Abstract
Intrinsically disordered proteins (IDPs) constitute a wide range of molecules that act in cells of living organisms and mediate many protein–protein interactions and many regulatory processes. Computational identification of disordered regions in protein amino acid sequences, thus, became an important branch of 3D protein structure prediction and modeling. In this chapter, we will see the IDP meta-predictor that applies an ensemble of primary predictors in order to increase the quality of IDP prediction. We will also see the highly scalable implementation of the meta-predictor on the Spark cluster (Spark-IDPP) that mitigates the problem of the exponentially growing number of protein amino acid sequences in public repositories. Spark-IDPP responds very well to the current needs of IDP prediction by parallelizing computations on the Spark cluster that can be scaled on demand on the Microsoft Azure cloud according to particular requirements for computing power.
Big data is mostly about taking numbers and using those numbers to make predictions about the future. The bigger the data set you have, the more accurate the predictions about the future will be
Anthony Goldbloom
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Altschul, S.F., Madden, T.L., Schäffer, A.A., Zhang, J., Zhang, Z., Miller, W., Lipman, D.J.: Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25(17), 3389–3402 (1997). https://doi.org/10.1093/nar/25.17.3389
Bai, C., Dhavale, D., Sarkis, J.: Complex investment decisions using rough set and fuzzy c-means: an example of investment in green supply chains. Eur. J. Oper. Res. 248(2), 507–521 (2016)
Baron, T.: Prediction of intrinsically disordered proteins in Apache Spark. Master’s thesis, Institute of Informatics, Silesian University of Technology, Gliwice, Poland (2016)
Bayer, P., Arndt, A., Metzger, S., Mahajan, R., Melchior, F., Jaenicke, R., Becker, J.: Structure determination of the small ubiquitin-related modifier SUMO-1. J. Mol. Biol. 280(2), 275–286 (1998). http://www.sciencedirect.com/science/article/pii/S0022283698918393
Benson, D.A., Cavanaugh, M., Clark, K., Karsch-Mizrachi, I., Lipman, D.J., Ostell, J., Sayers, E.W.: GenBank. Nucleic Acids Res. 45(D1), D37–D42 (2017). https://doi.org/10.1093/nar/gkw1070
Berman, H., et al.: The Protein Data Bank. Nucleic Acids Res. 28, 235–242 (2000)
Boutet, E., Lieberherr, D., Tognolli, M., Schneider, M., Bansal, P., Bridge, A.J., Poux, S., Bougueleret, L., Xenarios, I.: UniProtKB/Swiss-Prot, the Manually Annotated Section of the UniProt KnowledgeBase: How to Use the Entry View, pp. 23–54. Springer, New York (2016)
Ceri, S., Kaitoua, A., Masseroli, M., Pinoli, P., Venco, F.: Data management for heterogeneous genomic datasets. IEEE/ACM Trans. Comput. Biol. Bioinform. 99, 1–1 (2016)
Chang, H., Mishra, N., Lin, C.: IoT Big-Data centred knowledge granule analytic and cluster framework for BI applications: a case base analysis. Plos One 10, 1–23 (2015)
Cheng, J., Sweredoski, M.J., Baldi, P.: Accurate prediction of protein disordered regions by mining protein structure data. Data Min. Knowl. Discov. 11(3), 213–222 (2005), https://doi.org/10.1007/s10618-005-0001-y
Cupek, R., Ziebinski, A., Huczala, L., Erdogan, H.: Agent-based manufacturing execution systems for short-series production scheduling. Comput. Ind. 82, 245–258 (2016)
