Assessing the Quality of Cotranscriptional Folding Simulations.

Structural changes in RNAs are an important contributor to controlling gene expression not only at the posttranscriptional stage but also during transcription. A subclass of riboswitches and RNA thermometers located in the 5' region of the primary transcript regulates the downstream functional unit - usually an ORF - through premature termination of transcription. Not only such elements occur naturally, but they are also attractive devices in synthetic biology. The possibility to design such riboswitches or RNA thermometers is thus of considerable practical interest. Since these functional RNA elements act already during transcription, it is important to model and understand the dynamics of folding and, in particular, the formation of intermediate structures concurrently with transcription. Cotranscriptional folding simulations are therefore an important step to verify the functionality of design constructs before conducting expensive and labor-intensive wet lab experiments. For RNAs, full-fledged molecular dynamics simulations are far beyond practical reach because of both the size of the molecules and the timescales of interest. Even at the simplified level of secondary structures, further approximations are necessary. The BarMap approach is based on representing the secondary structure landscape for each individual transcription step by a coarse-grained representation that only retains a small set of low-energy local minima and the energy barriers between them. The folding dynamics between two transcriptional elongation steps is modeled as a Markov process on this representation. Maps between pairs of consecutive coarse-grained landscapes make it possible to follow the folding process as it changes in response to transcription elongation. In its original implementation, the BarMap software provides a general framework to investigate RNA folding dynamics on temporally changing landscapes. It is, however, difficult to use in particular for specific scenarios such as cotranscriptional folding. To overcome this limitation, we developed the user-friendly BarMap-QA pipeline described in detail in this contribution. It is illustrated here by an elaborate example that emphasizes the careful monitoring of several quality measures. Using an iterative workflow, a reliable and complete kinetics simulation of a synthetic, transcription-regulating riboswitch is obtained using minimal computational resources. All programs and scripts used in this contribution are free software and available for download as a source distribution for Linux® or as a platform-independent Docker® image including support for Apple macOS® and Microsoft Windows®.

Cavity approach for the approximation of spectral density of graphs with heterogeneous structures.

Graphs have become widely used to represent and study social, biological, and technological systems. Statistical methods to analyze empirical graphs were proposed based on the graph's spectral density. However, their running time is cubic in the number of vertices, precluding direct application to large instances. Thus, efficient algorithms to calculate the spectral density become necessary. For sparse graphs, the cavity method can efficiently approximate the spectral density of locally treelike undirected and directed graphs. However, it does not apply to most empirical graphs because they have heterogeneous structures. Thus, we propose methods for undirected and directed graphs with heterogeneous structures using a new vertex's neighborhood definition and the cavity approach. Our methods' time and space complexities are O(|E|h_{max}^{3}t) and O(|E|h_{max}^{2}t), respectively, where |E| is the number of edges, h_{max} is the size of the largest local neighborhood of a vertex, and t is the number of iterations required for convergence. We demonstrate the practical efficacy by estimating the spectral density of simulated and real-world undirected and directed graphs.

BioDeepfuse: a hybrid deep learning approach with integrated feature extraction techniques for enhanced non-coding RNA classification.

The accurate classification of non-coding RNA (ncRNA) sequences is pivotal for advanced non-coding genome annotation and analysis, a fundamental aspect of genomics that facilitates understanding of ncRNA functions and regulatory mechanisms in various biological processes. While traditional machine learning approaches have been employed for distinguishing ncRNA, these often necessitate extensive feature engineering. Recently, deep learning algorithms have provided advancements in ncRNA classification. This study presents BioDeepFuse, a hybrid deep learning framework integrating convolutional neural networks (CNN) or bidirectional long short-term memory (BiLSTM) networks with handcrafted features for enhanced accuracy. This framework employs a combination of k-mer one-hot, k-mer dictionary, and feature extraction techniques for input representation. Extracted features, when embedded into the deep network, enable optimal utilization of spatial and sequential nuances of ncRNA sequences. Using benchmark datasets and real-world RNA samples from bacterial organisms, we evaluated the performance of BioDeepFuse. Results exhibited high accuracy in ncRNA classification, underscoring the robustness of our tool in addressing complex ncRNA sequence data challenges. The effective melding of CNN or BiLSTM with external features heralds promising directions for future research, particularly in refining ncRNA classifiers and deepening insights into ncRNAs in cellular processes and disease manifestations. In addition to its original application in the context of bacterial organisms, the methodologies and techniques integrated into our framework can potentially render BioDeepFuse effective in various and broader domains.

