GLASSgo in Galaxy: high-throughput, reproducible and easy-to-integrate prediction of sRNA homologs.

MOTIVATION: The correct prediction of bacterial sRNA homologs is a prerequisite for many downstream analyses based on comparative genomics, but it is frequently challenging due to the short length and distinct heterogeneity of such homologs. GLobal Automatic Small RNA Search go (GLASSgo) is an efficient tool for the prediction of sRNA homologs from a single input query. To make the algorithm available to a broader community, we offer a Docker container along with a free-access web service. For non-computer scientists, the web service provides a user-friendly interface. However, capabilities were lacking so far for batch processing, version control and direct interaction with compatible software applications as a workflow management system can provide. RESULTS: Here, we present GLASSgo 1.5.2, an updated version that is fully incorporated into the workflow management system Galaxy. The improved version contains a new feature for extracting the upstream regions, allowing the search for conserved promoter elements. Additionally, it supports the use of accession numbers instead of the outdated GI numbers, which widens the applicability of the tool. AVAILABILITY AND IMPLEMENTATION: GLASSgo is available at https://github.com/lotts/GLASSgo/ under the MIT license and is accompanied by instruction and application data. Furthermore, it can be installed into any Galaxy instance using the Galaxy ToolShed.

A framework for the computational prediction and analysis of non-coding RNAs in microbial environmental populations and their experimental validation.

Small regulatory RNAs and antisense RNAs play important roles in the regulation of gene expression in bacteria but are underexplored, especially in natural populations. While environmentally relevant microbes often are not amenable to genetic manipulation or cannot be cultivated in the laboratory, extensive metagenomic and metatranscriptomic datasets for these organisms might be available. Hence, dedicated workflows for specific analyses are needed to fully benefit from this information. Here, we identified abundant sRNAs from oceanic environmental populations of the ecologically important primary producer Prochlorococcus starting from a metatranscriptomic differential RNA-Seq (mdRNA-Seq) dataset. We tracked their homologs in laboratory isolates, and we provide a framework for their further detailed characterization. Several of the experimentally validated sRNAs responded to ecologically relevant changes in cultivation conditions. The expression of the here newly discovered sRNA Yfr28 was highly stimulated in low-nitrogen conditions. Its predicted top targets include mRNAs encoding cell division proteins, a sigma factor, and several enzymes and transporters, suggesting a pivotal role of Yfr28 in the coordination of primary metabolism and cell division. A cis-encoded antisense RNA was identified as a possible positive regulator of atpF encoding subunit b' of the ATP synthase complex. The presented workflow will also be useful for other environmentally relevant microorganisms for which experimental validation abilities are frequently limiting although there is wealth of sequence information available.

The power of cooperation: Experimental and computational approaches in the functional characterization of bacterial sRNAs.

Trans-acting small regulatory RNAs (sRNAs) are key players in the regulation of gene expression in bacteria. There are hundreds of different sRNAs in a typical bacterium, which in contrast to eukaryotic microRNAs are more heterogeneous in length, sequence composition, and secondary structure. The vast majority of sRNAs function post-transcriptionally by binding to other RNAs (mRNAs, sRNAs) through rather short regions of imperfect sequence complementarity. Besides, every single sRNA may interact with dozens of different target RNAs and impact gene expression either negatively or positively. These facts contributed to the view that the entirety of the regulatory targets of a given sRNA, its targetome, is challenging to identify. However, recent developments show that a more comprehensive sRNAs targetome can be achieved through the combination of experimental and computational approaches. Here, we give a short introduction into these methods followed by a description of two sRNAs, RyhB, and RsaA, to illustrate the particular strengths and weaknesses of these approaches in more details. RyhB is an sRNA involved in iron homeostasis in Enterobacteriaceae, while RsaA is a modulator of virulence in Staphylococcus aureus. Using such a combined strategy, a better appreciation of the sRNA-dependent regulatory networks is now attainable.

Divergent methylation of CRISPR repeats and cas genes in a subtype I-D CRISPR-Cas-system.

