論文

RNA-RNA interactions are key mechanisms through which noncoding RNA (ncRNA) regions exert biological functions. Computational prediction of RNA-RNA interactions is an essential method for detecting novel RNA-RNA interactions because their comprehensive detection by biological experimentation is still quite difficult. Many RNA-RNA interaction prediction tools have been developed, but they tend to produce many false positives. Accordingly, assessment of the statistical significance of computationally predicted interactions is an important task. However, there is no method to evaluate the statistical significance of RNA-RNA interactions that is applicable to interactions between two long RNA sequences. We developed a method to calculate the p-value for the minimal interaction energy between two long RNA sequences. The developed method depends on the fact that minimum interaction energies of RNA-RNA interactions between long RNAs follow a Gumbel distribution when repeat sequences in RNAs are masked. To show the usefulness of the developed method, we applied it to whole human 5′-untranslated region (UTR) and 3′-UTR sequences to detect novel 5′-UTR-3′-UTR interactions. We thus identified two significant 5′-UTR-3′-UTR interactions. Specifically, the human small proline-rich repeat protein 3 shows conserved 5′-UTR-3′-UTR interactions with some nucleotide variations preserving base pairings among primates. Our developed method enables us to detect statistically significant RNA-RNA interactions between long RNAs such as long ncRNAs. Statistical significance estimates help in identification of interactions for experimental validation and provide novel insights into the function of ncRNA regions.

The coronavirus disease-2019 (COVID-19) pandemic has elucidated major limitations in the capacity of medical and research institutions to appropriately manage emerging infectious diseases. We can improve our understanding of infectious diseases by unveiling virus–host interactions through host range prediction and protein–protein interaction prediction. Although many algorithms have been developed to predict virus–host interactions, numerous issues remain to be solved, and the entire network remains veiled. In this review, we comprehensively surveyed algorithms used to predict virus–host interactions. We also discuss the current challenges, such as dataset biases toward highly pathogenic viruses, and the potential solutions. The complete prediction of virus–host interactions remains difficult; however, bioinformatics can contribute to progress in research on infectious diseases and human health.

RNA-binding proteins (RBPs) bind to their target RNA molecules by recognizing specific RNA sequences and structural contexts. The development of CLIP-seq and related protocols has made it possible to exhaustively identify RNA fragments that bind to RBPs. However, no efficient bioinformatics method exists to reveal the structural specificities of RBP-RNA interactions using these data. We present CapR, an efficient algorithm that calculates the probability that each RNA base position is located within each secondary structural context. Using CapR, we demonstrate that several RBPs bind to their target RNA molecules under specific structural contexts.

Transient and alternative structures of ribonucleic acids (RNAs) play essential roles in various regulatory processes, such as translation regulation in living cells. Because experimental analyses for RNA structures are difficult and time-consuming, computational approaches based on RNA secondary structures are promising. In this article, we review computational methods for detecting and analyzing transient/alternative secondary structures of RNAs, including static approaches based on probabilistic distributions of RNA secondary structures and dynamic approaches such as kinetic folding and folding pathway predictions.

RNA accessibility is a useful RNA secondary structural feature for predicting RNA-RNA interactions and translation efficiency in prokaryotes. However, conventional accessibility calculation tools, such as Raccess, are computationally expensive and require considerable computational time to perform transcriptome-scale analysis. In this study, we developed DeepRaccess, which predicts RNA accessibility based on deep learning methods. DeepRaccess was trained to take artificial RNA sequences as input and to predict the accessibility of these sequences as calculated by Raccess. Simulation and empirical dataset analyses showed that the accessibility predicted by DeepRaccess was highly correlated with the accessibility calculated by Raccess. In addition, we confirmed that DeepRaccess could predict protein abundance in E.coli with moderate accuracy from the sequences around the start codon. We also demonstrated that DeepRaccess achieved tens to hundreds of times software speed-up in a GPU environment. The source codes and the trained models of DeepRaccess are freely available at https://github.com/hmdlab/DeepRaccess.

A cancer genome includes many mutations derived from various mutagens and mutational processes, leading to specific mutation patterns. It is known that each mutational process leads to characteristic mutations, and when a mutational process has preferences for mutations, this situation is called a 'mutation signature.' Identification of mutation signatures is an important task for elucidation of carcinogenic mechanisms. In previous studies, analyses with statistical approaches (e.g. non-negative matrix factorization and latent Dirichlet allocation) revealed a number of mutation signatures. Nonetheless, strictly speaking, these existing approaches employ an ad hoc method or incorrect approximation to estimate the number of mutation signatures, and the whole picture of mutation signatures is unclear. Results: In this study, we present a novel method for estimating the number of mutation signatures- latent Dirichlet allocation with variational Bayes inference (VB-LDA)-where variational lower bounds are utilized for finding a plausible number of mutation patterns. In addition, we performed cluster analyses for estimated mutation signatures to extract novel mutation signatures that appear in multiple primary lesions. In a simulation with artificial data, we confirmed that our method estimated the correct number of mutation signatures. Furthermore, applying our method in combination with clustering procedures for real mutation data revealed many interesting mutation signatures that have not been previously reported.

