We report the results of a first, collective, blind experiment in RNA three-dimensional (3D) structure prediction, encompassing three prediction puzzles. The goals are to assess the leading edge of RNA structure prediction techniques; compare existing methods and tools; and evaluate their relative strengths, weaknesses, and limitations in terms of sequence length and structural complexity. The results should give potential users insight into the suitability of available methods for different applications and facilitate efforts in the RNA structure prediction community in ongoing efforts to improve prediction tools. We also report the creation of an automated evaluation pipeline to facilitate the analysis of future RNA structure prediction exercises.

RNA molecules have recently become attractive as potential drug targets due to the increased awareness of their importance in key biological processes. The increase of the number of experimentally determined RNA 3D structures enabled structure-based searches for small molecules that can specifically bind to defined sites in RNA molecules, thereby blocking or otherwise modulating their function. However, as of yet, computational methods for structure-based docking of small molecule ligands to RNA molecules are not as well established as analogous methods for protein-ligand docking. This motivated us to create LigandRNA, a scoring function for the prediction of RNA-small molecule interactions. Our method employs a grid-based algorithm and a knowledge-based potential derived from ligand-binding sites in the experimentally solved RNA-ligand complexes. As an input, LigandRNA takes an RNA receptor file and a file with ligand poses. As an output, it returns a ranking of the poses according to their score. The predictive power of LigandRNA favorably compares to five other publicly available methods. We found that the combination of LigandRNA and Dock6 into a "meta-predictor" leads to further improvement in the identification of near-native ligand poses. The LigandRNA program is available free of charge as a web server at http:// ligandrna.genesilico.pl.

Splicing aberrations induced as a consequence of the sequestration of muscleblind-like splicing factors on the dystrophia myotonica protein kinase transcript, which contains expanded CUG repeats, present a major pathomechanism of myotonic dystrophy type 1 (DM1). As muscleblind-like factors may also be important factors involved in the biogenesis of circular RNAs (circRNAs), we hypothesized that the level of circRNAs would be decreased in DM1. To test this hypothesis, we selected 20 well-validated circRNAs and analyzed their levels in several experimental systems (e.g., cell lines, DM muscle tissues, and a mouse model of DM1) using droplet digital PCR assays. We also explored the global level of circRNAs using two RNA-Seq datasets of DM1 muscle samples. Contrary to our original hypothesis, our results consistently showed a global increase in circRNA levels in DM1, and we identified numerous circRNAs that were increased in DM1. We also identified many genes (including muscle-specific genes) giving rise to numerous (>10) circRNAs. Thus, this study is the first to show an increase in global circRNA levels in DM1. We also provided preliminary results showing the association of circRNA level with muscle weakness and alternative splicing changes that are biomarkers of DM1 severity.

Motivation: Metal ions are essential for the folding of RNA molecules into stable tertiary structures and are often involved in the catalytic activity of ribozymes. However, the positions of metal ions in RNA 3D structures are difficult to determine experimentally. This motivated us to develop a computational predictor of metal ion sites for RNA structures.Results: We developed a statistical potential for predicting positions of metal ions (magnesium, sodium and potassium), based on the analysis of binding sites in experimentally solved RNA structures. The MetalionRNA program is available as a web server that predicts metal ions for RNA structures submitted by the user.Availability: The MetalionRNA web server is accessible at http://metalionrna.genesilico.pl/.Contact: iamb@genesilico.plSupplementary information: Supplementary data are available at Bioinformatics online.