BACKGROUND: The presence and activity of CRISPR-Cas defense systems is a hallmark of many prokaryotic microorganisms. Here, the distribution of sequences related to the highly iterated palindrome 1 (HIP1) element and the DNA methylation of CGATCG motifs embedded within HIP1 as a vital part of the CRISPR1 repeat sequence was analyzed in the cyanobacterium Synechocystis sp. PCC 6803. Previously suggested functions of HIP1 include organization of chromosomal structure, DNA recombination or gene regulation, all of which could be relevant in CRISPR-Cas functionality. RESULTS: The CRISPR1 repeat-spacer array contains more than 50 CGATCG elements that are double-methylated (5mCG6mATCG) by the enzymes M.Ssp6803I and M.Ssp6803III. Hence, more than 200 possible methylation events cluster over a stretch of 3600 bp of double-stranded DNA. Bisulfite sequencing showed that these motifs were highly methylated at the m5CGATCG positions whereas specific motifs within the CRISPR1 cas genes were hypomethylated suggesting a lowered accessibility for the DNA methylase to these regions. Assays for conjugation and CRISPR1-mediated DNA interference revealed a 50% drop in conjugation efficiency in the mutant lacking the 5mC methylation of CGATCG motifs, while the highly efficient DNA interference activity was not affected by the lack of m5CGATCG DNA-methylation, nor was the capability to differentiate between self and non-self targets based on the protospacer adjacent motifs (PAMs) GTA and GTC versus the non-PAM AGC. A third DNA methylation mediated by M.Ssp6803II modifies the first cytosine in the motif GGCC yielding GGm4CC. We found a remarkable absence of GGCC motifs and hence the corresponding methylation over an 11 kb stretch encompassing all the cas genes involved in interference and crRNA maturation but not adaptation of the CRISPR1 system. CONCLUSIONS: The lack of GGCC tetranucleotides along the CRISPR1 interference and maturation genes supports the reported hybrid character of subtype I-D CRISPR-Cas systems. We report tight and very high 5mC methylation of the CRISPR1 repeat sequences. Nevertheless, cells lacking the 5mC methylation activity were unaffected in their CRISPR1-mediated interference response but the efficiency of conjugation was reduced by 50%. These results point to an unknown role of m5CGATCG DNA-methylation marks in conjugation and DNA transformation.

GLASSgo - Automated and Reliable Detection of sRNA Homologs From a Single Input Sequence.

Bacterial small RNAs (sRNAs) are important post-transcriptional regulators of gene expression. The functional and evolutionary characterization of sRNAs requires the identification of homologs, which is frequently challenging due to their heterogeneity, short length and partly, little sequence conservation. We developed the GLobal Automatic Small RNA Search go (GLASSgo) algorithm to identify sRNA homologs in complex genomic databases starting from a single sequence. GLASSgo combines an iterative BLAST strategy with pairwise identity filtering and a graph-based clustering method that utilizes RNA secondary structure information. We tested the specificity, sensitivity and runtime of GLASSgo, BLAST and the combination RNAlien/cmsearch in a typical use case scenario on 40 bacterial sRNA families. The sensitivity of the tested methods was similar, while the specificity of GLASSgo and RNAlien/cmsearch was significantly higher than that of BLAST. GLASSgo was on average ∼87 times faster than RNAlien/cmsearch, and only ∼7.5 times slower than BLAST, which shows that GLASSgo optimizes the trade-off between speed and accuracy in the task of finding sRNA homologs. GLASSgo is fully automated, whereas BLAST often recovers only parts of homologs and RNAlien/cmsearch requires extensive additional bioinformatic work to get a comprehensive set of homologs. GLASSgo is available as an easy-to-use web server to find homologous sRNAs in large databases.

Freiburg RNA tools: a central online resource for RNA-focused research and teaching.

The Freiburg RNA tools webserver is a well established online resource for RNA-focused research. It provides a unified user interface and comprehensive result visualization for efficient command line tools. The webserver includes RNA-RNA interaction prediction (IntaRNA, CopraRNA, metaMIR), sRNA homology search (GLASSgo), sequence-structure alignments (LocARNA, MARNA, CARNA, ExpaRNA), CRISPR repeat classification (CRISPRmap), sequence design (antaRNA, INFO-RNA, SECISDesign), structure aberration evaluation of point mutations (RaSE), and RNA/protein-family models visualization (CMV), and other methods. Open education resources offer interactive visualizations of RNA structure and RNA-RNA interaction prediction as well as basic and advanced sequence alignment algorithms. The services are freely available at http://rna.informatik.uni-freiburg.de.

Identification of the DNA methyltransferases establishing the methylome of the cyanobacterium Synechocystis sp. PCC 6803.

