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Cloud computing for comparative genomics
2010
BMC Bioinformatics
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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
2012
Evolutionary Bioinformatics
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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
2010
Evolutionary Bioinformatics
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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
2012
Frontiers in Genetics
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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
2017
Journal of Applied Bioinformatics & Computational Biology
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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
2015
Advances and Applications in Bioinformatics and Chemistry
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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
2014
PLoS ONE
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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
2016
International Journal of Computer Applications
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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]
2014
biorxiv/medrxiv
pre-print
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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
2015
PLoS ONE
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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
2015
BioMed Research International
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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
2021
PLoS Computational Biology
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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
2008
Analytical Biochemistry
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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
2016
2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
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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
2022
Indonesian Journal of Electrical Engineering and Computer Science
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<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
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