Filters








22,274 Hits in 3.3 sec

Cloud computing for comparative genomics

Dennis P Wall, Parul Kudtarkar, Vincent A Fusaro, Rimma Pivovarov, Prasad Patil, Peter J Tonellato
2010 BMC Bioinformatics  
We then employed the RSD-cloud for ortholog calculations across a wide selection of fully sequenced genomes. Results: We ran more than 300,000 RSD-cloud processes within the EC2.  ...  Alternative computing architectures, in particular cloud computing environments, may help alleviate this increasing pressure and enable fast, large-scale, and cost-effective comparative genomics strategies  ...  Acknowledgements We would like to thank the Amazon Web Services product development team for support and assistance throughout the duration of the project.  ... 
doi:10.1186/1471-2105-11-259 pmid:20482786 pmcid:PMC3098063 fatcat:qtypyhf3h5fj5nkhsj6oz3bwci

Cloud Computing for Comparative Genomics with Windows Azure Platform

Insik Kim, Jae-Yoon Jung, Todd F. DeLuca, Tristan H. Nelson, Dennis P. Wall
2012 Evolutionary Bioinformatics  
Cloud computing services have emerged as a cost-effective alternative for cluster systems as the number of genomes and required computation power to analyze them increased in recent years.  ...  For Results Case studies Here we run our computationally intensive comparative genomics application, Roundup, 4 in two different case scenarios.  ...  processes that need to be completely redesigned for optimal cloud computing performance.  ... 
doi:10.4137/ebo.s9946 pmid:23032609 pmcid:PMC3433929 fatcat:2rjtkzwpgvduve3nbi5xhmzdnu

Cost-Effective Cloud Computing: A Case Study Using the Comparative Genomics Tool, Roundup

Parul Kudtarkar, Todd F. DeLuca, Vincent A. Fusaro, Peter J. Tonellato, Dennis P. Wall
2010 Evolutionary Bioinformatics  
Methods: Utilizing the comparative genomics tool, Roundup, as a case study, we computed orthologs among 902 fully sequenced genomes on Amazon's Elastic Compute Cloud.  ...  Results: We computed orthologous relationships for 245,323 genome-to-genome comparisons on Amazon's computing cloud, a computation that required just over 200 hours and cost $8,000 USD, at least 40% less  ...  Acknowledgements We would like to thank the Amazon Web Services for providing access to computational resources throughout the duration of the project.  ... 
doi:10.4137/ebo.s6259 pmid:21258651 pmcid:PMC3023304 fatcat:34vakinvanfvfgdvcxulobsofy

Now and Next-Generation Sequencing Techniques: Future of Sequence Analysis Using Cloud Computing

Radhe Shyam Thakur, Rajib Bandopadhyay, Bratati Chaudhary, Sourav Chatterjee
2012 Frontiers in Genetics  
Finally, the applications of cloud computing in biological systems, particularly in comparative genomics, genome informatics, and SNP detection are discussed with reference to traditional workflows.  ...  In this discussion, we focus on the basics of cloud computing, and go on to analyze the prerequisites and overall working of clouds.  ...  One of the intensive computation demands is Reciprocal Shortest Distance (RSD) algorithm for comparative genomics which further increases with the increase in genome size to be analyzed.  ... 
doi:10.3389/fgene.2012.00280 pmid:23248640 pmcid:PMC3518790 fatcat:d43ml5jbtzaivoezlxk4jnqqe4

New Era for Biocomputing

Momiao Xiong
2017 Journal of Applied Bioinformatics & Computational Biology  
Two types of popular parallel computing are Cloud computing and GPU (graphical process units) computing. Cloud is a metaphor for the Internet. Cloud computing is a type of Internet-based computing.  ...  The cloud computing has been applied to manage the deluge of 'big sequence data' in 1000 Genomes Project [6], comparative genomics [7], Chip-seq data analysis [24], translational medicine [25], transcriptome  ...  Two types of popular parallel computing are Cloud computing and GPU (graphical process units) computing. Cloud is a metaphor for the Internet. Cloud computing is a type of Internet-based computing.  ... 
doi:10.4172/2329-9533.1000e102 fatcat:cm3waiicyjg6xgbtagvydwy3iy

