Metabolic Analysis of Metatranscriptomic Data from Planktonic Communities

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

Abstract

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.

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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
  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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