N-of-one differential gene expression without control samples using a deep generative model.

Differential analysis of bulk RNA-seq data often suffers from lack of good controls. Here, we present a generative model that replaces controls, trained solely on healthy tissues. The unsupervised model learns a low-dimensional representation and can identify the closest normal representation for a given disease sample. This enables control-free, single-sample differential expression analysis. In breast cancer, we demonstrate how our approach selects marker genes and outperforms a state-of-the-art method. Furthermore, significant genes identified by the model are enriched in driver genes across cancers. Our results show that the in silico closest normal provides a more favorable comparison than control samples.

Hybrid Machine Learning Approach for Evapotranspiration Estimation of Fruit Tree in Agricultural Cyber-Physical Systems.

The flourish of the Internet of Things (IoT) and data-driven techniques provide new ideas for enhancing agricultural production, where evapotranspiration estimation is a crucial issue in crop irrigation systems. However, tremendous and unsynchronized data from agricultural cyber-physical systems bring large computational costs as well as complicate performing conventional machine learning methods. To precisely estimate evapotranspiration with acceptable computational costs under the background of IoT, we combine time granulation computing techniques and gradient boosting decision tree (GBDT) with Bayesian optimization (BO) to propose a hybrid machine learning approach. In the combination, a fuzzy granulation method and a time calibration technique are introduced to break voluminous and unsynchronized data into small-scale and synchronized granules with high representativeness. Subsequently, GBDT is implemented to predict evapotranspiration, and BO is utilized to find the optimal hyperparameter values from the reduced granules. IoT data from Xi'an Fruit Technology Promotion Center in Shaanxi Province, China, verify that the proposed granular-GBDT-BO is effective for cherry tree evapotranspiration estimation with reduced computational time, and acceptable and robust predictive accuracy. Consequently, the precise estimation of crop evapotranspiration could provide operational guidance for plant irrigation, plant conservations, and pest control in the agricultural greenhouse.

Context dependent prediction in DNA sequence using neural networks.

One way to better understand the structure in DNA is by learning to predict the sequence. Here, we trained a model to predict the missing base at any given position, given its left and right flanking contexts. Our best-performing model was a neural network that obtained an accuracy close to 54% on the human genome, which is 2% points better than modelling the data using a Markov model. In likelihood-ratio tests, the neural network performed significantly better than any of the alternative models by a large margin. We report on where the accuracy was obtained, first observing that the performance appeared to be uniform over the chromosomes. The models performed best in repetitive sequences, as expected, although their performance far from random in the more difficult coding sections, the proportions being ~70:40%. We further explored the sources of the accuracy, Fourier transforming the predictions revealed weak but clear periodic signals. In the human genome the characteristic periods hinted at connections to nucleosome positioning. We found similar periodic signals in GC/AT content in the human genome, which to the best of our knowledge have not been reported before. On other large genomes similarly high accuracy was found, while lower predictive accuracy was observed on smaller genomes. Only in the mouse genome did we see periodic signals in the same range as in the human genome, though weaker and of a different type. This indicates that the sources of these signals are other or more than nucleosome arrangement. Interestingly, applying a model trained on the mouse genome to the human genome resulted in a performance far below that of the human model, except in the difficult coding regions. Despite the clear outcomes of the likelihood-ratio tests, there is currently a limited superiority of the neural network methods over the Markov model. We expect, however, that there is great potential for better modelling DNA using different neural network architectures.

Context dependency of nucleotide probabilities and variants in human DNA.

BACKGROUND: Genomic DNA has been shaped by mutational processes through evolution. The cellular machinery for error correction and repair has left its marks in the nucleotide composition along with structural and functional constraints. Therefore, the probability of observing a base in a certain position in the human genome is highly context-dependent. RESULTS: Here we develop context-dependent nucleotide models. We first investigate models of nucleotides conditioned on sequence context. We develop a bidirectional Markov model that use an average of the probability from a Markov model applied to both strands of the sequence and thus depends on up to 14 bases to each side of the nucleotide. We show how the genome predictability varies across different types of genomic regions. Surprisingly, this model can predict a base from its context with an average of more than 50% accuracy. For somatic variants we show a tendency towards higher probability for the variant base than for the reference base. Inspired by DNA substitution models, we develop a model of mutability that estimates a mutation matrix (called the alpha matrix) on top of the nucleotide distribution. The alpha matrix can be estimated from a much smaller context than the nucleotide model, but the final model will still depend on the full context of the nucleotide model. With the bidirectional Markov model of order 14 and an alpha matrix dependent on just one base to each side, we obtain a model that compares well with a model of mutability that estimates mutation probabilities directly conditioned on three nucleotides to each side. For somatic variants in particular, our model fits better than the simpler model. Interestingly, the model is not very sensitive to the size of the context for the alpha matrix. CONCLUSIONS: Our study found strong context dependencies of nucleotides in the human genome. The best model uses a context of 14 nucleotides to each side. Based on these models, a substitution model was constructed that separates into the context model and a matrix dependent on a small context. The model fit somatic variants particularly well.

A metagenome-wide association study of gut microbiota in asthma in UK adults.

BACKGROUND: Asthma, one of the most common chronic respiratory disorders, is associated with the hyper-activation of the T-cell subset of adaptive immunity. The gut microbiota may be involved in the development of asthma through the production of short-chain fatty acids (SCFAs), exhibiting modulatory effects on Th. So, we performed a metagenome-wide association study (MWAS) of the fecal microbiota from individuals with asthma and healthy controls. And that was the first case to resolve the relationship between asthma and microbiome among UK adults. RESULTS: The microbiota of the individuals with asthma consisted of fewer microbial entities than the microbiota of healthy individuals. Faecalibacterium prausnitzii, Sutterella wadsworthensis and Bacteroides stercoris were depleted in cases, whereas Clostridiums with Eggerthella lenta were over-represented in individuals with asthma. Functional analysis shows that the SCFAs might be altered in the microbiota of asthma patients. CONCLUSION: In all, the adult human gut microbiome of asthma patients is clearly different from healthy controls. The functional and taxa results showed that the change of asthma patients might related to SCFAs.

Phylogenomics reveals multiple losses of nitrogen-fixing root nodule symbiosis.

The root nodule symbiosis of plants with nitrogen-fixing bacteria affects global nitrogen cycles and food production but is restricted to a subset of genera within a single clade of flowering plants. To explore the genetic basis for this scattered occurrence, we sequenced the genomes of 10 plant species covering the diversity of nodule morphotypes, bacterial symbionts, and infection strategies. In a genome-wide comparative analysis of a total of 37 plant species, we discovered signatures of multiple independent loss-of-function events in the indispensable symbiotic regulator NODULE INCEPTION in 10 of 13 genomes of nonnodulating species within this clade. The discovery that multiple independent losses shaped the present-day distribution of nitrogen-fixing root nodule symbiosis in plants reveals a phylogenetically wider distribution in evolutionary history and a so-far-underestimated selection pressure against this symbiosis.