DNA methylation in bacteria is important for defense against foreign DNA, but is also involved in DNA repair, replication, chromosome partitioning, and regulatory processes. Thus, characterization of the underlying DNA methyltransferases in genetically tractable bacteria is of paramount importance. Here, we characterized the methylome and orphan methyltransferases in the model cyanobacterium Synechocystis sp. PCC 6803. Single molecule real-time (SMRT) sequencing revealed four DNA methylation recognition sequences in addition to the previously known motif m5CGATCG, which is recognized by M.Ssp6803I. For three of the new recognition sequences, we identified the responsible methyltransferases. M.Ssp6803II, encoded by the sll0729 gene, modifies GGm4CC, M.Ssp6803III, encoded by slr1803, represents the cyanobacterial dam-like methyltransferase modifying Gm6ATC, and M.Ssp6803V, encoded by slr6095 on plasmid pSYSX, transfers methyl groups to the bipartite motif GGm6AN7TTGG/CCAm6AN7TCC. The remaining methylation recognition sequence GAm6AGGC is probably recognized by methyltransferase M.Ssp6803IV encoded by slr6050. M.Ssp6803III and M.Ssp6803IV were essential for the viability of Synechocystis, while the strains lacking M.Ssp6803I and M.Ssp6803V showed growth similar to the wild type. In contrast, growth was strongly diminished of the Δsll0729 mutant lacking M.Ssp6803II. These data provide the basis for systematic studies on the molecular mechanisms impacted by these methyltransferases.

Customized workflow development and data modularization concepts for RNA-Sequencing and metatranscriptome experiments.

RNA-Sequencing (RNA-Seq) has become a widely used approach to study quantitative and qualitative aspects of transcriptome data. The variety of RNA-Seq protocols, experimental study designs and the characteristic properties of the organisms under investigation greatly affect downstream and comparative analyses. In this review, we aim to explain the impact of structured pre-selection, classification and integration of best-performing tools within modularized data analysis workflows and ready-to-use computing infrastructures towards experimental data analyses. We highlight examples for workflows and use cases that are presented for pro-, eukaryotic and mixed dual RNA-Seq (meta-transcriptomics) experiments. In addition, we are summarizing the expertise of the laboratories participating in the project consortium "Structured Analysis and Integration of RNA-Seq experiments" (de.STAIR) and its integration with the Galaxy-workbench of the RNA Bioinformatics Center (RBC).

The RNA workbench: best practices for RNA and high-throughput sequencing bioinformatics in Galaxy.

RNA-based regulation has become a major research topic in molecular biology. The analysis of epigenetic and expression data is therefore incomplete if RNA-based regulation is not taken into account. Thus, it is increasingly important but not yet standard to combine RNA-centric data and analysis tools with other types of experimental data such as RNA-seq or ChIP-seq. Here, we present the RNA workbench, a comprehensive set of analysis tools and consolidated workflows that enable the researcher to combine these two worlds. Based on the Galaxy framework the workbench guarantees simple access, easy extension, flexible adaption to personal and security needs, and sophisticated analyses that are independent of command-line knowledge. Currently, it includes more than 50 bioinformatics tools that are dedicated to different research areas of RNA biology including RNA structure analysis, RNA alignment, RNA annotation, RNA-protein interaction, ribosome profiling, RNA-seq analysis and RNA target prediction. The workbench is developed and maintained by experts in RNA bioinformatics and the Galaxy framework. Together with the growing community evolving around this workbench, we are committed to keep the workbench up-to-date for future standards and needs, providing researchers with a reliable and robust framework for RNA data analysis. AVAILABILITY: The RNA workbench is available at https://github.com/bgruening/galaxy-rna-workbench.

CoVennTree: a new method for the comparative analysis of large datasets.

The visualization of massive datasets, such as those resulting from comparative metatranscriptome analyses or the analysis of microbial population structures using ribosomal RNA sequences, is a challenging task. We developed a new method called CoVennTree (Comparative weighted Venn Tree) that simultaneously compares up to three multifarious datasets by aggregating and propagating information from the bottom to the top level and produces a graphical output in Cytoscape. With the introduction of weighted Venn structures, the contents and relationships of various datasets can be correlated and simultaneously aggregated without losing information. We demonstrate the suitability of this approach using a dataset of 16S rDNA sequences obtained from microbial populations at three different depths of the Gulf of Aqaba in the Red Sea. CoVennTree has been integrated into the Galaxy ToolShed and can be directly downloaded and integrated into the user instance.

Dataset for metatranscriptome analysis of Prochlorococcus-rich marine picoplankton communities in the Gulf of Aqaba, Red Sea.

Regulatory RNAs play a central role in the regulation of gene expression and can act on several regulatory levels from transcriptional initiation and RNA processing to the control of initiation of translation and RNA stability. One class of these molecules is non-coding (nc)RNAs in bacteria that typically lack protein-coding potential, range in size between 50 and 500nt and originate from intergenic regions. Common methods for the identification of these RNAs are either based on computational predictions, or on transcriptomic analyses of laboratory cultures, whereas very little is known about ncRNAs in environmental microbial populations. Here, we have combined a metatranscriptomics approach with a selective enrichment protocol for ncRNAs. The primary objective of this study was the identification of novel, environmentally relevant ncRNAs focusing on the cyanobacterium Prochlorococcus, which was one of the dominant microorganisms of the marine community of the Gulf of Aqaba when samples were taken.