Éditeur(s) : HAL CCSD Wiley
Résumé : International audience
Analysis of microbial biodiversity is hampered by a lack of reference genomes from most bacteria, viruses, and algae. This necessitates either the cultivation of a restricted number of species for standard sequencing projects or the analysis of highly complex environmental DNA metagenome data. Single-cell genomics (SCG) offers a solution to this problem by constraining the studied DNA sample to an individual cell and its associated symbionts, prey, and pathogens. We used SCG to study marine heterotrophic amoebae related to Paulinella ovalis (A. Wulff) P.W. Johnson, P.E. Hargraves & J.M. Sieburth (Rhizaria). The genus Paulinella is best known for its photosynthetic members such as P.chromatophora Lauterborn that is the only case of plastid primary endosymbiosis known outside of algae and plants. Here, we studied the phagotrophic sister taxa of P.chromatophora that are related to P.ovalis and found one SCG assembly to contain -cyanobacterial DNA. These cyanobacterial contigs are presumably derived from prey. We also uncovered an associated cyanophage lineage (provisionally named phage PoL_MC2). Phylogenomic analysis of the fragmented genome assembly suggested a minimum genome size of 200Kbp for phage PoL_MC2 that encodes 179 proteins and is most closely related to Synechococcus phage S-SM2. For this phage, gene network analysis demonstrates a highly modular genome structure typical of other cyanophages. Our work demonstrates that SCG is a powerful approach for discovering algal and protist biodiversity and for elucidating biotic interactions in natural samples.
ISSN: 0022-3646

hal-01544797
https://hal.archives-ouvertes.fr/hal-01544797
DOI : 10.1111/jpy.12028

Éditeur(s) : HAL CCSD Oxford University Press (OUP)
Résumé : International audience
DNA sequencing technology is becoming more accessible to a variety of researchers as costs continue to decline. As researchers begin to sequence novel transcriptomes, most of these data sets lack a reference genome and will have to rely on de novo assemblers. Making comparisons across assemblies can be difficult: each program has its strengths and weaknesses, and no tool exists to comparatively evaluate these data sets. We developed software in R, called Sequence Comparative Analysis using Networks (SCAN), to perform statistical comparisons between distinct assemblies. SCAN uses a reference data set to identify the most accurate de novo assembly and the ``good'' transcripts in the user's data. We tested SCAN on three publicly available transcriptomes, each assembled using three assembly programs. Moreover, we sequenced the transcriptome of the oomycete Achlya hypogyna and compared de novo assemblies from Velvet, ABySS, and the CLC Genomics Workbench assembly algorithms. One thousand one hundred twenty-eight of the CLC transcripts were statistically similar to the reference, compared with 49 of the Velvet transcripts and 937 of the ABySS transcripts. SCAN's strength is providing statistical support for transcript assemblies in a biological context. However, SCAN is designed to compare distinct node sets in networks, therefore it can also easily be extended to perform statistical comparisons on any network graph regardless of what the nodes represent.
ISSN: 0737-4038

hal-01544800
https://hal.archives-ouvertes.fr/hal-01544800
DOI : 10.1093/molbev/mst087