Efficient Shared Peak Counting in Database Peptide Search Using Compact Data Structure for FragmentIon Index Database search is the most commonly employed method for identification of peptides from MSMS spectra data. The search involves comparing experimentally obtained MSMS spectra against a set of theoretical spectra predicted from a protein sequence database. One of the most commonly employed similarity metrics for spectral comparison is the sharedpeak count between a pair of MSMS spectra. Most modern methods index all generated fragmention data from theoretical spectra to speed up the shared peak count computations between a given experimental spectrum and all theoretical spectra. However, the bottleneck for this method is the gigantic memory footprint of fragmention index that leads to nonscalable solutions. In this paper, we present a novel data structure, called Compact FragmentIon Index Representation (CFIR), that efficiently compresses highly redundant ionmass information in the data to reduce the index size. Our proposed data structure outperforms all existing fragmention indexing data structures by at least 2xc3x97 in memory consumption while exhibiting the same time complexity for index construction and peptide search. The results also show comparable indexing speed, search speed and speedup scalability for CFIRindex and the stateoftheart algorithms.

Accelerating LargeScale Molecular Similarity Search through Exploiting High Performance Computing Molecular similarity search is a simple but powerful chemoinformatics tool to rapidly find molecules that are structurally similar to a known reference compound from a large molecular database. A variety of indexing structures had been developed to improve the performance of similarity search over the large compound database. However, those algorithms often require a large computational cost to build indices and process queries, especially for a largescale molecular dataset. We study the problem of accelerating similarity search using high performance computing (HPC) and design general algorithms to speed up existing indexing algorithms. We first propose a parallel algorithm based on data chunking, working for all indexing algorithms for similarity search. We theoretically analyze its computation cost and relationships between the speedup and number of data chunks. We further propose a parallel query algorithm for all graphbased indexing algorithms to accelerate their query processing in HPC. Both of our algorithms consistently offer a greater speedup than the baseline algorithm(s) when evaluated with different datasets and parameter settings.