Predicting congenital renal tract malformation genes using machine learning.

Congenital renal tract malformations (RTMs) are the major cause of severe kidney failure in children. Studies to date have identified defined genetic causes for only a minority of human RTMs. While some RTMs may be caused by poorly defined environmental perturbations affecting organogenesis, it is likely that numerous causative genetic variants have yet to be identified. Unfortunately, the speed of discovering further genetic causes for RTMs is limited by challenges in prioritising candidate genes harbouring sequence variants. Here, we exploited the computer-based artificial intelligence methodology of supervised machine learning to identify genes with a high probability of being involved in renal development. These genes, when mutated, are promising candidates for causing RTMs. With this methodology, the machine learning classifier determines which attributes are common to renal development genes and identifies genes possessing these attributes. Here we report the validation of an RTM gene classifier and provide predictions of the RTM association status for all protein-coding genes in the mouse genome. Overall, our predictions, whilst not definitive, can inform the prioritisation of genes when evaluating patient sequence data for genetic diagnosis. This knowledge of renal developmental genes will accelerate the processes of reaching a genetic diagnosis for patients born with RTMs.

Using an integrative machine learning approach utilising homology modelling to clinically interpret genetic variants: CACNA1F as an exemplar.

Advances in DNA sequencing technologies have revolutionised rare disease diagnostics and have led to a dramatic increase in the volume of available genomic data. A key challenge that needs to be overcome to realise the full potential of these technologies is that of precisely predicting the effect of genetic variants on molecular and organismal phenotypes. Notably, despite recent progress, there is still a lack of robust in silico tools that accurately assign clinical significance to variants. Genetic alterations in the CACNA1F gene are the commonest cause of X-linked incomplete Congenital Stationary Night Blindness (iCSNB), a condition associated with non-progressive visual impairment. We combined genetic and homology modelling data to produce CACNA1F-vp, an in silico model that differentiates disease-implicated from benign missense CACNA1F changes. CACNA1F-vp predicts variant effects on the structure of the CACNA1F encoded protein (a calcium channel) using parameters based upon changes in amino acid properties; these include size, charge, hydrophobicity, and position. The model produces an overall score for each variant that can be used to predict its pathogenicity. CACNA1F-vp outperformed four other tools in identifying disease-implicated variants (area under receiver operating characteristic and precision recall curves = 0.84; Matthews correlation coefficient = 0.52) using a tenfold cross-validation technique. We consider this protein-specific model to be a robust stand-alone diagnostic classifier that could be replicated in other proteins and could enable precise and timely diagnosis.

The Essentiality Status of Mouse Duplicate Gene Pairs Correlates with Developmental Co-Expression Patterns.

During the evolution of multicellular eukaryotes, gene duplication occurs frequently to generate new genes and/or functions. A duplicated gene may have a similar function to its ancestral gene. Therefore, it may be expected that duplicated genes are less likely to be critical for the survival of an organism, since there are multiple copies of the gene rendering each individual copy redundant. In this study, we explored the developmental expression patterns of duplicate gene pairs and the relationship between development co-expression and phenotypes resulting from the knockout of duplicate genes in the mouse. We define genes that generate lethal phenotypes in single gene knockout experiments as essential genes. We found that duplicate gene pairs comprised of two essential genes tend to be expressed at different stages of development, compared to duplicate gene pairs with at least one non-essential member, showing that the timing of developmental expression affects the ability of one paralogue to compensate for the loss of the other. Gene essentiality, developmental expression and gene duplication are thus closely linked.

Identifying mouse developmental essential genes using machine learning.

