HuD Is a Neural Translation Enhancer Acting on mTORC1-Responsive Genes and Counteracted by the Y3 Small Non-coding RNA.

The RNA-binding protein HuD promotes neurogenesis and favors recovery from peripheral axon injury. HuD interacts with many mRNAs, altering both stability and translation efficiency. We generated a nucleotide resolution map of the HuD RNA interactome in motor neuron-like cells, identifying HuD target sites in 1,304 mRNAs, almost exclusively in the 3' UTR. HuD binds many mRNAs encoding mTORC1-responsive ribosomal proteins and translation factors. Altered HuD expression correlates with the translation efficiency of these mRNAs and overall protein synthesis, in a mTORC1-independent fashion. The predominant HuD target is the abundant, small non-coding RNA Y3, amounting to 70% of the HuD interaction signal. Y3 functions as a molecular sponge for HuD, dynamically limiting its recruitment to polysomes and its activity as a translation and neuron differentiation enhancer. These findings uncover an alternative route to the mTORC1 pathway for translational control in motor neurons that is tunable by a small non-coding RNA.

Multi-omics profiling of mouse gastrulation at single-cell resolution.

Formation of the three primary germ layers during gastrulation is an essential step in the establishment of the vertebrate body plan and is associated with major transcriptional changes1-5. Global epigenetic reprogramming accompanies these changes6-8, but the role of the epigenome in regulating early cell-fate choice remains unresolved, and the coordination between different molecular layers is unclear. Here we describe a single-cell multi-omics map of chromatin accessibility, DNA methylation and RNA expression during the onset of gastrulation in mouse embryos. The initial exit from pluripotency coincides with the establishment of a global repressive epigenetic landscape, followed by the emergence of lineage-specific epigenetic patterns during gastrulation. Notably, cells committed to mesoderm and endoderm undergo widespread coordinated epigenetic rearrangements at enhancer marks, driven by ten-eleven translocation (TET)-mediated demethylation and a concomitant increase of accessibility. By contrast, the methylation and accessibility landscape of ectodermal cells is already established in the early epiblast. Hence, regulatory elements associated with each germ layer are either epigenetically primed or remodelled before cell-fate decisions, providing the molecular framework for a hierarchical emergence of the primary germ layers.

Bottom-up data integration in polymer models of chromatin organization.

Cellular functions crucially depend on the precise execution of complex biochemical reactions taking place on the chromatin fiber in the tightly packed environment of the cell nucleus. Despite the availability of large datasets probing this process from multiple angles, bottom-up frameworks that allow the incorporation of the sequence-specific nature of biochemistry in a unified model of 3D chromatin structure remain scarce. Here, we propose Sequence-Enhanced Magnetic Polymer (SEMPER), a novel stochastic polymer model that naturally incorporates observational data about sequence-driven biochemical processes, such as binding of transcription factor proteins, in a 3D model of chromatin structure. We introduce a novel approximate Bayesian algorithm to quantify a posteriori the relative importance of various factors, including the polymeric nature of DNA, in determining chromatin epigenetic state, thus providing a transparent way to generate biological hypotheses. Although accurate prediction of contact frequencies (a problem already extensively studied in the literature) is not our main aim, as a by-product of the inference procedure and without additional input from the genome 3D structure, our model can predict with reasonable accuracy some notable and nontrivial conformational features of chromatin folding within the nucleus. Our work highlights the importance of introducing physically realistic statistical models for predicting chromatin states from epigenetic data and opens the way to a new class of more systematic approaches to interpreting epigenomic data.

De novo prediction of RNA-protein interactions with graph neural networks.

RNA-binding proteins (RBPs) are key co- and post-transcriptional regulators of gene expression, playing a crucial role in many biological processes. Experimental methods like CLIP-seq have enabled the identification of transcriptome-wide RNA-protein interactions for select proteins; however, the time- and resource-intensive nature of these technologies call for the development of computational methods to complement their predictions. Here, we leverage recent, large-scale CLIP-seq experiments to construct a de novo predictor of RNA-protein interactions based on graph neural networks (GNN). We show that the GNN method allows us not only to predict missing links in an RNA-protein network, but to predict the entire complement of targets of previously unassayed proteins, and even to reconstruct the entire network of RNA-protein interactions in different conditions based on minimal information. Our results demonstrate the potential of modern machine learning methods to extract useful information on post-transcriptional regulation from large data sets.

SCRaPL: A Bayesian hierarchical framework for detecting technical associates in single cell multiomics data.

Single-cell multi-omics assays offer unprecedented opportunities to explore epigenetic regulation at cellular level. However, high levels of technical noise and data sparsity frequently lead to a lack of statistical power in correlative analyses, identifying very few, if any, significant associations between different molecular layers. Here we propose SCRaPL, a novel computational tool that increases power by carefully modelling noise in the experimental systems. We show on real and simulated multi-omics single-cell data sets that SCRaPL achieves higher sensitivity and better robustness in identifying correlations, while maintaining a similar level of false positives as standard analyses based on Pearson and Spearman correlation.