Taking identity-by-descent analysis into the wild: Estimating realized relatedness in free-ranging macaques.

Biological relatedness is a key consideration in studies of behavior, population structure, and trait evolution. Except for parent-offspring dyads, pedigrees capture relatedness imperfectly. The number and length of DNA segments that are identical-by-descent (IBD) yield the most precise estimates of relatedness. Here, we leverage novel methods for estimating locus-specific IBD from low coverage whole genome resequencing data to demonstrate the feasibility and value of resolving fine-scaled gradients of relatedness in free-living animals. Using primarily 4-6× coverage data from a rhesus macaque (Macaca mulatta) population with available long-term pedigree data, we show that we can call the number and length of IBD segments across the genome with high accuracy even at 0.5× coverage. The resulting estimates demonstrate substantial variation in genetic relatedness within kin classes, leading to overlapping distributions between kin classes. They identify cryptic genetic relatives that are not represented in the pedigree and reveal elevated recombination rates in females relative to males, which allows us to discriminate maternal and paternal kin using genotype data alone. Our findings represent a breakthrough in the ability to understand the predictors and consequences of genetic relatedness in natural populations, contributing to our understanding of a fundamental component of population structure in the wild.

MuDoGeR: Multi-Domain Genome recovery from metagenomes made easy.

Several computational frameworks and workflows that recover genomes from prokaryotes, eukaryotes and viruses from metagenomes exist. Yet, it is difficult for scientists with little bioinformatics experience to evaluate quality, annotate genes, dereplicate, assign taxonomy and calculate relative abundance and coverage of genomes belonging to different domains. MuDoGeR is a user-friendly tool tailored for those familiar with Unix command-line environment that makes it easy to recover genomes of prokaryotes, eukaryotes and viruses from metagenomes, either alone or in combination. We tested MuDoGeR using 24 individual-isolated genomes and 574 metagenomes, demonstrating the applicability for a few samples and high throughput. While MuDoGeR can recover eukaryotic viral sequences, its characterization is predominantly skewed towards bacterial and archaeal viruses, reflecting the field's current state. However, acting as a dynamic wrapper, the MuDoGeR is designed to constantly incorporate updates and integrate new tools, ensuring its ongoing relevance in the rapidly evolving field. MuDoGeR is open-source software available at https://github.com/mdsufz/MuDoGeR. Additionally, MuDoGeR is also available as a Singularity container.

Proteinortho6: pseudo-reciprocal best alignment heuristic for graph-based detection of (co-)orthologs.

Proteinortho is a widely used tool to predict (co)-orthologous groups of genes for any set of species. It finds application in comparative and functional genomics, phylogenomics, and evolutionary reconstructions. With a rapidly increasing number of available genomes, the demand for large-scale predictions is also growing. In this contribution, we evaluate and implement major algorithmic improvements that significantly enhance the speed of the analysis without reducing precision. Graph-based detection of (co-)orthologs is typically based on a reciprocal best alignment heuristic that requires an all vs. all comparison of proteins from all species under study. The initial identification of similar proteins is accelerated by introducing an alternative search tool along with a revised search strategy-the pseudo-reciprocal best alignment heuristic-that reduces the number of required sequence comparisons by one-half. The clustering algorithm was reworked to efficiently decompose very large clusters and accelerate processing. Proteinortho6 reduces the overall processing time by an order of magnitude compared to its predecessor while maintaining its small memory footprint and good predictive quality.

Relative timing information and orthology in evolutionary scenarios.

BACKGROUND: Evolutionary scenarios describing the evolution of a family of genes within a collection of species comprise the mapping of the vertices of a gene tree T to vertices and edges of a species tree S. The relative timing of the last common ancestors of two extant genes (leaves of T) and the last common ancestors of the two species (leaves of S) in which they reside is indicative of horizontal gene transfers (HGT) and ancient duplications. Orthologous gene pairs, on the other hand, require that their last common ancestors coincides with a corresponding speciation event. The relative timing information of gene and species divergences is captured by three colored graphs that have the extant genes as vertices and the species in which the genes are found as vertex colors: the equal-divergence-time (EDT) graph, the later-divergence-time (LDT) graph and the prior-divergence-time (PDT) graph, which together form an edge partition of the complete graph. RESULTS: Here we give a complete characterization in terms of informative and forbidden triples that can be read off the three graphs and provide a polynomial time algorithm for constructing an evolutionary scenario that explains the graphs, provided such a scenario exists. While both LDT and PDT graphs are cographs, this is not true for the EDT graph in general. We show that every EDT graph is perfect. While the information about LDT and PDT graphs is necessary to recognize EDT graphs in polynomial-time for general scenarios, this extra information can be dropped in the HGT-free case. However, recognition of EDT graphs without knowledge of putative LDT and PDT graphs is NP-complete for general scenarios. In contrast, PDT graphs can be recognized in polynomial-time. We finally connect the EDT graph to the alternative definitions of orthology that have been proposed for scenarios with horizontal gene transfer. With one exception, the corresponding graphs are shown to be colored cographs.

