Transcriptome reconstruction and functional analysis of eukaryotic marine plankton communities via high-throughput metagenomics and metatranscriptomics.

Large-scale metagenomic and metatranscriptomic data analyses are often restricted by their gene-centric approach, limiting the ability to understand organismal and community biology. De novo assembly of large and mosaic eukaryotic genomes from complex meta-omics data remains a challenging task, especially in comparison with more straightforward bacterial and archaeal systems. Here, we use a transcriptome reconstruction method based on clustering co-abundant genes across a series of metagenomic samples. We investigated the co-abundance patterns of ∼37 million eukaryotic unigenes across 365 metagenomic samples collected during the Tara Oceans expeditions to assess the diversity and functional profiles of marine plankton. We identified ∼12,000 co-abundant gene groups (CAGs), encompassing ∼7 million unigenes, including 924 metagenomics-based transcriptomes (MGTs, CAGs larger than 500 unigenes). We demonstrated the biological validity of the MGT collection by comparing individual MGTs with available references. We identified several key eukaryotic organisms involved in dimethylsulfoniopropionate (DMSP) biosynthesis and catabolism in different oceanic provinces, thus demonstrating the potential of the MGT collection to provide functional insights on eukaryotic plankton. We established the ability of the MGT approach to capture interspecies associations through the analysis of a nitrogen-fixing haptophyte-cyanobacterial symbiotic association. This MGT collection provides a valuable resource for analyses of eukaryotic plankton in the open ocean by giving access to the genomic content and functional potential of many ecologically relevant eukaryotic species.

Analysis of the genomic basis of functional diversity in dinoflagellates using a transcriptome-based sequence similarity network.

Dinoflagellates are one of the most abundant and functionally diverse groups of eukaryotes. Despite an overall scarcity of genomic information for dinoflagellates, constantly emerging high-throughput sequencing resources can be used to characterize and compare these organisms. We assembled de novo and processed 46 dinoflagellate transcriptomes and used a sequence similarity network (SSN) to compare the underlying genomic basis of functional features within the group. This approach constitutes the most comprehensive picture to date of the genomic potential of dinoflagellates. A core-predicted proteome composed of 252 connected components (CCs) of putative conserved protein domains (pCDs) was identified. Of these, 206 were novel and 16 lacked any functional annotation in public databases. Integration of functional information in our network analyses allowed investigation of pCDs specifically associated with functional traits. With respect to toxicity, sequences homologous to those of proteins found in species with toxicity potential (e.g., sxtA4 and sxtG) were not specific to known toxin-producing species. Although not fully specific to symbiosis, the most represented functions associated with proteins involved in the symbiotic trait were related to membrane processes and ion transport. Overall, our SSN approach led to identification of 45,207 and 90,794 specific and constitutive pCDs of, respectively, the toxic and symbiotic species represented in our analyses. Of these, 56% and 57%, respectively (i.e., 25,393 and 52,193 pCDs), completely lacked annotation in public databases. This stresses the extent of our lack of knowledge, while emphasizing the potential of SSNs to identify candidate pCDs for further functional genomic characterization.