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نتیجه جستجو - Sequence Alignment

تعداد مقالات یافته شده: 10
ردیف عنوان نوع
1 Comprehensive Comparison of Cloud-Based NGS Data Analysis and Alignment Tools
مقایسه جامع ابزارهای تحلیل و تراز داده NGS مبتنی بر ابر-2020
Next-Generation Sequencing (NGS) is very helpful for conducting DeoxyriboNucleic Acid (DNA) Sequencing. DNA sequencing is the process for determining the order (sequence) of the main chemical bases in the DNA. Analyzing human DNA sequencing is important for determining the possibility that a person will develop certain diseases, and/or the ability to respond to medication. However, the NGS process is a complicated and resource-hungry technical process. To solve this dilemma, the majority of NGS software systems are deployed as cloud-based services distributed over cloud-based platforms. Cloud-based platforms provide promising solutions for the computationally intensive tasks required by the NGS data analysis. This work provides a comprehensive investigation of cloud-based NGS data analysis and alignment tools, both the commercial and the open-source tools. We also discuss in detail the main features and setup requirements for each tool, and then compare and contrast between them. Moreover, we extensively analyze and classify the studied NGS data analysis and alignment tools to help NGS biomedical researchers and clinicians in finding appropriate tools for their work, while understanding the similarities and the differences between them.
Keywords: Next-Generation Sequencing (NGS) | Sequence Alignment | Cloud Computing | Big Data | Bioinformatics
مقاله انگلیسی
2 DINeR: Database for Insect Neuropeptide Research
DINeR: پایگاه داده برای تحقیقات نوروپپتید حشرات-2017
Neuropeptides are responsible for regulating a variety of functions, including development, metabolism, water and ion homeostasis, and as neuromodulators in circuits of the central nervous system. Numerous neuropeptides have been identified and characterized. However, both discovery and functional charac terization of neuropeptides across the massive Class Insecta has been sporadic. To leverage advances in post-genomic technologies for this rapidly growing field, insect neuroendocrinology requires a consol idated, comprehensive and standardised resource for managing neuropeptide information. The Database for Insect Neuropeptide Research (DINeR) is a web-based database-application used for search and retrieval of neuropeptide information of various insect species detailing their isoform se quences, physiological functionality and images of their receptor-binding sites, in an intuitive, accessible and user-friendly format. The curated data includes representatives of 50 well described neuropeptide families from over 400 different insect species. Approximately 4700 FASTA formatted, neuropeptide isoform amino acid sequences and over 200 records of physiological functionality have been recorded based on published literature. Also available are images of neuropeptide receptor locations. In addition, the data include comprehensive summaries for each neuropeptide family, including their function, location, known functionality, as well as cladograms, sequence alignments and logos covering most insect orders. Moreover, we have adopted a standardised nomenclature to address inconsistent classi fication of neuropeptides. As part of the H2020 nEUROSTRESSPEP project, the data will be actively maintained and curated, ensuring a comprehensive and standardised resource for the scientific community. DINeR is publicly available at the project website: http://www.neurostresspep.eu/diner/.
Keywords: Insect neuropeptide | Database | DINeR | Neuroendocrinology | Neuropeptide hormones | Neuropeptide signalling
مقاله انگلیسی
3 parSRA: A framework for the parallel execution of short read aligners on compute clusters
parSRA: چارچوبی برای اجرای موازی aligners خواندن کوتاه بر روی خوشه های محاسبه-2017
The growth of next generation sequencing datasets poses as a challenge to the alignment of reads to reference genomes in terms of both accuracy and speed. In this work we present parSRA, a parallel framework to accelerate the execution of existing short read aligners on distributed-memory systems. parSRA can be used to parallelize a variety of short read alignment tools installed in the system without any modification to their source code. We show that our framework provides good scalability on a compute cluster for accelerating the popular BWA-MEM and Bowtie2 aligners. On average, it is able to accelerate sequence alignments on 16 64-core nodes (in total, 1024 cores) with speedup of 10.48 compared to the original multithreaded tools running with 64 threads on one node. It is also faster and more scalable than the pMap and BigBWA frameworks. Source code of parSRA in C++ and UPC++ running on Linux systems with support for FUSE is freely available at https://sourceforge.net/projects/parsra/.
