NBC Biomedical Informatics Core
From Informatics
Contents |
Current projects
From NBC side managed by Sean Conlan [sconlan@gmail.com]
Bioinformatics support: Pavel Morozov [pm259@columbia.edu]
Molecular Beacons for "Barcoding" Bacteria
Project description
Dr. David Alland's group is developing methods for differentiating bacteria using molecular beacons designed against the variable regions of the 16S ribosomal RNA. The method has been evaluated using a small set of bacteria and shows great promise. An algorithm is being developed at the BIC to find short sequence segments capable of differentiating between species over a range of temperatures. This will be accomplished using multiple alignments of ribosomal RNA from bacteria species combined with a Bayesian approach and multiparametric constrained optimization algorithms. A number of optimization techniques will be evaluated in order to find the most suitable model for beacon-target hybridization, including: simplex, annealing and Markov chain Monte Carlo methods.
People
- Sean Conlan [sc2623@columbia.edu]
- Pavel Morozov [pm259@columbia.edu]
- David Alland [Allandda@umdnj.edu]
- Soumitesh Chakravorty [chakraso@umdnj.edu]
Mass Tag PCR
Project description
MassTag PCR is a fast, sensitive and inexpensive tool for detecting pathogens in a multiplex format. The presence of an agent (or agents) is detected through the amplification of nucleic acids with primers coupled to discretely sized-tags (MassTags). Microbial nucleic acids (RNA, DNA, or both) are amplified by multiplex reverse transcription (RT)-PCR using primers coupled to photocleavable mass tag pairs. After removing unincorporated primers, tags are released by UV irradiation and analyzed by mass spectrometry. The identity of the microbe in the clinical sample is determined by its cognate tags. While the experimental part of the system uses traditional mass spectrometry equipment and technology, the data processing and analysis has a number of unique problems. In addition, MassTag PCR is highly scalable (96-well and 384-well formats) which means that a considerable volume of data is generated from each experiment. Dr. Thomas Briese is collaborating with the BIC to develop an analysis program for: signal preprocessing, background subtraction and automatic pathogen identification.
People
- Sean Conlan [sc2623@columbia.edu]
- Pavel Morozov [pm259@columbia.edu]
- Thomas Briese [thomas.briese@columbia.edu]
- Joseph Villari [jcv2112@columbia.edu]
- Michael Honig [mhonig@c2b2.columbia.edu]
