Metabolic Analysis of Metatranscriptomic Data from Planktonic Communities

• Published in: Bioinformatics Research and Applications Vol. 10330; pp. 396 - 402
• Main Authors: Mandric, Igor, Knyazev, Sergey, Padilla, Cory, Stewart, Frank, Măndoiu, Ion I., Zelikovsky, Alex
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 01.01.2017; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Contig Abundance; Infer Pathway Activity; KEGG Orthology; Maximum Likelihood Model; Pathway Activity Level
• ISBN: 3319595741; 9783319595740
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-59575-7_41

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.