If you are using this Shiny App for your own research please cite:

Matthias Riediger, Philipp Spaet, Raphael Bilger, Karsten Voigt, Boris Macek, Wolfgang R Hess, (2021) Analysis of a Photosynthetic Cyanobacterium Rich in Internal Membrane Systems via Gradient Profiling by Sequencing (Grad-seq), The Plant Cell, Volume 33, koaa017, https://doi.org/10.1093/plcell/koaa017

and visit our Cyanolab Homepage for the latest updates of our research.


Although regulatory small RNAs have been reported in photosynthetic cyanobacteria, the lack of clear RNA chaperones involved in their regulation poses a conundrum. Here, we analyzed the full complement of cellular RNAs and proteins using gradient profiling by sequencing (Grad-seq) in Synechocystis 6803. Complexes with overlapping subunits such as the CpcG1-type versus the CpcL-type phycobilisomes or the PsaK1 versus PsaK2 photosystem I (pre)complexes could be distinguished, supporting the quality of this approach. Clustering of the in-gradient distribution profiles followed by the calculation of a support vector machine score for the prediction of RNA-binding proteins and the application of several additional criteria yielded a short list of potential RNA chaperones. The data suggest previously undetected complexes between accessory proteins and CRISPR-Cas systems, between the core RNA polymerase complex and other factors, and many other. This database provides a comprehensive resource for the functional assignment of RNA-protein complexes and multisubunit protein complexes in a photosynthetic organism.

General Description

Customizable visualizations for the Synechocystis sp. PCC6803 proteome and transcriptome, according to the separation of RNA-protein and multi-protein complexes in a sucrose density gradient. The dataset has been subjected to a hierarchical clustering approach and all detected proteins and RNAs were assigned to one of 17 distinct clusters, based on their individual sedimentation characteristics. These visualizations are also available as download in '.png' format (300 dpi).

Grad-Seq Explorer

Heatmap visualizations of sedimentation profiles ('Grad-Seq Heatmap'). The options can be set to search for 'locus tags/gene names', 'functional categories' (in hierarchical search order of KEGG BRITE -- database - category - pathway - complex/type) or comparison between selected locus tags and selected functional categories. Multiple filter options are available for search within 'functional categories'. Further visualization tools are available for proteins only. The 'Phylogeny Heatmap' shows the phylogenetic occurrence of selected proteins within selected cyanobacteria, A.thaliana, E.coli or S.enterica based on domclust of the Microbial Genome Database (MBGD) and all selected genomes, given in the supplementary information tab (Table S1). The 'SVM score Boxplot' represents the SVM score for the prediction of RNA-binding proteins by RNApred and all selected Synechocystis proteins (red dot) and their respective orthologues (boxplot), determined by MBGD. For details of the color classification refer to Figure S1 in the supplementary information tab. A tabular output of the selected proteins and RNAs with all information is available as well.

Gradient Composition

Barplots of proteins and RNA types by cluster assignment and peak fractions.

Protein Categories

Barplots of proteins of selected 'functional categories' by cluster assignment and peak fractions. (in hierarchical search order of (KEGG BRITE -- database - category - pathway - complex/type)

Download Grad-Seq Heatmap Download Phylogeny Heatmap Download SVM score Boxplot
Download Barplot of Gradient Composition
Download Databse Barplot Download Category Barplot Download Pathway Barplot Download Complex / Type Barplot Download Phylogenetic group Barplot Download Location Barplot

Table S1: All genomes which were used in this study are shown in this table, based on the genbank/refeq files listed in the MBGD genomelist.

Figure S1: SVM scores for proteins of Synechocystis sp. PCC6803, grouped by Gene Ontology Terms (Nucleotide binding, DNA binding, RNA binding) or 'No binding' if no such term existed. A SVM score < 0.49 is reached by the majority of proteins with no reported binding to nucleic acids, while almost all proteins with reported RNA binding properties exceeded that threshold, showing the validity of the algorithm. Therefore, all proteins of the dataset have been categorized into three groups in the SVM score Boxplots. Those proteins which fail to pass a SVM score > 0.49 (grey group, probably no RNA binding), those proteins which are above that threshold but are still in the range of the upper quartile of the DNA binding group (yellow group, probably RNA binding) and those proteins which surpass the upper quartile of the DNA binding group with an SVM score >1.07 (green group, RNA binding very likely).