Large-scale benchmarking of circRNA detection tools reveals large differences in sensitivity but not in precision.

The detection of circular RNA molecules (circRNAs) is typically based on short-read RNA sequencing data processed using computational tools. Numerous such tools have been developed, but a systematic comparison with orthogonal validation is missing. Here, we set up a circRNA detection tool benchmarking study, in which 16 tools detected more than 315,000 unique circRNAs in three deeply sequenced human cell types. Next, 1,516 predicted circRNAs were validated using three orthogonal methods. Generally, tool-specific precision is high and similar (median of 98.8%, 96.3% and 95.5% for qPCR, RNase R and amplicon sequencing, respectively) whereas the sensitivity and number of predicted circRNAs (ranging from 1,372 to 58,032) are the most significant differentiators. Of note, precision values are lower when evaluating low-abundance circRNAs. We also show that the tools can be used complementarily to increase detection sensitivity. Finally, we offer recommendations for future circRNA detection and validation.

Led-Seq: ligation-enhanced double-end sequence-based structure analysis of RNA.

Structural analysis of RNA is an important and versatile tool to investigate the function of this type of molecules in the cell as well as in vitro. Several robust and reliable procedures are available, relying on chemical modification inducing RT stops or nucleotide misincorporations during reverse transcription. Others are based on cleavage reactions and RT stop signals. However, these methods address only one side of the RT stop or misincorporation position. Here, we describe Led-Seq, a new approach based on lead-induced cleavage of unpaired RNA positions, where both resulting cleavage products are investigated. The RNA fragments carrying 2', 3'-cyclic phosphate or 5'-OH ends are selectively ligated to oligonucleotide adapters by specific RNA ligases. In a deep sequencing analysis, the cleavage sites are identified as ligation positions, avoiding possible false positive signals based on premature RT stops. With a benchmark set of transcripts in Escherichia coli, we show that Led-Seq is an improved and reliable approach based on metal ion-induced phosphodiester hydrolysis to investigate RNA structures in vivo.

The expansion of chemical space in 1826 and in the 1840s prompted the convergence to the periodic system.

The periodic system, which intertwines order and similarity among chemical elements, arose from knowledge about substances constituting the chemical space. Little is known, however, about how the expansion of the space contributed to the emergence of the system-formulated in the 1860s. Here, we show by analyzing the space between 1800 and 1869 that after an unstable period culminating around 1826, chemical space led the system to converge to a backbone structure clearly recognizable in the 1840s. Hence, the system was already encoded in the space for about two and half decades before its formulation. Chemical events in 1826 and in the 1840s were driven by the discovery of new forms of combination standing the test of time. Emphasis of the space upon organic chemicals after 1830 prompted the recognition of relationships among elements participating in the organic turn and obscured some of the relationships among transition metals. To account for the role of nineteenth century atomic weights upon the system, we introduced an algorithm to adjust the space according to different sets of weights, which allowed for estimating the resulting periodic systems of chemists using one or the other weights. By analyzing these systems, from Dalton up to Mendeleev, Gmelin's atomic weights of 1843 produce systems remarkably similar to that of 1869, a similarity that was reinforced by the atomic weights on the years to come. Although our approach is computational rather than historical, we hope it can complement other tools of the history of chemistry.

A guide to naming human non-coding RNA genes.

Research on non-coding RNA (ncRNA) is a rapidly expanding field. Providing an official gene symbol and name to ncRNA genes brings order to otherwise potential chaos as it allows unambiguous communication about each gene. The HUGO Gene Nomenclature Committee (HGNC, www.genenames.org) is the only group with the authority to approve symbols for human genes. The HGNC works with specialist advisors for different classes of ncRNA to ensure that ncRNA nomenclature is accurate and informative, where possible. Here, we review each major class of ncRNA that is currently annotated in the human genome and describe how each class is assigned a standardised nomenclature.

Synthetic riboswitches for the analysis of tRNA processing by eukaryotic RNase P enzymes.

