5,583 research outputs found
Applications and Challenges of Real-time Mobile DNA Analysis
The DNA sequencing is the process of identifying the exact order of
nucleotides within a given DNA molecule. The new portable and relatively
inexpensive DNA sequencers, such as Oxford Nanopore MinION, have the potential
to move DNA sequencing outside of laboratory, leading to faster and more
accessible DNA-based diagnostics. However, portable DNA sequencing and analysis
are challenging for mobile systems, owing to high data throughputs and
computationally intensive processing performed in environments with unreliable
connectivity and power.
In this paper, we provide an analysis of the challenges that mobile systems
and mobile computing must address to maximize the potential of portable DNA
sequencing, and in situ DNA analysis. We explain the DNA sequencing process and
highlight the main differences between traditional and portable DNA sequencing
in the context of the actual and envisioned applications. We look at the
identified challenges from the perspective of both algorithms and systems
design, showing the need for careful co-design
HPC Cloud for Scientific and Business Applications: Taxonomy, Vision, and Research Challenges
High Performance Computing (HPC) clouds are becoming an alternative to
on-premise clusters for executing scientific applications and business
analytics services. Most research efforts in HPC cloud aim to understand the
cost-benefit of moving resource-intensive applications from on-premise
environments to public cloud platforms. Industry trends show hybrid
environments are the natural path to get the best of the on-premise and cloud
resources---steady (and sensitive) workloads can run on on-premise resources
and peak demand can leverage remote resources in a pay-as-you-go manner.
Nevertheless, there are plenty of questions to be answered in HPC cloud, which
range from how to extract the best performance of an unknown underlying
platform to what services are essential to make its usage easier. Moreover, the
discussion on the right pricing and contractual models to fit small and large
users is relevant for the sustainability of HPC clouds. This paper brings a
survey and taxonomy of efforts in HPC cloud and a vision on what we believe is
ahead of us, including a set of research challenges that, once tackled, can
help advance businesses and scientific discoveries. This becomes particularly
relevant due to the fast increasing wave of new HPC applications coming from
big data and artificial intelligence.Comment: 29 pages, 5 figures, Published in ACM Computing Surveys (CSUR
GPUmotif: An Ultra-Fast and Energy-Efficient Motif Analysis Program Using Graphics Processing Units
Computational detection of TF binding patterns has become an indispensable tool in functional genomics research. With the rapid advance of new sequencing technologies, large amounts of protein-DNA interaction data have been produced. Analyzing this data can provide substantial insight into the mechanisms of transcriptional regulation. However, the massive amount of sequence data presents daunting challenges. In our previous work, we have developed a novel algorithm called Hybrid Motif Sampler (HMS) that enables more scalable and accurate motif analysis. Despite much improvement, HMS is still time-consuming due to the requirement to calculate matching probabilities position-by-position. Using the NVIDIA CUDA toolkit, we developed a graphics processing unit (GPU)-accelerated motif analysis program named GPUmotif. We proposed a “fragmentation" technique to hide data transfer time between memories. Performance comparison studies showed that commonly-used model-based motif scan and de novo motif finding procedures such as HMS can be dramatically accelerated when running GPUmotif on NVIDIA graphics cards. As a result, energy consumption can also be greatly reduced when running motif analysis using GPUmotif. The GPUmotif program is freely available at http://sourceforge.net/projects/gpumotif
QuASeR -- Quantum Accelerated De Novo DNA Sequence Reconstruction
In this article, we present QuASeR, a reference-free DNA sequence
reconstruction implementation via de novo assembly on both gate-based and
quantum annealing platforms. Each one of the four steps of the implementation
(TSP, QUBO, Hamiltonians and QAOA) is explained with simple proof-of-concept
examples to target both the genomics research community and quantum application
developers in a self-contained manner. The details of the implementation are
discussed for the various layers of the quantum full-stack accelerator design.
We also highlight the limitations of current classical simulation and available
quantum hardware systems. The implementation is open-source and can be found on
https://github.com/prince-ph0en1x/QuASeR.Comment: 24 page
GPU Accelerated Browser for Neuroimaging Genomics
Neuroimaging genomics is an emerging field that provides exciting opportunities to understand the genetic basis of brain structure and function. The unprecedented scale and complexity of the imaging and genomics data, however, have presented critical computational bottlenecks. In this work we present our initial efforts towards building an interactive visual exploratory system for mining big data in neuroimaging genomics. A GPU accelerated browsing tool for neuroimaging genomics is created that implements the ANOVA algorithm for single nucleotide polymorphism (SNP) based analysis and the VEGAS algorithm for gene-based analysis, and executes them at interactive rates. The ANOVA algorithm is 110 times faster than the 4-core OpenMP version, while the VEGAS algorithm is 375 times faster than its 4-core OpenMP counter part. This approach lays a solid foundation for researchers to address the challenges of mining large-scale imaging genomics datasets via interactive visual exploration
Bid-Centric Cloud Service Provisioning
Bid-centric service descriptions have the potential to offer a new cloud
service provisioning model that promotes portability, diversity of choice and
differentiation between providers. A bid matching model based on requirements
and capabilities is presented that provides the basis for such an approach. In
order to facilitate the bidding process, tenders should be specified as
abstractly as possible so that the solution space is not needlessly restricted.
To this end, we describe how partial TOSCA service descriptions allow for a
range of diverse solutions to be proposed by multiple providers in response to
tenders. Rather than adopting a lowest common denominator approach, true
portability should allow for the relative strengths and differentiating
features of cloud service providers to be applied to bids. With this in mind,
we describe how TOSCA service descriptions could be augmented with additional
information in order to facilitate heterogeneity in proposed solutions, such as
the use of coprocessors and provider-specific services
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