Weakly supervised learning of RNA modifications from low-resolution epitranscriptome data

Since the discovery of the first ribonucleic acid (RNA) modifications in transfer RNAs (tRNAs) and ribosomal RNAs (rRNAs), scientists have been on a quest to decipher the identities and functions of RNA modifications in biological systems. The last decade has seen monumental growth in the number of studies that have characterized and assessed the functionalities of RNA modifications in the field of plant biology. Owing to these studies, we now categorize RNA modifications based on their chemical nature and the RNA on which they are found, as well as the array of proteins that are involved in the processes that add, read, and remove them from an RNA molecule. Beyond their identity, another key piece of the puzzle is the functional significance of the various types of RNA modifications. Here, we shed light on recent studies that help establish our current understanding of the diversity of RNA modifications found in plant transcriptomes and the functions they play at both the molecular (e.g., RNA stability, translation, and transport) and organismal (e.g., stress response and development) levels. Finally, we consider the key research questions related to plant gene expression and biology in general and highlight developments in various technologies that are driving our insights forward in this research area. Expected final online publication date for the Annual Review of Plant Biology, Volume 74 is May 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Ribonucleic acid (RNA) plays a pivotal role in regulating biological processes within living systems, with modified nucleosides serving as critical modulators of various aspects of biological functions. Therefore, the development of efficient methodologies for late-stage, site-selective RNA modification is of considerable interest, as it facilitates the functional exploration of RNA chemical modifications and their implications for therapeutic applications. Precise RNA modification holds significant promise for the treatment of genetic diseases by enabling the correction of mutated nucleobases to their wild-type forms. Additionally, the site-selective incorporation of synthetic labeling groups into RNA provides invaluable tools for structural and functional studies, thereby uncovering previously hidden dimensions of RNA's role in biological systems. In this review, we provide a comprehensive overview of three principal approaches to site-selective, late-stage RNA modifications: enzyme-mediated strategies, catalytic nucleic acid-based techniques, and chemical methodologies. These approaches predominantly target the nucleobase or the 2'-hydroxyl (2'-OH) group of RNA nucleosides. We evaluate the advantages and limitations of each strategy and discuss future directions for advancing this field of research.

This paper describes changes in the ultraviolet spectra of ribonucleic acid (RNA) that occur in the presence of inorganic anionic sulfur species. As part of a larger program for validating an in vitro screen for testing the safety and efficacy of potential sulfur mustard (HD) antagonists, this work suggests that modification of RNA may occur in the presence of anionic sulfur m oieties. Although the exact mechanism of the m odification is unclear, it may explain why compounds such as thiosulfate are effective as pretreatments for sulfur mustard intoxication under certain conditions. Alteration of certain intracellular components may act to preserve signal transduction at the RNA m essage step.

Myogenesis involves skeletal muscle stem cells (MuSCs) that produce and regenerate skeletal muscle during regular growth and repair. However, when this system fails to function normally, it can lead to musculoskeletal diseases like Duchenne Muscular Dystrophy (DMD). Therefore, it is important to study the molecular mechanisms behind this developmental process in order to seek therapies and solutions for these types of diseases. Our interest lies in the field of epitranscriptomics, which focuses on post-transcriptional ribonucleic acid (RNA) modifications, particularly N6-methyladenosine (m6A), which involves the addition of a methyl group to the adenosine nucleotide, a process that is mediated by the methyltransferase-like protein 3 (METTL3) enzyme. Little is known regarding the contribution of epitranscriptomics and m6A to the development of DMD. The goal of this library thesis is to review and analyze existing scientific literature to evaluate the regulatory role that the METTL3-catalyzed m6A modification plays in skeletal muscle development, regeneration, and disease for the purposes of identifying therapeutic opportunities.

Transcription Factor Binding Site (TFBS) prediction is crucial for understanding gene regulation and various biological processes. This study introduces a novel Mixture of Experts (MoE) approach for TFBS prediction, integrating multiple pre-trained Convolutional Neural Network (CNN) models, each specializing in different TFBS patterns. We evaluate the performance of our MoE model against individual expert models on both in-distribution and out-of-distribution (OOD) datasets, using six randomly selected transcription factors (TFs) for OOD testing. Our results demonstrate that the MoE model achieves competitive or superior performance across diverse TF binding sites, particularly excelling in OOD scenarios. The Analysis of Variance (ANOVA) statistical test confirms the significance of these performance differences. Additionally, we introduce ShiftSmooth, a novel attribution mapping technique that provides more robust model interpretability by considering small shifts in input sequences. Through comprehensive explainability analysis, we show that ShiftSmooth offers superior attribution for motif discovery and localization compared to traditional Vanilla Gradient methods. Our work presents an efficient, generalizable, and interpretable solution for TFBS prediction, potentially enabling new discoveries in genome biology and advancing our understanding of transcriptional regulation.