Removal of the 5'-leader region is an essential step in the maturation of tRNA molecules in all domains of life. This reaction is catalyzed by various RNase P activities, ranging from ribonucleoproteins with ribozyme activity to protein-only forms. In Escherichia coli, the efficiency of RNase P-mediated cleavage can be controlled by computationally designed riboswitch elements in a ligand-dependent way, where the 5'-leader sequence of a tRNA precursor is either sequestered in a hairpin structure or presented as a single-stranded region accessible for maturation. In the presented work, the regulatory potential of such artificial constructs is tested on different forms of eukaryotic RNase P enzymes-two protein-only RNase P enzymes (PRORP1 and PRORP2) from Arabidopsis thaliana and the ribonucleoprotein of Homo sapiens The PRORP enzymes were analyzed in vitro as well as in vivo in a bacterial RNase P complementation system. We also tested in HEK293T cells whether the riboswitches remain functional with human nuclear RNase P. While the regulatory principle of the synthetic riboswitches applies for all tested RNase P enzymes, the results also show differences in the substrate requirements of the individual enzyme versions. Hence, such designed RNase P riboswitches represent a novel tool to investigate the impact of the structural composition of the 5'-leader on substrate recognition by different types of RNase P enzymes.

BioAutoML: automated feature engineering and metalearning to predict noncoding RNAs in bacteria.

Recent technological advances have led to an exponential expansion of biological sequence data and extraction of meaningful information through Machine Learning (ML) algorithms. This knowledge has improved the understanding of mechanisms related to several fatal diseases, e.g. Cancer and coronavirus disease 2019, helping to develop innovative solutions, such as CRISPR-based gene editing, coronavirus vaccine and precision medicine. These advances benefit our society and economy, directly impacting people's lives in various areas, such as health care, drug discovery, forensic analysis and food processing. Nevertheless, ML-based approaches to biological data require representative, quantitative and informative features. Many ML algorithms can handle only numerical data, and therefore sequences need to be translated into a numerical feature vector. This process, known as feature extraction, is a fundamental step for developing high-quality ML-based models in bioinformatics, by allowing the feature engineering stage, with design and selection of suitable features. Feature engineering, ML algorithm selection and hyperparameter tuning are often manual and time-consuming processes, requiring extensive domain knowledge. To deal with this problem, we present a new package: BioAutoML. BioAutoML automatically runs an end-to-end ML pipeline, extracting numerical and informative features from biological sequence databases, using the MathFeature package, and automating the feature selection, ML algorithm(s) recommendation and tuning of the selected algorithm(s) hyperparameters, using Automated ML (AutoML). BioAutoML has two components, divided into four modules: (1) automated feature engineering (feature extraction and selection modules) and (2) Metalearning (algorithm recommendation and hyper-parameter tuning modules). We experimentally evaluate BioAutoML in two different scenarios: (i) prediction of the three main classes of noncoding RNAs (ncRNAs) and (ii) prediction of the eight categories of ncRNAs in bacteria, including housekeeping and regulatory types. To assess BioAutoML predictive performance, it is experimentally compared with two other AutoML tools (RECIPE and TPOT). According to the experimental results, BioAutoML can accelerate new studies, reducing the cost of feature engineering processing and either keeping or improving predictive performance. BioAutoML is freely available at https://github.com/Bonidia/BioAutoML.

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.

Virus Evolution on Fitness Landscapes.

The landscape paradigm is revisited in the light of evolution in simple systems. A brief overview of different classes of fitness landscapes is followed by a more detailed discussion of the RNA model, which is currently the only evolutionary model that allows for a comprehensive molecular analysis of a fitness landscape. Neutral networks of genotypes are indispensable for the success of evolution. Important insights into the evolutionary mechanism are gained by considering the topology of sequence and shape spaces. The dynamic concept of molecular quasispecies is viewed in the light of the landscape paradigm. The distribution of fitness values in state space is mirrored by the population structures of mutant distributions. Two classes of thresholds for replication error or mutations are important: (i) the-conventional-genotypic error threshold, which separates ordered replication from random drift on neutral networks, and (ii) a phenotypic error threshold above which the molecular phenotype is lost. Empirical landscapes are reviewed and finally, the implications of the landscape concept for virus evolution are discussed.

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.

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.

Mitochondrial Genomes in Perkinsus Decode Conserved Frameshifts in All Genes.