Uncertainties surrounding the evolutionary origin of the epipelagic fish family Scombridae (tunas and mackerels) are symptomatic of the difficulties in resolving suprafamilial relationships within Percomorpha, a hyperdiverse teleost radiation that contains approximately 17,000 species placed in 13 ill-defined orders and 269 families. Here we find that scombrids share a common ancestry with 14 families based on (i) bioinformatic analyses using partial mitochondrial and nuclear gene sequences from all percomorphs deposited in GenBank (10,733 sequences) and (ii) subsequent mitogenomic analysis based on 57 species from those targeted 15 families and 67 outgroup taxa. Morphological heterogeneity among these 15 families is so extraordinary that they have been placed in six different perciform suborders. However, members of the 15 families are either coastal or oceanic pelagic in their ecology with diverse modes of life, suggesting that they represent a previously undetected adaptive radiation in the pelagic realm. Time-calibrated phylogenies imply that scombrids originated from a deep-ocean ancestor and began to radiate after the end-Cretaceous when large predatory epipelagic fishes were selective victims of the Cretaceous-Paleogene mass extinction. We name this clade of open-ocean fishes containing Scombridae "Pelagia" in reference to the common habitat preference that links the 15 families. © 2013 Miya et al.

Quantitative analysis of behaviors shown by interacting multiple animals can provide a key for revealing high-order functions of their nervous systems. To resolve these complex behaviors, a video tracking system that preserves individual identity even under severe overlap in positions, i.e., occlusion, is needed. We developed GroupTracker, a multiple animal tracking system that accurately tracks individuals even under severe occlusion. As maximum likelihood estimation of Gaussian mixture model whose components can severely overlap is theoretically an ill-posed problem, we devised an expectation-maximization scheme with additional constraints on the eigenvalues of the covariance matrix of the mixture components. Our system was shown to accurately track multiple medaka (Oryzias latipes) which freely swim around in three dimensions and frequently overlap each other. As an accurate multiple animal tracking system, GroupTracker will contribute to revealing unexplored structures and patterns behind animal interactions. The Java source code of GroupTracker is available at https://sites.google.com/site/fukunagatsu/software/group-tracker.

Background: Although the number of discovered long non-coding RNAs (lncRNAs) has increased dramatically, their biological roles have not been established. Many recent studies have used ribosome profiling data to assess the protein-coding capacity of lncRNAs. However, very little work has been done to identify ribosome-associated lncRNAs, here defined as lncRNAs interacting with ribosomes related to protein synthesis as well as other unclear biological functions. Results: On average, 39.17% of expressed lncRNAs were observed to interact with ribosomes in human and 48.16% in mouse. We developed the ribosomal association index (RAI), which quantifies the evidence for ribosomal associability of lncRNAs over various tissues and cell types, to catalog 691 and 409 lncRNAs that are robustly associated with ribosomes in human and mouse, respectively. Moreover, we identified 78 and 42 lncRNAs with a high probability of coding peptides in human and mouse, respectively. Compared with ribosome-free lncRNAs, ribosome-associated lncRNAs were observed to be more likely to be located in the cytoplasm and more sensitive to nonsense-mediated decay. Conclusion: Our results suggest that RAI can be used as an integrative and evidence-based tool for distinguishing between ribosome-associated and free lncRNAs, providing a valuable resource for the study of lncRNA functions.

Background: With rapid advances in genome sequencing and editing technologies, systematic and quantitative analysis of animal behavior is expected to be another key to facilitating data-driven behavioral genetics. The nematode Caenorhabditis elegans is a model organism in this field. Several video-tracking systems are available for automatically recording behavioral data for the nematode, but computational methods for analyzing these data are still under development. Results: In this study, we applied the Gaussian mixture model-based binning method to time-series postural data for 322 C. elegans strains. We revealed that the occurrence patterns of the postural states and the transition patterns among these states have a relationship as expected, and such a relationship must be taken into account to identify strains with atypical behaviors that are different from those of wild type. Based on this observation, we identified several strains that exhibit atypical transition patterns that cannot be fully explained by their occurrence patterns of postural states. Surprisingly, we found that two simple factors-overall acceleration of postural movement and elimination of inactivity periods-explained the behavioral characteristics of strains with very atypical transition patterns; therefore, computational analysis of animal behavior must be accompanied by evaluation of the effects of these simple factors. Finally, we found that the npr-1 and npr-3 mutants have similar behavioral patterns that were not predictable by sequence homology, proving that our data-driven approach can reveal the functions of genes that have not yet been characterized. Conclusion: We propose that elimination of inactivity periods and overall acceleration of postural change speed can explain behavioral phenotypes of strains with very atypical postural transition patterns. Our methods and results constitute guidelines for effectively finding strains that show "truly" interesting behaviors and systematically uncovering novel gene functions by bioimage-informatic approaches.

