Programs and computational frameworks for predicting RNA sequences with desired folding properties are continually being developed and expanded. A decade has passed since they were last reviewed in this journal, and this brief review provides an update to the review published at that time. Given a target secondary structure, these programs aim to predict RNA sequences that fold into the desired structure while satisfying various constraints. This procedure is known as inverse RNA folding. Traditionally, inverse RNA folding has been used to design optimized RNAs with favorable properties. This updated review covers some of the most widely used freeware programs developed for this purpose over the past decade. RNAinverse, part of the Vienna RNA package, was the first program devised to address the inverse RNA folding problem, and many subsequent programs were described in the earlier review. Some of the most important computational frameworks are the Infrared framework and DesiRNA. In addition, RNA design capabilities have been incorporated into the RNAstructure package, while NUPACK, as well as MoiRNAiFold, MODENA, incaRNAfbinv, and related tools have undergone recent updates. A variety of strategies have also emerged to address the problem of 3D RNA design and RNA-RNA interactions. The various programs mentioned employ distinct approaches, ranging from replica exchange Monte Carlo to constraint satisfaction, as well as Boltzmann sampling and machine learning approaches. Machine learning methods are being developed for emerging applications in biotechnology such as messenger RNA(mRNA) design and CRISPR guide RNA (gRNA) design. This brief review examines these programs and provides a timely update.

Riboswitches are conserved structural ribonucleic acid (RNA) sensors that are mainly found to regulate a large number of genes/operons in bacteria. Presently, >50 bacterial riboswitch classes have been discovered, but only the thiamine pyrophosphate riboswitch class is detected in a few eukaryotes like fungi, plants and algae. One of the most important challenges in riboswitch research is to discover existing riboswitch classes in eukaryotes and to understand the evolution of bacterial riboswitches. However, traditional search methods for riboswitch detection have failed to detect eukaryotic riboswitches besides just one class and any distant structural homologs of riboswitches. We developed a novel approach based on inverse RNA folding that attempts to find sequences that match the shape of the target structure with minimal sequence conservation based on key nucleotides that interact directly with the ligand. Then, to support our matched candidates, we expanded the results into a covariance model representing similar sequences preserving the structure. Our method transforms a structure-based search into a sequence-based search that considers the conservation of secondary structure shape and ligand-binding residues. This method enables us to identify a potential structural candidate in fungi that could be the distant homolog of bacterial purine riboswitches. Further, phylogenomic analysis and evolutionary distribution of this structural candidate indicate that the most likely point of origin of this structural candidate in these organisms is associated with the loss of traditional purine riboswitches. The computational approach could be applicable to other domains and problems in RNA research.

We study high-dimensional bayesian linear regression with product priors. Using the nascent theory of “non-linear large deviations" (Chatterjee and Dembo, 2016), we derive sufficient conditions for the leading-order correctness of the naive mean-field approximation to the log-normalizing constant of the posterior distribution. Subsequently, assuming a true linear model for the observed data, we derive a limiting infinite dimensional variational formula for the log normalizing constant for the posterior. Furthermore, we establish that under an additional “separation" condition, the variational problem has a unique optimizer, and this optimizer governs the probabilistic properties of the posterior distribution. We provide intuitive sufficient conditions for the validity of this “separation" condition. Finally, we illustrate our results on concrete examples with specific design matrices.

AI for good (AI4G) projects involve developing and applying artificial intelligence (AI) based solutions to further goals in areas such as sustainability, health, humanitarian aid, and social justice. Developing and deploying such solutions must be done in collaboration with partners who are experts in the domain in question and who already have experience in making progress towards such goals. Based on our experiences, we detail the different aspects of this type of collaboration broken down into four high-level categories: communication, data, modeling, and impact, and distill eleven takeaways to guide such projects in the future. We briefly describe two case studies to illustrate how some of these takeaways were applied in practice during our past collaborations.