Czerniak, J.M., Dobrosielski, W.T., Apiecionek, Ł., Ewald, D.: Representation of a trend in OFN during fuzzy observance of the water level from the Crisis control center. In: Proceedings of the 2015 Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 443–447 (2015)
Davis, G.B., Carley, K.M.: Clearing the fog: fuzzy, overlapping groups for social networks. Soc. Netw. 30(3), 201–212 (2008)
De Maio, C., Fenza, G., Loia, V., Parente, M.: Time aware knowledge extraction for microblog summarization on Twitter. Inf. Fus. 28, 60–74 (2016)
Dosztányi, Z., Csizmok, V., Tompa, P., Simon, I.: IUPred: web server for the prediction of intrinsically unstructured regions of proteins based on estimated energy content. Bioinformatics 21(16), 3433–3434 (2005). https://doi.org/10.1093/bioinformatics/bti541
Dunker, A.K., Silman, I., Uversky, V.N., Sussman, J.L.: Function and structure of inherently disordered proteins. Curr. Opin. Struct. Biol. 18(6), 756–764 (2008)
Feng, X., Grossman, R., Stein, L.: PeakRanger: a cloud-enabled peak caller for ChIP-seq data. BMC Bioinform.12(1), 1–11 (2011), https://doi.org/10.1186/1471-2105-12-139
Guo, K., Zhang, R., Kuang, L.: TMR: towards an efficient semantic-based heterogeneous transportation media Big Data retrieval. Neurocomputing 181, 122–131 (2016)
Hazelhurst, S.: PH2: an Hadoop-based framework for mining structural properties from the PDB database. In: Proceedings of the 2010 Annual Research Conference of the South African Institute of Computer Scientists and Information Technologists, pp. 104–112 (2010)
Hirose, S., Shimizu, K., Kanai, S., Kuroda, Y., Noguchi, T.: POODLE-L: a two-level SVM prediction system for reliably predicting long disordered regions. Bioinformatics 23(16), 2046–2053 (2007). https://doi.org/10.1093/bioinformatics/btm302
Hu, C., Ren, G., Liu, C., Li, M., Jie, W.: A Spark-based genetic algorithm for sensor placement in large scale drinking water distribution systems. Clust. Comput. 20(2), 1089–1099 (2017). https://doi.org/10.1007/s10586-017-0838-z
Hung, C.L., Hua, G.J.: Cloud Computing for protein-ligand binding site comparison. Biomed Res. Int. 170356 (2013)
Hung, C.L., Lin, C.Y.: Open reading frame phylogenetic analysis on the cloud. Int. J. Genomics 2013(614923), 1–9 (2013)
Hung, C.L., Lin, Y.L.: Implementation of a parallel protein structure alignment service on cloud. Int. J. Genomics 439681, 1–8 (2013)
Ishida, T., Kinoshita, K.: PrDOS: prediction of disordered protein regions from amino acid sequence. Nucleic Acids Res. 35(suppl\(\_\)2), W460–W464 (2007). https://doi.org/10.1093/nar/gkm363
Jensen, K., Nguyen, H.T., Do, T.V., Årnes, A.: a big data analytics approach to combat telecommunication vulnerabilities. Clust. Comput. 20(3), 2363–2374 (2017). https://doi.org/10.1007/s10586-017-0811-x
Jin, Y., Dunbrack, R.: Assessment of disorder predictions in CASP6. Proteins 61, 167–175 (2005)
Kabsch, W., Sander, C.: Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 22(12), 2577–2637 (1987)
Kelley, D.R., Schatz, M.C., Salzberg, S.L.: Quake: quality-aware detection and correction of sequencing errors. Genome Biol. 11(11), 1–13 (2010). https://doi.org/10.1186/gb-2010-11-11-r116
Kozlowski, L.P., Bujnicki, J.M.: MetaDisorder: a meta-server for the prediction of intrinsic disorder in proteins. BMC Bioinform. 13(1), 111 (2012). https://doi.org/10.1186/1471-2105-13-111
