Types Of Genomic Data Research Articles

Causal Mediation Analysis for Integrating Exposure, Genomic, and Phenotype Data

Causal mediation analysis provides an attractive framework for integrating diverse types of exposure, genomic, and phenotype data. Recently, this field has seen a surge of interest, largely driven by the increasing need for causal mediation analyses in health and social sciences. This article aims to provide a review of recent developments in mediation analysis, encompassing mediation analysis of a single mediator and a large number of mediators, as well as mediation analysis with multiple exposures and mediators. Our review focuses on the recent advancements in statistical inference for causal mediation analysis, especially in the context of high-dimensional mediation analysis. We delve into the complexities of testing mediation effects, especially addressing the challenge of testing a large number of composite null hypotheses. Through extensive simulation studies, we compare the existing methods across a range of scenarios. We also include an analysis of data from the Normative Aging Study, which examines DNA methylation CpG sites as potential mediators of the effect of smoking status on lung function. We discuss the pros and cons of these methods and future research directions.

A Low Transformed Tubal Rank Tensor Model Using a Spatial-Tubal Constraint for Sample Clustering with Cancer Multi-omics Data

Background: Since each dimension of a tensor can store different types of genomics data, compared to matrix methods, utilizing tensor structure can provide a deeper understanding of multi-dimensional data while also facilitating the discovery of more useful information related to cancer. However, in reality, there are issues such as insufficient utilization of prior knowledge in multi-omics data and limitations in the recovery of low-tubal-rank tensors. Therefore, the method proposed in this article was developed. Objective: In this paper, we proposed a low transformed tubal rank tensor model (LTTRT) using a spatial-tubal constraint to accurately partition different types of cancer samples and provide reliable theoretical support for the identification, diagnosis, and treatment of cancer. Method: In the LTTRT method, the transformed tensor nuclear norm based on the transformed tensor singular value decomposition is characterized by the low-rank tensor, which can explore the global low-rank property of the tensor, resolving the challenge of the tensor nuclear norm-based method not achieving the lowest tubal rank. Additionally, the introduction of weighted total variation regularization is conducive to extracting more information from sequencing data in both spatial and tubal dimensions, exploring cross-correlation features of multiple genomic data, and addressing the problem of overlooking prior knowledge from various perspectives. In addition, the L1-norm is used to improve sparsity. A symmetric Gauss‒Seidel-based alternating direction method of multipliers (sGS-ADMM) is used to update the LTTRT model iteratively. Results: The experiments of sample clustering on multiple integrated cancer multi-omics datasets show that the proposed LTTRT method is better than existing methods. Experimental results validate the effectiveness of LTTRT in accurately partitioning different types of cancer samples. result: The experiments of sample clustering on multiple integrated cancer multi-omics datasets show that the proposed LTTRT method is better than existing methods. Experimental results validate the effectiveness of LTTRT in accurately partitioning different types of cancer samples. Conclusion: The LTTRT method achieves precise segmentation of different types of cancer samples.

The TELCoMB Protocol for High-Sensitivity Detection of ARG-MGE Colocalizations in Complex Microbial Communities.

Understanding the genetic basis of antimicrobial resistance is crucial for developing effective mitigation strategies. One necessary step is to identify the antimicrobial resistance genes (ARGs) within a microbial population, referred to as the resistome, as well as the mobile genetic elements (MGEs) harboring ARGs. Although shotgun metagenomics has been successful in detecting ARGs and MGEs within a microbiome, it is limited by low sensitivity. Enrichment using cRNA biotinylated probes has been applied to address this limitation, enhancing the detection of rare ARGs and MGEs, especially when combined with long-read sequencing. Here, we present the TELCoMB protocol, a Snakemake workflow that elucidates resistome and mobilome composition and diversity and uncovers ARG-MGE colocalizations. The protocol supports both short- and long-read sequencing and does not require enrichment, making it versatile for various genomic data types. TELCoMB generates publication-ready figures and CSV files for comprehensive analysis, improving our understanding of antimicrobial resistance mechanisms and spread. © 2024 The Author(s). Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Installing TELCOMB Locally Alternate Protocol: Installing TELCOMB on a SLURM Cluster Basic Protocol 2: Data Preprocessing Basic Protocol 3: Calculation of Resistome Distribution and Composition Basic Protocol 4: Identification of ARG-MGE Colocalizations.