Mitochondrial genomes of apicomplexans, dinoflagellates, and chrompodellids that collectively make up the Myzozoa, encode only three proteins (Cytochrome b [COB], Cytochrome c oxidase subunit 1 [COX1], Cytochrome c oxidase subunit 3 [COX3]), contain fragmented ribosomal RNAs, and display extensive recombination, RNA trans-splicing, and RNA-editing. The early-diverging Perkinsozoa is the final major myzozoan lineage whose mitochondrial genomes remained poorly characterized. Previous reports of Perkinsus genes indicated independent acquisition of non-canonical features, namely the occurrence of multiple frameshifts. To determine both ancestral myzozoan and novel perkinsozoan mitochondrial genome features, we sequenced and assembled mitochondrial genomes of four Perkinsus species. These data show a simple ancestral genome with the common reduced coding capacity but disposition for rearrangement. We identified 75 frameshifts across the four species that occur as distinct types and that are highly conserved in gene location. A decoding mechanism apparently employs unused codons at the frameshift sites that advance translation either +1 or +2 frames to the next used codon. The locations of frameshifts are seemingly positioned to regulate protein folding of the nascent protein as it emerges from the ribosome. The cox3 gene is distinct in containing only one frameshift and showing strong selection against residues that are otherwise frequently encoded at the frameshift positions in cox1 and cob. All genes lack cysteine codons implying a reduction to 19 amino acids in these genomes. Furthermore, mitochondrion-encoded rRNA fragment complements are incomplete in Perkinsus spp. but some are found in the nuclear DNA suggesting import into the organelle. Perkinsus demonstrates further remarkable trajectories of organelle genome evolution including pervasive integration of frameshift translation into genome expression.

Comprehensive benchmarking of software for mapping whole genome bisulfite data: from read alignment to DNA methylation analysis.

Whole genome bisulfite sequencing is currently at the forefront of epigenetic analysis, facilitating the nucleotide-level resolution of 5-methylcytosine (5mC) on a genome-wide scale. Specialized software have been developed to accommodate the unique difficulties in aligning such sequencing reads to a given reference, building on the knowledge acquired from model organisms such as human, or Arabidopsis thaliana. As the field of epigenetics expands its purview to non-model plant species, new challenges arise which bring into question the suitability of previously established tools. Herein, nine short-read aligners are evaluated: Bismark, BS-Seeker2, BSMAP, BWA-meth, ERNE-BS5, GEM3, GSNAP, Last and segemehl. Precision-recall of simulated alignments, in comparison to real sequencing data obtained from three natural accessions, reveals on-balance that BWA-meth and BSMAP are able to make the best use of the data during mapping. The influence of difficult-to-map regions, characterized by deviations in sequencing depth over repeat annotations, is evaluated in terms of the mean absolute deviation of the resulting methylation calls in comparison to a realistic methylome. Downstream methylation analysis is responsive to the handling of multi-mapping reads relative to mapping quality (MAPQ), and potentially susceptible to bias arising from the increased sequence complexity of densely methylated reads.

Functional ecology of planktonic ciliates: Measuring mortality rates in response to starvation.

Population dynamics of aquatic ciliates are controlled "bottom-up" via food supply and "top-down" by grazing and parasitism. While intrinsic growth rates of ciliates under saturating food conditions have been studied in some detail, mortality rates induced by starvation have received little attention thus far. To this end, we examined the response of three algivorous freshwater ciliate species to starvation using three different optical methods. Two of these methods, i.e. ciliate mortality rates (δ) estimated from (i) numerical response experiments and (ii) the rate of decline (ROD) in cell numbers, investigated the response of the ciliate population using conventional light microscopy. The third method, imaging cytometry using a FlowCAM instrument, monitored single cells during the starvation experiment. Like light microscopy, the FlowCAM approach estimated δ based on ROD in the experimental containers. However, imaging cytometry also measured the relative cellular chlorophyll a content in the ciliates' food vacuoles as a proxy for the nutritional status of the cells. The linear decline of the cellular chl. a yielded an independent estimate of δ that was similar to δ calculated from ROD. Additionally, the FlowCAM measurements revealed a high degree of phenotypic plasticity of the ciliates when exposed to starvation.

Ligand-dependent tRNA processing by a rationally designed RNase P riboswitch.

We describe a synthetic riboswitch element that implements a regulatory principle which directly addresses an essential tRNA maturation step. Constructed using a rational in silico design approach, this riboswitch regulates RNase P-catalyzed tRNA 5'-processing by either sequestering or exposing the single-stranded 5'-leader region of the tRNA precursor in response to a ligand. A single base pair in the 5'-leader defines the regulatory potential of the riboswitch both in vitro and in vivo. Our data provide proof for prior postulates on the importance of the structure of the leader region for tRNA maturation. We demonstrate that computational predictions of ligand-dependent structural rearrangements can address individual maturation steps of stable non-coding RNAs, thus making them amenable as promising target for regulatory devices that can be used as functional building blocks in synthetic biology.