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Metagenome analysis has become a common source of information about microbial communities that occupy a wide range of niches, including archaeological specimens. It has been shown that the vast majority of DNA extracted from ancient samples come from bacteria (presumably modern contaminants). However, characterization of microbial DNA accompanying human remains has never been done systematically for a wide range of different samples. We used metagenomic approaches to perform comparative analyses of microorganism communities present in 161 archaeological human remains. DNA samples were isolated from the teeth of human skeletons dated from 100 AD to 1200 AD. The skeletons were collected from 7 archaeological sites in Central Europe and stored under different conditions. The majority of identified microbes were ubiquitous environmental bacteria that most likely contaminated the host remains not long ago. We observed that the composition of microbial communities was sample-specific and not correlated with its temporal or geographical origin. Additionally, traces of bacteria and archaea typical for human oral/gut flora, as well as potential pathogens, were identified in two-thirds of the samples. The genetic material of human-related species, in contrast to the environmental species that accounted for the majority of identified bacteria, displayed DNA damage patterns comparable with endogenous human ancient DNA, which suggested that these microbes might have accompanied the individual before death. Our study showed that the microbiome observed in an individual sample is not reliant on the method or duration of sample storage. Moreover, shallow sequencing of DNA extracted from ancient specimens and subsequent bioinformatics analysis allowed both the identification of ancient microbial species, including potential pathogens, and their differentiation from contemporary species that colonized human remains more recently.

Despite the increase in our knowledge about the factors that shaped the genetic structure of the human population in Europe, the demographic processes that occurred during and after the Early Bronze Age (EBA) in Central-East Europe remain unclear. To fill the gap, we isolated and sequenced DNAs of 60 individuals from Kowalewko, a bi-ritual cemetery of the Iron Age (IA) Wielbark culture, located between the Oder and Vistula rivers (Kow-OVIA population). The collected data revealed high genetic diversity of Kow-OVIA, suggesting that it was not a small isolated population. Analyses of mtDNA haplogroup frequencies and genetic distances performed for Kow-OVIA and other ancient European populations showed that Kow-OVIA was most closely linked to the Jutland Iron Age (JIA) population. However, the relationship of both populations to the preceding Late Neolithic (LN) and EBA populations were different. We found that this phenomenon is most likely the consequence of the distinct genetic history observed for Kow-OVIA women and men. Females were related to the Early-Middle Neolithic farmers, whereas males were related to JIA and LN Bell Beakers. In general, our findings disclose the mechanisms that could underlie the formation of the local genetic substructures in the South Baltic region during the IA.

RNA-seq is currently the only method that can provide a comprehensive landscape of circular RNA (circRNAs) in the whole organism and its particular organs. Recent years have brought an increasing number of RNA-seq-based reports on plant circRNAs. Notably, the picture they revealed is questionable and depends on the applied circRNA identification and quantification techniques. In consequence, little is known about the biogenesis and functions of circRNAs in plants. In this work, we tested two experimental and six bioinformatics procedures of circRNA analysis to determine the optimal approach for studying the profiles of circRNAs in Arabidopsis thaliana. Then using the optimized strategy, we determined the accumulation of circular and corresponding linear transcripts in plant seedlings and organs. We observed that only a small fraction of circRNAs was reproducibly generated. Among them, two groups of circRNAs were discovered: ubiquitous and organ-specific. The highest number of circRNAs with significantly increased accumulation in comparison to other organs/seedlings was found in roots. The circRNAs in seedlings, leaves and flowers originated mainly from genes involved in photosynthesis and the response to stimulus. The levels of circular and linear transcripts were not correlated. Although RNase R treatment enriches the analyzed RNA samples in circular transcripts, it may also have a negative impact on the stability of some of the circRNAs. We also showed that the normalization of NGS data by the library size is not proper for circRNAs quantification. Alternatively, we proposed four other normalization types whose accuracy was confirmed by ddPCR. Moreover, we provided a comprehensive characterization of circRNAs in A. thaliana organs and in seedlings. Our analyses revealed that plant circRNAs are formed in both stochastic and controlled processes. The latter are less frequent and likely engage circRNA-specific mechanisms. Only a few circRNAs were organ-specific. The lack of correlation between the accumulation of linear and circular transcripts indicated that their biogenesis depends on different mechanisms.