Towards Index-based Similarity Search for Protein Structure Databases Authors: Orhan Çamoǧlu, Tamer Kahveci, Ambuj K. Singh Keywords: Protein structures, feature vectors, indexing, dataset join
Abstract:
We propose two methods for finding similarities in protein structure databases. Our techniques extract feature vectors on triplets of SSEs (Secondary Structure Elements) of proteins. These feature vectors are then indexed using a multidimensional index structure. Our first technique considers the problem of finding proteins similar to a given query protein in a protein dataset. This technique quickly finds promising proteins using the index structure. These proteins are then aligned to the query protein using a popular pairwise alignment tool such as VAST. We also develop a novel statistical model to estimate the goodness of a match using the SSEs. Our second technique considers the problem of joining two protein datasets to find an all-to-all similarity. Experimental results show that our techniques improve the pruning time of VAST 3 to 3.5 times while keeping the sensitivity similar.
Unless you want to do a project on bioinformatics indexing and topic maps, this paper probably isn’t of much interest.
I include it as an illustration of fashioning an domain specific index and for those who are interested, what subjects and their definitions lurk therein.
Questions (for those who want to pursue both topic maps and bioinformatics):
- Isolate all the “we chose” aspects of the paper. What results would have been different with other choices? The “we obtained best results…” is unsatisfying. In what sense “best results?”
- What aspects of this process would be amenable to use of a topic map?
- What about the results (if anything) would have to be different to make these results meaningful in a topic map to be merged with results by other researchers?