Challenges for machine learning in RNA-protein interaction prediction.

RNA-protein interactions have long being recognised as crucial regulators of gene expression. Recently, the development of scalable experimental techniques to measure these interactions has revolutionised the field, leading to the production of large-scale datasets which offer both opportunities and challenges for machine learning techniques. In this brief note, we will discuss some of the major stumbling blocks towards the use of machine learning in computational RNA biology, focusing specifically on the problem of predicting RNA-protein interactions from next-generation sequencing data.

Model reduction for the Chemical Master Equation: An information-theoretic approach.

The complexity of mathematical models in biology has rendered model reduction an essential tool in the quantitative biologist's toolkit. For stochastic reaction networks described using the Chemical Master Equation, commonly used methods include time-scale separation, Linear Mapping Approximation, and state-space lumping. Despite the success of these techniques, they appear to be rather disparate, and at present, no general-purpose approach to model reduction for stochastic reaction networks is known. In this paper, we show that most common model reduction approaches for the Chemical Master Equation can be seen as minimizing a well-known information-theoretic quantity between the full model and its reduction, the Kullback-Leibler divergence defined on the space of trajectories. This allows us to recast the task of model reduction as a variational problem that can be tackled using standard numerical optimization approaches. In addition, we derive general expressions for propensities of a reduced system that generalize those found using classical methods. We show that the Kullback-Leibler divergence is a useful metric to assess model discrepancy and to compare different model reduction techniques using three examples from the literature: an autoregulatory feedback loop, the Michaelis-Menten enzyme system, and a genetic oscillator.

Quantitative analysis questions the role of MeCP2 as a global regulator of alternative splicing.

MeCP2 is an abundant protein in mature nerve cells, where it binds to DNA sequences containing methylated cytosine. Mutations in the MECP2 gene cause the severe neurological disorder Rett syndrome (RTT), provoking intensive study of the underlying molecular mechanisms. Multiple functions have been proposed, one of which involves a regulatory role in splicing. Here we leverage the recent availability of high-quality transcriptomic data sets to probe quantitatively the potential influence of MeCP2 on alternative splicing. Using a variety of machine learning approaches that can capture both linear and non-linear associations, we show that widely different levels of MeCP2 have a minimal effect on alternative splicing in three different systems. Alternative splicing was also apparently indifferent to developmental changes in DNA methylation levels. Our results suggest that regulation of splicing is not a major function of MeCP2. They also highlight the importance of multi-variate quantitative analyses in the formulation of biological hypotheses.

Transcription rate strongly affects splicing fidelity and cotranscriptionality in budding yeast.

The functional consequences of alternative splicing on altering the transcription rate have been the subject of intensive study in mammalian cells but less is known about effects of splicing on changing the transcription rate in yeast. We present several lines of evidence showing that slow RNA polymerase II elongation increases both cotranscriptional splicing and splicing efficiency and that faster elongation reduces cotranscriptional splicing and splicing efficiency in budding yeast, suggesting that splicing is more efficient when cotranscriptional. Moreover, we demonstrate that altering the RNA polymerase II elongation rate in either direction compromises splicing fidelity, and we reveal that splicing fidelity depends largely on intron length together with secondary structure and splice site score. These effects are notably stronger for the highly expressed ribosomal protein coding transcripts. We propose that transcription by RNA polymerase II is tuned to optimize the efficiency and accuracy of ribosomal protein gene expression, while allowing flexibility in splice site choice with the nonribosomal protein transcripts.

Detecting repeated cancer evolution from multi-region tumor sequencing data.

Recurrent successions of genomic changes, both within and between patients, reflect repeated evolutionary processes that are valuable for the anticipation of cancer progression. Multi-region sequencing allows the temporal order of some genomic changes in a tumor to be inferred, but the robust identification of repeated evolution across patients remains a challenge. We developed a machine-learning method based on transfer learning that allowed us to overcome the stochastic effects of cancer evolution and noise in data and identified hidden evolutionary patterns in cancer cohorts. When applied to multi-region sequencing datasets from lung, breast, renal, and colorectal cancer (768 samples from 178 patients), our method detected repeated evolutionary trajectories in subgroups of patients, which were reproduced in single-sample cohorts (n = 2,935). Our method provides a means of classifying patients on the basis of how their tumor evolved, with implications for the anticipation of disease progression.

The MOBSTER R package for tumour subclonal deconvolution from bulk DNA whole-genome sequencing data.

