Tiny microbes, enormous impacts: what matters in gut microbiome studies?

Many factors affect the microbiomes of humans, mice, and other mammals, but substantial challenges remain in determining which of these factors are of practical importance. Considering the relative effect sizes of both biological and technical covariates can help improve study design and the quality of biological conclusions. Care must be taken to avoid technical bias that can lead to incorrect biological conclusions. The presentation of quantitative effect sizes in addition to P values will improve our ability to perform meta-analysis and to evaluate potentially relevant biological effects. A better consideration of effect size and statistical power will lead to more robust biological conclusions in microbiome studies

Big Data Proteogenomics and High Performance Computing: Challenges and Opportunities

Proteogenomics is an emerging field of systems biology research at the intersection of proteomics and genomics. Two high-throughput technologies, Mass Spectrometry (MS) for proteomics and Next Generation Sequencing (NGS) machines for genomics are required to conduct proteogenomics studies. Independently both MS and NGS technologies are inflicted with data deluge which creates problems of storage, transfer, analysis and visualization. Integrating these big data sets (NGS+MS) for proteogenomics studies compounds all of the associated computational problems. Existing sequential algorithms for these proteogenomics datasets analysis are inadequate for big data and high performance computing (HPC) solutions are almost non-existent. The purpose of this paper is to introduce the big data problem of proteogenomics and the associated challenges in analyzing, storing and transferring these data sets. Further, opportunities for high performance computing research community are identified and possible future directions are discussed

Multiple Comparative Metagenomics using Multiset k-mer Counting

Background. Large scale metagenomic projects aim to extract biodiversity knowledge between different environmental conditions. Current methods for comparing microbial communities face important limitations. Those based on taxonomical or functional assignation rely on a small subset of the sequences that can be associated to known organisms. On the other hand, de novo methods, that compare the whole sets of sequences, either do not scale up on ambitious metagenomic projects or do not provide precise and exhaustive results. Methods. These limitations motivated the development of a new de novo metagenomic comparative method, called Simka. This method computes a large collection of standard ecological distances by replacing species counts by k-mer counts. Simka scales-up today's metagenomic projects thanks to a new parallel k-mer counting strategy on multiple datasets. Results. Experiments on public Human Microbiome Project datasets demonstrate that Simka captures the essential underlying biological structure. Simka was able to compute in a few hours both qualitative and quantitative ecological distances on hundreds of metagenomic samples (690 samples, 32 billions of reads). We also demonstrate that analyzing metagenomes at the k-mer level is highly correlated with extremely precise de novo comparison techniques which rely on all-versus-all sequences alignment strategy or which are based on taxonomic profiling

Ecological succession and viability of human-associated microbiota on restroom surfaces

Author Posting. © The Author(s), 2014. This is the author's version of the work. It is posted here by permission of American Society for Microbiology for personal use, not for redistribution. The definitive version was published in Applied and Environmental Microbiology (2014), doi:10.1128/AEM.03117-14.Human-associated bacteria dominate the built environment (BE). Following decontamination of floors, toilet seats, and soap dispensers in 4 public restrooms, in situ bacterial communities were characterized hourly, daily, and weekly to determine their successional ecology. The viability of cultivable bacteria, following the removal of dispersal agents (humans), was also assessed hourly. A late successional community developed within 5-8 hours on restroom floors, and showed remarkable stability over weeks to months. Despite late successional dominance by skin- and outdoor-associated bacteria, the most ubiquitous organisms were predominantly gut-associated taxa, which persisted following exclusion of humans. Staphylococcus represented the majority of the cultivable community, even after several hours of human-exclusion. MRSA-associated virulence genes were found on floors, but were not present in assembled Staphylococcus pan-genomes. Viral abundances, which were predominantly enterophage, human papilloma and herpes viruses, were significantly correlated with bacteria abundances, and showed an unexpectedly low virus-to-bacteria ratio in surface-associated samples, suggesting that bacterial hosts are mostly dormant on BE surfaces.S.M.G. was supported by an EPA STAR Graduate Fellowship and the National Institutes of Health Training Grant 5T-32EB-009412. We acknowledge funding from the Alfred P Sloan Foundation’s Microbiology of the Built Environment Program.2015-05-1

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