Keywords: Short read alignment | High performance computing | Multicore clusters | Bioinformatics | PGAS
مقاله انگلیسی
4 Computing Platforms for Big Biological Data Analytics: Perspectives and Challenges
سیستم های محاسباتی برای تجزیه و تحلیل داده های زیستی بزرگ: چشم اندازها و چالش ها-2017
The last decade has witnessed an explosion in the amount of available biological sequence data, due to the rapid progress of high-throughput sequencing projects. However, the biological data amount is becoming so great that traditional data analysis platforms and methods can no longer meet the need to rapidly perform data analysis tasks in life sciences. As a result, both biologists and computer scientists are facing the challenge of gaining a profound insight into the deep est biological functions from big biological data. This in turn requires massive computational resources. Therefore, high performance computing (HPC) plat forms are highly needed as well as efficient and scalable algorithms that can take advantage of these platforms. In this paper, we survey the state-of-the-art HPC platforms for big biological data analytics. We first list the characteristics of big biological data and popular computing platforms. Then we provide a taxonomy of different biological data analysis applications and a survey of the way they have been mapped onto various computing platforms. After that, we present a case study to compare the efficiency of different computing platforms for handling the classical biological sequence alignment problem. At last we discuss the open issues in big biological data analytics.
Keywords: Computational Biology Applications | Computing Platforms | Big Biological Data | NGS | GPU | Intel MIC
مقاله انگلیسی
5 Energy efficiency of sequence alignment tools—Software and hardware perspectives
بهره وری انرژی ابزار همترازی ترتیب - دیدگاههای نرم افزاری و سخت افزاری-2017
Pairwise sequence alignment is ubiquitous in modern bioinformatics. It may be performed either explicitly, e.g. to find the most similar sequences in a database, or implicitly as a hidden building block of more complex methods, e.g. for reads mapping. The alignment algorithms have been widely investigated over the last few years, mainly with respect to their speed. However, no attention was given to their energy efficiency, which is becoming critical in high performance computing and cloud environment. We compare the energy efficiency of the most established software tools performing exact pairwise sequence alignment on various computational architectures: CPU, GPU and Intel Xeon Phi. The results show that the energy consumption may differ as much as nearly 5 times. Substantial differences are reported even for different implementations running on the same hardware. Moreover, we present an FPGA implementation of one of the tested tools—G-DNA, and show how it outperforms all the others on the energy efficiency front. Finally, some details regarding the special RECS⃝ R |Box servers used in our study are outlined. This hardware is designed and manufactured within the FiPS project by the Bielefeld University and christmann informationstechnik + medien with a special purpose to deliver highly heterogeneous computational environment supporting energy efficiency and green ICT.
Keywords: Sequence alignment | Energy efficiency | FiPS project | Heterogeneous hardware | Bioinformatics | FPGA
مقاله انگلیسی
6 PA-Star: A disk-assisted parallel A-Star strategy with locality-sensitive hash for multiple sequence alignment
PA-Star: یک راهبرد موازی دیسکی PA-Star با اختلاط حساس به مکان برای همترازی ترتیبی چندگانه-2017
Multiple Sequence Alignment (MSA) is a basic operation in Bioinformatics, and is used to highlight the similarities among a set of sequences. The MSA problem was proven NP-Hard, thus requiring a high amount of memory and computing power. This problem can be modeled as a search for the path with minimum cost in a graph, and the A-Star algorithm has been adapted to solve it sequentially and in parallel. The design of a parallel version for MSA with A-Star is subject to challenges such as irregular dependency pattern and substantial memory requirements. In this paper, we propose PA-Star, a locality sensitive multithreaded strategy based on A-Star, which computes optimal MSAs using both RAM and disk to store nodes. The experimental results obtained in 3 different machines show that the optimizations used in PA-Star can achieve an acceleration of 1.88× in the serial execution, and the parallel execution can attain an acceleration of 5.52× with 8 cores. We also show that PA-Star outperforms a state-of-the-art MSA tool based on A-Star, executing up to 4.77× faster. Finally, we show that our disk-assisted strategy is able to retrieve the optimal alignment when other tools fail.
Keywords: Multiple sequence alignment | Locality-sensitive hash | A-Star | Parallel algorithms
مقاله انگلیسی
7 Chemical principles additive model aligns low consensus DNA targets of p53 tumor suppressor protein
مدل اصول افزودنی های شیمیایی همتراز با گونه های با دی ان ای پایین از پروتئین جلوگیری کننده از تومور -2017
Computational prediction of the interaction between protein transcription factors and their cognate DNA binding sites in genomic promoters constitutes a formidable challenge in two situations: when the number of discriminatory interactions is small compared to the length of the binding site, and when DNA binding sites possess a poorly conserved consensus binding motif. The transcription factor p53 tumor suppressor protein and its target DNA exhibit both of these issues. From crystal structure analysis, only three discriminatory amino acid side chains contact the DNA for a binding site spanning ten base pairs. Furthermore, our analysis of a dataset of genome wide fragments binding to p53 revealed many sequences lacking the expected consensus. The low information content leads to an overestimation of binding sites, and the lack of conservation equates to a computational alignment problem. Within a fragment of DNA known to bind to p53, computationally locating the position of the site equates to aligning the DNA with the binding interface. From a molecular perspective, that alignment implies a specification of which DNA bases are interacting with which amino acid side chains, and aligning many sequences to the same protein interface concomitantly produces a multiple sequence alignment. From this vantage, we propose to cast prediction of p53 binding sites as an alignment to the protein binding surface with the novel approach of optimizing the alignment of DNA fragments to the p53 binding interface based on chemical principles. A scoring scheme based on this premise was successfully implemented to score a dataset of biological DNA fragments known to contain p53 binding sites. The results illuminate the mechanism of recognition for the protein-DNA system at the forefront of cancer research. These findings implicate that p53 may recognize its target binding sites via several different mechanisms which may include indirect readout.