Does combining numerous data types in multi-omics data improve or hinder performance in survival prediction? Insights from a large-scale benchmark study

BackgroundPredictive modeling based on multi-omics data, which incorporates several types of omics data for the same patients, has shown potential to outperform single-omics predictive modeling. Most research in this domain focuses on incorporating numerous data types, despite the complexity and cost of acquiring them. The prevailing assumption is that increasing the number of data types necessarily improves predictive performance. However, the integration of less informative or redundant data types could potentially hinder this performance. Therefore, identifying the most effective combinations of omics data types that enhance predictive performance is critical for cost-effective and accurate predictions.MethodsIn this study, we systematically evaluated the predictive performance of all 31 possible combinations including at least one of five genomic data types (mRNA, miRNA, methylation, DNAseq, and copy number variation) using 14 cancer datasets with right-censored survival outcomes, publicly available from the TCGA database. We employed various prediction methods and up-weighted clinical data in every model to leverage their predictive importance. Harrell’s C-index and the integrated Brier Score were used as performance measures. To assess the robustness of our findings, we performed a bootstrap analysis at the level of the included datasets. Statistical testing was conducted for key results, limiting the number of tests to ensure a low risk of false positives.ResultsContrary to expectations, we found that using only mRNA data or a combination of mRNA and miRNA data was sufficient for most cancer types. For some cancer types, the additional inclusion of methylation data led to improved prediction results. Far from enhancing performance, the introduction of more data types most often resulted in a decline in performance, which varied between the two performance measures.ConclusionsOur findings challenge the prevailing notion that combining multiple omics data types in multi-omics survival prediction improves predictive performance. Thus, the widespread approach in multi-omics prediction of incorporating as many data types as possible should be reconsidered to avoid suboptimal prediction results and unnecessary expenditure.

Exploring combined effects of DNA methylation and copy number on gene expression with a two-stage approach.

DNA methylation and copy number may be associated with each other to some extent, in positive or negative ways. Whether differential methylation and copy number variation have combined effects on gene expression is largely unknown. We use a multivariate linear model to formulate the relationship among the three genomic measurements, gene expression, copy number, and methylation levels. We propose a method that combines a distance covariance measure and the group LASSO to analyze multiple types of genomic data collectively for the purpose of insightfully revealing how gene expression is potentially affected by both copy number variation and differential methylation levels in cellular process. Our approach is of two stages, the first is a variable screening process in which a variable selection method is utilized by employing the concept of joint distance covariance (JdCov) of random vectors, and the second is to implement a penalized regression approach, a group LASSO, on the screened data of much lower dimension. The two-stage approach is tested in extensive simulation studies and shown to be effective. The approach is then applied to the TCGA Melanoma data, which consists of gene expression, methylation and copy number measurements of more than 300 patients and relationship of some genes with methylation and copy number measurements were revealed for the involved subjects.

Genomic analysis reveals HDAC1 regulates clinically relevant transcriptional programs in Pancreatic cancer

Novel strategies are needed to combat multidrug resistance in pancreatic ductal adenocarcinoma (PDAC). We applied genomic approaches to understand mechanisms of resistance in order to better inform treatment and precision medicine. Altered function of chromatin remodeling complexes contribute to chemoresistance. Our study generates and analyzes genomic and biochemical data from PDAC cells overexpressing HDAC1, a histone deacetylase involved in several chromatin remodeling complexes. We characterized the impact of overexpression on drug response, gene expression, HDAC1 binding, and chromatin structure using RNA-sequencing and ChIP-sequencing for HDAC1 and H3K27 acetylation. Integrative genomic analysis shows that HDAC1 overexpression promotes activation of key resistance pathways including epithelial to mesenchymal transition, cell cycle, and apoptosis through global chromatin remodeling. Target genes are similarly altered in patient tissues and show correlation with patient survival. We also demonstrate that direct targets of HDAC1 that also show altered chromatin are enriched near genes associated with altered GTPase activity. HDAC1 target genes identified using in vitro methods and observed in patient tissues were used to develop a clinically relevant nine-transcript signature associated with patient prognosis. Integration of multiple genomic and biochemical data types enables understanding of multidrug resistance and tumorigenesis in PDAC, a disease in desperate need of novel treatment strategies.

