BACKGROUND: Computation of reaction similarity is a pre-requisite for several bioinformatics applications including enzyme identification for specific biochemical reactions, enzyme classification and mining for specific inhibitors. Reaction similarity is often assessed at either two levels: (i) comparison across all the constituent substrates and products of a reaction, reaction level similarity, (ii) comparison at the transformation center with various degrees of neighborhood, transformation level similarity. Existing reaction similarity computation tools are designed for specific applications and use different features and similarity measures. A single system integrating these diverse features enables comparison of the impact of different molecular properties on similarity score computation. RESULTS: To address these requirements, we present SimCAL, an integrated system to calculate reaction similarity with novel features and capability to perform comparative assessment. SimCAL provides reaction similarity computation at both whole reaction level and transformation level. Novel physicochemical features such as stereochemistry, mass, volume and charge are included in computing reaction fingerprint. Users can choose from four different fingerprint types and nine molecular similarity measures. Further, a comparative assessment of these features is also enabled. The performance of SimCAL is assessed on 3,688,122 reaction pairs with Enzyme Commission (EC) number from MetaCyc and achieved an area under the curve (AUC) of > 0.9. In addition, SimCAL results showed strong correlation with state-of-the-art EC-BLAST and molecular signature based reaction similarity methods. CONCLUSIONS: SimCAL is developed in java and is available as a standalone tool, with intuitive, user-friendly graphical interface and also as a console application. With its customizable feature selection and similarity calculations, it is expected to cater a wide audience interested in studying and analyzing biochemical reactions and metabolic networks.

MOTIVATION: Biochemical pathways engineering is often used to synthesize or degrade target chemicals. In silico screening of the biochemical transformation space allows predicting feasible reactions, constituting these pathways. Current enabling tools are customized to predict reactions based on pre-defined biochemical transformations or reaction rule sets. Reaction rule sets are usually curated manually and tailored to specific applications. They are not exhaustive. In addition, current systems are incapable of regulating and refining data with an aim to tune specificity and sensitivity. A robust and flexible tool that allows automated reaction rule set creation along with regulated pathway prediction and analyses is a need. ReactPRED aims to address the same. RESULTS: ReactPRED is an open source flexible and customizable tool enabling users to predict biochemical reactions and pathways. The tool allows automated reaction rule creation from a user defined reaction set. Additionally, reaction rule degree and rule tolerance features allow refinement of predicted data. It is available as a flexible graphical user interface and a console application. AVAILABILITY AND IMPLEMENTATION: ReactPRED is available at: https://sourceforge.net/projects/reactpred/ CONTACT: [email protected] or [email protected] information: Supplementary data are available at Bioinformatics online.

This paper presents an efficient parallel-and-distributed string matching scheme in a heterogeneous environment. The contribution of the proposed approach is in two-fold; exploiting a non-trivial parallelism in each node that possibly is SMP, and distributed processing with dynamic load balancing on a network-connected system. For the parallel processing in each node, a multithreaded computation model is designed and implemented with Java Threads. For the distributed processing, a dynamic load balancing method is developed and implemented with Java RMI. The proposed approach manipulates both exact string matching and approximate string matching with k-mismatches. Experimental results demonstrate that the approach is an efficient parallel-and-distributed solution to the problem in a clustered system that is either homogeneous or heterogeneous.

This paper presents a design and implementation of a reconfigurable parallel approximate string matching hardware on FPGAs. The design is based on a linear systolic dataflow algorithm, and control logic is added to reconfigure the resulting hardware. For the k-differences version of the approximate string matching problem, the proposed approach finds all approximate occurrences of a pattern in the reference string, with the time complexity O(n+m) where n and m are lengths of the reference string and the pattern, respectively. Unlike other hardware approaches found in the literature, the design is size optimized since it uses only m PEs that are independent on the reference string length. Also the design is flexible for handling arbitrary size pattern strings within the maximum bound. The design is implemented and tested on the target device Xilinx Spartan 2S XC2S200EPQ208.