Multiscale, multimodal analysis of tumor heterogeneity in IDH1 mutant vs wild-type diffuse gliomas.

Glioma is recognized to be a highly heterogeneous CNS malignancy, whose diverse cellular composition and cellular interactions have not been well characterized. To gain new clinical- and biological-insights into the genetically-bifurcated IDH1 mutant (mt) vs wildtype (wt) forms of glioma, we integrated data from protein, genomic and MR imaging from 20 treatment-naïve glioma cases and 16 recurrent GBM cases. Multiplexed immunofluorescence (MxIF) was used to generate single cell data for 43 protein markers representing all cancer hallmarks, Genomic sequencing (exome and RNA (normal and tumor) and magnetic resonance imaging (MRI) quantitative features (protocols were T1-post, FLAIR and ADC) from whole tumor, peritumoral edema and enhancing core vs equivalent normal region were also collected from patients. Based on MxIF analysis, 85,767 cells (glioma cases) and 56,304 cells (GBM cases) were used to generate cell-level data for 24 biomarkers. K-means clustering was used to generate 7 distinct groups of cells with divergent biomarker profiles and deconvolution was used to assign RNA data into three classes. Spatial and molecular heterogeneity metrics were generated for the cell data. All features were compared between IDH mt and IDHwt patients and were finally combined to provide a holistic/integrated comparison. Protein expression by hallmark was generally lower in the IDHmt vs wt patients. Molecular and spatial heterogeneity scores for angiogenesis and cell invasion also differed between IDHmt and wt gliomas irrespective of prior treatment and tumor grade; these differences also persisted in the MR imaging features of peritumoral edema and contrast enhancement volumes. A coherent picture of enhanced angiogenesis in IDHwt tumors was derived from multiple platforms (genomic, proteomic and imaging) and scales from individual proteins to cell clusters and heterogeneity, as well as bulk tumor RNA and imaging features. Longer overall survival for IDH1mt glioma patients may reflect mutation-driven alterations in cellular, molecular, and spatial heterogeneity which manifest in discernable radiological manifestations

Regulatory motif discovery using a population clustering evolutionary algorithm

This paper describes a novel evolutionary algorithm for regulatory motif discovery in DNA promoter sequences. The algorithm uses data clustering to logically distribute the evolving population across the search space. Mating then takes place within local regions of the population, promoting overall solution diversity and encouraging discovery of multiple solutions. Experiments using synthetic data sets have demonstrated the algorithm's capacity to find position frequency matrix models of known regulatory motifs in relatively long promoter sequences. These experiments have also shown the algorithm's ability to maintain diversity during search and discover multiple motifs within a single population. The utility of the algorithm for discovering motifs in real biological data is demonstrated by its ability to find meaningful motifs within muscle-specific regulatory sequences

Development of New Bioinformatic Approaches for Human Genetic Studies

The development of bioinformatics methods for human genetic studies utilizes the vast amount of data to generate new valuable information. Machine learning and statistical coupling analysis can be used in the study of human diseases. These diseases include intellectual disabilities (ID), prevalent in 1-3% of the population and caused primarily by genetics. Although many cases of ID are caused by mutations in protein-coding genes, the possible involvement of long non-coding RNAs (lncRNAs) in ID due to their role in gene expression regulation, has been explored. In this study, we used machine learning to develop a new expression-based model trained using ID genes encoded with the developing brain transcriptome. The model was fine-tuned using the class-balancing approach of synthetic over-sampling of the minority class, resulting in improved performance. We used the model to predict candidate ID-associated lncRNAs. Our model identified several candidates that overlapped with previously reported ID-associated lncRNAs, enriched with neurodevelopmental functions, and highly expressed in brain tissues. Machine learning was also used to predict protein stability changes caused by missense mutations, which can lead to disease conditions including ID. We tested Random Forests, Support Vector Machines (SVM) and Naïve Bayes to find the best-performing algorithm to develop a multi-class classifier. We developed an SVM model using relevant physico-chemical features after feature selection. Our work identified new features for predicting the effect of amino acid substitutions on protein stability and a well-performing multi-class classifier solely based on sequence information. Statistical approaches were used to analyze the association between mutations and phenotypes. In this study, we used statistical coupling analysis (SCA) to cluster disease-causing mutations and ID phenotypes. Using SCA we identified groups of co-evolving residues, known as protein sectors, in ID protein families. Within each distinct sector, mutations associated with different phenotypic manifestations associated with a syndromic ID were identified. Our results suggest that protein sector analysis can be used to associate mutations with phenotypic manifestations in human diseases. The bioinformatic methods developed in this dissertation can be used in human genetic research to understand the role of new genes and proteins in human disease

Identification of functionally related enzymes by learning-to-rank methods

Enzyme sequences and structures are routinely used in the biological sciences as queries to search for functionally related enzymes in online databases. To this end, one usually departs from some notion of similarity, comparing two enzymes by looking for correspondences in their sequences, structures or surfaces. For a given query, the search operation results in a ranking of the enzymes in the database, from very similar to dissimilar enzymes, while information about the biological function of annotated database enzymes is ignored. In this work we show that rankings of that kind can be substantially improved by applying kernel-based learning algorithms. This approach enables the detection of statistical dependencies between similarities of the active cleft and the biological function of annotated enzymes. This is in contrast to search-based approaches, which do not take annotated training data into account. Similarity measures based on the active cleft are known to outperform sequence-based or structure-based measures under certain conditions. We consider the Enzyme Commission (EC) classification hierarchy for obtaining annotated enzymes during the training phase. The results of a set of sizeable experiments indicate a consistent and significant improvement for a set of similarity measures that exploit information about small cavities in the surface of enzymes

The Expanding Landscape of Alternative Splicing Variation in Human Populations.

Alternative splicing is a tightly regulated biological process by which the number of gene products for any given gene can be greatly expanded. Genomic variants in splicing regulatory sequences can disrupt splicing and cause disease. Recent developments in sequencing technologies and computational biology have allowed researchers to investigate alternative splicing at an unprecedented scale and resolution. Population-scale transcriptome studies have revealed many naturally occurring genetic variants that modulate alternative splicing and consequently influence phenotypic variability and disease susceptibility in human populations. Innovations in experimental and computational tools such as massively parallel reporter assays and deep learning have enabled the rapid screening of genomic variants for their causal impacts on splicing. In this review, we describe technological advances that have greatly increased the speed and scale at which discoveries are made about the genetic variation of alternative splicing. We summarize major findings from population transcriptomic studies of alternative splicing and discuss the implications of these findings for human genetics and medicine