Abstract Generative Adversarial Networks (GANs) have made releasing of synthetic images a viable approach to share data without releasing the original dataset. It has been shown that such synthetic data can be used for a variety of downstream tasks such as training classifiers that would otherwise require the original dataset to be shared. However, recent work has shown that the GAN models and their synthetically generated data can be used to infer the training set membership by an adversary who has access to the entire dataset and some auxiliary information. Current approaches to mitigate this problem (such as DPGAN [1]) lead to dramatically poorer generated sample quality than the original non–private GANs. Here we develop a new GAN architecture (privGAN), where the generator is trained not only to cheat the discriminator but also to defend membership inference attacks. The new mechanism is shown to empirically provide protection against this mode of attack while leading to negligible loss in downstream performances. In addition, our algorithm has been shown to explicitly prevent memorization of the training set, which explains why our protection is so effective. The main contributions of this paper are: i) we propose a novel GAN architecture that can generate synthetic data in a privacy preserving manner with minimal hyperparameter tuning and architecture selection, ii) we provide a theoretical understanding of the optimal solution of the privGAN loss function, iii) we empirically demonstrate the effectiveness of our model against several white and black–box attacks on several benchmark datasets, iv) we empirically demonstrate on three common benchmark datasets that synthetic images generated by privGAN lead to negligible loss in downstream performance when compared against non– private GANs. While we have focused on benchmarking privGAN exclusively on image datasets, the architecture of privGAN is not exclusive to image datasets and can be easily extended to other types of datasets. Repository link: https://github.com/microsoft/privGAN .

In the United States, the opioid epidemic is a serious public health crisis which claimed over 130 lives per day in 2018, according to the CDC. While there are many efforts to design effective interventions to prevent drug related deaths, much of them are focused around better prescribing practices. A promising line of inquiry has focused on utilizing machine learning tools to predict addiction or overdose related hospital admission using prior health record information. However, these are strongly reliant on the private health record information of individuals. Here, we propose using publicly available historic death records along with publicly available internet search trends of drug related search terms to predict the number of overdose deaths in the upcoming week. Our model is able to predict both the number of, and spikes in drug overdose deaths with good accuracy compared to several baselines, demonstrating the utility of search data in forecasting overdose deaths. While we demonstrate this approach as a case study in the State of Connecticut, which collects and publishes overdose data, our findings could encourage other state governments to similarly invest in collection, publication and analysis of such data.

What is the chance that among a group of $n$ friends, there are $s$ friends all of whom have the same birthday? This is the celebrated birthday problem which can be formulated as the existence of a monochromatic $s$-clique $K_s$ ($s$-matching birthdays) in the complete graph $K_n$, where every vertex of $K_n$ is uniformly colored with 365 colors (corresponding to birthdays). More generally, for a general connected graph $H$, let $T(H, G_n)$ be the number of monochromatic copies of $H$ in a uniformly random coloring of the vertices of the graph $G_n$ with $c_n$ colors. In this paper we show that $T(H, G_n)$ converges to ${Pois}(\lambda)$ whenever $\mathbb{E} T(H, G_n) \rightarrow \lambda$ and ${Var} T(H, G_n) \rightarrow \lambda$, that is, the asymptotic Poisson distribution of $T(H, G_n)$ is determined just by the convergence of its mean and variance. Moreover, this condition is necessary if and only if $H$ is a star-graph. In fact, the second-moment phenomenon is a consequence of a more general theorem about the convergence of $T(H,G_n)$ to a finite linear combination of independent Poisson random variables. As an application, we derive the limiting distribution of $T(H, G_n)$, when $G_n\sim G(n, p)$ is the Erdös--Rényi random graph. Multiple phase transitions emerge as $p$ varies from 0 to 1, depending on whether the graph $H$ is balanced or unbalanced.

SUMMARY: Riboswitches are cis-regulatory non-coding genomic segments that control the expression of downstream genes by undergoing conformational change upon ligand binding. We present a comprehensive database of prokaryotic riboswitches that allows the user to search for riboswitches using multiple criteria, extract information about riboswitch location and gene/operon it regulates. RiboD provides a very useful resource that can be utilized for the better understanding of riboswitch-based gene regulation in bacteria and archaea. AVAILABILITY AND IMPLEMENTATION: RiboD can be freely accessed on the web at http://ribod.iiserkol.ac.in/.

