Metabolic Analysis of Metatranscriptomic Data from Planktonic Communities

  • Igor Mandric
  • Sergey Knyazev
  • Cory Padilla
  • Frank Stewart
  • Ion I. Măndoiu
  • Alex ZelikovskyEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10330)


This paper describes an enhanced method for analyzing microbial metatranscriptomic (community RNA-seq) data using Expectation - Maximization (EM)-based differentiation and quantification of predicted gene, enzyme, and metabolic pathway activity. Here, we demonstrate the method by analyzing the metatranscriptome of planktonic communities in surface waters from the Northern Louisiana Shelf (Gulf of Mexico) during contrasting light and dark conditions. The analysis reveals that the level of transcripts encoding proteins of oxidative phosphorylation varys little between day and night. In contrast, transcripts of pyrimidine metabolism are significantly more abundant at night, whereas those of carbon fixation by photosynthetic organisms increase 2-fold in abundance from night to day.


KEGG Orthology Maximum Likelihood Model Pathway Activity Level Contig Abundance Infer Pathway Activity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



IM, SK and AZ were partially supported from NSF Grants 1564899 and 16119110, IM and SK were partially supported by GSU Molecular Basis of Disease Fellowship, IIM was partially supported from NSF Grants 1564936 and 1618347, CP and FS were partially supported by NSF Grants 1151698, 1558916, and 1564559, and Simons Foundation award 346253.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Igor Mandric
    • 1
  • Sergey Knyazev
    • 1
  • Cory Padilla
    • 2
  • Frank Stewart
    • 2
  • Ion I. Măndoiu
    • 3
  • Alex Zelikovsky
    • 1
    Email author
  1. 1.Department of Computer ScienceGeorgia State UniversityAtlantaUSA
  2. 2.School of Biological SciencesGeorgia Institute of TechnologyAtlantaUSA
  3. 3.Computer Science and Engineering DepartmentUniversity of ConnecticutStorrsUSA

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