Langmead, B., Hansen, K.D., Leek, J.T.: Cloud-scale RNA-sequencing differential expression analysis with Myrna. Genome Biol. 11(8), 1–11 (2010). https://doi.org/10.1186/gb-2010-11-8-r83
Langmead, B., Schatz, M.C., Lin, J., Pop, M., Salzberg, S.L.: Searching for SNPs with Cloud computing. Genome Biol. 10(11), 1–10 (2009). https://doi.org/10.1186/gb-2009-10-11-r134
Lewis, S., Csordas, A., Killcoyne, S., Hermjakob, H., et al.: Hydra: a scalable proteomic search engine which utilizes the Hadoop distributed computing framework. BMC Bioinform. 13, 324 (2012)
Linding, R., Jensen, L.J., Diella, F., Bork, P., Gibson, T.J., Russell, R.B.: Protein disorder prediction: implications for structural proteomics. Structure 11(11), 1453–1459 (2003). http://www.sciencedirect.com/science/article/pii/S0969212603002351
Linding, R., Russell, R.B., Neduva, V., Gibson, T.J.: GlobPlot: exploring protein sequences for globularity and disorder. Nucleic Acids Res. 31(13), 3701–3708 (2003). https://doi.org/10.1093/nar/gkg519
Lipman, D., Pearson, W.: Rapid and sensitive protein similarity searches. Science 227(4693), 1435–1441 (1985)
Lu, H., Sun, Z., Qu, W.: Big Data-driven based real-time traffic flow state identification and prediction. Discret. Dyn. Nat. Soc. 2015, 1–11 (2015)
Lu, H., Sun, Z., Qu, W., Wang, L.: Real-time corrected traffic correlation model for traffic flow forecasting. Math. Probl. Eng. 2015, 1–7 (2015)
Mahmud, S., Iqbal, R., Doctor, F.: Cloud enabled data analytics and visualization framework for health-shocks prediction. Future Gener. Comput. Syst. 65, 169–181 (2016). http://www.sciencedirect.com/science/article/pii/S0167739X15003271. (special Issue on Big Data in the Cloud)
Małysiak-Mrozek, B., Baron, T., Mrozek, D.: Spark-IDPP: High throughput and scalable prediction of intrinsically disordered protein regions with Spark clusters on the Cloud, J. Clus. Comp, 1–35 (in review)
Małysiak-Mrozek, B., Stabla, M., Mrozek, D.: Soft and declarative fishing of information in Big Data lake. IEEE Trans. Fuzzy Syst. 99, 1–1 (2018)
Małysiak-Mrozek, B., Zur, K., Mrozek, D.: In-memory management system for 3D protein macromolecular structures. Curr. Proteomics 15 (2018). https://doi.org/10.2174/1570164615666180320151452
Matsunaga, A., Tsugawa, M., Fortes, J.: Cloudblast: combining MapReduce and virtualization on distributed resources for bioinformatics applications. In: Proceedings of the IEEE Fourth International Conference on eScience (ESCIENCE ’08), pp. 222–229 (2008)
Matthews, S.J., Williams, T.L.: MrsRF: an efficient MapReduce algorithm for analyzing large collections of evolutionary trees. BMC Bioinform. 11(1), 1–9 (2010). https://doi.org/10.1186/1471-2105-11-S1-S15
Meng, L., Tan, A., Wunsch, D.: Adaptive scaling of cluster boundaries for large-scale social media data clustering. IEEE Trans. Neural Netw. Learn. 27(12), 2656–2669 (2015)
Mrozek, D.: High-Performance Computational Solutions in Protein Bioinformatics. SpringerBriefs in Computer Science. Springer International Publishing, Cham (2014)
Mrozek, D., Daniłowicz, P., Małysiak-Mrozek, B.: HDInsight4PSi: boosting performance of 3D protein structure similarity searching with HDInsight clusters in Microsoft Azure cloud. Inf. Sci. 349–350, 77–101 (2016)
Mrozek, D., Gosk, P., Małysiak-Mrozek, B.: Scaling Ab Initio predictions of 3D protein structures in Microsoft Azure cloud. J Grid Comput. 13, 561–585 (2015)
