Skip to main content
SpringerLink
Log in
Book cover

Italian Workshop on Artificial Life and Evolutionary Computation

WIVACE 2016: Advances in Artificial Life, Evolutionary Computation, and Systems Chemistry pp 53–64Cite as

MapReduce in Computational Biology - A Synopsis

  • Conference paper
  • First Online:

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 708))

Abstract

In the past 20 years, the Life Sciences have witnessed a paradigm shift in the way research is performed. Indeed, the computational part of biological and clinical studies has become central or is becoming so. Correspondingly, the amount of data that one needs to process, compare and analyze, has experienced an exponential growth. As a consequence, High Performance Computing (HPC, for short) is being used intensively, in particular in terms of multi-core architectures. However, recently and thanks to the advances in the processing of other scientific and commercial data, Distributed Computing is also being considered for Bioinformatics applications. In particular, the MapReduce paradigm, together with the main middleware supporting it, i.e., Hadoop and Spark, is becoming increasingly popular.

Here we provide a short review in which the state of the art of MapReduce bioinformatics applications is presented, together with a qualitative evaluation of each of the software systems that have been here included. In order to make the paper self-contained, computer architectural and middleware issues are also briefly presented.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Kahn, S.D.: On the future of genomic data. Science 331, 728–729 (2011)

    Article  Google Scholar 

  2. Mardis, E.R.: The $1,000 genome, the $100,000 analysis? Genome Med. 2, 1–3 (2010)

    Article  Google Scholar 

  3. Compeau, P.E.C., Pevzner, P.A., Tesler, G.: How to apply de Bruijn graphs to genome assembly. Nat. Biotechnol. 29, 987–991 (2011)

    Article  Google Scholar 

  4. Giancarlo, R., Rombo, S.E., Utro, F.: Epigenomic k-mer dictionaries: shedding light on how sequence composition influences in vivo nucleosome positioning. Bioinformatics 31(18), 2939–2946 (2015)

    Article  Google Scholar 

  5. Utro, F., Di Benedetto, V., Corona, D.F., Giancarlo, R.: The intrinsic combinatorial organization and information theoretic content of a sequence are correlated to the DNA encoded nucleosome organization of eukaryotic genomes. Bioinformatics 32(6), 835–842 (2015)

    Article  Google Scholar 

  6. Deorowicz, S., Kokot, M., Grabowski, S., Debudaj-Grabysz, A.: KMC 2: fast and resource-frugal k-mer counting. Bioinformatics 31(10), 1569–1576 (2015)

    Article  Google Scholar 

  7. National Human Genome Research Institute (NIH): The cost of sequencing a human genome (2016). https://www.genome.gov/sequencingcosts/

  8. Tanenbaum, A.S., Van Steen, M.: Distributed Systems. Prentice-Hall, Upper Saddle River (2007)

    MATH  Google Scholar 

  9. Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. Commun. ACM 51, 107–113 (2008)

    Article  Google Scholar 

  10. Apache Software Foundation: Hadoop (2016). http://hadoop.apache.org/

  11. Apache Software Foundation: Spark (2016). http://spark.apache.org/

  12. Zaharia, M., Chowdhury, M., Franklin, M.J., Shenker, S., Stoica, I.: Spark: cluster computing with working sets. In: Proceedings of the 2nd USENIX Conference on Hot Topics in Cloud Computing, vol. 10, pp. 1–7 (2010)

    Google Scholar 

  13. Vavilapalli, V.K., Murthy, A.C., Douglas, C., Agarwal, S., Konar, M., Evans, R., Graves, T., Lowe, J., Shah, H., Seth, S., et al.: Apache Hadoop YARN: yet another resource negotiator. In: Proceedings of the 4th annual Symposium on Cloud Computing, pp. 1–16. ACM (2013)

    Google Scholar 

  14. Shvachko, K., Kuang, H., Radia, S., Chansler, R.: The Hadoop distributed file system. In: IEEE 26th Symposium on Mass Storage Systems and Technologies (MSST), pp. 1–10. IEEE Computer Society, Washington, DC (2010)

