DOPA: GPU-based protein alignment using database and memory access optimizations

Background Smith-Waterman (S-W) algorithm is an optimal sequence alignment method for biological databases, but its computational complexity makes it too slow for practical purposes. Heuristics based approximate methods like FASTA and BLAST provide faster solutions but at the cost of reduced accuracy. Also, the expanding volume and varying lengths of sequences necessitate performance efficient restructuring of these databases. Thus to come up with an accurate and fast solution, it is highly desired to speed up the S-W algorithm. Findings This paper presents a high performance protein sequence alignment implementation for Graphics Processing Units (GPUs). The new implementation improves performance by optimizing the database organization and reducing the number of memory accesses to eliminate bandwidth bottlenecks. The implementation is called Database Optimized Protein Alignment (DOPA) and it achieves a performance of 21.4 Giga Cell Updates Per Second (GCUPS), which is 1.13 times better than the fastest GPU implementation to date. Conclusions In the new GPU-based implementation for protein sequence alignment (DOPA), the database is organized in equal length sequence sets. This equally distributes the workload among all the threads on the GPU's multiprocessors. The result is an improved performance which is better than the fastest available GPU implementation.MicroelectronicsElectrical Engineering, Mathematics and Computer Scienc

Accelerated large-scale multiple sequence alignment

<p>Abstract</p> <p>Background</p> <p>Multiple sequence alignment (MSA) is a fundamental analysis method used in bioinformatics and many comparative genomic applications. Prior MSA acceleration attempts with reconfigurable computing have only addressed the first stage of progressive alignment and consequently exhibit performance limitations according to Amdahl's Law. This work is the first known to accelerate the third stage of progressive alignment on reconfigurable hardware.</p> <p>Results</p> <p>We reduce subgroups of aligned sequences into discrete profiles before they are pairwise aligned on the accelerator. Using an FPGA accelerator, an overall speedup of up to 150 has been demonstrated on a large data set when compared to a 2.4 GHz Core2 processor.</p> <p>Conclusions</p> <p>Our parallel algorithm and architecture accelerates large-scale MSA with reconfigurable computing and allows researchers to solve the larger problems that confront biologists today. Program source is available from <url>http://dna.cs.byu.edu/msa/</url>.</p

A Systematic Survey of Mini-Proteins in Bacteria and Archaea

BACKGROUND: Mini-proteins, defined as polypeptides containing no more than 100 amino acids, are ubiquitous in prokaryotes and eukaryotes. They play significant roles in various biological processes, and their regulatory functions gradually attract the attentions of scientists. However, the functions of the majority of mini-proteins are still largely unknown due to the constraints of experimental methods and bioinformatic analysis. METHODOLOGY/PRINCIPAL FINDINGS: In this article, we extracted a total of 180,879 mini-proteins from the annotations of 532 sequenced genomes, including 491 strains of Bacteria and 41 strains of Archaea. The average proportion of mini-proteins among all genomic proteins is approximately 10.99%, but different strains exhibit remarkable fluctuations. These mini-proteins display two notable characteristics. First, the majority are species-specific proteins with an average proportion of 58.79% among six representative phyla. Second, an even larger proportion (70.03% among all strains) is hypothetical proteins. However, a fraction of highly conserved hypothetical proteins potentially play crucial roles in organisms. Among mini-proteins with known functions, it seems that regulatory and metabolic proteins are more abundant than essential structural proteins. Furthermore, domains in mini-proteins seem to have greater distributions in Bacteria than Eukarya. Analysis of the evolutionary progression of these domains reveals that they have diverged to new patterns from a single ancestor. CONCLUSIONS/SIGNIFICANCE: Mini-proteins are ubiquitous in bacterial and archaeal species and play significant roles in various functions. The number of mini-proteins in each genome displays remarkable fluctuation, likely resulting from the differential selective pressures that reflect the respective life-styles of the organisms. The answers to many questions surrounding mini-proteins remain elusive and need to be resolved experimentally

Heterogeneous Cloud Systems Based on Broadband Embedded Computing

Computing systems continue to evolve from homogeneous systems of commodity-based servers within a single data-center towards modern Cloud systems that consist of numerous data-center clusters virtualized at the infrastructure and application layers to provide scalable, cost-effective and elastic services to devices connected over the Internet. There is an emerging trend towards heterogeneous Cloud systems driven from growth in wired as well as wireless devices that incorporate the potential of millions, and soon billions, of embedded devices enabling new forms of computation and service delivery. Service providers such as broadband cable operators continue to contribute towards this expansion with growing Cloud system infrastructures combined with deployments of increasingly powerful embedded devices across broadband networks. Broadband networks enable access to service provider Cloud data-centers and the Internet from numerous devices. These include home computers, smart-phones, tablets, game-consoles, sensor-networks, and set-top box devices. With these trends in mind, I propose the concept of broadband embedded computing as the utilization of a broadband network of embedded devices for collective computation in conjunction with centralized Cloud infrastructures. I claim that this form of distributed computing results in a new class of heterogeneous Cloud systems, service delivery and application enablement. To support these claims, I present a collection of research contributions in adapting distributed software platforms that include MPI and MapReduce to support simultaneous application execution across centralized data-center blade servers and resource-constrained embedded devices. Leveraging these contributions, I develop two complete prototype system implementations to demonstrate an architecture for heterogeneous Cloud systems based on broadband embedded computing. Each system is validated by executing experiments with applications taken from bioinformatics and image processing as well as communication and computational benchmarks. This vision, however, is not without challenges. The questions on how to adapt standard distributed computing paradigms such as MPI and MapReduce for implementation on potentially resource-constrained embedded devices, and how to adapt cluster computing runtime environments to enable heterogeneous process execution across millions of devices remain open-ended. This dissertation presents methods to begin addressing these open-ended questions through the development and testing of both experimental broadband embedded computing systems and in-depth characterization of broadband network behavior. I present experimental results and comparative analysis that offer potential solutions for optimal scalability and performance for constructing broadband embedded computing systems. I also present a number of contributions enabling practical implementation of both heterogeneous Cloud systems and novel application services based on broadband embedded computing

From Sequence to Structure And Back Again: An Alignment Tale

Heringa, J. [Promotor