Motivation: Phylogenetic profiling is a powerful computational method for revealing the functions of function-unknown genes. Although conventional similarity metrics in phylogenetic profiling achieved high prediction accuracy, they have two estimation biases: an evolutionary bias and a spurious correlation bias. While previous studies reduced the evolutionary bias by considering a phylogenetic tree, few studies have analyzed the spurious correlation bias. Results: To reduce the spurious correlation bias, we developed metrics based on the inverse Potts model (IPM) for phylogenetic profiling. We also developed a metric based on both the IPM and a phylogenetic tree. In an empirical dataset analysis, we demonstrated that these IPM-based metrics improved the prediction performance of phylogenetic profiling. In addition, we found that the integration of several metrics, including the IPM-based metrics, had superior performance to a single metric.

Motivation: RNA consensus secondary structure prediction from aligned sequences is a powerful approach for improving the secondary structure prediction accuracy. However, because the computational complexities of conventional prediction tools scale with the cube of the alignment lengths, their application to long RNA sequences, such as viral RNAs or long non-coding RNAs, requires significant computational time. Results: In this study, we developed LinAliFold and CentroidLinAliFold, fast RNA consensus secondary structure prediction tools based on minimum free energy and maximum expected accuracy principles, respectively. We achieved software acceleration using beam search methods that were successfully used for fast secondary structure prediction from a single RNA sequence. Benchmark analyses showed that LinAliFold and CentroidLinAliFold were much faster than the existing methods while preserving the prediction accuracy. As an empirical application, we predicted the consensus secondary structure of coronaviruses with approximately 30 000 nt in 5 and 79 min by LinAliFold and CentroidLinAliFold, respectively. We confirmed that the predicted consensus secondary structure of coronaviruses was consistent with the experimental results.

With a large number of annotated non-coding RNAs (ncRNAs), repetitive sequences are found to constitute functional components (termed as repetitive elements) in ncRNAs that perform specific biological functions. Bioinformatics analysis is a powerful tool for improving our understanding of the role of repetitive elements in ncRNAs. This chapter summarizes recent findings that reveal the role of repetitive elements in ncRNAs. Furthermore, relevant bioinformatics approaches are systematically reviewed, which promises to provide valuable resources for studying the functional impact of repetitive elements on ncRNAs.

The computational prediction of RNA-RNA interactions has long been studied in RNA informatics. Most of the existing approaches focused on the interaction prediction of short RNAs in small datasets. However, in recent years, two fast prediction methods, RIsearch2 and RIblast, have been developed to predict transcriptome-scale interactions or long RNA interactions. The key idea of the software acceleration of these tools was the integration of a seed-and-extend method, which is used in fast sequence alignment tools, into RNA-RNA interaction prediction. As a result, the two software programs were ten to a thousand times faster than the existing tools; because of this acceleration, detection of genome-wide microRNA target sites or interaction partners of function-unknown long noncoding RNAs has become possible. In this review, we describe the basic concept of the algorithm, its applications, and the future perspectives of the fast RNA-RNA interaction prediction tools.

We developed a set of universal PCR primers (MiFish-U/E) for metabarcoding environmental DNA (eDNA) from fishes. Primers were designed using aligned whole mitochondrial genome (mitogenome) sequences from 880 species, supplemented by partial mitogenome sequences from 160 elasmobranchs (sharks and rays). The primers target a hypervariable region of the 12S rRNA gene (163–185 bp), which contains sufficient information to identify fishes to taxonomic family, genus and species except for some closely related congeners. To test versatility of the primers across a diverse range of fishes, we sampled eDNA from four tanks in the Okinawa Churaumi Aquarium with known species compositions, prepared dual-indexed libraries and performed paired-end sequencing of the region using high-throughput next-generation sequencing technologies. Out of the 180 marine fish species contained in the four tanks with reference sequences in a custom database, we detected 168 species (93.3%) distributed across 59 families and 123 genera. These fishes are not only taxonomically diverse, ranging from sharks and rays to higher teleosts, but are also greatly varied in their ecology, including both pelagic and benthic species living in shallow coastal to deep waters. We also sampled natural seawaters around coral reefs near the aquarium and detected 93 fish species using this approach. Of the 93 species, 64 were not detected in the four aquarium tanks, rendering the total number of species detected to 232 (from 70 families and 152 genera). The metabarcoding approach presented here is non-invasive, more efficient, more cost-effective and more sensitive than the traditional survey methods. It has the potential to serve as an alternative (or complementary) tool for biodiversity monitoring that revolutionizes natural resource management and ecological studies of fish communities on larger spatial and temporal scales.

Non-coding RNAs have various biological functions such as translational regulation, and RNA-RNA interactions play essential roles in the mechanisms of action of these RNAs. Therefore, RNA-RNA interaction prediction is an important problem in bioinformatics, and many tools have been developed for the computational prediction of RNA-RNA interactions. In addition to the development of novel algorithms with high accuracy, the development and maintenance of web services is essential for enhancing usability by experimental biologists. In this review, we survey web services for RNA-RNA interaction predictions and introduce how to use primary web services. We present various prediction tools, including general interaction prediction tools, prediction tools for specific RNA classes, and RNA-RNA interaction-based RNA design tools. Additionally, we discuss the future perspectives of the development of RNA-RNA interaction prediction tools and the sustainability of web services.