Parallel computing in genomic research: advances and applications

Daniel de Oliveira, Kary Ocaña
2015 Advances and Applications in Bioinformatics and Chemistry  
However, running bioinformatics experiments in HPC environments such as clouds, grids, clusters, and graphics processing unit requires the expertise from scientists to integrate computational, biological  ...  Several solutions have already been proposed to allow scientists for processing their genomic experiments using HPC capabilities and parallelism techniques.  ...  Author contributions All authors contributed toward data analysis, drafting and revising the paper and agree to be accountable for all aspects of the work.  ... 
doi:10.2147/aabc.s64482 pmid:26604801 pmcid:PMC4655901 fatcat:qyslams5evftjm7euma5vg2rtq

Benchmarking Undedicated Cloud Computing Providers for Analysis of Genomic Datasets

Seyhan Yazar, George E. C. Gooden, David A. Mackey, Alex W. Hewitt, Maureen J. Donlin
2014 PLoS ONE  
Our findings confirm that cloud computing is an efficient and potentially cost-effective alternative for analysis of large genomic datasets.  ...  We benchmarked two established cloud computing services, Amazon Web Services Elastic MapReduce (EMR) on Amazon EC2 instances and Google Compute Engine (GCE), using publicly available genomic datasets (  ...  Cloud computing offers a dynamic, economical and versatile solution for large-scale computational analysis.  ... 
doi:10.1371/journal.pone.0108490 pmid:25247298 pmcid:PMC4172764 fatcat:jlby6nkxhvbpxcdnwv5eigug2u

An Efficient Bulk Synchronous Parallelized Scheduler for Bioinformatics Application on Public Cloud

Siddu P., Leena I.
2016 International Journal of Computer Applications  
Bioinformatics computation require super computer for sequence alignment computation which involves huge cost.  ...  This work introduces a Smith-Waterman Alignment on the Bulk synchronous Parallel Map Reduce (SW-BSPMR) cloud platform for bioinformatics gene sequence alignment.  ...  The SW-BSPMR adopts a cloud computing infrastructure framework for MapReduce computation.  ... 
doi:10.5120/ijca2016910882 fatcat:bmtfpzyxczdodcjwf2qxkkk4hi

Benchmarking undedicated cloud computing providers for analysis of genomic datasets [article]

Seyhan Yazar, George EC Gooden, David A Mackey, Alex Hewitt
2014 biorxiv/medrxiv   pre-print
Our findings confirm that cloud computing is an efficient and potentially cost-effective alternative for analysis of large genomic datasets.  ...  We benchmarked two established cloud computing services, Amazon Web Services Elastic MapReduce (EMR) on Amazon EC2 instances and Google Compute Engine (GCE), using publicly available genomic datasets (  ...  Cloud computing offers a dynamic, economical and versatile solution for large-scale computational analysis.  ... 
doi:10.1101/007724 fatcat:oioi6djamfe5pmxqjnot3qciom

Inexpensive and Highly Reproducible Cloud-Based Variant Calling of 2,535 Human Genomes

Suyash S. Shringarpure, Andrew Carroll, Francisco M. De La Vega, Carlos D. Bustamante, Lars Kaderali
2015 PLoS ONE  
Here, we describe a cloud-based pipeline for joint variant calling in large samples using the Real Time Genomics population caller.  ...  Analysis of cost dependence and running time on the data size suggests that, given near linear scalability, cloud computing can be a cheap and efficient platform for analyzing even larger sequencing studies  ...  Acknowledgments The authors would like to acknowledge Len Trigg and Brian Hilbush (Real Time Genomics, Inc.) for helpful suggestions and Katie Kanagawa for comments on the manuscript.  ... 
doi:10.1371/journal.pone.0129277 pmid:26110529 pmcid:PMC4482534 fatcat:jc6obwhltfefndom2wa5mbt4tm

