Biology subjects

Ramos, M.

Publications and source records attributed to Ramos, M..

4 recordsLinked to original sources

bioRxiv2018

Human pancreatic islet 3D chromatin architecture provides insights into the genetics of type 2 diabetes

Genetic studies promise to provide insight into the molecular mechanisms underlying type 2 diabetes (T2D). Variants associated with T2D are often located in tissue-specific enhancer regions (enhancer clusters, stretch enhancers or super-enhancers). So far, such domains have been defined through clustering of enhancers in linear genome maps rather than in 3D-space. Furthermore, their target genes are generally unknown. We have now created promoter capture Hi-C maps in human pancreatic islets. This linked diabetes-associated enhancers with their target genes, often located hundreds of kilobases away. It further revealed sets of islet enhancers, super-enhancers and active promoters that form 3D higher-order hubs, some of which show coordinated glucose-dependent activity. Hub genetic variants impact the heritability of insulin secretion, and help identify individuals in whom genetic variation of islet function is important for T2D. Human islet 3D chromatin architecture thus provides a framework for interpretation of T2D GWAS signals.

genomics↗

bioRxiv2018

HMP16SData: Efficient Access to the Human Microbiome Project through Bioconductor

Phase 1 of the NIH Human Microbiome Project (HMP) investigated 18 body subsites of 239 healthy American adults, to produce the first comprehensive reference for the composition and variation of the \"healthy\" human microbiome. Publicly-available data sets from amplicon sequencing of two 16S rRNA variable regions, with extensive controlled-access participant data, provide a reference for ongoing microbiome studies. However, utilization of these data sets can be hindered by the complex bioinformatic steps required to access, import, decrypt, and merge the various components in formats suitable for ecological and statistical analysis. The HMP16SData package provides count data for both 16S variable regions, integrated with phylogeny, taxonomy, public participant data, and controlled participant data for authorized researchers, using standard integrative Bioconductor data objects. By removing bioinformatic hurdles of data access and management, HMP16SData enables epidemiologists with only basic R skills to quickly analyze HMP data.

bioinformatics↗

bioRxiv2017

Software For The Integration Of Multi-Omics Experiments In Bioconductor

Multi-omics experiments are increasingly commonplace in biomedical research, and add layers of complexity to experimental design, data integration, and analysis. R and Bioconductor provide a generic framework for statistical analysis and visualization, as well as specialized data classes for a variety of high-throughput data types, but methods are lacking for integrative analysis of multi-omics experiments. The MultiAssayExperiment software package, implemented in R and leveraging Bioconductor software and design principles, provides for the coordinated representation of, storage of, and operation on multiple diverse genomics data. We provide all of the multiple omics data for each cancer tissue in The Cancer Genome Atlas (TCGA) as ready-to-analyze MultiAssayExperiment objects, and demonstrate in these and other datasets how the software simplifies data representation, statistical analysis, and visualization. The MultiAssayExperiment Bioconductor package reduces major obstacles to efficient, scalable and reproducible statistical analysis of multi-omics data and enhances data science applications of multiple omics datasets.

bioinformatics↗

bioRxiv2017

Accessible, curated metagenomic data through ExperimentHub

We present curatedMetagenomicData, a Bioconductor and command-line interface to thousands of metagenomic profiles from the Human Microbiome Project and other publicly available datasets, and ExperimentHub, a platform for convenient cloud-based distribution of data to the R desktop. The resource provides standardized per-participant metadata linked to bacterial, fungal, archaeal, and viral taxonomic abundances, as well as quantitative metabolic functional profiles. The datasets can be immediately analyzed in R or other software with a minimum of bioinformatic expertise and no preprocessing of data. We demonstrate identification of taxonomic/functional correlations, an investigation of gut \"enterotypes\", and a comparison of the accuracy of disease classification from different data types. These documented analyses can be reproduced efficiently on a laptop, without the barriers of working with large-scale, raw sequencing data. The building and expansion of curatedMetagenomicData is based entirely on open source software and pipelines, to facilitate the addition of new microbiome datasets and methods.

bioinformatics↗