Doctor of Philosophy

dissertationRapidly evolving technologies such as chip arrays and next-generation sequencing are uncovering human genetic variants at an unprecedented pace. Unfortunately, this ever growing collection of gene sequence variation has limited clinical utility without clear association to disease outcomes. As electronic medical records begin to incorporate genetic information, gene variant classification and accurate interpretation of gene test results plays a critical role in customizing patient therapy. To verify the functional impact of a given gene variant, laboratories rely on confirming evidence such as previous literature reports, patient history and disease segregation in a family. By definition variants of uncertain significance (VUS) lack this supporting evidence and in such cases, computational tools are often used to evaluate the predicted functional impact of a gene mutation. This study evaluates leveraging high quality genotype-phenotype disease variant data from 20 genes and 3986 variants, to develop gene-specific predictors utilizing a combination of changes in primary amino acid sequence, amino acid properties as descriptors of mutation severity and Naïve Bayes classification. A Primary Sequence Amino Acid Properties (PSAAP) prediction algorithm was then combined with well established predictors in a weighted Consensus sum in context of gene-specific reference intervals for known phenotypes. PSAAP and Consensus were also used to evaluate known variants of uncertain significance in the RET proto-oncogene as a model gene. The PSAAP algorithm was successfully extended to many genes and diseases. Gene-specific algorithms typically outperform generalized prediction tools. Characteristic mutation properties of a given gene and disease may be lost when diluted into genomewide data sets. A reliable computational phenotype classification framework with quantitative metrics and disease specific reference ranges allows objective evaluation of novel or uncertain gene variants and augments decision making when confirming clinical information is limited

GeneFishing to reconstruct context specific portraits of biological processes.

Rapid advances in genomic technologies have led to a wealth of diverse data, from which novel discoveries can be gleaned through the application of robust statistical and computational methods. Here, we describe GeneFishing, a semisupervised computational approach to reconstruct context-specific portraits of biological processes by leveraging gene-gene coexpression information. GeneFishing incorporates multiple high-dimensional statistical ideas, including dimensionality reduction, clustering, subsampling, and results aggregation, to produce robust results. To illustrate the power of our method, we applied it using 21 genes involved in cholesterol metabolism as "bait" to "fish out" (or identify) genes not previously identified as being connected to cholesterol metabolism. Using simulation and real datasets, we found that the results obtained through GeneFishing were more interesting for our study than those provided by related gene prioritization methods. In particular, application of GeneFishing to the GTEx liver RNA sequencing (RNAseq) data not only reidentified many known cholesterol-related genes, but also pointed to glyoxalase I (GLO1) as a gene implicated in cholesterol metabolism. In a follow-up experiment, we found that GLO1 knockdown in human hepatoma cell lines increased levels of cellular cholesterol ester, validating a role for GLO1 in cholesterol metabolism. In addition, we performed pantissue analysis by applying GeneFishing on various tissues and identified many potential tissue-specific cholesterol metabolism-related genes. GeneFishing appears to be a powerful tool for identifying related components of complex biological systems and may be used across a wide range of applications

Classification in biological networks with hypergraphlet kernels

MOTIVATION: Biological and cellular systems are often modeled as graphs in which vertices represent objects of interest (genes, proteins and drugs) and edges represent relational ties between these objects (binds-to, interacts-with and regulates). This approach has been highly successful owing to the theory, methodology and software that support analysis and learning on graphs. Graphs, however, suffer from information loss when modeling physical systems due to their inability to accurately represent multiobject relationships. Hypergraphs, a generalization of graphs, provide a framework to mitigate information loss and unify disparate graph-based methodologies. RESULTS: We present a hypergraph-based approach for modeling biological systems and formulate vertex classification, edge classification and link prediction problems on (hyper)graphs as instances of vertex classification on (extended, dual) hypergraphs. We then introduce a novel kernel method on vertex- and edge-labeled (colored) hypergraphs for analysis and learning. The method is based on exact and inexact (via hypergraph edit distances) enumeration of hypergraphlets; i.e. small hypergraphs rooted at a vertex of interest. We empirically evaluate this method on fifteen biological networks and show its potential use in a positive-unlabeled setting to estimate the interactome sizes in various species. AVAILABILITY AND IMPLEMENTATION: https://github.com/jlugomar/hypergraphlet-kernels. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online

