Machine Learning and Integrative Analysis of Biomedical Big Data.

Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues

GIVE: portable genome browsers for personal websites.

Growing popularity and diversity of genomic data demand portable and versatile genome browsers. Here, we present an open source programming library called GIVE that facilitates the creation of personalized genome browsers without requiring a system administrator. By inserting HTML tags, one can add to a personal webpage interactive visualization of multiple types of genomics data, including genome annotation, "linear" quantitative data, and genome interaction data. GIVE includes a graphical interface called HUG (HTML Universal Generator) that automatically generates HTML code for displaying user chosen data, which can be copy-pasted into user's personal website or saved and shared with collaborators. GIVE is available at: https://www.givengine.org/

The Metagenomics and Metadesign of the Subways and Urban Biomes (MetaSUB) International Consortium inaugural meeting report.

The Metagenomics and Metadesign of the Subways and Urban Biomes (MetaSUB) International Consortium is a novel, interdisciplinary initiative comprised of experts across many fields, including genomics, data analysis, engineering, public health, and architecture. The ultimate goal of the MetaSUB Consortium is to improve city utilization and planning through the detection, measurement, and design of metagenomics within urban environments. Although continual measures occur for temperature, air pressure, weather, and human activity, including longitudinal, cross-kingdom ecosystem dynamics can alter and improve the design of cities. The MetaSUB Consortium is aiding these efforts by developing and testing metagenomic methods and standards, including optimized methods for sample collection, DNA/RNA isolation, taxa characterization, and data visualization. The data produced by the consortium can aid city planners, public health officials, and architectural designers. In addition, the study will continue to lead to the discovery of new species, global maps of antimicrobial resistance (AMR) markers, and novel biosynthetic gene clusters (BGCs). Finally, we note that engineered metagenomic ecosystems can help enable more responsive, safer, and quantified cities

A comprehensive resource for retrieving, visualizing, and integrating functional genomics data

The enormous amount of freely accessible functional genomics data is an invaluable resource for interrogating the biological function of multiple DNA-interacting players and chromatin modifications by large-scale comparative analyses. However, in practice, interrogating large collections of public data requires major efforts for (i) reprocessing available raw reads, (ii) incorporating quality assessments to exclude artefactual and low-quality data, and (iii) processing data by using high-performance computation. Here, we present qcGenomics, a user-friendly online resource for ultrafast retrieval, visualization, and comparative analysis of tens of thousands of genomics datasets to gain new functional insight from global or focused multidimensional data integration.\ua0\ua9 2019 Blum et al

High-Throughput Polygenic Biomarker Discovery Using Condition-Specific Gene Coexpression Networks

Biomarkers can be described as molecular signatures that are associated with a trait or disease. RNA expression data facilitates discovery of biomarkers underlying complex phenotypes because it can capture dynamic biochemical processes that are regulated in tissue-specific and time-specific manners. Gene Coexpression Network (GCN) analysis is a method that utilizes RNA expression data to identify binary gene relationships across experimental conditions. Using a novel GCN construction algorithm, Knowledge Independent Network Construction (KINC), I provide evidence for novel polygenic biomarkers in both plant and animal use cases. Kidney cancer is comprised of several distinct subtypes that demonstrate unique histological and molecular signatures. Using KINC, I have identified gene correlations that are specific to clear cell renal cell carcinoma (ccRCC), the most common form of kidney cancer. ccRCC is associated with two common mutation profiles that respond differently to targeted therapy. By identifying GCN edges that are specific to patients with each of these two mutation profiles, I discovered unique genes with similar biological function, suggesting a role for T cell exhaustion in the development of ccRCC. Medicago truncatula is a legume that is capable of atmospheric nitrogen fixation through a symbiotic relationship between plant and rhizobium that results in root nodulation. This process is governed by complex gene expression patterns that are dynamically regulated across tissues over the course of rhizobial infection. Using de novo RNA sequencing data generated from the root maturation zone at five distinct time points, I identified hundreds of genes that were differentially expressed between control and inoculated plants at specific time points. To discover genes that were co-regulated during this experiment, I constructed a GCN using the KINC software. By combining GCN clustering analysis with differentially expressed genes, I present evidence for novel root nodulation biomarkers. These biomarkers suggest that temporal regulation of pathogen response related genes is an important process in nodulation. Large-scale GCN analysis requires computational resources and stable data-processing pipelines. Supercomputers such as Clemson University’s Palmetto Cluster provide data storage and processing resources that enable terabyte-scale experiments. However, with the wealth of public sequencing data available for mining, petabyte-scale experiments are required to provide novel insights across the tree of life. I discuss computational challenges that I have discovered with large scale RNA expression data mining, and present two workflows, OSG-GEM and OSG-KINC, that enable researchers to access geographically distributed computing resources to handle petabyte-scale experiments