Human pathways in animal models: possibilities and limitations.

Animal models are crucial for advancing our knowledge about the molecular pathways involved in human diseases. However, it remains unclear to what extent tissue expression of pathways in healthy individuals is conserved between species. In addition, organism-specific information on pathways in animal models is often lacking. Within these limitations, we explore the possibilities that arise from publicly available data for the animal models mouse, rat, and pig. We approximate the animal pathways activity by integrating the human counterparts of curated pathways with tissue expression data from the models. Specifically, we compare whether the animal orthologs of the human genes are expressed in the same tissue. This is complicated by the lower coverage and worse quality of data in rat and pig as compared to mouse. Despite that, from 203 human KEGG pathways and the seven tissues with best experimental coverage, we identify 95 distinct pathways, for which the tissue expression in one animal model agrees better with human than the others. Our systematic pathway-tissue comparison between human and three animal modes points to specific similarities with human and to distinct differences among the animal models, thereby suggesting the most suitable organism for modeling a human pathway or tissue.

HumanMetagenomeDB: a public repository of curated and standardized metadata for human metagenomes.

Metagenomics became a standard strategy to comprehend the functional potential of microbial communities, including the human microbiome. Currently, the number of metagenomes in public repositories is increasing exponentially. The Sequence Read Archive (SRA) and the MG-RAST are the two main repositories for metagenomic data. These databases allow scientists to reanalyze samples and explore new hypotheses. However, mining samples from them can be a limiting factor, since the metadata available in these repositories is often misannotated, misleading, and decentralized, creating an overly complex environment for sample reanalysis. The main goal of the HumanMetagenomeDB is to simplify the identification and use of public human metagenomes of interest. HumanMetagenomeDB version 1.0 contains metadata of 69 822 metagenomes. We standardized 203 attributes, based on standardized ontologies, describing host characteristics (e.g. sex, age and body mass index), diagnosis information (e.g. cancer, Crohn's disease and Parkinson), location (e.g. country, longitude and latitude), sampling site (e.g. gut, lung and skin) and sequencing attributes (e.g. sequencing platform, average length and sequence quality). Further, HumanMetagenomeDB version 1.0 metagenomes encompass 58 countries, 9 main sample sites (i.e. body parts), 58 diagnoses and multiple ages, ranging from just born to 91 years old. The HumanMetagenomeDB is publicly available at https://webapp.ufz.de/hmgdb/.

Multiomics reveal unique signatures of human epiploic adipose tissue related to systemic insulin resistance.

OBJECTIVE: Human white adipose tissue (AT) is a metabolically active organ with distinct depot-specific functions. Despite their locations close to the gastrointestinal tract, mesenteric AT and epiploic AT (epiAT) have only scarcely been investigated. Here, we aim to characterise these ATs in-depth and estimate their contribution to alterations in whole-body metabolism. DESIGN: Mesenteric, epiploic, omental and abdominal subcutaneous ATs were collected from 70 patients with obesity undergoing Roux-en-Y gastric bypass surgery. The metabolically well-characterised cohort included nine subjects with insulin sensitive (IS) obesity, whose AT samples were analysed in a multiomics approach, including methylome, transcriptome and proteome along with samples from subjects with insulin resistance (IR) matched for age, sex and body mass index (n=9). Findings implying differences between AT depots in these subgroups were validated in the entire cohort (n=70) by quantitative real-time PCR. RESULTS: While mesenteric AT exhibited signatures similar to those found in the omental depot, epiAT was distinct from all other studied fat depots. Multiomics allowed clear discrimination between the IS and IR states in all tissues. The highest discriminatory power between IS and IR was seen in epiAT, where profound differences in the regulation of developmental, metabolic and inflammatory pathways were observed. Gene expression levels of key molecules involved in AT function, metabolic homeostasis and inflammation revealed significant depot-specific differences with epiAT showing the highest expression levels. CONCLUSION: Multi-omics epiAT signatures reflect systemic IR and obesity subphenotypes distinct from other fat depots. Our data suggest a previously unrecognised role of human epiploic fat in the context of obesity, impaired insulin sensitivity and related diseases.