    Google Scholar 

  15. McKenna, A., Hanna, M., Banks, E., Sivachenko, A., Cibulskis, K., Kernytsky, A., Garimella, K., Altshuler, D., Gabriel, S., Daly, M., et al.: The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010)

    Article  Google Scholar 

  16. Nordberg, H., Bhatia, K., Wang, K., Wang, Z.: BioPig: a Hadoop-based analytic toolkit for large-scale sequence data. Bioinformatics 29, 3014–3019 (2013)

    Article  Google Scholar 

  17. Matsunaga, A., Tsugawa, M., Fortes, J.: CloudBLAST: combining MapReduce and virtualization on distributed resources for bioinformatics applications. In: IEEE Fourth International Conference on eScience. eScience 2008, pp. 222–229. IEEE (2008)

    Google Scholar 

  18. Altschul, S.F., Gish, W., Miller, W., Myers, E.W., Lipman, D.J.: Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990)

    Article  Google Scholar 

  19. Piotto, S., Di Biasi, L., Concilio, S., Castiglione, A., Cattaneo, G.: GRIMD: distributed computing for chemists and biologists. Bioinformation 10, 43–47 (2014)

    Article  Google Scholar 

  20. Lopez, D.H., Fiol-deRoque, M.A., Noguera-Salvà, M.A., Terés, S., Campana, F., Piotto, S., Castro, J.A., Mohaibes, R.J., Escribá, P.V., Busquets, X.: 2-Hydroxy arachidonic acid: a new non-steroidal anti-inflammatory drug. PloS ONE 8, 1–10 (2013)

    Google Scholar 

  21. Piotto, S., Concilio, S., Bianchino, E., Iannelli, P., López, D.J., Terés, S., Ibarguren, M., Barceló-Coblijn, G., Martin, M.L., Guardiola-Serrano, F., Alonso-Sande, M., Funari, S.S., Busquets, X., Escribá, P.V.: Differential effect of 2-hydroxyoleic acid enantiomers on protein (sphingomyelin synthase) and lipid (membrane) targets. Biochimica et Biophysica Acta (BBA)-Biomembranes 1838, 1628–1637 (2014)

    Article  Google Scholar 

  22. Piotto, S., Trapani, A., Bianchino, E., Ibarguren, M., López, D.J., Busquets, X., Concilio, S.: The effect of hydroxylated fatty acid-containing phospholipids in the remodeling of lipid membranes. Biochimica et Biophysica Acta (BBA)-Biomembranes 1838, 1509–1517 (2014)

    Article  Google Scholar 

  23. Nguyen, T., Shi, W., Ruden, D.: CloudAligner: a fast and full-featured MapReduce based tool for sequence mapping. BMC Res. Notes 4, 171 (2011)

    Article  Google Scholar 

  24. Cattaneo, G., Italiano, G.F.: Algorithm engineering. ACM Comput. Surv. (CSUR) 31, 582–585 (1999)

    Article  Google Scholar 

  25. Demetrescu, C., Finocchi, I., Italiano, G.F.: Algorithm engineering. Bull. EATCS 79, 48–63 (2003)

    MATH  Google Scholar 

  26. Niemenmaa, M., Kallio, A., Schumacher, A., Klemelä, P., Korpelainen, E., Heljanko, K.: Hadoop-BAM: directly manipulating next generation sequencing data in the Cloud. Bioinformatics 28, 876–877 (2012)

    Article  Google Scholar 

  27. Schönherr, S., Forer, L., Weißensteiner, H., Kronenberg, F., Specht, G., Kloss-Brandstätter, A.: Cloudgene: a graphical execution platform for MapReduce programs on private and public clouds. BMC Bioinform. 13, 1–9 (2012)

    Article  Google Scholar 

  28. Ferraro Petrillo, U., Roscigno, G., Cattaneo, G., Giancarlo, R.: FASTdoop: a versatile and efficient library for the input of FASTA and FASTQ files for MapReduce Hadoop bioinformatics applications. Bioinformatics (2017). https://dx.doi.org/10.1093/bioinformatics/btx010