Classification in biological networks with hypergraphlet kernels

Abstract Motivation Biological and cellular systems are often modeled as graphs in which vertices represent objects of interest (genes, proteins and drugs) and edges represent relational ties between these objects (binds-to, interacts-with and regulates). This approach has been highly successful owing to the theory, methodology and software that support analysis and learning on graphs. Graphs, however, suffer from information loss when modeling physical systems due to their inability to accurately represent multiobject relationships. Hypergraphs, a generalization of graphs, provide a framework to mitigate information loss and unify disparate graph-based methodologies. Results We present a hypergraph-based approach for modeling biological systems and formulate vertex classification, edge classification and link prediction problems on (hyper)graphs as instances of vertex classification on (extended, dual) hypergraphs. We then introduce a novel kernel method on vertex- and edge-labeled (colored) hypergraphs for analysis and learning. The method is based on exact and inexact (via hypergraph edit distances) enumeration of hypergraphlets; i.e. small hypergraphs rooted at a vertex of interest. We empirically evaluate this method on fifteen biological networks and show its potential use in a positive-unlabeled setting to estimate the interactome sizes in various species.This work was partially supported by the National Science Foundation (NSF) [DBI-1458477], National Institutes of Health (NIH) [R01 MH105524], the Indiana University Precision Health Initiative, the European Research Council (ERC) [Consolidator Grant 770827], UCL Computer Science, the Slovenian Research Agency project [J1-8155], the Serbian Ministry of Education and Science Project [III44006] and the Prostate Project.Peer ReviewedPostprint (author's final draft

Binding Affinity and Specificity of SH2 Domain Interactions in Receptor Tyrosine Kinase Signaling Networks

Receptor tyrosine kinase (RTK) signaling mechanisms play a central role in intracellular signaling and control development of multicellular organisms, cell growth, cell migration, and programmed cell death. Dysregulation of these signaling mechanisms results in defects of development and diseases such as cancer. Control of this network relies on the specificity and selectivity of Src Homology 2 (SH2) domain interactions with phosphorylated target peptides. In this work, we review and identify the limitations of current quantitative understanding of SH2 domain interactions, and identify severe limitations in accuracy and availability of SH2 domain interaction data. We propose a framework to address some of these limitations and present new results which improve the quality and accuracy of currently available data. Furthermore, we supplement published results with a large body of negative interactions of high-confidence extracted from rejected data, allowing for improved modeling and prediction of SH2 interactions. We present and analyze new experimental results for the dynamic response of downstream signaling proteins in response to RTK signaling. Our data identify differences in downstream response depending on the character and dose of the receptor stimulus, which has implications for previous studies using high-dose stimulation. We review some of the methods used in this work, focusing on pitfalls of clustering biological data, and address the high-dimensional nature of biological data from high-throughput experiments, the failure to consider more than one clustering method for a given problem, and the difficulty in determining whether clustering has produced meaningful results

Machine Learning based Protein Sequence to (un)Structure Mapping and Interaction Prediction

Proteins are the fundamental macromolecules within a cell that carry out most of the biological functions. The computational study of protein structure and its functions, using machine learning and data analytics, is elemental in advancing the life-science research due to the fast-growing biological data and the extensive complexities involved in their analyses towards discovering meaningful insights. Mapping of protein’s primary sequence is not only limited to its structure, we extend that to its disordered component known as Intrinsically Disordered Proteins or Regions in proteins (IDPs/IDRs), and hence the involved dynamics, which help us explain complex interaction within a cell that is otherwise obscured. The objective of this dissertation is to develop machine learning based effective tools to predict disordered protein, its properties and dynamics, and interaction paradigm by systematically mining and analyzing large-scale biological data. In this dissertation, we propose a robust framework to predict disordered proteins given only sequence information, using an optimized SVM with RBF kernel. Through appropriate reasoning, we highlight the structure-like behavior of IDPs in disease-associated complexes. Further, we develop a fast and effective predictor of Accessible Surface Area (ASA) of protein residues, a useful structural property that defines protein’s exposure to partners, using regularized regression with 3rd-degree polynomial kernel function and genetic algorithm. As a key outcome of this research, we then introduce a novel method to extract position specific energy (PSEE) of protein residues by modeling the pairwise thermodynamic interactions and hydrophobic effect. PSEE is found to be an effective feature in identifying the enthalpy-gain of the folded state of a protein and otherwise the neutral state of the unstructured proteins. Moreover, we study the peptide-protein transient interactions that involve the induced folding of short peptides through disorder-to-order conformational changes to bind to an appropriate partner. A suite of predictors is developed to identify the residue-patterns of Peptide-Recognition Domains from protein sequence that can recognize and bind to the peptide-motifs and phospho-peptides with post-translational-modifications (PTMs) of amino acid, responsible for critical human diseases, using the stacked generalization ensemble technique. The involved biologically relevant case-studies demonstrate possibilities of discovering new knowledge using the developed tools