Systems Analytics and Integration of Big Omics Data

A “genotype"" is essentially an organism's full hereditary information which is obtained from its parents. A ""phenotype"" is an organism's actual observed physical and behavioral properties. These may include traits such as morphology, size, height, eye color, metabolism, etc. One of the pressing challenges in computational and systems biology is genotype-to-phenotype prediction. This is challenging given the amount of data generated by modern Omics technologies. This “Big Data” is so large and complex that traditional data processing applications are not up to the task. Challenges arise in collection, analysis, mining, sharing, transfer, visualization, archiving, and integration of these data. In this Special Issue, there is a focus on the systems-level analysis of Omics data, recent developments in gene ontology annotation, and advances in biological pathways and network biology. The integration of Omics data with clinical and biomedical data using machine learning is explored. This Special Issue covers new methodologies in the context of gene–environment interactions, tissue-specific gene expression, and how external factors or host genetics impact the microbiome

The CHARME "Advanced Big Data Training School for Life Sciences": an example of good practices for training on current bioinformatics challenges

The CHARME “Advanced Big Data Training School for Life Sciences” took place during 3-7 September 2018, at the Campus Nord of the Technical University of Catalonia (UPC) in Barcelona (ES). The school was organised by the Data Management Group (DAMA) of the UPC in collaboration with EMBnet as a follow-up of the first CHARME-EMBnet “Big Data Training School for Life Sciences”, held in Uppsala, Sweden, in September 2017. The learning objectives of the school were defined and agreed during the CHARME “Think Tank Hackathon” that was held in Ljubljana, Slovenia, in February 2018. This article explains in detail the step forward organisation of the training school, the covered contents and the interaction/relationships that thanks to this school have been established between the trainees, the trainers and the organisers.Peer ReviewedPostprint (published version

The Pharmacoepigenomics Informatics Pipeline and H-GREEN Hi-C Compiler: Discovering Pharmacogenomic Variants and Pathways with the Epigenome and Spatial Genome

Over the last decade, biomedical science has been transformed by the epigenome and spatial genome, but the discipline of pharmacogenomics, the study of the genetic underpinnings of pharmacological phenotypes like drug response and adverse events, has not. Scientists have begun to use omics atlases of increasing depth, and inferences relating to the bidirectional causal relationship between the spatial epigenome and gene expression, as a foundational underpinning for genetics research. The epigenome and spatial genome are increasingly used to discover causative regulatory variants in the significance regions of genome-wide association studies, for the discovery of the biological mechanisms underlying these phenotypes and the design of genetic tests to predict them. Such variants often have more predictive power than coding variants, but in the area of pharmacogenomics, such advances have been radically underapplied. The majority of pharmacogenomics tests are designed manually on the basis of mechanistic work with coding variants in candidate genes, and where genome wide approaches are used, they are typically not interpreted with the epigenome. This work describes a series of analyses of pharmacogenomics association studies with the tools and datasets of the epigenome and spatial genome, undertaken with the intent of discovering causative regulatory variants to enable new genetic tests. It describes the potent regulatory variants discovered thereby to have a putative causative and predictive role in a number of medically important phenotypes, including analgesia and the treatment of depression, bipolar disorder, and traumatic brain injury with opiates, anxiolytics, antidepressants, lithium, and valproate, and in particular the tendency for such variants to cluster into spatially interacting, conceptually unified pathways which offer mechanistic insight into these phenotypes. It describes the Pharmacoepigenomics Informatics Pipeline (PIP), an integrative multiple omics variant discovery pipeline designed to make this kind of analysis easier and cheaper to perform, more reproducible, and amenable to the addition of advanced features. It described the successes of the PIP in rediscovering manually discovered gene networks for lithium response, as well as discovering a previously unknown genetic basis for warfarin response in anticoagulation therapy. It describes the H-GREEN Hi-C compiler, which was designed to analyze spatial genome data and discover the distant target genes of such regulatory variants, and its success in discovering spatial contacts not detectable by preceding methods and using them to build spatial contact networks that unite disparate TADs with phenotypic relationships. It describes a potential featureset of a future pipeline, using the latest epigenome research and the lessons of the previous pipeline. It describes my thinking about how to use the output of a multiple omics variant pipeline to design genetic tests that also incorporate clinical data. And it concludes by describing a long term vision for a comprehensive pharmacophenomic atlas, to be constructed by applying a variant pipeline and machine learning test design system, such as is described, to thousands of phenotypes in parallel. Scientists struggled to assay genotypes for the better part of a century, and in the last twenty years, succeeded. The struggle to predict phenotypes on the basis of the genotypes we assay remains ongoing. The use of multiple omics variant pipelines and machine learning models with omics atlases, genetic association, and medical records data will be an increasingly significant part of that struggle for the foreseeable future.PHDBioinformaticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145835/1/ariallyn_1.pd

