Bioinformatics Methods Development for Short-read Sequencing ("nextgen") Genomics Studies in Neuroscience
The major challenge in basic biological research is rapidly shifting: From the challenge of efficient data acquisition (lots of pipetting, numerous experiments) to a challenge of data analysis and integration.
Every molecular neurobiology experiment currently performed in our laboratory (such as experiments seeking to analyze RNA transcript structure or abundance) is designed to provide unbiased whole genome views of the molecular problem under consideration. We are taking advantage of the revolutionary sequencing technologies developed over the past five years. Covering thousands of genes, responsible for tens of thousands of transcripts, under the control of hundreds of thousands of epigenomic controls is exhilarating yet at times overwhelming. The result from a single tissue site such as the dorsal root ganglion may be billions and billions of base pairs. How can we make sense of it?
Computer science is rapidly moving toward the center of most of our work. Our questions resemble the alignment problems at the National Security Agency — Hidden Markov Models were originally developed for speech recognition — or data mining at Google more closely than classic analysis of laboratory experiments (bye-bye Excel… welcome Perl, C and R). Our laboratory team currently includes a full-time statistician and two bioinformaticians with primary degrees in quantitative sciences, all are fluent programmers.
In addition, we are enjoying ongoing guidance from international leaders in statistics and bioinformatics among the faculty of our own institution (Dr. Terry Therneau and Dr. Peter Li), as well as active national (Drs. Gary Schroth and Irina Khrebtukova) and international collaborations (Dr. Peter Beyerlein) in bioinformatics.
Please, visit the site of the Beyerlein laboratory at the University of Applied Sciences, Wildau, Germany, and check out our new software package for illumina mRNA-seq analysis.