The genes that are required for organismal survival are annotated as 'essential genes'. Identifying all the essential genes of an animal species can reveal critical functions that are needed during the development of the organism. To inform studies on mouse development, we developed a supervised machine learning classifier based on phenotype data from mouse knockout experiments. We used this classifier to predict the essentiality of mouse genes lacking experimental data. Validation of our predictions against a blind test set of recent mouse knockout experimental data indicated a high level of accuracy (>80%). We also validated our predictions for other mouse mutagenesis methodologies, demonstrating that the predictions are accurate for lethal phenotypes isolated in random chemical mutagenesis screens and embryonic stem cell screens. The biological functions that are enriched in essential and non-essential genes have been identified, showing that essential genes tend to encode intracellular proteins that interact with nucleic acids. The genome distribution of predicted essential and non-essential genes was analysed, demonstrating that the density of essential genes varies throughout the genome. A comparison with human essential and non-essential genes was performed, revealing conservation between human and mouse gene essentiality status. Our genome-wide predictions of mouse essential genes will be of value for the planning of mouse knockout experiments and phenotyping assays, for understanding the functional processes required during mouse development, and for the prioritisation of disease candidate genes identified in human genome and exome sequence datasets.

Properties of genes essential for mouse development.

Essential genes are those that are critical for life. In the specific case of the mouse, they are the set of genes whose deletion means that a mouse is unable to survive after birth. As such, they are the key minimal set of genes needed for all the steps of development to produce an organism capable of life ex utero. We explored a wide range of sequence and functional features to characterise essential (lethal) and non-essential (viable) genes in mice. Experimental data curated manually identified 1301 essential genes and 3451 viable genes. Very many sequence features show highly significant differences between essential and viable mouse genes. Essential genes generally encode complex proteins, with multiple domains and many introns. These genes tend to be: long, highly expressed, old and evolutionarily conserved. These genes tend to encode ligases, transferases, phosphorylated proteins, intracellular proteins, nuclear proteins, and hubs in protein-protein interaction networks. They are involved with regulating protein-protein interactions, gene expression and metabolic processes, cell morphogenesis, cell division, cell proliferation, DNA replication, cell differentiation, DNA repair and transcription, cell differentiation and embryonic development. Viable genes tend to encode: membrane proteins or secreted proteins, and are associated with functions such as cellular communication, apoptosis, behaviour and immune response, as well as housekeeping and tissue specific functions. Viable genes are linked to transport, ion channels, signal transduction, calcium binding and lipid binding, consistent with their location in membranes and involvement with cell-cell communication. From the analysis of the composite features of essential and viable genes, we conclude that essential genes tend to be required for intracellular functions, and viable genes tend to be involved with extracellular functions and cell-cell communication. Knowledge of the features that are over-represented in essential genes allows for a deeper understanding of the functions and processes implemented during mammalian development.

Reverse engineering gene regulatory network from microarray data using linear time-variant model.

BACKGROUND: Gene regulatory network is an abstract mapping of gene regulations in living cells that can help to predict the system behavior of living organisms. Such prediction capability can potentially lead to the development of improved diagnostic tests and therapeutics. DNA microarrays, which measure the expression level of thousands of genes in parallel, constitute the numeric seed for the inference of gene regulatory networks. In this paper, we have proposed a new approach for inferring gene regulatory networks from time-series gene expression data using linear time-variant model. Here, Self-Adaptive Differential Evolution, a versatile and robust Evolutionary Algorithm, is used as the learning paradigm. RESULTS: To assess the potency of the proposed work, a well known nonlinear synthetic network has been used. The reconstruction method has inferred this synthetic network topology and the associated regulatory parameters with high accuracy from both the noise-free and noisy time-series data. For validation purposes, the proposed approach is also applied to the simulated expression dataset of cAMP oscillations in Dictyostelium discoideum and has proved it's strength in finding the correct regulations. The strength of this work has also been verified by analyzing the real expression dataset of SOS DNA repair system in Escherichia coli and it has succeeded in finding more correct and reasonable regulations as compared to various existing works. CONCLUSION: By the proposed approach, the gene interaction networks have been inferred in an efficient manner from both the synthetic, simulated cAMP oscillation expression data and real expression data. The computational time of this approach is also considerably smaller, which makes it to be more suitable for larger network reconstruction. Thus the proposed approach can serve as an initiate for the future researches regarding the associated area.