Hennig/Yan/Breland/Harris Lab Computer Science Project #1

Title:		Accelerated small RNA sequence matching using GPU & MP technologies.
Expertise:	Multi-processing, fragment shaders, OpenGL, GLSL, Text I/O

Background:	
			
			The expression and regulation of genetic information in cells has undergone rapid development over the last few years.  The well known paradigm of message RNA being transcribed from genes, which is then used as a template to construct proteins (translation) does not do justice to the layers of complexity which are now being appreciated by biologists.  With the discovery of small interfering RNA molecules (awarded the Nobel prize in ….) that can bind and inactivate message RNA, the idea that short sequences of RNA may regulate many parts of the genome is now being unravelled.  It is estimated that there are about 20 million RNA molecules in each cell of our body.  These RNA molecules can be transcribed from genes (mRNA) or from areas of the genome that have no genes - so called "non-coding" regions (e.g., miRNA, piRNA).  RNA molecules often undergo "maturation" after they are transcribed from DNA, where regions are deleted and the ends reconnected, in addition to shape changes (e.g. micro RNA hairpin structure).  It appears that short RNA sequences are crucial in the normal development of some cells, and are useful indicators to detect problems within cells.

New tools have made it much easier to extract small RNA molecules from cells and work out their sequence (A U G C nucleotides/bases).  Currently about 1 million RNA "reads" can be made from a cell, however it is expected to reach 10 million in the next year.  Within a few years the entire RNA content in a cell (20 million) will be able to be sequenced, the so called "transcriptome".  This gives researchers an unprecedented look at how cells operate and is likely to usher in a golden age of genomic biology.

Analysis typically is farmed out to large CPU clusters to do sequence alignment (matching the small RNA to the region of the ~4GB of DNA that it was transcribed from).  Many other forms of analysis require more flexible code which may not need such "heavy-lifting" capacity, such as aligning different types of RNA to each other (e.g. siRNA to mRNA).  Over the past few years, I have developed flexible software (C / OpenGL interface) that can be used for a variety of analysis scenarios.  While the analysis works well, it suffers from being single threaded - and some alignments can lasts for days…

The project described below would have a major impact in this emerging field, allowing biologist to leverage the power of multi-processes and GPU streams to do the majority of RNA analysis on a desktop computer.  This would advance the field much more rapidly compared to what is currently done, and would put the control and flexibility of analysis back in the hands of the biologist.

Outline:

			• Conversion of 4 character text in RNAs (multiple) to  2D / 3D RGBA textures.
			• Coding sequence comparison engine/s for multi-CPU and GPU environments.
			• Prioritization of texture memory for both fixed (CPU or GPU) or mixed (CPU+GPU) environments.
			• Sequence composition analysis for faster alignment (Breland).