Facilitating and Enhancing Biomedical Knowledge Translation: An in Silico Approach to Patient-centered Pharmacogenomic Outcomes Research

Current research paradigms such as traditional randomized control trials mostly rely on relatively narrow efficacy data which results in high internal validity and low external validity. Given this fact and the need to address many complex real-world healthcare questions in short periods of time, alternative research designs and approaches should be considered in translational research. In silico modeling studies, along with longitudinal observational studies, are considered as appropriate feasible means to address the slow pace of translational research. Taking into consideration this fact, there is a need for an approach that tests newly discovered genetic tests, via an in silico enhanced translational research model (iS-TR) to conduct patient-centered outcomes research and comparative effectiveness research studies (PCOR CER). In this dissertation, it was hypothesized that retrospective EMR analysis and subsequent mathematical modeling and simulation prediction could facilitate and accelerate the process of generating and translating pharmacogenomic knowledge on comparative effectiveness of anticoagulation treatment plan(s) tailored to well defined target populations which eventually results in a decrease in overall adverse risk and improve individual and population outcomes. To test this hypothesis, a simulation modeling framework (iS-TR) was proposed which takes advantage of the value of longitudinal electronic medical records (EMRs) to provide an effective approach to translate pharmacogenomic anticoagulation knowledge and conduct PCOR CER studies. The accuracy of the model was demonstrated by reproducing the outcomes of two major randomized clinical trials for individualizing warfarin dosing. A substantial, hospital healthcare use case that demonstrates the value of iS-TR when addressing real world anticoagulation PCOR CER challenges was also presented

Advanced Analysis on Temporal Data

Due to the increase in CPU power and the ever increasing data storage capabilities, more and more data of all kind is recorded, including temporal data. Time series, the most prevalent type of temporal data are derived in a broad number of application domains. Prominent examples include stock price data in economy, gene expression data in biology, the course of environmental parameters in meteorology, or data of moving objects recorded by traffic sensors. This large amount of raw data can only be analyzed by automated data mining algorithms in order to generate new knowledge. One of the most basic data mining operations is the similarity query, which computes a similarity or distance value for two objects. Two aspects of such an similarity function are of special interest. First, the semantics of a similarity function and second, the computational cost for the calculation of a similarity value. The semantics is the actual similarity notion and is highly dependant on the analysis task at hand. This thesis addresses both aspects. We introduce a number of new similarity measures for time series data and show how they can efficiently be calculated by means of index structures and query algorithms. The first of the new similarity measures is threshold-based. Two time series are considered as similar, if they exceed a user-given threshold during similar time intervals. Aside from formally defining this similarity measure, we show how to represent time series in such a way that threshold-based queries can be efficiently calculated. Our representation allows for the specification of the threshold value at query time. This is for example useful for data mining task that try to determine crucial thresholds. The next similarity measure considers a relevant amplitude range. This range is scanned with a certain resolution and for each considered amplitude value features are extracted. We consider the change in the feature values over the amplitude values and thus, generate so-called feature sequences. Different features can finally be combined to answer amplitude-level-based similarity queries. In contrast to traditional approaches which aggregate global feature values along the time dimension, we capture local characteristics and monitor their change for different amplitude values. Furthermore, our method enables the user to specify a relevant range of amplitude values to be considered and so the similarity notion can be adapted to the current requirements. Next, we introduce so-called interval-focused similarity queries. A user can specify one or several time intervals that should be considered for the calculation of the similarity value. Our main focus for this similarity measure was the efficient support of the corresponding query. In particular we try to avoid loading the complete time series objects into main memory, if only a relatively small portion of a time series is of interest. We propose a time series representation which can be used to calculate upper and lower distance bounds, so that only a few time series objects have to be completely loaded and refined. Again, the relevant time intervals do not have to be known in advance. Finally, we define a similarity measure for so-called uncertain time series, where several amplitude values are given for each point in time. This can be due to multiple recordings or to errors in measurements, so that no exact value can be specified. We show how to efficiently support queries on uncertain time series. The last part of this thesis shows how data mining methods can be used to discover crucial threshold parameters for the threshold-based similarity measure. Furthermore we present a data mining tool for time series

