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Big data analytics in bioinformatics: architectures, techniques, tools and issues

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Abstract

Bioinformatics research is characterized by voluminous and incremental datasets and complex data analytics methods. The machine learning methods used in bioinformatics are iterative and parallel. These methods can be scaled to handle big data using the distributed and parallel computing technologies. Usually big data tools perform computation in batch mode and are not optimized for iterative processing and high data dependency among operations. In the recent years, parallel, incremental, and multi-view machine learning algorithms have been proposed. Similarly, graph-based architectures and in-memory big data tools have been developed to minimize I/O cost and optimize iterative processing. However, standard big data architectures are still lacking. Also appropriate tools are not available for many important bioinformatics problems, such as fast construction of co-expression and regulatory networks and salient module identification, detection of complexes over growing protein-protein interaction data, fast analysis of massive DNA, RNA, and protein sequence data, and fast querying on incremental and heterogeneous disease networks. This paper addresses the issues and challenges posed by several big data problems in bioinformatics, and gives an overview of the state of the art and the future research opportunities.

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Notes

  1. www.cs.cmu.edu/~coke/.

  2. http://bhuvan.nrsc.gov.in.

  3. http://www.bina.com.

  4. http://www.ebi.ac.uk/arrayexpress.

  5. http://www.ncbi.nlm.nih.gov/geo.

  6. http://smd.princeton.edu.

  7. http://www.ddbj.nig.ac.jp.

  8. http://rdp.cme.msu.edu.

  9. http://www.mirbase.org.

  10. http://dip.doe-mbi.ucla.edu.

  11. http://string.embl.de.

  12. http://thebiogrid.org.

  13. http://www.geneontology.org.

  14. http://giraph.apache.org.

  15. http://www.mongodb.org.

  16. http://couchdb.apache.org.

  17. http://spark.apache.org/mllib.

  18. http://hadoop.apache.org.

  19. http://spark.apache.org.

  20. http://hama.apache.org.

  21. http://spark.apache.org/streaming.

  22. http://illumina.com/applications/microarrays/microarray-software/beeline.html.

  23. http://www.bioinformatics.bbsrc.ac.uk/projects/seqmonk.

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Authors and Affiliations

  1. Department of Computer Science, Donald Bren School of Information and Computer Sciences, University of California Irvine, 3019 Donald Bren Hall, Irvine, CA, 92697-3435, USA

    Hirak Kashyap

  2. Department of Computer Science and Engineering, Tezpur University, Tezpur, 784028, India

    Hasin Afzal Ahmed, Nazrul Hoque & Dhruba Kumar Bhattacharyya

  3. Department of Information Technology, North Eastern Hill University, Shillong, 22, India

    Swarup Roy

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Correspondence to Dhruba Kumar Bhattacharyya.

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Kashyap, H., Ahmed, H.A., Hoque, N. et al. Big data analytics in bioinformatics: architectures, techniques, tools and issues. Netw Model Anal Health Inform Bioinforma 5, 28 (2016). https://doi.org/10.1007/s13721-016-0135-4

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