Mrozek, D., Kutyła, T., Małysiak-Mrozek, B.: Accelerating 3D protein structure similarity searching on Microsoft Azure Cloud with local replicas of macromolecular data. In: Wyrzykowski, R. (ed.) Parallel Processing and Applied Mathematics - PPAM 2015. Lecture Notes in Computer Science, vol. 9574, pp. 1–12. Springer, Heidelberg (2016)
Mrozek, D., Małysiak-Mrozek, B., Kłapciński, A.: Cloud4Psi: cloud computing for 3D protein structure similarity searching. Bioinformatics 30(19), 2822–2825 (2014)
Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B., Kozielski, S.: Life sciences data analysis. Inform. Sci. 384, 86–89 (2017)
Piovesan, D., Tabaro, F., Mičetić, I., Necci, M., Quaglia, F., Oldfield, C.J., Aspromonte, M.C., Davey, N.E., Davidović, R., Dosztányi, Z., Elofsson, A., Gasparini, A., Hatos, A., Kajava, A.V., Kalmar, L., Leonardi, E., Lazar, T., Macedo-Ribeiro, S., Macossay-Castillo, M., Meszaros, A., Minervini, G., Murvai, N., Pujols, J., Roche, D.B., Salladini, E., Schad, E., Schramm, A., Szabo, B., Tantos, A., Tonello, F., Tsirigos, K.D., Veljković, N., Ventura, S., Vranken, W., Warholm, P., Uversky, V.N., Dunker, A.K., Longhi, S., Tompa, P., Tosatto, S.C.: DisProt 7.0: a major update of the database of disordered proteins. Nucleic Acids Res. 45(D1), D219–D227 (2017). https://doi.org/10.1093/nar/gkw1056
Powers, D.: Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. Int. J. Mach. Learn. Technol. 2, 37–63 (2011)
Qiu, X., Ekanayake, J., Beason, S., Gunarathne, T., Fox, G., Barga, R., Gannon, D.: Cloud technologies for bioinformatics applications. In: Proceedings of the 2nd Workshop on Many-Task Computing on Grids and Supercomputers, pp. 6:1–6:10. MTAGS ’09, ACM, New York, NY, USA (2009). https://doi.org/10.1145/1646468.1646474
Radenski, A., Ehwerhemuepha, L.: Speeding-up codon analysis on the cloud with local MapReduce aggregation. Inf. Sci. 263, 175–185 (2014)
Rose, A.S., Hildebrand, P.W.: NGL viewer: a web application for molecular visualization. Nucleic Acids Res. 43(W1), W576–W579 (2015). https://doi.org/10.1093/nar/gkv402
Schatz, M.C.: CloudBurst: highly sensitive read mapping with MapReduce. Bioinformatics 25(11), 1363–1369 (2009)
Shimizu, K., Hirose, S., Noguchi, T.: POODLE-S: web application for predicting protein disorder by using physicochemical features and reduced amino acid set of a position-specific scoring matrix. Bioinformatics 23(17), 2337–2338 (2007). https://doi.org/10.1093/bioinformatics/btm330
Sickmeier, M., Hamilton, J.A., LeGall, T., Vacic, V., Cortese, M.S., Tantos, A., Szabo, B., Tompa, P., Chen, J., Uversky, V.N., Obradovic, Z., Dunker, A.K.: DisProt: the database of disordered proteins. Nucleic Acids Res. 35\((\text{suppl}\_1)\), D786–D793 (2007). https://doi.org/10.1093/nar/gkl893
Su, C.T., Chen, C.Y., Hsu, C.M.: iPDA: integrated protein disorder analyzer. Nucleic Acids Res. 35(suppl\({\_}\)2), W465–W472 (2007). https://doi.org/10.1093/nar/gkm353
Teijeiro, D., Pardo, X.C., Penas, D.R., González, P., Banga, J.R., Doallo, R.: A cloud-based enhanced differential evolution algorithm for parameter estimation problems in computational systems biology. Clust. Comput. 20(3), 1937–1950 (2017). https://doi.org/10.1007/s10586-017-0860-1
The UniProt consortium: Uniprot: the universal protein knowledgebase. Nucleic Acids Res. 45(D1), D158–D169 (2017). https://doi.org/10.1093/nar/gkw1099
