High-throughput genome research has long been associated with bioinformatics, as it assists genome sequencing and annotation projects. Along with databases, to store, properly manage, and retrieve biological data, a large number of computational tools have been developed to decode biological information from this data. However, with the advent of next-generation sequencing (NGS) technology the sequence data starts generating at a pace never before seen. Consequently researchers are facing a threat as they are experiencing a potential shortage of storage space and tools to analyze the data. Moreover, the voluminous data increases traffic in the network by uploading and downloading large data sets, and thus consume much of the network's available bandwidth. All of these obstacles have led to the solution in the form of cloud computing.
Get full access to this article
View all access options for this article.
References
1.
AdamsM.D., CelnikerS.E., HoltR.A.et al.2000. The genome sequence of Drosophila melanogaster. Science, 287:2185–2195.
AltschulS.F., MaddenT.L., SchafferA.A., ZhangJ., ZhangZ., MillerW., LipmanD.J.1997. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res, 25:3389–3402.
4.
Arabidopsis Genome Initiative. 2000. Analysis of the genome sequence of the flowering plant Arabidopsis thaliana. Nature, 408:796–815.
Caenorhabditis elegans Sequencing Consortium. 1998. Genome sequence of the nematode Caenorhabditis elegans: a platform for investigating biology. Science, 282:2012–2018.
7.
ColeS.T., BroschR., ParkhillJ.et al.1998. Deciphering the biology of Mycobacterium tuberculosis from the complete genome sequence. Nature, 393:537–544.
8.
DeanJ., GhemawatS.2004. MapReduce: simplified data processing on large clusters. Proceedings of the 6th Symposium on Operating Systems Design & Implementation. ACM: New York6137–150.
9.
DudleyJ.T., ButteA.J.2010. In silico research in the era of cloud computing. Nat Biotechnol, 28:1181–1185.
10.
DudleyJ.T., PouliotY., ChenR., MorganA.A., ButteA.J.2010. Translational bioinformatics in the cloud: an affordable alternative. Genome Med, 2:51.
11.
EdgarR.C.2004. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res, 32:1792–1797.
12.
FleischmannR.D., AdamsM.D., WhiteO.et al.1995. Whole-genome random sequencing and assembly of Haemophilus influenzae Rd. Science, 269:496–512.
13.
FoxA.2011. Cloud computing—what's in it for me as a scientist?Science, 331:406–407.
14.
FraserC.M., GocayneJ.D., WhiteO.et al.1995. The minimal gene complement of Mycoplasma genitalium. Science, 270:397–403.
15.
GoffeauA., BarrellB.G., BusseyH.et al.1996. Life with 6000 genes. Science, 274:563–567.
16.
HofmannP., WoodsD.2010. Cloud computing: The limits of public clouds for business applications. IEEE Internet Computing, 90–93.
17.
HongD., RhieA., ParkS.S.et al.2012. FX: an RNA-Seq analysis tool on the cloud. Bioinformatics, 28:721–723.
18.
KatohK., MisawaK., KumaK., MiyataT.2002. MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res, 30:3059–3066.
LangmeadB., SchatzM.C., LinJ., PopM., SalzbergS.L.2009. Searching for SNPs with cloud computing. Genome Biol, 10:R134.
21.
LangmeadB., TrapnellC., PopM., SalzbergS.L.2009a. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol, 10:R25.
22.
LiR., LiY., FangX., YangH., WangJ., KristiansenK.2009. SNP detection for massively parallel whole-genome resequencing. Genome Res, 9:1124–1132.
23.
LiuY., SchmidtB., MaskellD.L.2010. MSAProbs: multiple sequence alignment based on pair hidden Markov models and partition function posterior probabilities. Bioinformatics, 26:1958–1964.
24.
MatsunagaA., TsugawaM., FortesJ.2008. CloudBLAST: combining MapReduce and virtualization on distributed resources for bioinformatics applications. Proceedings of the IEEE Fourth International Conference on eScience, 222–229. 10.1109/eScience.2008.62.
25.
MetzkerM.L.2011. Sequencing technologies—the next generation. Nat Rev Genet, 11:31–46.
26.
NeedlemanS.B., WunschC.D.1970. A general method applicable to the search for similarities in the amino acid sequence of two proteins. J Mol Biol, 48:443–453.
27.
NingZ., CoxA.J., MullikinC.2001. SSAHA: a fast search method for large DNA databases. Genome Res, 11:1725–1729.
28.
NotredameC., HigginsD.G., HeringaJ.2000. T-Coffee: A novel method for fast and accurate multiple sequence alignment. J Mol Biol, 302:205–217.
29.
PearsonW.R., LipmanD.J.1988. Improved tools for biological sequence comparison. Proc Natl Acad Sci USA, 85:2444–2448.
30.
QiuJ., EkanayakeJ., GunarathneT.et al.2010. Hybrid cloud and cluster computing paradigms for life science applications. BMC Bioinformatics, 11,Suppl 12:S3.
31.
SangerF., NicklenS., CoulsonA.R.1977. DNA sequencing with chain-terminating inhibitors. Proc Natl Acad Sci USA, 74:5463–5467.
32.
SchadtE.E., LindermanM.D., SorensonJ., LeeL., NolanG.P.2010. Computational solutions to large-scale data management and analysis. Nat Rev Genet, 11:647–657.
33.
SchatzM.C.2009. CloudBurst: highly sensitive read mapping with MapReduce. Bioinformatics, 25:1363–1369.
34.
SchatzM.C., LangmeadB., SalzbergS.L.2010. Cloud computing and the DNA data race. Nat Biotechnol, 28:691–693.
SmithT.F., WatermanM.S.1981. Identification of common molecular subsequences. J Mol Biol, 147:195–197.
37.
SteinL.D.2010. The case for cloud computing in genome informatics. Genome Biol, 11:207.
38.
TaylorR.C.2010. An overview of the Hadoop/MapReduce/HBase framework and its current applications in bioinformatics. BMC Bioinformatics, 11,Suppl 12:S1.
39.
ThompsonJ.D., HigginsD.G., GibsonT.J.1994. CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res, 22:4673–4680.
40.
TommasoP.D., OrobitgM., GuiradoF., CoresF., EspinosaT., NotredameC.2010. Cloud-Coffee: implementation of a parallel consistency-based multiple alignment algorithm in the T-Coffee package and its benchmarking on the Amazon Elastic-Cloud. Bioinformatics, 26:1903–1904.
41.
VaqueroL.M., Rodero-MerinoL., CaceresJ., LindnerM.2009. A break in the clouds: towards a cloud definition. ACM SIGCOMM Computer Communication Review, 39:50–55.
42.
VenterJ.C., AdamsM.D., MyersE.W.et al.2001. The sequence of the human genome. Science, 291:1304–1351.
43.
VoukM.A.2008. Cloud computing—issues, research and implementations. J Comput Information Technol, 4:235–246.