BACKGROUND: The large-scale availability of whole-genome sequencing profiles from bulk DNA sequencing of cancer tissues is fueling the application of evolutionary theory to cancer. From a bulk biopsy, subclonal deconvolution methods are used to determine the composition of cancer subpopulations in the biopsy sample, a fundamental step to determine clonal expansions and their evolutionary trajectories. RESULTS: In a recent work we have developed a new model-based approach to carry out subclonal deconvolution from the site frequency spectrum of somatic mutations. This new method integrates, for the first time, an explicit model for neutral evolutionary forces that participate in clonal expansions; in that work we have also shown that our method improves largely over competing data-driven methods. In this Software paper we present mobster, an open source R package built around our new deconvolution approach, which provides several functions to plot data and fit models, assess their confidence and compute further evolutionary analyses that relate to subclonal deconvolution. CONCLUSIONS: We present the mobster package for tumour subclonal deconvolution from bulk sequencing, the first approach to integrate Machine Learning and Population Genetics which can explicitly model co-existing neutral and positive selection in cancer. We showcase the analysis of two datasets, one simulated and one from a breast cancer patient, and overview all package functionalities.

Robust statistical modeling improves sensitivity of high-throughput RNA structure probing experiments.

Structure probing coupled with high-throughput sequencing could revolutionize our understanding of the role of RNA structure in regulation of gene expression. Despite recent technological advances, intrinsic noise and high sequence coverage requirements greatly limit the applicability of these techniques. Here we describe a probabilistic modeling pipeline that accounts for biological variability and biases in the data, yielding statistically interpretable scores for the probability of nucleotide modification transcriptome wide. Using two yeast data sets, we demonstrate that our method has increased sensitivity, and thus our pipeline identifies modified regions on many more transcripts than do existing pipelines. Our method also provides confident predictions at much lower sequence coverage levels than those recommended for reliable structural probing. Our results show that statistical modeling extends the scope and potential of transcriptome-wide structure probing experiments.

Neural field models for latent state inference: Application to large-scale neuronal recordings.

Large-scale neural recording methods now allow us to observe large populations of identified single neurons simultaneously, opening a window into neural population dynamics in living organisms. However, distilling such large-scale recordings to build theories of emergent collective dynamics remains a fundamental statistical challenge. The neural field models of Wilson, Cowan, and colleagues remain the mainstay of mathematical population modeling owing to their interpretable, mechanistic parameters and amenability to mathematical analysis. Inspired by recent advances in biochemical modeling, we develop a method based on moment closure to interpret neural field models as latent state-space point-process models, making them amenable to statistical inference. With this approach we can infer the intrinsic states of neurons, such as active and refractory, solely from spiking activity in large populations. After validating this approach with synthetic data, we apply it to high-density recordings of spiking activity in the developing mouse retina. This confirms the essential role of a long lasting refractory state in shaping spatiotemporal properties of neonatal retinal waves. This conceptual and methodological advance opens up new theoretical connections between mathematical theory and point-process state-space models in neural data analysis.

MeCP2 recognizes cytosine methylated tri-nucleotide and di-nucleotide sequences to tune transcription in the mammalian brain.

Mutations in the gene encoding the methyl-CG binding protein MeCP2 cause several neurological disorders including Rett syndrome. The di-nucleotide methyl-CG (mCG) is the classical MeCP2 DNA recognition sequence, but additional methylated sequence targets have been reported. Here we show by in vitro and in vivo analyses that MeCP2 binding to non-CG methylated sites in brain is largely confined to the tri-nucleotide sequence mCAC. MeCP2 binding to chromosomal DNA in mouse brain is proportional to mCAC + mCG density and unexpectedly defines large genomic domains within which transcription is sensitive to MeCP2 occupancy. Our results suggest that MeCP2 integrates patterns of mCAC and mCG in the brain to restrain transcription of genes critical for neuronal function.

BPRMeth: a flexible Bioconductor package for modelling methylation profiles.

Motivation: High-throughput measurements of DNA methylation are increasingly becoming a mainstay of biomedical investigations. While the methylation status of individual cytosines can sometimes be informative, several recent papers have shown that the functional role of DNA methylation is better captured by a quantitative analysis of the spatial variation of methylation across a genomic region. Results: Here, we present BPRMeth, a Bioconductor package that quantifies methylation profiles by generalized linear model regression. The original implementation has been enhanced in two important ways: we introduced a fast, variational inference approach that enables the quantification of Bayesian posterior confidence measures on the model, and we adapted the method to use several observation models, making it suitable for a diverse range of platforms including single-cell analyses and methylation arrays. Availability and implementation: http://bioconductor.org/packages/BPRMeth. Supplementary information: Supplementary data are available at Bioinformatics online.

Autoregressive Point Processes as Latent State-Space Models: A Moment-Closure Approach to Fluctuations and Autocorrelations.