Tripathy, B.K., Mittal, D.: Hadoop based uncertain possibilistic kernelized c-means algorithms for image segmentation and a comparative analysis. Appl. Soft Comput. 46, 886–923 (2016)
Vullo, A., Bortolami, O., Pollastri, G., Tosatto, S.C.E.: Spritz: a server for the prediction of intrinsically disordered regions in protein sequences using kernel machines. Nucleic Acids Res. 34\((\text{ suppl }\_2)\), W164–W168 (2006). https://doi.org/10.1093/nar/gkl166
Wang, H., Li, J., Hou, Z., Fang, R., Mei, W., Huang, J.: Research on parallelized real-time map matching algorithm for massive GPS data. Clust. Comput. 20(2), 1123–1134 (2017). https://doi.org/10.1007/s10586-017-0869-5
Wang, C., Li, X., Zhou, X., Wang, A., Nedjah, N.: Soft computing in Big Data intelligent transportation systems. Appl. Soft Comput. 38, 1099–1108 (2016)
Wang, Z., Tu, L., Guo, Z., Yang, L.T., Huang, B.: Analysis of user behaviors by mining large network data sets. Future Gener. Comput. Syst. 37, 429–437 (2014)
Ward, J.J., McGuffin, L.J., Bryson, K., Buxton, B.F., Jones, D.T.: The DISOPRED server for the prediction of protein disorder. Bioinformatics 20(13), 2138–2139 (2004). https://doi.org/10.1093/bioinformatics/bth195
Xu, Z., Mei, L., Hu, C., Liu, Y.: The big data analytics and applications of the surveillance system using video structured description technology. Clust. Comput. 19(3), 1283–1292 (2016). https://doi.org/10.1007/s10586-016-0581-x
Xue, B., Dunbrack, R.L., Williams, R.W., Dunker, A.K., Uversky, V.N.: Pondr-fit: a meta-predictor of intrinsically disordered amino acids. Biochim. Biophys. Acta (BBA) - Proteins Proteomics 1804(4), 996–1010 (2010). http://www.sciencedirect.com/science/article/pii/S1570963910000130
Yang, C.T., Chen, S.T., Yan, Y.Z.: The implementation of a cloud city traffic state assessment system using a novel big data architecture. Clust. Comput. 20(2), 1101–1121 (2017). https://doi.org/10.1007/s10586-017-0846-z
Yang, Z.R., Thomson, R., McNeil, P., Esnouf, R.M.: RONN: the bio-basis function neural network technique applied to the detection of natively disordered regions in proteins. Bioinformatics 21(16), 3369–3376 (2005). https://doi.org/10.1093/bioinformatics/bti534
Zaharia, M., Xin, R.S., Wendell, P., Das, T., Armbrust, M., Dave, A., Meng, X., Rosen, J., Venkataraman, S., Franklin, M.J., Ghodsi, A., Gonzalez, J., Shenker, S., Stoica, I.: Apache Spark: a unified engine for big data processing. Commun. ACM 59(11), 56–65 (2016). https://doi.org/10.1145/2934664
Zhang, T., Faraggi, E., Li, Z., Zhou, Y.: Intrinsic disorder and Semi-disorder prediction by SPINE-D, pp. 159–174. Springer, New York (2017). https://doi.org/10.1007/978-1-4939-6406-2_12
Zhong, Y., Zhang, L., Xing, S., Li, F., Wan, B.: The Big Data processing algorithm for water environment monitoring of the three gorges reservoir area. Abstr. Appl. Anal. 2014 (2014)
Zou, Q., Hu, Q., Guo, M., Wang, G.: HAlign: fast multiple similar DNA/RNA sequence alignment based on the centre star strategy. Bioinformatics 31(15), 2475–2481 (2015)
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Mrozek, D. (2018). Scalable Prediction of Intrinsically Disordered Protein Regions with Spark Clusters on Microsoft Azure Cloud. In: Scalable Big Data Analytics for Protein Bioinformatics. Computational Biology, vol 28. Springer, Cham. https://doi.org/10.1007/978-3-319-98839-9_9
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