RNA interaction format: a general data format for RNA interactions.

SUMMARY: RNA molecules play crucial roles in various biological processes. They mediate their function mainly by interacting with other RNAs or proteins. At present, information about these interactions is distributed over different resources, often providing the data in simple tab-delimited formats that differ between the databases. There is no standardized data format that can capture the nature of all these different interactions in detail. AVAILABILITY AND IMPLEMENTATION: Here, we propose the RNA interaction format (RIF) for the detailed representation of RNA-RNA and RNA-Protein interactions and provide reference implementations in C/C++, Python, and JavaScript. RIF is released under licence GNU General Public License version 3 (GNU GPLv3) and is available on https://github.com/RNABioInfo/rna-interaction-format.

The AnimalAssociatedMetagenomeDB reveals a bias towards livestock and developed countries and blind spots in functional-potential studies of animal-associated microbiomes.

BACKGROUND: Metagenomic data can shed light on animal-microbiome relationships and the functional potential of these communities. Over the past years, the generation of metagenomics data has increased exponentially, and so has the availability and reusability of data present in public repositories. However, identifying which datasets and associated metadata are available is not straightforward. We created the Animal-Associated Metagenome Metadata Database (AnimalAssociatedMetagenomeDB - AAMDB) to facilitate the identification and reuse of publicly available non-human, animal-associated metagenomic data, and metadata. Further, we used the AAMDB to (i) annotate common and scientific names of the species; (ii) determine the fraction of vertebrates and invertebrates; (iii) study their biogeography; and (iv) specify whether the animals were wild, pets, livestock or used for medical research. RESULTS: We manually selected metagenomes associated with non-human animals from SRA and MG-RAST.  Next, we standardized and curated 51 metadata attributes (e.g., host, compartment, geographic coordinates, and country). The AAMDB version 1.0 contains 10,885 metagenomes associated with 165 different species from 65 different countries. From the collected metagenomes, 51.1% were recovered from animals associated with medical research or grown for human consumption (i.e., mice, rats, cattle, pigs, and poultry). Further, we observed an over-representation of animals collected in temperate regions (89.2%) and a lower representation of samples from the polar zones, with only 11 samples in total. The most common genus among invertebrate animals was Trichocerca (rotifers). CONCLUSION: Our work may guide host species selection in novel animal-associated metagenome research, especially in biodiversity and conservation studies. The data available in our database will allow scientists to perform meta-analyses and test new hypotheses (e.g., host-specificity, strain heterogeneity, and biogeography of animal-associated metagenomes), leveraging existing data. The AAMDB WebApp is a user-friendly interface that is publicly available at https://webapp.ufz.de/aamdb/ .

Tailored machine learning models for functional RNA detection in genome-wide screens.

The in silico prediction of non-coding and protein-coding genetic loci has received considerable attention in comparative genomics aiming in particular at the identification of properties of nucleotide sequences that are informative of their biological role in the cell. We present here a software framework for the alignment-based training, evaluation and application of machine learning models with user-defined parameters. Instead of focusing on the one-size-fits-all approach of pervasive in silico annotation pipelines, we offer a framework for the structured generation and evaluation of models based on arbitrary features and input data, focusing on stable and explainable results. Furthermore, we showcase the usage of our software package in a full-genome screen of Drosophila melanogaster and evaluate our results against the well-known but much less flexible program RNAz.

RNAcode_Web - Convenient identification of evolutionary conserved protein coding regions.

The differentiation of regions with coding potential from non-coding regions remains a key task in computational biology. Methods such as RNAcode that exploit patterns of sequence conservation for this task have a substantial advantage in classification accuracy in particular for short coding sequences, compared to methods that rely on a single input sequence. However, they require sequence alignments as input. Frequently, suitable multiple sequence alignments are not readily available and are tedious, and sometimes difficult to construct. We therefore introduce here a new web service that provides access to the well-known coding sequence detector RNAcode with minimal user overhead. It requires as input only a single target nucleotide sequence. The service automates the collection, selection, and preparation of homologous sequences from the NCBI database, as well as the construction of the multiple sequence alignment that are needed as input for RNAcode. The service automatizes the entire pre- and postprocessing and thus makes the investigation of specific genomic regions for previously unannotated coding regions, such as small peptides or additional introns, a simple task that is easily accessible to non-expert users. RNAcode_Web is accessible online at rnacode.bioinf.uni-leipzig.de.