Long Read Alignment with Parallel MapReduce Cloud Platform

Ahmed Abdulhakim Al-Absi, Dae-Ki Kang
2015 BioMed Research International  
Existing genes sequencing tools for cloud platforms predominantly consider short read gene sequences and adopt the Hadoop MapReduce framework for computation.  ...  The experimental study proves the efficiency of BWASW-PMR for aligning long genomic sequences on cloud platforms.  ...  The necessity for cloud computing for genomic analysis has been well discussed by leaders in bioinformatics and computational biology [23] .  ... 
doi:10.1155/2015/807407 pmid:26839887 pmcid:PMC4709609 fatcat:ezku2olljbbcdfex2n3dlijpue

Swarm: A federated cloud framework for large-scale variant analysis

Amir Bahmani, Kyle Ferriter, Vandhana Krishnan, Arash Alavi, Amir Alavi, Philip S. Tsao, Michael P. Snyder, Cuiping Pan, Mihaela Pertea
2021 PLoS Computational Biology  
Here, we present Swarm, a framework for federated computation that promotes minimal data motion and facilitates crosstalk between genomic datasets stored on various cloud platforms.  ...  Compared to single-cloud platforms, the Swarm framework significantly reduced computational costs, run-time delays and risks of security breach and privacy violation.  ...  , runtimes between CSV input versus Parquet input were compared and significant P values were indicated (two sample t-tests).  ... 
doi:10.1371/journal.pcbi.1008977 pmid:33979321 fatcat:s62adyyabffwzkoczg7j36mr4i

Identification of repeat structure in large genomes using repeat probability clouds

Wanjun Gu, Todd A. Castoe, Dale J. Hedges, Mark A. Batzer, David D. Pollock
2008 Analytical Biochemistry  
Our goal was to develop a fast algorithm for de novo identification of repeated structures applicable to entire eukaryotic genomes that could be reasonably implemented using existing desktop computers.  ...  All require extensive computational effort and/or capability that limit the ability of individual genomic researchers to extensively investigate repeat structure, particularly for mammalian and other large  ...  P-clouds may also be applicable for comparative analysis of repeat structure among multiple vertebrate genomes.  ... 
doi:10.1016/j.ab.2008.05.015 pmid:18541131 pmcid:PMC2533575 fatcat:6nm2pu7y3nb6nhenrpu6jk2yfy

The role of high performance, grid and cloud computing in high-throughput sequencing

Gaye Lightbody, Fiona Browne, Huiru Zheng, Valeriia Haberland, Jaine Blayney
2016 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)  
We have reached the era of full genome sequencing using high throughput sequencing technologies pouring out gigabases of reads in a day.  ...  GPU Computing Many researchers [29] , [40] , [41] , compare Graphics Processor Unit (GPU)-based computing with a traditional CPU-based parallel computing.  ...  3 http://www.pacb.com/products-and-services/pacbio-systems/rsii/ comparative genomics, transcriptome analysis and therapeutic decision-making for somatic cancers [5] .  ... 
doi:10.1109/bibm.2016.7822643 dblp:conf/bibm/LightbodyBZHB16 fatcat:emjxedmvrnfshdtrynakwurl4q

An efficient and robust parallel scheduler for bioinformatics applications in a public cloud: A bigdata approach

Leena Ammanna, Jagadeeshgowda Jagadeeshgowda, Jagadeesh Pujari
2022 Indonesian Journal of Electrical Engineering and Computer Science  
<p>In bioinformatics, genomic sequence alignment is a simple method for handling and analysing data, and it is one of the most important applications in determining the structure and function of protein  ...  To address the issue with Hadoop's MapReduce framework, a customised MapReduce framework is developed on the Azure cloud platform.  ...  Google introduced the Pregel framework for cloud computations in [21] for similar applications. Pregel is built on valiant's bulk synchronous parallel (BSP) computation model [22] .  ... 
doi:10.11591/ijeecs.v25.i2.pp1078-1086 fatcat:cp3alt77wbebbpax4hiya5fpnu
« Previous Showing results 1 — 15 out of 22,274 results