Deep learning (DL) has become an indispensable tool in hyperspectral data analysis, automatically extracting valuable features from complex, high-dimensional datasets. Super-resolution reconstruction, an essential aspect of hyperspectral data, involves enhancing spatial resolution, particularly relevant to low-resolution hyperspectral data. Yet, the pursuit of super-resolution in hyperspectral analysis is fraught with challenges, including acquiring ground truth high-resolution data for training, generalization, and scalability. The pressing issue of extended spectral acquisition times, notably for high-resolution scans, is a significant roadblock in hyperspectral imaging. Super-resolution methods offer a promising solution by providing higher spatial resolution data to expedite data collection and yield more efficient outcomes. This paper delves into a practical application of these concepts using Raman imaging, where spectral acquisition times can be prohibitively long. In this context, DL-based super-resolution models demonstrate their efficacy by predicting and reconstructing high-resolution Raman data from low-resolution input, eliminating the need for resource-intensive high-resolution scans. While previous work often relied on substantial high-resolution datasets, this study showcases the ability to achieve similar outcomes even with limited data, presenting a more practical and cost-effective approach. The results offer a glimpse into the transformative potential of this technology to streamline hyperspectral imaging applications by saving valuable time and resources through the successful generation of high-resolution data from low-resolution inputs.

N6-methyladenosine (m6A) methylation is proved to play a significant role in human cancers. This study aimed to explore the association between m6A ribonucleic acid (RNA) methylation regulators and uterine corpus endometrial carcinoma (UCEC), and build a prognostic signature of m6A regulators for UCEC.RNA-seq transcriptome data and clinicopathological data of UCEC were downloaded from the Cancer Genome Atlas database. We compared the expression of 23 m6A-regulators in tumor tissues and nontumor tissues. Then we classified the data into 3 clusters by consensus clustering analysis. Several regulators were picked out as the prognostic signature of patients with UCEC based on least absolute shrinkage and selection operator Cox regression analysis. Additionally, we established a predictive nomogram to calculate survival times. Finally, we used receiver operating characteristic curve, univariate Cox regression analysis, and multivariate Cox regression analysis to further verify the prognostic value of the risk signature consisting of m6A regulators.The expression of 18/23 m6A regulators was significantly different in UCEC compared with normal samples. Gene ontology functional analysis of these regulators revealed that they were mainly participated in RNA splicing, stabilization, modification, and degradation. LRPPRC, IGFBP2, KIAA1429, IGFBP3, FMR1, YTHDF1, METTL14, and YTHDF2 were selected to construct the risk signature and predictive nomogram. The results of receiver operating characteristic curve, univariate Cox regression analysis, and multivariate Cox regression analysis for the risk signature showed a good predictive performance for UCEC.The risk signature of 8-m6A regulators has potential prognostic value for patients with UCEC.

Coastal zones are recognised sinks for plastic debris, although studying the transport and dispersion of plastic debris in nearshore waters remains challenging. The understanding of coastal processes and how these can be applied to numerical modelling studies is not yet fully mature. Most Lagrangian numerical modelling studies of plastic debris transport to date have been conducted at the basin or sub-basin level, typically using low-resolution hydrodynamic data (>2.5 km). These resolutions are generally sufficient at larger scales, but at smaller scales they create uncertainties regarding mainly the beaching of particles on the coastline and poor resolution of coastal structures or complex geometries.The LOCATE numerical model addresses these constraints by incorporating high-resolution hydrodynamic data in nested grid configurations, focusing on areas of high interest. Specifically, this has been applied to the Barcelona coastline, where the LOCATE model used a coupled current-wave dispersion module with a three-way nested grid for current data, of 2.5 km resolution from CMEMS within the study domain, a 350 m high-resolution grid from the Spanish Port Authority (Puertos del Estado) covering the Barcelona metropolitan area, and a 70 m resolution grid covering the Port and the urban coastline. The use of high-resolution hydrodynamic data with the LOCATE model was validated using drifter data. Complex geometric structures were resolved, demonstrating that particles had residence times over 18 times longer within the Port when using high-resolution data compared to only using low-resolution data. Furthermore, a beaching module that calculated the real-time distance of a particle to the shoreline was developed using high-resolution shoreline data. This provided much more realistic beaching patterns, compared to determining particle beaching using its advected velocity, as well as shoreline continuity between the different resolutions in the hydrodynamic data.These adaptations alone, however, do not address coastline or beach processes not included even in the highest-resolution hydrodynamic data. For this, a probabilistic framework was assumed, where a beaching timescale of 26.35 h was determined using a sensitivity analysis for backtracking simulations initiated in nearshore environments where particles could only cross the land-water boundary at known discharge sources. A further condition based on a minimum particle trajectory distance was also introduced to avoid artefacts. This final probabilistic configuration allowed for particle advection near the shoreline with more realistic trajectories within complex shorelines, considering stochastic elements of particle transport at localised scales. These parameterisations highlight the adaptations required to coastal and nearshore Lagrangian numerical modelling and serve as an area of future research which may be transposed to other locations.AcknowledgementsThis study was carried out within the TRAP project (Participatory Strategies for the Management of Plastic Pollution on the Transboundary Coast), which was 65% co-financed by the European Union through the Interreg VI-A Spain-France-Andorra Programme (POCTEFA 2021-2027). The objective of POCTEFA is to strengthen the economic and social integration of the Spain-France-Andorra border region