Clustering systems of phylogenetic networks.

Rooted acyclic graphs appear naturally when the phylogenetic relationship of a set X of taxa involves not only speciations but also recombination, horizontal transfer, or hybridization that cannot be captured by trees. A variety of classes of such networks have been discussed in the literature, including phylogenetic, level-1, tree-child, tree-based, galled tree, regular, or normal networks as models of different types of evolutionary processes. Clusters arise in models of phylogeny as the sets [Formula: see text] of descendant taxa of a vertex v. The clustering system [Formula: see text] comprising the clusters of a network N conveys key information on N itself. In the special case of rooted phylogenetic trees, T is uniquely determined by its clustering system [Formula: see text]. Although this is no longer true for networks in general, it is of interest to relate properties of N and [Formula: see text]. Here, we systematically investigate the relationships of several well-studied classes of networks and their clustering systems. The main results are correspondences of classes of networks and clustering systems of the following form: If N is a network of type [Formula: see text], then [Formula: see text] satisfies [Formula: see text], and conversely if [Formula: see text] is a clustering system satisfying [Formula: see text] then there is network N of type [Formula: see text] such that [Formula: see text].This, in turn, allows us to investigate the mutual dependencies between the distinct types of networks in much detail.

Defining the landscape of circular RNAs in neuroblastoma unveils a global suppressive function of MYCN.

Circular RNAs (circRNAs) are a regulatory RNA class. While cancer-driving functions have been identified for single circRNAs, how they modulate gene expression in cancer is not well understood. We investigate circRNA expression in the pediatric malignancy, neuroblastoma, through deep whole-transcriptome sequencing in 104 primary neuroblastomas covering all risk groups. We demonstrate that MYCN amplification, which defines a subset of high-risk cases, causes globally suppressed circRNA biogenesis directly dependent on the DHX9 RNA helicase. We detect similar mechanisms in shaping circRNA expression in the pediatric cancer medulloblastoma implying a general MYCN effect. Comparisons to other cancers identify 25 circRNAs that are specifically upregulated in neuroblastoma, including circARID1A. Transcribed from the ARID1A tumor suppressor gene, circARID1A promotes cell growth and survival, mediated by direct interaction with the KHSRP RNA-binding protein. Our study highlights the importance of MYCN regulating circRNAs in cancer and identifies molecular mechanisms, which explain their contribution to neuroblastoma pathogenesis.

Local RNA folding revisited.

Most of the functional RNA elements located within large transcripts are local. Local folding therefore serves a practically useful approximation to global structure prediction. Due to the sensitivity of RNA secondary structure prediction to the exact definition of sequence ends, accuracy can be increased by averaging local structure predictions over multiple, overlapping sequence windows. These averages can be computed efficiently by dynamic programming. Here we revisit the local folding problem, present a concise mathematical formalization that generalizes previous approaches and show that correct Boltzmann samples can be obtained by local stochastic backtracing in McCaskill's algorithms but not from local folding recursions. Corresponding new features are implemented in the ViennaRNA package to improve the support of local folding. Applications include the computation of maximum expected accuracy structures from RNAplfold data and a mutual information measure to quantify the sensitivity of individual sequence positions.

Mono-valent salt corrections for RNA secondary structures in the ViennaRNA package.

BACKGROUND: RNA features a highly negatively charged phosphate backbone that attracts a cloud of counter-ions that reduce the electrostatic repulsion in a concentration dependent manner. Ion concentrations thus have a large influence on folding and stability of RNA structures. Despite their well-documented effects, salt effects are not handled consistently by currently available secondary structure prediction algorithms. Combining Debye-Hückel potentials for line charges and Manning's counter-ion condensation theory, Einert et al. (Biophys J 100: 2745-2753, 2011) modeled the energetic contributions of monovalent cations on loops and helices. RESULTS: The model of Einert et al. is adapted to match the structure of the dynamic programming recursion of RNA secondary structure prediction algorithms. An empirical term describing the salt dependence of the duplex initiation energy is added to improve co-folding predictions for two or more RNA strands. The slightly modified model is implemented in the ViennaRNA package in such way that only the energy parameters but not the algorithmic structure is affected. A comparison with data from the literature show that predicted free energies and melting temperatures are in reasonable agreement with experiments. CONCLUSION: The new feature in the ViennaRNA package makes it possible to study effects of salt concentrations on RNA folding in a systematic manner. Strictly speaking, the model pertains only to mono-valent cations, and thus covers the most important parameter, i.e., the NaCl concentration. It remains a question for future research to what extent unspecific effects of bi- and tri-valent cations can be approximated in a similar manner. AVAILABILITY: Corrections for the concentration of monovalent cations are available in the ViennaRNA package starting from version 2.6.0.