Keywords: p53 | DNA sequence alignment | Low consensus | Additive energy | DNA binding site prediction | Bioinformatics of DNA
مقاله انگلیسی
8 Higher accuracy protein multiple sequence alignments by genetic algorithm
ترازبندی توالی چندگانه پروتئین با دقت بالا توسط الگوریتم ژنتیک-2017
A Multiple sequence alignment (MSA) gives insight into the evolutionary, structural and functional relationships among the protein sequences. Here, the initial MSAs are chosen as the output of the two important protein sequence alignment programs: ProbCons and MCoffee. We have used the evolutionary operators of a genetic algorithm to find the optimized protein alignment after several iterations of the algorithm. Thus, we have developed a new MSA computational tool called as the Protein Alignment by Stochastic Algorithm (PASA). The efficiency of protein alignments is evaluated in terms of Total Column (TC) score. The TC score is basically the number of correctly aligned columns between the test alignments and the reference alignments divided by the total number of columns. The PASA is found to be statistically more accurate protein alignment method in our analysis in comparison to other popular bioinformatics tools.
Keywords: Genetic algorithm | Multiple sequence alignment | Protein sequence alignment | Friedman rank test | Balibase | MCoffee | ProbCons | Ranksum
مقاله انگلیسی
9 parSRA: A framework for the parallel execution of short read aligners on compute clusters
parSRA: چارچوبی برای اجرای موازی هماهنگ کنندگان خواندن کوتاه در خوشه های محاسبه ای-2017
The growth of next generation sequencing datasets poses as a challenge to the alignment of reads to reference genomes in terms of both accuracy and speed. In this work we present parSRA, a parallel framework to accelerate the execution of existing short read aligners on distributed-memory systems. parSRA can be used to parallelize a variety of short read alignment tools installed in the system without any modification to their source code. We show that our framework provides good scalability on a compute cluster for accelerating the popular BWA-MEM and Bowtie2 aligners. On average, it is able to accelerate sequence alignments on 16 64-core nodes (in total, 1024 cores) with speedup of 10.48 compared to the original multithreaded tools running with 64 threads on one node. It is also faster and more scalable than the pMap and BigBWA frameworks. Source code of parSRA in C++ and UPC++ running on Linux systems with support for FUSE is freely available at https://sourceforge.net/projects/parsra/.
Keywords: Short read alignment | High performance computing | Multicore clusters | Bioinformatics | PGAS
مقاله انگلیسی
10 Multi-objective two-level swarm intelligence approach for multiple RNA sequence-structure alignment
رویکرد هوش گروهی دو سطحی چند هدفه برای تراز توالی-ساختار RNA چندگانه-2017
This paper proposes a novel two-level particle swarm optimization algorithm for multi-objective optimization (MO-TLPSO) employed to a challenging problem of bioinformatics i.e. RNA sequence-structure alignment. Level one of the proposed approach optimizes the dimension of each swarm which is sequence length for the addressed problem, whereas level two optimizes the particle positions and then evaluates both the conflicting objectives. The conflicting objectives of the addressed problem are obtaining optimal multiple sequence alignment as well as optimal secondary structure. Optimal secondary structure is obtained by TL-PSOfold, the structure is further used for computing the contribution of base pairing of individual sequence and the co variation between aligned positions of sequences so as to make the structure closer to the natural one. The results are tested against the popular softwares for pairwise and multiple alignment at BRAlibase benchmark datasets. Proposed work is so far the first multi-objective optimization based approach for structural alignment of multiple RNA sequences without converting the problem into single objective. Also, it is the first swarm intelligence based approach that addresses sequence-structure alignment issue of RNA sequences. Simulation results are compared with the state-of-the-art and competitive approaches. MO-TLPSO is found well competent in producing pairwise as well as multiple sequence-structure alignment of RNA. The claim is supported by performing statistical significance testing using one way ANOVA followed by Bonferroni post-hoc analysis for both kind of alignments.
Keywords: Multi-objective optimization | RNA secondary structure | Multiple sequence alignment | Particle swarm optimization | Non-dominated solutions | Pareto optimal solution | Minimum free energy | Conflicting objectives
مقاله انگلیسی
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