  29. Schumacher, A., Pireddu, L., Niemenmaa, M., Kallio, A., Korpelainen, E., Zanetti, G., Heljanko, K.: SeqPig: simple and scalable scripting for large sequencing data sets in Hadoop. Bioinformatics 30, 119–120 (2014)

    Article  Google Scholar 

  30. Wiewiórka, M.S., Messina, A., Pacholewska, A., Maffioletti, S., Gawrysiak, P., Okoniewski, M.J.: SparkSeq: fast, scalable, Cloud-ready tool for the interactive genomic data analysis with nucleotide precision. Bioinformatics 30, 2652–2653 (2014)

    Article  Google Scholar 

  31. Huang, H., Tata, S., Prill, R.J.: BlueSNP: R package for highly scalable genome-wide association studies using Hadoop clusters. Bioinformatics 29, 135–136 (2013)

    Article  Google Scholar 

  32. Langmead, B., Schatz, M.C., Lin, J., Pop, M., Salzberg, S.L.: Searching for SNPs with Cloud computing. Genome Biol. 10, 1–10 (2009)

    Article  Google Scholar 

  33. Jourdren, L., Bernard, M., Dillies, M.A., Le Crom, S.: Eoulsan: a Cloud computing-based framework facilitating high throughput sequencing analyses. Bioinformatics 28, 1542–1543 (2012)

    Article  Google Scholar 

  34. Hong, D., Rhie, A., Park, S.S., Lee, J., Ju, Y.S., Kim, S., Yu, S.B., Bleazard, T., Park, H.S., Rhee, H., et al.: FX: an RNA-Seq analysis tool on the Cloud. Bioinformatics 28, 721–723 (2012)

    Article  Google Scholar 

  35. Langmead, B., Hansen, K.D., Leek, J.T., et al.: Cloud-scale RNA-sequencing differential expression analysis with Myrna. Genome Biol. 11, 1–11 (2010)

    Article  Google Scholar 

  36. Zhang, L., Gu, S., Liu, Y., Wang, B., Azuaje, F.: Gene set analysis in the Cloud. Bioinformatics 28, 294–295 (2012)

    Article  Google Scholar 

  37. Almeida, J.S., Grüneberg, A., Maass, W., Vinga, S.: Fractal MapReduce decomposition of sequence alignment. Algorithms Mol. Biol. 7, 1–12 (2012)

    Article  Google Scholar 

  38. Cattaneo, G., Ferraro Petrillo, U., Giancarlo, R., Roscigno, G.: An effective extension of the applicability of alignment-free biological sequence comparison algorithms with Hadoop. J. Supercomput. 1–17 (2016). http://dx.doi.org/10.1007/s11227-016-1835-3

  39. Hill, C.M., Albach, C.H., Angel, S.G., Pop, M.: K-mulus: strategies for BLAST in the Cloud. In: Wyrzykowski, R., Dongarra, J., Karczewski, K., Waśniewski, J. (eds.) PPAM 2013. LNCS, vol. 8385, pp. 237–246. Springer, Heidelberg (2014). doi:10.1007/978-3-642-55195-6_22

    Chapter  Google Scholar 

  40. Colosimo, M.E., Peterson, M.W., Mardis, S., Hirschman, L.: Nephele: genotyping via complete composition vectors and MapReduce. Source Code Biol. Med. 6, 1–10 (2011)

    Article  Google Scholar 

  41. Drew, J., Hahsler, M.: Strand: fast sequence comparison using MapReduce and locality sensitive hashing. In: Proceedings of the 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, pp. 506–513. ACM (2014)

    Google Scholar 

  42. Chang, Y.J., Chen, C.C., Chen, C.L., Ho, J.M.: A de novo next generation genomic sequence assembler based on string graph and MapReduce Cloud computing framework. BMC Genomics 13, 1–17 (2012)