StandEnA: a customizable workflow for standardized annotation and generating a presence-absence matrix of proteins.

Motivation: Several genome annotation tools standardize annotation outputs for comparability. During standardization, these tools do not allow user-friendly customization of annotation databases; limiting their flexibility and applicability in downstream analysis. Results: StandEnA is a user-friendly command-line tool for Linux that facilitates the generation of custom databases by retrieving protein sequences from multiple databases. Directed by a user-defined list of standard names, StandEnA retrieves synonyms to search for corresponding sequences in a set of public databases. Custom databases are used in prokaryotic genome annotation to generate standardized presence-absence matrices and reference files containing standard database identifiers. To showcase StandEnA, we applied it to six metagenome-assembled genomes to analyze three different pathways. Availability and implementation: StandEnA is an open-source software available at https://github.com/mdsufz/StandEnA. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

Detecting gene breakpoints in noisy genome sequences using position-annotated colored de-Bruijn graphs.

BACKGROUND: Identifying the locations of gene breakpoints between species of different taxonomic groups can provide useful insights into the underlying evolutionary processes. Given the exact locations of their genes, the breakpoints can be computed without much effort. However, often, existing gene annotations are erroneous, or only nucleotide sequences are available. Especially in mitochondrial genomes, high variations in gene orders are usually accompanied by a high degree of sequence inconsistencies. This makes accurately locating breakpoints in mitogenomic nucleotide sequences a challenging task. RESULTS: This contribution presents a novel method for detecting gene breakpoints in the nucleotide sequences of complete mitochondrial genomes, taking into account possible high substitution rates. The method is implemented in the software package DeBBI. DeBBI allows to analyze transposition- and inversion-based breakpoints independently and uses a parallel program design, allowing to make use of modern multi-processor systems. Extensive tests on synthetic data sets, covering a broad range of sequence dissimilarities and different numbers of introduced breakpoints, demonstrate DeBBI 's ability to produce accurate results. Case studies using species of various taxonomic groups further show DeBBI 's applicability to real-life data. While (some) multiple sequence alignment tools can also be used for the task at hand, we demonstrate that especially gene breaks between short, poorly conserved tRNA genes can be detected more frequently with the proposed approach. CONCLUSION: The proposed method constructs a position-annotated de-Bruijn graph of the input sequences. Using a heuristic algorithm, this graph is searched for particular structures, called bulges, which may be associated with the breakpoint locations. Despite the large size of these structures, the algorithm only requires a small number of graph traversal steps.

Toward a Systematic Assessment of Sex Differences in Cystic Fibrosis.

(1) Background: Cystic fibrosis (CF) is a disease with well-documented clinical differences between female and male patients. However, this gender gap is very poorly studied at the molecular level. (2) Methods: Expression differences in whole blood transcriptomics between female and male CF patients are analyzed in order to determine the pathways related to sex-biased genes and assess their potential influence on sex-specific effects in CF patients. (3) Results: We identify sex-biased genes in female and male CF patients and provide explanations for some sex-specific differences at the molecular level. (4) Conclusion: Genes in key pathways associated with CF are differentially expressed between sexes, and thus may account for the gender gap in morbidity and mortality in CF.

The six stages of the convergence of the periodic system to its final structure.

The periodic system encodes order and similarity among chemical elements arising from known substances at a given time that constitute the chemical space. Although the system has incorporated new elements, the connection with the remaining space is still to be analysed, which leads to the question of how the exponentially growing space has affected the periodic system. Here we show, by analysing the space between 1800 and 2021, that the system has converged towards its current stable structure through six stages, respectively characterised by the finding of elements (1800-1826), the emergence of the core structure of the system (1826-1860), its organic chemistry bias (1860-1900) and its further stabilisation (1900-1948), World War 2 new chemistry (1948-1980) and the system final stabilisation (1980-). Given the self-reinforced low diversity of the space and the limited chemical possibilities of the elements to be synthesised, we hypothesise that the periodic system will remain largely untouched.