MC64: A web platform to test bioinformatics algorithms in a many-core architecture

New analytical methodologies, like the so-called "next-generation sequencing" (NGS), allow the sequencing of full genomes with high speed and reduced price. Yet, such technologies generate huge amounts of data that demand large raw computational power. Many-core technologies can be exploited to overcome the involved bioinformatics bottleneck. Indeed, such hardware is currently in active development. We have developed parallel bioinformatics algorithms for many-core microprocessors containing 64 cores each. Thus, the MC64 web platform allows executing high-performance alignments (Needleman-Wunsch, Smith-Waterman and ClustalW) of long sequences. The MC64 platform can be accessed via web browsers, allowing easy resource integration into third-party tools. Furthermore, the results obtained from the MC64 include time-performance statistics that can be compared with other platform

Towards Reliable Automatic Protein Structure Alignment

A variety of methods have been proposed for structure similarity calculation, which are called structure alignment or superposition. One major shortcoming in current structure alignment algorithms is in their inherent design, which is based on local structure similarity. In this work, we propose a method to incorporate global information in obtaining optimal alignments and superpositions. Our method, when applied to optimizing the TM-score and the GDT score, produces significantly better results than current state-of-the-art protein structure alignment tools. Specifically, if the highest TM-score found by TMalign is lower than (0.6) and the highest TM-score found by one of the tested methods is higher than (0.5), there is a probability of (42%) that TMalign failed to find TM-scores higher than (0.5), while the same probability is reduced to (2%) if our method is used. This could significantly improve the accuracy of fold detection if the cutoff TM-score of (0.5) is used. In addition, existing structure alignment algorithms focus on structure similarity alone and simply ignore other important similarities, such as sequence similarity. Our approach has the capacity to incorporate multiple similarities into the scoring function. Results show that sequence similarity aids in finding high quality protein structure alignments that are more consistent with eye-examined alignments in HOMSTRAD. Even when structure similarity itself fails to find alignments with any consistency with eye-examined alignments, our method remains capable of finding alignments highly similar to, or even identical to, eye-examined alignments.Comment: Peer-reviewed and presented as part of the 13th Workshop on Algorithms in Bioinformatics (WABI2013

Soft topographic map for clustering and classification of bacteria

In this work a new method for clustering and building a topographic representation of a bacteria taxonomy is presented. The method is based on the analysis of stable parts of the genome, the so-called “housekeeping genes”. The proposed method generates topographic maps of the bacteria taxonomy, where relations among different type strains can be visually inspected and verified. Two well known DNA alignement algorithms are applied to the genomic sequences. Topographic maps are optimized to represent the similarity among the sequences according to their evolutionary distances. The experimental analysis is carried out on 147 type strains of the Gammaprotebacteria class by means of the 16S rRNA housekeeping gene. Complete sequences of the gene have been retrieved from the NCBI public database. In the experimental tests the maps show clusters of homologous type strains and present some singular cases potentially due to incorrect classification or erroneous annotations in the database

SimSearch: A new variant of dynamic programming based on distance series for optimal and near-optimal similarity discovery in biological sequences

http://www.informatik.uni-trier.de/%7Eley/db/conf/iwpacbb/iwpacbb2008.htmlIn this paper, we propose SimSearch, an algorithm implementing a new variant of dynamic programming based on distance series for optimal and near-optimal similarity discovery in biological sequences. The initial phase of SimSearch is devoted to fulfil the binary similarity matrices by signalling the distances between occurrences of the same symbol. The scoring scheme is further applied, when analysed the maximal extension of the pattern. Employing bit parallelism to analyse the global similarity matrix’s upper triangle, the new methodology searches the sequence(s) for all the exact and approximate patterns in regular or reverse order. The algorithm accepts parameterization to work with greater seeds for near-optimal results. Performance tests show significant efficiency improvement over traditional optimal methods based on dynamic programming. Comparing the new algorithm’s efficiency against heuristic based methods, equalizing the required sensitivity, the proposed algorithm remains acceptable.This work has been partially supported by PRODEP

Computer-aided design of nano-filter construction using DNA self-assembly

Computer-aided design plays a fundamental role in both top-down and bottom-up nano-system fabrication. This paper presents a bottom-up nano-filter patterning process based on DNA self-assembly. In this study we designed a new method to construct fully designed nano-filters with the pores between 5 nm and 9 nm in diameter. Our calculations illustrated that by constructing such a nano-filter we would be able to separate many molecules

Rigid and Non-rigid Shape Matching for Mechanical Components Retrieval

Reducing the setup time for a new production line is critical to the success of a manufacturer within the current competitive and cost-conscious market. To this end, being able to reuse already available machines, toolings and parts is paramount. However, matching a large warehouse of previously engineered parts to a new component to produce, is often more a matter of art and personal expertise rather than predictable science. In order to ease this process we developed a database retrieval approach for mechanical components that is able to deal with both rigid matching and deformable shapes. The intended use for the system is to match parts acquired with a 3D scanning system to a large database of components and to supply a list of results sorted according with a metric that expresses a structural distance. © 2012 IFIP International Federation for Information Processing