Osteoarthritis (OA) is a common chronic degenerative disease of the skeletal system and muscular system, and its pathogenesis remains unclear, leading to a lack of effective therapeutic strategies. Ribonucleic acid binding proteins (RBP), as key regulators of post-transcriptional processes, can specifically bind to targeted ribonucleic acids (RNA) and modulate their function and fate. By regulating various aspects of RNA metabolism, including transcription, splicing, modification, stabilization, and translation, RBPs influence the onset and progression of OA. Exploring the regulatory mechanisms of RBPs under physiological and pathological conditions, elucidating the role of RBPs in the occurrence and development of OA, and discussing current challenges and future directions in RBPs research, hold significant importance for the treatment of OA by targeting RBPs.

BepiColombo is an interdisciplinary mission to Mercury scheduled for launch in 2015, arriving at Mercury in 2021. It is a joint mission between ESA and JAXA consisting of 2 complementary spacecraft, the Mercury Planetary Orbiter (MPO) and the Mercury Magnetospheric Orbiter (MMO). Some of the instruments on the MPO either rely on certain solar events or are interested in variations of the magnetic or plasma environment around Mercury, which cannot be predicted or planned for. Traditionally, these instruments would measure continuously, downlink all their data back to Earth and the Scientists would identify the interesting periods. The Selective Data Downlink approach would allow experiments to have the possibility to detect these variations in a way that only interesting data will be selected for downlink. This approach would make far better use of the limited available data downlink volume, returning only high-resolution science data for the interesting periods. To achieve this, the instruments would have to generate simultaneously low-resolution and high-resolution data of the same observation that will be stored in the on-board SSMM (Solid State Mass Memory). This high-resolution science data will be stored in a dedicated instrument packet store that will not be automatically returned to the Earth. Low-resolution science data, which will include selective downlink support data from the observation, used for quick-look data analysis on ground, will be downlinked, typically via the X-band, arriving at Earth nominally 1 or 2 days after its generation. If the low-resolution science data, data from other MPO instruments, or even MMO data indicate an interesting feature, a selection of the high-resolution data stored on-board can be copied to a separate packet store for subsequent downlink via Ka-band. This concept would guarantee that only scientifically useful data will be downlinked.

This paper covers predicting high-resolution electricity peak demand features given lower-resolution data. This is a relevant setup as it answers whether limited higher-resolution monitoring helps to estimate future high-resolution peak loads when the high-resolution data is no longer available. That question is particularly interesting for network operators considering replacing high-resolution monitoring by predictive models due to economic considerations. We propose models to predict half-hourly minima and maxima of high-resolution (every minute) electricity load data while model inputs are of a lower resolution (30 min). We combine predictions of generalized additive models (GAM) and deep artificial neural networks (DNN), which are popular in load forecasting. We extensively analyze the prediction models, including the input parameters’ importance, focusing on load, weather, and seasonal effects. The proposed method won a data competition organized by Western Power Distribution, a British distribution network operator. In addition, we provide a rigorous evaluation study that goes beyond the competition frame to analyze the models’ robustness. The results show that the proposed methods are superior to the competition benchmark concerning the out-of-sample root mean squared error (RMSE). This holds regarding the competition month and the supplementary evaluation study, which covers an additional eleven months. Overall, our proposed model combination reduces the out-of-sample RMSE by 57.4% compared to the benchmark.