A Bayesian framework for pathway-guided identification of cancer subgroups by integrating multiple types of genomic data.

In recent years, comprehensive cancer genomics platforms, such as The Cancer Genome Atlas (TCGA), provide access to an enormous amount of high throughput genomic datasets for each patient, including gene expression, DNA copy number alterations, DNA methylation, and somatic mutation. While the integration of these multi-omics datasets has the potential to provide novel insights that can lead to personalized medicine, most existing approaches only focus on gene-level analysis and lack the ability to facilitate biological findings at the pathway-level. In this article, we propose Bayes-InGRiD (Bayesian Integrative Genomics Robust iDentification of cancer subgroups), a novel pathway-guided Bayesian sparse latent factor model for the simultaneous identification of cancer patient subgroups (clustering) and key molecular features (variable selection) within a unified framework, based on the joint analysis of continuous, binary, and count data. By utilizing pathway (gene set) information, Bayes-InGRiD does not only enhance the accuracy and robustness of cancer patient subgroup and key molecular feature identification, but also promotes biological understanding and interpretation. Finally, to facilitate an efficient posterior sampling, an alternative Gibbs sampler for logistic and negative binomial models is proposed using Pólya-Gamma mixtures of normal to represent latent variables for binary and count data, which yields a conditionally Gaussian representation of the posterior. The R package "INGRID" implementing the proposed approach is currently available in our research group GitHub webpage (https://dongjunchung.github.io/INGRID/).

VBASS enables integration of single cell gene expression data in Bayesian association analysis of rare variants

Rare or de novo variants have substantial contribution to human diseases, but the statistical power to identify risk genes by rare variants is generally low due to rarity of genotype data. Previous studies have shown that risk genes usually have high expression in relevant cell types, although for many conditions the identity of these cell types are largely unknown. Recent efforts in single cell atlas in human and model organisms produced large amount of gene expression data. Here we present VBASS, a Bayesian method that integrates single-cell expression and de novo variant (DNV) data to improve power of disease risk gene discovery. VBASS models disease risk prior as a function of expression profiles, approximated by deep neural networks. It learns the weights of neural networks and parameters of Gamma-Poisson likelihood models of DNV counts jointly from expression and genetics data. On simulated data, VBASS shows proper error rate control and better power than state-of-the-art methods. We applied VBASS to published datasets and identified more candidate risk genes with supports from literature or data from independent cohorts. VBASS can be generalized to integrate other types of functional genomics data in statistical genetics analysis.

Somatic mutations inferred from RNA-seq data highlight the contribution of replication timing to mutation rate variation in a model plant.

Variation in the rates and characteristics of germline and somatic mutations across the genome of an organism is informative about DNA damage and repair processes and can also shed light on aspects of organism physiology and evolution. We adapted a recently developed method for inferring somatic mutations from bulk RNA-seq data and applied it to a large collection of Arabidopsis thaliana accessions. The wide range of genomic data types available for A. thaliana enabled us to investigate the relationships of multiple genomic features with the variation in the somatic mutation rate across the genome of this model plant. We observed that late replicated regions showed evidence of an elevated rate of somatic mutation compared to genomic regions that are replicated early. We identified transcriptional strand asymmetries, consistent with the effects of transcription-coupled damage and/or repair. We also observed a negative relationship between the inferred somatic mutation count and the H3K36me3 histone mark which is well documented in the literature of human systems. In addition, we were able to support previous reports of an inverse relationship between inferred somatic mutation count and guanine-cytosine content as well as a positive relationship between inferred somatic mutation count and DNA methylation for both cytosine and noncytosine mutations.

Orchestrating information across tissues via a novel multitask GAT framework to improve quantitative gene regulation relation modeling for survival analysis.