PTEN regulates adipose progenitor cell growth, differentiation, and replicative aging.

The tumor suppressor phosphatase and tensin homolog (PTEN) negatively regulates the insulin signaling pathway. Germline PTEN pathogenic variants cause PTEN hamartoma tumor syndrome (PHTS), associated with lipoma development in children. Adipose progenitor cells (APCs) lose their capacity to differentiate into adipocytes during continuous culture, whereas APCs from lipomas of patients with PHTS retain their adipogenic potential over a prolonged period. It remains unclear which mechanisms trigger this aberrant adipose tissue growth. To investigate the role of PTEN in adipose tissue development, we performed functional assays and RNA-Seq of control and PTEN knockdown APCs. Reduction of PTEN levels using siRNA or CRISPR led to enhanced proliferation and differentiation of APCs. Forkhead box protein O1 (FOXO1) transcriptional activity is known to be regulated by insulin signaling, and FOXO1 was downregulated at the mRNA level while its inactivation through phosphorylation increased. FOXO1 phosphorylation initiates the expression of the lipogenesis-activating transcription factor sterol regulatory element-binding protein 1 (SREBP1). SREBP1 levels were higher after PTEN knockdown and may account for the observed enhanced adipogenesis. To validate this, we overexpressed constitutively active FOXO1 in PTEN CRISPR cells and found reduced adipogenesis, accompanied by SREBP1 downregulation. We observed that PTEN CRISPR cells showed less senescence compared with controls and the senescence marker CDKN1A (p21) was downregulated in PTEN knockdown cells. Cellular senescence was the most significantly enriched pathway found in RNA-Seq of PTEN knockdown versus control cells. These results provide evidence that PTEN is involved in the regulation of APC proliferation, differentiation, and senescence, thereby contributing to aberrant adipose tissue growth in patients with PHTS.

Manipulating base quality scores enables variant calling from bisulfite sequencing alignments using conventional bayesian approaches.

BACKGROUND: Calling germline SNP variants from bisulfite-converted sequencing data poses a challenge for conventional software, which have no inherent capability to dissociate true polymorphisms from artificial mutations induced by the chemical treatment. Nevertheless, SNP data is desirable both for genotyping and to understand the DNA methylome in the context of the genetic background. The confounding effect of bisulfite conversion however can be conceptually resolved by observing differences in allele counts on a per-strand basis, whereby artificial mutations are reflected by non-complementary base pairs. RESULTS: Herein, we present a computational pre-processing approach for adapting sequence alignment data, thus indirectly enabling downstream analysis on a per-strand basis using conventional variant calling software such as GATK or Freebayes. In comparison to specialised tools, the method represents a marked improvement in precision-sensitivity based on high-quality, published benchmark datasets for both human and model plant variants. CONCLUSION: The presented "double-masking" procedure represents an open source, easy-to-use method to facilitate accurate variant calling using conventional software, thus negating any dependency on specialised tools and mitigating the need to generate additional, conventional sequencing libraries alongside bisulfite sequencing experiments. The method is available at https://github.com/bio15anu/revelio and an implementation with Freebayes is available at https://github.com/EpiDiverse/SNP.

Evolution of DNA Methylation Across Ecdysozoa.

DNA methylation is a crucial, abundant mechanism of gene regulation in vertebrates. It is less prevalent in many other metazoan organisms and completely absent in some key model species, such as Drosophila melanogaster and Caenorhabditis elegans. We report here a comprehensive study of the presence and absence of DNA methyltransferases (DNMTs) in 138 Ecdysozoa, covering Arthropoda, Nematoda, Priapulida, Onychophora, and Tardigrada. Three of these phyla have not been investigated for the presence of DNA methylation before. We observe that the loss of individual DNMTs independently occurred multiple times across ecdysozoan phyla. We computationally predict the presence of DNA methylation based on CpG rates in coding sequences using an implementation of Gaussian Mixture Modeling, MethMod. Integrating both analysis we predict two previously unknown losses of DNA methylation in Ecdysozoa, one within Chelicerata (Mesostigmata) and one in Tardigrada. In the early-branching Ecdysozoa Priapulus caudatus, we predict the presence of a full set of DNMTs and the presence of DNA methylation. We are therefore showing a very diverse and independent evolution of DNA methylation in different ecdysozoan phyla spanning a phylogenetic range of more than 700 million years.