The resolution of seismic data determines the ability to characterize individual geological structures in a seismic image. Sparse spike inversion (SSI) is an effective approach for improving the resolution of seismic data. However, the basic assumption of SSI is that the strong reflectivity of the formation is sparse, which may not be a reasonable fit for weak thin-layer reflections. In this study, we propose a deep learning-based method to reconstruct high-resolution seismic data by combining information from the longitudinal reflectivity distribution and lateral geological structure features in the field data. A U-shaped network that fuses residual block and attention mechanism is used to learn the relationship between low resolution and high resolution. In addition, we use a hybrid loss function that combines <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\ell _{1}}$ </tex-math></inline-formula> loss and structural similarity (SSIM) loss to optimize the network parameters for better distinguishing the geometrical features characterized by structural amplitude changes. To train the network, we adopt a workflow to automatically generate numerous 2-D low-resolution data and their corresponding high-resolution data. In this workflow, the prior information, such as statistical reflectivity distribution of well logs and structural features of the data are considered. The synthetic data and field data tests show that our method can work well compared to the traditional method even though only a few well logs are available. Especially in the field data example, our method recovers thin layers better and yields laterally more consistent high-resolution results.

MotivationMachine learning models for predicting cell-type-specific transcription factor (TF) binding sites have become increasingly more accurate thanks to the increased availability of next-generation sequencing data and more standardized model evaluation criteria. However, knowledge transfer from data-rich to data-limited TFs and cell types remains crucial for improving TF binding prediction models because available binding labels are highly skewed towards a small collection of TFs and cell types. Transfer prediction of TF binding sites can potentially benefit from a multitask learning approach; however, existing methods typically use shallow single-task models to generate low-resolution predictions. Here, we propose NetTIME, a multitask learning framework for predicting cell-type-specific TF binding sites with base-pair resolution.ResultsWe show that the multitask learning strategy for TF binding prediction is more efficient than the single-task approach due to the increased data availability. NetTIME trains high-dimensional embedding vectors to distinguish TF and cell-type identities. We show that this approach is critical for the success of the multitask learning strategy and allows our model to make accurate transfer predictions within and beyond the training panels of TFs and cell types. We additionally train a linear-chain conditional random field (CRF) to classify binding predictions and show that this CRF eliminates the need for setting a probability threshold and reduces classification noise. We compare our method’s predictive performance with two state-of-the-art methods, Catchitt and Leopard, and show that our method outperforms previous methods under both supervised and transfer learning settings.Availability and implementationNetTIME is freely available at https://github.com/ryi06/NetTIME and the code is also archived at https://doi.org/10.5281/zenodo.6994897.Supplementary informationSupplementary data are available at Bioinformatics online.

Transcription factor binding site (TFBS), one of the DNA-protein binding sites, plays important roles in understanding regulation of gene expression and drug design. Recently, deep-learning based methods have been widely used in the prediction of TFBS. In this work, we propose a novel deep-learning model, called Combination of Multi-Scale Convolutional Network and Long Short-Term Memory Network (MCNN-LSTM), which utilizes multi-scale convolution for feature processing, and the long short-term memory network to recognize TFBS in DNA sequences. Moreover, we design a new encoding method, called multi-nucleotide one-hot (MNOH), which considers the correlation between nucleotides in adjacent positions, to further improve the prediction performance of TFBS. Stringent cross-validation and independent tests on benchmark datasets demonstrated the efficacy of MNOH and MCNN-LSTM. Based on the proposed methods, we further implement a new TFBS predictor, called DeepTF. The computational experimental results show that our predictor outperformed several existing TFBS predictors.

In this paper, we wanted to test whether it is possible to use genetical genomics information such as expression quantitative trait loci (eQTL) mapping results as input to a transcription factor binding site (TFBS) prediction algorithm. Furthermore, this new approach was compared to the more traditional cluster based TFBS prediction. The results of eQTL mapping are used as input to one of the top ranking TFBS prediction algorithms. Genes with observed expression profiles showing the same eQTL region are collected into eQTL groups. The promoter sequences of all the genes within the same eQTL group are used as input in the transcription factor binding site search. This approach is tested with a real data set of a recombinant inbred line population of Arabidopsis thaliana. The predicted motifs are compared to results obtained from the conventional approach of first clustering the gene expression values and then using the promoter sequences of the genes within the same cluster as input for the transcription factor binding site prediction. Our eQTL based approach produced different motifs compared to the cluster based method. Furthermore the score of the eQTL based motifs was higher than the score of the cluster based motifs. In a comparison to already predicted motifs from the AtcisDB database, the eQTL based and the cluster based method produced about the same number of hits with binding sites from AtcisDB. In conclusion, the results of this study clearly demonstrate the usefulness of eQTL to predict transcription factor binding sites.