Survival analysis is critical to cancer prognosis estimation. High-throughput technologies facilitate the increase in the dimension of genic features, but the number of clinical samples in cohorts is relatively small due to various reasons, including difficulties in participant recruitment and high data-generation costs. Transcriptome is one of the most abundantly available OMIC (referring to the high-throughput data, including genomic, transcriptomic, proteomic and epigenomic) data types. This study introduced a multitask graph attention network (GAT) framework DQSurv for the survival analysis task. We first used a large dataset of healthy tissue samples to pretrain the GAT-based HealthModel for the quantitative measurement of the gene regulatory relations. The multitask survival analysis framework DQSurv used the idea of transfer learning to initiate the GAT model with the pretrained HealthModel and further fine-tuned this model using two tasks i.e. the main task of survival analysis and the auxiliary task of gene expression prediction. This refined GAT was denoted as DiseaseModel. We fused the original transcriptomic features with the difference vector between the latent features encoded by the HealthModel and DiseaseModel for the final task of survival analysis. The proposed DQSurv model stably outperformed the existing models for the survival analysis of 10 benchmark cancer types and an independent dataset. The ablation study also supported the necessity of the main modules. We released the codes and the pretrained HealthModel to facilitate the feature encodings and survival analysis of transcriptome-based future studies, especially on small datasets. The model and the code are available at http://www.healthinformaticslab.org/supp/.

PoliViews: A comprehensive and modular approach to the conceptual modeling of genomic data

The human genome complexity is captured by many signals, representing for instance DNA variations, the expression of gene activity, or DNA’s structural rearrangements; a rich set of data types and formats is used to record these signals. Conceptual models can support the description and explanation of the genome’s elaborate structure and behavior. Among others, the Conceptual Schema of the Human Genome (CSG) provides a concept-oriented, top-down representation of the genome behavior, which is independent of data formats. The Genomic Conceptual Model (GCM) provides instead a data-oriented, bottom-up representation, targeting a well-organized, unified description of these formats. In this research, we join the two approaches to achieve PoliViews, a comprehensive model that links (1) a concepts layer, describing genome elements and their conceptual connections, with (2) a data layer, describing datasets derived from genome sequencing with specific technologies. Their dynamic connection is established when specific genomic data types are chosen in the data layer, thereby triggering the selection of a view in the concepts layer. The benefit is mutual: data records can be semantically described by high-level concepts exploiting their links and, in turn, the continuously evolving abstract model can be extended thanks to the input provided by real datasets. PoliViews enables expressing queries that employ a holistic conceptual perspective on the genome, directly translated onto data-oriented terms and organization. Here, we demonstrate the approach by linking two major genomic data types, namely DNA variation and gene expression. For each type, we consider different eminent data sources; we describe their mapping with the corresponding view in the concepts layer, enabling an intra-data-type integration. Then, leveraging on the connections available in the concepts layer, we show how the distinct data types can be interoperated, enabling an inter-data-type integration. The PoliViews approach is shown through several examples of biological interest and can be further extended to any kind of genomic information.

Synergy between imputed genetic pathway and clinical information for predicting recurrence in early stage non-small cell lung cancer

Objective:Lung cancer exhibits unpredictable recurrence in low-stage tumors and variable responses to different therapeutic interventions. Predicting relapse in early-stage lung cancer can facilitate precision medicine and improve patient survivability. While existing machine learning models rely on clinical data, incorporating genomic information could enhance their efficiency. This study aims to impute and integrate specific types of genomic data with clinical data to improve the accuracy of machine learning models for predicting relapse in early-stage, non-small cell lung cancer patients. Methods:The study utilized a publicly available TCGA lung cancer cohort and imputed genetic pathway scores into the Spanish Lung Cancer Group (SLCG) data, specifically in 1348 early-stage patients. Initially, tumor recurrence was predicted without imputed pathway scores. Subsequently, the SLCG data were augmented with pathway scores imputed from TCGA. The integrative approach aimed to enhance relapse risk prediction performance. Results:The integrative approach achieved improved relapse risk prediction with the following evaluation metrics: an area under the precision–recall curve (PR-AUC) score of 0.75, an area under the ROC (ROC-AUC) score of 0.80, an F1 score of 0.61, and a Precision of 0.80. The prediction explanation model SHAP (SHapley Additive exPlanations) was employed to explain the machine learning model’s predictions. Conclusion:We conclude that our explainable predictive model is a promising tool for oncologists that addresses an unmet clinical need of post-treatment patient stratification based on the relapse risk while also improving the predictive power by incorporating proxy genomic data not available for specific patients.

HiCube: Interactive visualization of multiscale and multimodal Hi-C and 3D genome data.