    Article  Google Scholar 

  43. Schatz, M.C., Sommer, D., Kelley, D., Pop, M.: De novo assembly of large genomes using Cloud computing. In: Proceedings of the Cold Spring Harbor Biology of Genomes Conference (2010)

    Google Scholar 

  44. Schatz, M.C.: BlastReduce: high performance short read mapping with MapReduce. University of Maryland (2008). http://cgis.cs.umd.edu/Grad/scholarlypapers/papers/MichaelSchatz.pdf

  45. Schatz, M.C.: CloudBurst: highly sensitive read mapping with MapReduce. Bioinformatics 25, 1363–1369 (2009)

    Article  Google Scholar 

  46. Pireddu, L., Leo, S., Zanetti, G.: SEAL: a distributed short read mapping and duplicate removal tool. Bioinformatics 27, 2159–2160 (2011)

    Article  Google Scholar 

  47. Zhao, G., Ling, C., Sun, D.: SparkSW: scalable distributed computing system for large-scale biological sequence alignment. In: 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), pp. 845–852. IEEE (2015)

    Google Scholar 

  48. Leo, S., Santoni, F., Zanetti, G.: Biodoop: bioinformatics on Hadoop. In: International Conference on Parallel Processing Workshops (ICPPW 2009), pp. 415–422. IEEE (2009)

    Google Scholar 

  49. Radenski, A., Ehwerhemuepha, L.: Speeding-up codon analysis on the Cloud with local MapReduce aggregation. Inf. Sci. 263, 175–185 (2014)

    Article  Google Scholar 

  50. Rasheed, Z., Rangwala, H.: A Map-Reduce framework for clustering metagenomes. In: IEEE 27th International Parallel and Distributed Processing Symposium Workshops & Ph.D. Forum (IPDPSW), pp. 549–558. IEEE (2013)

    Google Scholar 

  51. Matthews, S.J., Williams, T.L.: MrsRF: an efficient MapReduce algorithm for analyzing large collections of evolutionary trees. BMC Bioinform. 11, 1–9 (2010)

    Article  Google Scholar 

  52. Feng, X., Grossman, R., Stein, L.: PeakRanger: a Cloud-enabled peak caller for ChIP-seq data. BMC Bioinform. 12, 1–11 (2011)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dipartimento di Informatica, Università degli Studi di Salerno, 84084, Fisciano, Salerno, Italy

    Giuseppe Cattaneo & Gianluca Roscigno

  2. Dipartimento di Matematica ed Informatica, Università di Palermo, 90133, Palermo, Palermo, Italy

    Raffaele Giancarlo

  3. Dipartimento di Farmacia, Università degli Studi di Salerno, 84084, Fisciano, Salerno, Italy

    Stefano Piotto & Luigi Di Biasi

  4. Dipartimento di Scienze Statistiche, Università di Roma “La Sapienza”, 00185, Roma, Italy

    Umberto Ferraro Petrillo

Authors
  1. Giuseppe Cattaneo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Raffaele Giancarlo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Stefano Piotto
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Umberto Ferraro Petrillo
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Gianluca Roscigno
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Luigi Di Biasi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gianluca Roscigno .

Editor information

Editors and Affiliations

  1. Chemistry and Biology, University of Salerno, Fisciano, Italy

    Federico Rossi

  2. Department of Pharmacy, University of Salerno, Fisciano, Italy

    Stefano Piotto

  3. Department of Industrial Engineering, University of Salerno, Fisciano, Italy

    Simona Concilio

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Cattaneo, G., Giancarlo, R., Piotto, S., Ferraro Petrillo, U., Roscigno, G., Di Biasi, L. (2017). MapReduce in Computational Biology - A Synopsis. In: Rossi, F., Piotto, S., Concilio, S. (eds) Advances in Artificial Life, Evolutionary Computation, and Systems Chemistry. WIVACE 2016. Communications in Computer and Information Science, vol 708. Springer, Cham. https://doi.org/10.1007/978-3-319-57711-1_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-57711-1_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-57710-4

  • Online ISBN: 978-3-319-57711-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation