Grid-Enabled BLASTZ: Application to Comparative Genomics

Chen, Chunxi; Rajapakse, Jagath C.

doi:10.1007/s11265-007-0065-6

Cited by 3 publications

(6 citation statements)

References 18 publications

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“…Sequence alignment by using BLASTZ accounts for most of the running time of the workflow. We use a computational grid to speed up the alignment process, which allows one to utilize the distributed computing and memory resources at a reasonable cost [18], [19]. In a computational grid, heterogeneous computing servers and data are distributed at distinct locations connected through Intranet/Internet.…”

Section: Grid-enabled Pipelinementioning

confidence: 99%

“…We illustrate a grid-enabled implementation of comparative genomics pipeline in Figure 1 with Grid-BLASTZ [18]. The parallelization of the sequence alignment in BLASTZ is embedded in the workflow such that extracted noncoding sequences can be processed simultaneously at different computing nodes.…”

Section: Grid-enabled Pipelinementioning

confidence: 99%

“…Figure 2 shows speedups of our grid-enabled pipeline with and without disturbances. The speedup of a cluster is measured by the sum of the running time of each processor in a corresponding fraction of tasks, divided by the running time when multiple processes are equally distributed in the cluster [18]. A disturbance is simulated by running an application in one of the 2.60-GHz CPU.…”

Section: Performancementioning

confidence: 99%

See 2 more Smart Citations

Comparative genomic workflow

Rajapakse¹,

Pooja²,

Chen³

et al. 2009

IEEE Eng. Med. Biol. Mag.

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Section: Grid-enabled Pipelinementioning

confidence: 99%

Section: Grid-enabled Pipelinementioning

confidence: 99%

Section: Performancementioning

confidence: 99%

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Comparative genomic workflow

Rajapakse¹,

Pooja²,

Chen³

et al. 2009

IEEE Eng. Med. Biol. Mag.

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“…In particular, with the continuously increasing number of fully sequenced genomes being reported each ATTENTION: The Singapore Copyright Act applies to the use of this document. Nanyang Technological University Library year, it has already become necessary to involve high-performance computing solutions and acceleration techniques for the complex and flexible approaches to DNA processing to be efficient in practice [27,28].…”

Section: Objectivesmentioning

confidence: 99%

“…Since many bioinformatics algorithms require repetition of a huge number of the similar processing steps on different sets of data, the massively parallel and adjustableto-application computational capacities of FPGAs seem to be offering a viable, readily available solution when added to the host computer's general purpose processor as an acceleration co-processor. The pioneering examples of the "bioinformatics on-chip" are the hardware-accelerated solutions for the famous database search tool BLAST [28,116], distributed hardware acceleration of the popular hidden Markov modelbased search tool HMMer [27], and the program for the phylogenetic tree inference…”

Section: Reconfigurable Computingmentioning

confidence: 99%

Adaptive computing for In Silico recognition of hormone response elements

Stepanova¹

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An important step in understanding the conditions which specify gene expression is to recognize gene regulatory areas that are associated with regulation of transcription. Due to high diversity of different types of transcription factors and their DNA binding preferences, it is a challenging problem to establish an accurate model for computational prediction of functioning regulatory elements in promoters of eukaryotic genes. In silico modeling of transcription factor binding sites becomes even more complicated in the case of some specialized transcription factors, e.g. nuclear receptors, which interact with their target DNA sequences as homo-or hetero-dimers, thus stipulating the multi-component structure of the binding sites. This thesis is mostly a methodology research aimed to solve the problem of recognition of symmetrically structured DNA motifs by using bioinformatics tools. Steroid hormone response elements (HREs), which are known to hold partially symmetric structure, are selected for our study. Addressed in the thesis are two novel methods for recognizing symmetrically structured DNA motifs; their applicability is demonstrated through a set of designed experiments. The first method exploits sequence-specific statistical modeling of the HRE pattern. Though characterized with certain limitations, for the particular problem of accurate HRE recognition, the simple and easily interpretable statistical model largely benefits from application-specific adaptation. The second method is based on a more accurate object-specific motif recognition paradigm, exploiting a two-phase neural architecture. The algorithmic research is preceded by the collection and preprocessing of the HRE training data, thus providing some interesting findings about HRE composition at the early stage of research. The collected dataset of functioning HREs currently has no analogs. In the statistical HRE modeling, we consider both nucleotide position and internucleotide transition patterns of HRE sequences. We implemented a position-transition recognition model with reference to the complex structure of the HRE motif. A cascade Markov model was invented for modeling the multi-component HRE structure, and it was implemented by hardware using FPGAs (Field-Programmable Gate Arrays). The designed chip was further used as a co-processing unit for computational efficiency when applied for predicting HREs in large genomic

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Alignment-free genetic sequence comparisons: a review of recent approaches by word analysis

Bonham-Carter¹,

Steele²,

Bastola³

2013

Briefings in Bioinformatics

118

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Modern sequencing and genome assembly technologies have provided a wealth of data, which will soon require an analysis by comparison for discovery. Sequence alignment, a fundamental task in bioinformatics research, may be used but with some caveats. Seminal techniques and methods from dynamic programming are proving ineffective for this work owing to their inherent computational expense when processing large amounts of sequence data. These methods are prone to giving misleading information because of genetic recombination, genetic shuffling and other inherent biological events. New approaches from information theory, frequency analysis and data compression are available and provide powerful alternatives to dynamic programming. These new methods are often preferred, as their algorithms are simpler and are not affected by synteny-related problems. In this review, we provide a detailed discussion of computational tools, which stem from alignment-free methods based on statistical analysis from word frequencies. We provide several clear examples to demonstrate applications and the interpretations over several different areas of alignment-free analysis such as base-base correlations, feature frequency profiles, compositional vectors, an improved string composition and the D2 statistic metric. Additionally, we provide detailed discussion and an example of analysis by Lempel-Ziv techniques from data compression.

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Grid-Enabled BLASTZ: Application to Comparative Genomics

Cited by 3 publications

References 18 publications

Comparative genomic workflow

Comparative genomic workflow

Adaptive computing for In Silico recognition of hormone response elements

Alignment-free genetic sequence comparisons: a review of recent approaches by word analysis

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