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ردیف | عنوان | نوع |
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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 |