HiCube is a lightweight web application for interactive visualization and exploration of diverse types of genomics data at multiscale resolutions. Especially, HiCube displays synchronized views of Hi-C contact maps and three-dimensional (3D) genome structures with user-friendly annotation and configuration tools, thereby facilitating the study of 3D genome organization and function. HiCube is implemented in Javascript and can be installed via NPM. The source code is freely available at GitHub (https://github.com/wmalab/HiCube). Supplementary data are available at Bioinformatics online.

2022 updates to the Rat Genome Database: a Findable, Accessible, Interoperable, and Reusable (FAIR) resource.

The Rat Genome Database (RGD, https://rgd.mcw.edu) has evolved from simply a resource for rat genetic markers, maps, and genes, by adding multiple genomic data types and extensive disease and phenotype annotations and developing tools to effectively mine, analyze, and visualize the available data, to empower investigators in their hypothesis-driven research. Leveraging its robust and flexible infrastructure, RGD has added data for human and eight other model organisms (mouse, 13-lined ground squirrel, chinchilla, naked mole-rat, dog, pig, African green monkey/vervet, and bonobo) besides rat to enhance its translational aspect. This article presents an overview of the database with the most recent additions to RGD's genome, variant, and quantitative phenotype data. We also briefly introduce Virtual Comparative Map (VCMap), an updated tool that explores synteny between species as an improvement to RGD's suite of tools, followed by a discussion regarding the refinements to the existing PhenoMiner tool that assists researchers in finding and comparing quantitative data across rat strains. Collectively, RGD focuses on providing a continuously improving, consistent, and high-quality data resource for researchers while advancing data reproducibility and fulfilling Findable, Accessible, Interoperable, and Reusable (FAIR) data principles.

KARAJ: An Efficient Adaptive Multi-Processor Tool to Streamline Genomic and Transcriptomic Sequence Data Acquisition.

Here we developed KARAJ, a fast and flexible Linux command-line tool to automate the end-to-end process of querying and downloading a wide range of genomic and transcriptomic sequence data types. The input to KARAJ is a list of PMCIDs or publication URLs or various types of accession numbers to automate four tasks as follows; firstly, it provides a summary list of accessible datasets generated by or used in these scientific articles, enabling users to select appropriate datasets; secondly, KARAJ calculates the size of files that users want to download and confirms the availability of adequate space on the local disk; thirdly, it generates a metadata table containing sample information and the experimental design of the corresponding study; and lastly, it enables users to download supplementary data tables attached to publications. Further, KARAJ provides a parallel downloading framework powered by Aspera connect which reduces the downloading time significantly.

Abstract LB503: Using JBrowse 2 plugins to visualize cancer genomic data

Abstract Genome browsers are essential tools for integrating, exploring, and visualizing data from large cancer genomics datasets. JBrowse 2 is a JavaScript genome browser with novel features for visualizing structural variants, syntenic alignments, and multiple types of genomic data. Although JBrowse 2 is available as a web-based or desktop application, the capabilities and features are not fixed and can be expanded through a comprehensive plugin system. To help address users' needs, we launched the new JBrowse 2 Plugin Store (https://jbrowse.org/jb2/plugin_store/). The overarching goals of the Plugin Store are to showcase the crucial features plugins add to JBrowse 2, to enable researchers to search for plugins they need, and for developers to highlight their plugins. The plugin system spans all aspects of the JBrowse 2 application and enables new track types (e.g. Manhattan plots, Hi-C data, ideograms), data adapters (e.g. API endpoint adapters for NCI Genomic Data Commons and the International Cancer Genome Consortium), and views (e.g. dot plots and multiple sequence alignments). Here, we present the capabilities of JBrowse 2 plugins and describe usage scenarios for cancer biologists and bioinformaticians, and software developers. Citation Format: Robin Andrew Haw, Colin Diesh, Caroline Bridge, Rob Buels, Garrett Stevens, Peter Xie, Teresa Martinez, Elliot Hershberg, Junjun Zhang, Shihab Dider, Scott Cain, Lincoln Stein, Ian Holmes. Using JBrowse 2 plugins to visualize cancer genomic data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr LB503.

