Summary: Chemical-genomic approaches that map interactions between small molecules and genetic perturbations offer a promising strategy for functional annotation of uncharacterized bioactive compounds. We recently developed a new high-throughput platform for mapping chemical-genetic (CG) interactions in yeast that can be scaled to screen large compound collections, and we applied this system to generate CG interaction profiles for more than 13 000 compounds. When integrated with the existing global yeast genetic interaction network, CG interaction profiles can enable mode-of-action prediction for previously uncharacterized compounds as well as discover unexpected secondary effects for known drugs. To facilitate future analysis of these valuable data, we developed a public database and web interface named MOSAIC. The website provides a convenient interface for querying compounds, bioprocesses (Gene Ontology terms) and genes for CG information including direct CG interactions, bioprocesses and gene-level target predictions. MOSAIC also provides access to chemical structure information of screened molecules, chemical-genomic profiles and the ability to search for compounds sharing structural and functional similarity. This resource will be of interest to chemical biologists for discovering new small molecule probes with specific modes-of-action as well as computational biologists interested in analysing CG interaction networks. Availability and implementation: MOSAIC is available at http://mosaic.cs.umn.edu. Contact: [email protected], [email protected], [email protected] or [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

Chemical-genetic interactions-observed when the treatment of mutant cells with chemical compounds reveals unexpected phenotypes-contain rich functional information linking compounds to their cellular modes of action. To systematically identify these interactions, an array of mutants is challenged with a compound and monitored for fitness defects, generating a chemical-genetic interaction profile that provides a quantitative, unbiased description of the cellular function(s) perturbed by the compound. Genetic interactions, obtained from genome-wide double-mutant screens, provide a key for interpreting the functional information contained in chemical-genetic interaction profiles. Despite the utility of this approach, integrative analyses of genetic and chemical-genetic interaction networks have not been systematically evaluated. We developed a method, called CG-TARGET (Chemical Genetic Translation via A Reference Genetic nETwork), that integrates large-scale chemical-genetic interaction screening data with a genetic interaction network to predict the biological processes perturbed by compounds. In a recent publication, we applied CG-TARGET to a screen of nearly 14,000 chemical compounds in Saccharomyces cerevisiae, integrating this dataset with the global S. cerevisiae genetic interaction network to prioritize over 1500 compounds with high-confidence biological process predictions for further study. We present here a formal description and rigorous benchmarking of the CG-TARGET method, showing that, compared to alternative enrichment-based approaches, it achieves similar or better accuracy while substantially improving the ability to control the false discovery rate of biological process predictions. Additional investigation of the compatibility of chemical-genetic and genetic interaction profiles revealed that one-third of observed chemical-genetic interactions contributed to the highest-confidence biological process predictions and that negative chemical-genetic interactions overwhelmingly formed the basis of these predictions. We also present experimental validations of CG-TARGET-predicted tubulin polymerization and cell cycle progression inhibitors. Our approach successfully demonstrates the use of genetic interaction networks in the high-throughput functional annotation of compounds to biological processes.

Future computing systems will feature many cores that run fast, but might show more faults compared to existing CMOS technologies. New software methodologies must be adopted to utilize communication bandwidth and the computational power of few slow, reliable cores that could be employed in such systems to verify the results of the fast, faulty cores. Employing the traditional Triple Module Redundancy (TMR) at core instruction level would not be as effective due to its blind replication of computations. We propose two software development methods that utilize what we call Smart TMR (STMR) and fingerprinting to statistically monitor the results of computations and selectively replicate computations that exhibit faults. Experimental results show significant speedup and reliability improvement over traditional TMR approaches.

The recent growth of cellular phone systems, voice over IP devices, and other multimedia applications has created a considerable need for efficient voice coding algorithms. These algorithms usually require intensive amount of signal processing capabilities and demand significant signal processing power. The current market trend of integrating multiple voice channels into a single die has further intensified the need for more powerful hardware platforms. Some new design ideas such as vocoder-specialized DSP architectures, combined RISC/DSP platforms, and adding hardware accelerators or coprocessors to the general-purpose processors have been proposed. In this paper, a new hardware accelerator design has been proposed which executes macro instructions (MIs). The proposed coprocessor can be added to each processor type that can support at least one coprocessor without modifying the compiler and redesigning the processor. It can handle computationally intensive loops in speech coding algorithms parallel with the main processor. The coprocessor along with software optimization reduces clock cycles required for G.723.1 by 80% and G.729 by 64% while MIPS R3000 RISC is used as the host.