are submitted to the cluster through a Moab/Torque scheduler and resource manager. One node of the cluster is dedicated to remote visualization use; 2) Silvertip2 and Silvertip3 clusters each with 24cores and 512 GB RAM and 3) IBM BladeCenter, 22 AMD Opteron nodes.  The SDSU HPC is supported by one lead, two science domain specialist and two staff systems specialists.

Inter-institute connectivity: All partner institutes of BioSNTR are part of South Dakota’s 10G grid (REED). Therefore, data transfer between institutions is possible for both genomics and imaging experiments. The major limitation is that only specific buildings in each institution have 10G infrastructure hardware. We have identified specific locations in each institution and will situate genomics and imaging equipment in these locations to enable fast data transfer.

Location: SDSU
Contact: Anne Fennell, Padmapriya Swaminathan

Algorithm Development

Systems-level analysis to predict genotype-phenotype linkages. Pathway-level modeling to associate protein-signaling activities with transcriptional activities. New image analysis methods for high-content data and systems models.

Location: Multiple campuses
Contact: Please contact us for more information about the faculty contacts at each campus

BioQuery Tool Discovery System (Bio-TDS)

The Bio-TDS (Bioscience Query Tool Discovery Systems) has been developed to assist researchers in retrieving the most applicable analytic tools by allowing them to formulate their questions as free text. It is a flexible retrieval system that affords users from multiple bioscience domains (e.g. genomic, proteomic, bio-imaging) the ability to query over 12 000 analytic tool descriptions integrated from well-established, community repositories. One of the primary components of the Bio-TDS is the ontology and natural language processing workflow for annotation, curation, query processing, and evaluation.Click here for more information.

Location: Online at
Contact: Carol Lushbough, Etienne Gnimpieba

Servers & Data Storage

BioSNTR server 'Gene', is a 32-core Linux (OpenSUSE 13.1) machine with 1 TB RAM that has been equipped with a 100 TB SAN storage to facilitate processing space for users to run jobs requiring high RAM applications.

SDSU BioSNTR Tier 4 storage is a file server shared through the 10 GB Ethernet. This storage is facilitates BioSNTR researchers ability to store their files through immediate access storage.  BioSNTR Tier 4 storage is backed up to tapes.

OMERO (Open Microscopy Environment Remote Objects) is a client-server software platform for visualizing, managing, and annotating scientific image data. It enables researchers to import and archive images, annotate and tag them, record the experimental protocols, and export images in a number of formats. Click here for more information.

Genomic Editing

Dr. Hoppe is collaborating with the Drubin Lab at UC Berkeley to use genome-editing to insert genes coding fluorescent proteins (FP) in-frame with the coding regions for proteins of interest. They have recently launched a new project that will use genome-editing to study the interplay between membrane curvature and the assembly of molecular signaling complexes on the plasma membranes of living cells. Here this technology will be used to make precise genetic manipulations in short periods of time for modulating signaling pathways for testing tissue engineering approaches. The infrastructure for application of genome-editing to tissue engineering will be disseminated to BioSNTR by technician Jason Kerkvliet, who is currently working with the technology in the Hoppe Lab. He will provide the expertise and training needed for genomic editing in plant and animal cells.