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Brian Hilbush, VP of Discovery Biology, Digital Gene Technologies



 BS in cell and molecular biology from the University of Washington, PhD in neurobiology from the State University of New York at Stony Brook.

Focused on gene expression in the central nervous system as a postdoctoral fellow in the laboratory of James Morgan at the Roche Institute of Molecular Biology.

Enjoys cooking, running, and hiking.

QWhat role does bioinformatics play at Digital Gene Technologies? What are some of the unique bioinformatics aspects of the company’s TOGA technology?

AWe have a bioinformatics department that really plays a support role within the company’s gene expression-based research. They provide integrated software and data management tools for our TOGA gene expression technology. We have a LIMS group and a gene expression informatics group. Those groups, along with the computational biology group, are the three teams that we have in bioinformatics.

When we first started here it was clear that bioinformatics was going to play a big role. We saw that with gene expression profiling, you needed an unambiguous way to account for both known and novel mRNAs that are in a population of RNAs when you’re doing gene expression. TOGA allows you to derive a unique digital address for every mRNA. With that, you can run TOGA virtually on any public sequence database and that information can be merged alongside our gene expression data.

That allows us to predict which genes are known and active in a cell and we can instantly identify any of those that are potentially novel. These can be from RNA samples from any eukaryotic species. So unlike chip experiments where you’re limited to those species for which chips have been built — mouse, human, rat, and so forth — we really can go into any experimental model and do a large-scale gene expression study and then build databases from that.

QWhat bioinformatics software do you use?

AWe have built the software, algorithms, and tools that are most critical to our business: for TOGA gene expression. Most of the third-party software is for sequence database- and sequence analysis-related things. We use, for example, the Accelrys products. We have the SeqStore database and the GCG Wisconsin package for the analysis programs. We also have Paracel’s PCP product for EST clustering and assembly.

QWhich databases do you use?

AWe use both public and in-house sequence databases. We don’t go to any third-party sequence databases. We have our own gene expression database.

QHow do you integrate your data?

AWe’ve spent a lot of effort in this area and have taken two approaches on a technical level to do that. We have middle-tier web applications that use languages like Perl to bring together data that come from different sources. We tie that together in a web-enabled front-end user interface. We also take an object-oriented approach with Enterprise Java Beans to accomplish the same thing in another Java-based application for the gene expression data.

QWhat are the biggest challenges bioinformatics must overcome?

AFrom an organizational standpoint, you’re bringing together talented individuals from a variety of disciplines and they have to work together as a team to create really good software tools to solve complex analysis issues. We’ve had tremendous success when this works well, but by nature these guys are individualistic and they have a personal interest in what they’re developing, so it’s always a challenge to make everyone work in a team format to pull off a project.

QWhat non-existing technology do you most wish you had?

APersonally, it would be great to be able to do real-time in vivo monitoring of gene expression, receptor-ligand interactions, and neural activity. Being able to get into living systems and instead of just taking little snapshots in the experiments that we now do, place in nano-scale sensors and track a lot of this activity simultaneously — sort of Buck Rogers kind of stuff!

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