MVEX01-15-27 Time performance optimization for attC sites detection

​Antibiotics are efficient drugs against infections caused by bacteria. However, bacteria can quickly become resistant against antibiotics which require new drugs to be constantly developed resulting in a process that is associated with high costs. Identification and understanding of antibiotic resistance and how it spreads is important to keep the design of new antibiotics feasible. Antibiotic resistance is often caused by the horizontal transfer of genes in which the bacteria can share genetic information between different organisms. Antibiotic resistance can therefore spread rapidly, causing untreatable infections all over the world.

In our group, we have created a probabilistic model, namely a conditional random field (CRF), to identify genes that have been horizontally transferred by detecting the attC sites, a 59 bases element that mediates gene transfer. The model has so far been implemented in R. Now it needs to be optimized so that it can deal with our large datasets.
The tasks in this project comprise:
1. Reading the existing code
2. Implement the model into C
3. Evaluation the performance gain and present the results
An introductory lecture on the related biology and our model will be held in the beginning. Our existing code will be provided for part 1. For part 2 your programming skills will be developed further. Part 3 will develop presentation and discussion of results skills; in an iterative loop it will get back to part 2 and improve the code further.

 

Obs! För GU-studenter räknas projektet som ett projekt i Matematisk Statistik (MSG900/MSG910).
 
Projektkod MVEX01-15-27
Gruppstorlek 3-4
Speciella förkunskapskrav Very good programming skills, experience in C or C++.
Handledare Mariana Buongermino Pereira, 031-7723558 , mariana.pereira@chalmers.se
Examinator Maria Roginskaya
Institution Matematiska vetenskaper

Publicerad: ti 04 nov 2014. Ändrad: to 06 nov 2014