A two-base encoded DNA sequence alignment problem in computational biology

The recent introduction of instruments capable of producing millions of DNA sequence reads in a single run is rapidly changing the landscape of genetics. The primary objective of the "sequence alignment" problem is to search for a new algorithm that facilitates the use of two-base encoded data for large-scale re-sequencing projects. This algorithm should be able to perform local sequence alignment as well as error detection and correction in a reliable and systematic manner, enabling the direct comparison of encoded DNA sequence reads to a candidate reference DNA sequence. We will first briefly review two well-known sequence alignment approaches and provide a rudimentary improvement for implementation on parallel systems. Then, we carefully examin a unique sequencing technique known as the SOLiDTM System that can be implemented, and follow by the results from the global and local sequence alignment. In this report, the team presents an explanation of the algorithms for color space sequence data from the high-throughput re-sequencing technology and a theoretical parallel approach to the dynamic programming method for global and local alignment. The combination of the di-base approach and dynamic programming provides a possible viewpoint for large-scale re-sequencing projects. We anticipate the use of distributed computing to be the next-generation engine for large-scale problems like such

A Parallel Non-Alignment Based Approach to Efficient Sequence Comparison using Longest Common Subsequences

Biological sequence comparison programs have revolutionized the practice of biochemistry, and molecular and evolutionary biology. Pairwise comparison of genomic sequences is a popular method of choice for analyzing genetic sequence data. However the quality of results from most sequence comparison methods are significantly affected by small perturbations in the data and furthermore, there is a dearth of computational tools to compare sequences beyond a certain length. In this paper, we describe a parallel algorithm for comparing genetic sequences using an alignment free-method based on computing the Longest Common Subsequence (LCS) between genetic sequences. We validate the quality of our results by comparing the phylogenetic tress obtained from ClustalW and LCS. We also show through complexity analysis of the isoefficiency and by empirical measurement of the running time that our algorithm is very scalable

Protein Alignment Systolic Array Throughput Optimization

Protein comparison is gaining importance year after year since it has been demonstrated that biologists can find cor- relation between different species, or genetic mutations that can lead to cancer and genetic diseases. Protein sequence alignment is the most computational intensive task when performing protein comparison. In order to speed-up alignment, dedicated processors that can perform different computations in parallel have been designed. Among them, the best performance have been achieved using Systolic Arrays. However, when the Processing Elements of the Systolic Array have an internal loop, performance could be highly reduced. In this work we present an architectural strategy to address this problem applying pipeline interleaving; this strategy is applied to a Systolic Array for Smith Waterman algorithm that we designed. Results encourage the adoption of pipeline interleaving for parallel circuits with loop based Processing Elements. We demonstrate that important benefits in terms of higher operating frequency can be derived without so relevant costs as increased complexity, area and power required

Local DNA sequence alignment in a cluster of workstations : algorithms and tools

Distributed Shared Memory systems allow the use of the shared memory programming paradigm in distributed architectures where no physically shared memory exist. Scope consistent software DSMs provide a relaxed memory model that reduces the coherence overhead by ensuring consistency only at synchronization operations, on a per-lock basis. Much of the work in DSM systems is validated by benchmarks and there are only a few examples of real parallel applications running on DSM systems. Sequence comparison is a basic operation in DNA sequencing projects, and most of sequence comparison methods used are based on heuristics, that are faster but do not produce optimal alignments. Recently, many organisms had their DNA entirely sequenced, and this reality presents the need for comparing long DNA sequences, which is a challenging task due to its high demands for computational power and memory. In this article, we present and evaluate a parallelization strategy for implementing a sequence alignment algorithm for long sequences. This strategy was implemented in JIAJIA, a scope consistent software DSM system. Our results on an eight-machine cluster presented good speedups, showing that our parallelization strategy and programming support were appropriate

Pash 3.0: A versatile software package for read mapping and integrative analysis of genomic and epigenomic variation using massively parallel DNA sequencing

<p>Abstract</p> <p>Background</p> <p>Massively parallel sequencing readouts of epigenomic assays are enabling integrative genome-wide analyses of genomic and epigenomic variation. Pash 3.0 performs sequence comparison and read mapping and can be employed as a module within diverse configurable analysis pipelines, including ChIP-Seq and methylome mapping by whole-genome bisulfite sequencing.</p> <p>Results</p> <p>Pash 3.0 generally matches the accuracy and speed of niche programs for fast mapping of short reads, and exceeds their performance on longer reads generated by a new generation of massively parallel sequencing technologies. By exploiting longer read lengths, Pash 3.0 maps reads onto the large fraction of genomic DNA that contains repetitive elements and polymorphic sites, including indel polymorphisms.</p> <p>Conclusions</p> <p>We demonstrate the versatility of Pash 3.0 by analyzing the interaction between CpG methylation, CpG SNPs, and imprinting based on publicly available whole-genome shotgun bisulfite sequencing data. Pash 3.0 makes use of gapped k-mer alignment, a non-seed based comparison method, which is implemented using multi-positional hash tables. This allows Pash 3.0 to run on diverse hardware platforms, including individual computers with standard RAM capacity, multi-core hardware architectures and large clusters.</p

Multiple structural alignment for distantly related all b structures using TOPS pattern discovery and simulated annealing

Topsalign is a method that will structurally align diverse protein structures, for example, structural alignment of protein superfolds. All proteins within a superfold share the same fold but often have very low sequence identity and different biological and biochemical functions. There is often signi®cant structural diversity around the common scaffold of secondary structure elements of the fold. Topsalign uses topological descriptions of proteins. A pattern discovery algorithm identi®es equivalent secondary structure elements between a set of proteins and these are used to produce an initial multiple structure alignment. Simulated annealing is used to optimize the alignment. The output of Topsalign is a multiple structure-based sequence alignment and a 3D superposition of the structures. This method has been tested on three superfolds: the b jelly roll, TIM (a/b) barrel and the OB fold. Topsalign outperforms established methods on very diverse structures. Despite the pattern discovery working only on b strand secondary structure elements, Topsalign is shown to align TIM (a/b) barrel superfamilies, which contain both a helices and b strands