Modeling and interpreting spike train data is a task of central importance in computational neuroscience, with significant translational implications. Two popular classes of data-driven models for this task are autoregressive point-process generalized linear models (PPGLM) and latent state-space models (SSM) with point-process observations. In this letter, we derive a mathematical connection between these two classes of models. By introducing an auxiliary history process, we represent exactly a PPGLM in terms of a latent, infinite-dimensional dynamical system, which can then be mapped onto an SSM by basis function projections and moment closure. This representation provides a new perspective on widely used methods for modeling spike data and also suggests novel algorithmic approaches to fitting such models. We illustrate our results on a phasic bursting neuron model, showing that our proposed approach provides an accurate and efficient way to capture neural dynamics.

Statistical modeling of isoform splicing dynamics from RNA-seq time series data.

MOTIVATION: Isoform quantification is an important goal of RNA-seq experiments, yet it remains problematic for genes with low expression or several isoforms. These difficulties may in principle be ameliorated by exploiting correlated experimental designs, such as time series or dosage response experiments. Time series RNA-seq experiments, in particular, are becoming increasingly popular, yet there are no methods that explicitly leverage the experimental design to improve isoform quantification. RESULTS: Here, we present DICEseq, the first isoform quantification method tailored to correlated RNA-seq experiments. DICEseq explicitly models the correlations between different RNA-seq experiments to aid the quantification of isoforms across experiments. Numerical experiments on simulated datasets show that DICEseq yields more accurate results than state-of-the-art methods, an advantage that can become considerable at low coverage levels. On real datasets, our results show that DICEseq provides substantially more reproducible and robust quantifications, increasing the correlation of estimates from replicate datasets by up to 10% on genes with low or moderate expression levels (bottom third of all genes). Furthermore, DICEseq permits to quantify the trade-off between temporal sampling of RNA and depth of sequencing, frequently an important choice when planning experiments. Our results have strong implications for the design of RNA-seq experiments, and offer a novel tool for improved analysis of such datasets. AVAILABILITY AND IMPLEMENTATION: Python code is freely available at http://diceseq.sf.net CONTACT: G.Sanguinetti@ed.ac.uk SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Higher order methylation features for clustering and prediction in epigenomic studies.

MOTIVATION: DNA methylation is an intensely studied epigenetic mark, yet its functional role is incompletely understood. Attempts to quantitatively associate average DNA methylation to gene expression yield poor correlations outside of the well-understood methylation-switch at CpG islands. RESULTS: Here, we use probabilistic machine learning to extract higher order features associated with the methylation profile across a defined region. These features quantitate precisely notions of shape of a methylation profile, capturing spatial correlations in DNA methylation across genomic regions. Using these higher order features across promoter-proximal regions, we are able to construct a powerful machine learning predictor of gene expression, significantly improving upon the predictive power of average DNA methylation levels. Furthermore, we can use higher order features to cluster promoter-proximal regions, showing that five major patterns of methylation occur at promoters across different cell lines, and we provide evidence that methylation beyond CpG islands may be related to regulation of gene expression. Our results support previous reports of a functional role of spatial correlations in methylation patterns, and provide a mean to quantitate such features for downstream analyses. AVAILABILITY AND IMPLEMENTATION: https://github.com/andreaskapou/BPRMeth CONTACT: G.Sanguinetti@ed.ac.uk SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Strand-specific, high-resolution mapping of modified RNA polymerase II.

Reversible modification of the RNAPII C-terminal domain links transcription with RNA processing and surveillance activities. To better understand this, we mapped the location of RNAPII carrying the five types of CTD phosphorylation on the RNA transcript, providing strand-specific, nucleotide-resolution information, and we used a machine learning-based approach to define RNAPII states. This revealed enrichment of Ser5P, and depletion of Tyr1P, Ser2P, Thr4P, and Ser7P in the transcription start site (TSS) proximal ~150 nt of most genes, with depletion of all modifications close to the poly(A) site. The TSS region also showed elevated RNAPII relative to regions further 3', with high recruitment of RNA surveillance and termination factors, and correlated with the previously mapped 3' ends of short, unstable ncRNA transcripts. A hidden Markov model identified distinct modification states associated with initiating, early elongating and later elongating RNAPII. The initiation state was enriched near the TSS of protein-coding genes and persisted throughout exon 1 of intron-containing genes. Notably, unstable ncRNAs apparently failed to transition into the elongation states seen on protein-coding genes.

Efficient Low-Order Approximation of First-Passage Time Distributions.

We consider the problem of computing first-passage time distributions for reaction processes modeled by master equations. We show that this generally intractable class of problems is equivalent to a sequential Bayesian inference problem for an auxiliary observation process. The solution can be approximated efficiently by solving a closed set of coupled ordinary differential equations (for the low-order moments of the process) whose size scales with the number of species. We apply it to an epidemic model and a trimerization process and show good agreement with stochastic simulations.