MOTIVATION: Late onset Alzheimer's disease is currently a disease with no known effective treatment options. To better understand disease, new multi-omic data-sets have recently been generated with the goal of identifying molecular causes of disease. However, most analytic studies using these datasets focus on uni-modal analysis of the data. Here, we propose a data driven approach to integrate multiple data types and analytic outcomes to aggregate evidences to support the hypothesis that a gene is a genetic driver of the disease. The main algorithmic contributions of our article are: (i) a general machine learning framework to learn the key characteristics of a few known driver genes from multiple feature sets and identifying other potential driver genes which have similar feature representations, and (ii) A flexible ranking scheme with the ability to integrate external validation in the form of Genome Wide Association Study summary statistics. While we currently focus on demonstrating the effectiveness of the approach using different analytic outcomes from RNA-Seq studies, this method is easily generalizable to other data modalities and analysis types. RESULTS: We demonstrate the utility of our machine learning algorithm on two benchmark multiview datasets by significantly outperforming the baseline approaches in predicting missing labels. We then use the algorithm to predict and rank potential drivers of Alzheimer's. We show that our ranked genes show a significant enrichment for single nucleotide polymorphisms associated with Alzheimer's and are enriched in pathways that have been previously associated with the disease. AVAILABILITY AND IMPLEMENTATION: Source code and link to all feature sets is available at https://github.com/Sage-Bionetworks/EvidenceAggregatedDriverRanking.

We consider the problem of estimating the probability matrix governing a tournament or linkage in graphs from incomplete observations under the assumption that the probability matrix satisfies natural monotonicity constraints after being permuted in both rows and columns by some latent permutation. We propose a natural estimator which bypasses the need to search over all possible latent permutations and hence is computationally tractable. We then derive asymptotic risk bounds for our estimator. Pertinently, we demonstrate an automatic adaptation property of our estimator for several sub classes of our parameter space which are of natural interest, including generalizations of the popular Bradley-Terry model in the tournament case, the $\beta $ model and stochastic block model in the graph case, and Hölder continuous matrices in the tournament and graph settings.

Motivation: Single cell RNA-seq (scRNA-seq) data contains a wealth of information which has to be inferred computationally from the observed sequencing reads. As the ability to sequence more cells improves rapidly, existing computational tools suffer from three problems. (i) The decreased reads-per-cell implies a highly sparse sample of the true cellular transcriptome. (ii) Many tools simply cannot handle the size of the resulting datasets. (iii) Prior biological knowledge such as bulk RNA-seq information of certain cell types or qualitative marker information is not taken into account. Here we present UNCURL, a preprocessing framework based on non-negative matrix factorization for scRNA-seq data, that is able to handle varying sampling distributions, scales to very large cell numbers and can incorporate prior knowledge. Results: We find that preprocessing using UNCURL consistently improves performance of commonly used scRNA-seq tools for clustering, visualization and lineage estimation, both in the absence and presence of prior knowledge. Finally we demonstrate that UNCURL is extremely scalable and parallelizable, and runs faster than other methods on a scRNA-seq dataset containing 1.3 million cells. Availability and implementation: Source code is available at https://github.com/yjzhang/uncurl_python. Supplementary information: Supplementary data are available at Bioinformatics online.

UNLABELLED: Riboswitches are non-coding RNA located in the 5' untranslated regions where they bind a target metabolite used to specify the riboswitch class and control the expression of associated genes. Accurate identification of riboswitches is the first step towards understanding their regulatory and functional roles in the cell. In this article, we describe a new web application named Riboswitch Scanner which provides an automated pipeline for pHMM-based detection of riboswitches in partial as well as complete genomic sequences rapidly, with high sensitivity and specificity. AVAILABILITY AND IMPLEMENTATION: Riboswitch Scanner can be freely accessed on the web at http://service.iiserkol.ac.in/∼riboscan/ CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.