Genomics and Informatics, Conjoined Tools Vital for Understanding and Protecting Plant Health

Genomics' impact on crop production continuously expands. The number of sequenced plant and microbial species and strains representing diverse populations of individual species rapidly increases thanks to the advent of next-generation sequencing technologies. Their genomic blueprints revealed candidate genes involved in various functions and processes crucial for crop health and helped in understanding how the sequenced organisms have evolved at the genome level. Functional genomics quickly translates these blueprints into a detailed mechanistic understanding of how such functions and processes work and are regulated; this understanding guides and empowers efforts to protect crops from diverse biotic and abiotic threats. Metagenome analyses help identify candidate microbes crucial for crop health and uncover how microbial communities associated with crop production respond to environmental conditions and cultural practices, presenting opportunities to enhance crop health by judiciously configuring microbial communities. Efficient conversion of disparate types of massive genomics data into actionable knowledge requires a robust informatics infrastructure supporting data preservation, analysis, and sharing. This review starts with an overview of how genomics came about and has quickly transformed life science. We illuminate how genomics and informatics can be applied to investigate various crop health-related problems using selected studies. We end the review by noting why community empowerment via crowdsourcing is crucial to harnessing genomics to protect global food and nutrition security without continuously expanding the environmental footprint of crop production.

The passivists: Managing risk through institutionalized ignorance in genomic medicine

As the era of big data transforms modern medicine, clinicians have access to more health data than ever. How do medical providers determine which data are relevant to patient care, which are irrelevant, and which may be inappropriately used to justify potentially harmful interventions? One of the most prominent medical fields to address these questions head on – clinical genomics – is actively debating how to assess the value of genomic data. In-depth interviews with clinicians and a content analysis of policy documents demonstrate that while many clinicians believe that collecting as much patient data as possible will lead to better patient care, a sizeable minority of clinicians preferred to collect less data. These clinicians worried that large genomic tests provided too much data, leading to confusion and inappropriate treatment. Clinical geneticists have also started developing the concept of “actionability” to assess which types of genomic data are worth collecting and interpreting. By classifying data as useful when it can or should lead to action, clinicians can formalize and institutionalize what types of data should be ignored. But achieving consensus about what counts as “actionable” has proven difficult and highlights the different values and risk philosophies of clinicians. At the same time, many clinicians are fighting against the ignorance arising from genomic databases predominantly filled with samples from European ancestry populations. Debates about how and when to institutionalize ignorance of health data are not unique to clinical genomics, but have spread throughout many fields of medicine. As the amount of health data available to clinicians and patients grows, social science research on the politics of knowledge and ignorance should inform debates about the value of data in medicine.

Polygenomic Interrogation of Drug Resistance Genes.

Understanding drug resistance in cancer is paramount to improving patient outcomes, quality of life and reducing toxicities in patients receiving chemotherapy. Pharmacogenomic methods seek to understand the interaction of genomic variation and response to chemotherapeutic treatment. This chapter presents a workflow to interrogate multiple genomic inputs and individually assess their relationship with the phenotype of drug resistance using hierarchical clustering to determine the set of features that can best describe what features are associated with drug resistance. Then in a gene-centric manner regulatory features such as miRNAs, SNPs, or DNA methylation can be related back to the differential expression of genes to give understanding to the mechanism underlying resistance. In this chapter, we describe a computational method that can be adapted to a number of different diseases and phenotypes in which there are multiple genomic data types available with concordant phenotypic drug resistance information.

Biological Network Inference With GRASP: A Bayesian Network Structure Learning Method Using Adaptive Sequential Monte Carlo.

Bayesian networks (BNs) provide a probabilistic, graphical framework for modeling high-dimensional joint distributions with complex correlation structures. BNs have wide applications in many disciplines, including biology, social science, finance and biomedical science. Despite extensive studies in the past, network structure learning from data is still a challenging open question in BN research. In this study, we present a sequential Monte Carlo (SMC)-based three-stage approach, GRowth-based Approach with Staged Pruning (GRASP). A double filtering strategy was first used for discovering the overall skeleton of the target BN. To search for the optimal network structures we designed an adaptive SMC (adSMC) algorithm to increase the quality and diversity of sampled networks which were further improved by a third stage to reclaim edges missed in the skeleton discovery step. GRASP gave very satisfactory results when tested on benchmark networks. Finally, BN structure learning using multiple types of genomics data illustrates GRASP’s potential in discovering novel biological relationships in integrative genomic studies.