The application of omics techniques to understand the role of the gut microbiota in inflammatory bowel disease

The aetiopathogenesis of inflammatory bowel diseases (IBD) involves the complex interaction between a patient’s genetic predisposition, environment, gut microbiota and immune system. Currently, however, it is not known if the distinctive perturbations of the gut microbiota that appear to accompany both Crohn’s disease and ulcerative colitis are the cause of, or the result of, the intestinal inflammation that characterizes IBD. With the utilization of novel systems biology technologies, we can now begin to understand not only details about compositional changes in the gut microbiota in IBD, but increasingly also the alterations in microbiota function that accompany these. Technologies such as metagenomics, metataxomics, metatranscriptomics, metaproteomics and metabonomics are therefore allowing us a deeper understanding of the role of the microbiota in IBD. Furthermore, the integration of these systems biology technologies through advancing computational and statistical techniques are beginning to understand the microbiome interactions that both contribute to health and diseased states in IBD. This review aims to explore how such systems biology technologies are advancing our understanding of the gut microbiota, and their potential role in delineating the aetiology, development and clinical care of IBD

Artificial Intelligence in Oncology Drug Discovery and Development

There exists a profound conflict at the heart of oncology drug development. The efficiency of the drug development process is falling, leading to higher costs per approved drug, at the same time personalised medicine is limiting the target market of each new medicine. Even as the global economic burden of cancer increases, the current paradigm in drug development is unsustainable. In this book, we discuss the development of techniques in machine learning for improving the efficiency of oncology drug development and delivering cost-effective precision treatment. We consider how to structure data for drug repurposing and target identification, how to improve clinical trials and how patients may view artificial intelligence

Recursive model for dose-time responses in pharmacological studies

Background: Clinical studies often track dose-response curves of subjects over time. One can easily model the dose-response curve at each time point with Hill equation, but such a model fails to capture the temporal evolution of the curves. On the other hand, one can use Gompertz equation to model the temporal behaviors at each dose without capturing the evolution of time curves across dosage Results: In this article, we propose a parametric model for dose-time responses that follows Gompertz law in time and Hill equation across dose approximately. We derive a recursion relation for dose-response curves over time capturing the temporal evolution and then specify a regression model connecting the parameters controlling the dose-time response with individual level proteomic data. The resultant joint model allows us to predict the dose-response curves over time for new individuals. Conclusion: We have compared the efficacy of our proposed Recursive Hybrid model with individual dose-response predictive models at desired time points. We note that our proposed model exhibits a superior performance compared to the individual ones for both synthetic data and actual pharamcological data. For the desired dose-time varying genetic characterization and drug response values, we have used the HMS-LINCS database and demonstrated the effectiveness of our model for all available anticancer compounds