scholarly journals EMPress Enables Tree-Guided, Interactive, and Exploratory Analyses of Multi-omic Data Sets

mSystems ◽  
2021 ◽  
Vol 6 (2) ◽  
Author(s):  
Kalen Cantrell ◽  
Marcus W. Fedarko ◽  
Gibraan Rahman ◽  
Daniel McDonald ◽  
Yimeng Yang ◽  
...  
Keyword(s):  
Phylogenetic Trees ◽  
Data Sets ◽  
Metabolomic Data ◽  
Microbiome Research ◽  
Community Data ◽  
Diverse Data ◽  
Standard Tree ◽  
Omic Data ◽  
Integral Data ◽  
Tree Visualization

ABSTRACT Standard workflows for analyzing microbiomes often include the creation and curation of phylogenetic trees. Here we present EMPress, an interactive web tool for visualizing trees in the context of microbiome, metabolome, and other community data scalable to trees with well over 500,000 nodes. EMPress provides novel functionality—including ordination integration and animations—alongside many standard tree visualization features and thus simplifies exploratory analyses of many forms of ‘omic data. IMPORTANCE Phylogenetic trees are integral data structures for the analysis of microbial communities. Recent work has also shown the utility of trees constructed from certain metabolomic data sets, further highlighting their importance in microbiome research. The ever-growing scale of modern microbiome surveys has led to numerous challenges in visualizing these data. In this paper we used five diverse data sets to showcase the versatility and scalability of EMPress, an interactive web visualization tool. EMPress addresses the growing need for exploratory analysis tools that can accommodate large, complex multi-omic data sets.

scholarly journals EMPress enables tree-guided, interactive, and exploratory analyses of multi-omic datasets

2020 ◽  
Author(s):  
Kalen Cantrell ◽  
Marcus W. Fedarko ◽  
Gibraan Rahman ◽  
Daniel McDonald ◽  
Yimeng Yang ◽  
...  
Keyword(s):  
Phylogenetic Trees ◽  
Large Datasets ◽  
The Earth ◽  
Interactive Tool ◽  
Community Data ◽  
The Creation ◽  
Standard Tree ◽  
Omic Data ◽  
Tree Visualization

AbstractStandard workflows for analyzing microbiomes often include the creation and curation of phylogenetic trees. Here we present EMPress, an interactive tool for visualizing trees in the context of microbiome, metabolome, etc. community data scalable beyond modern large datasets like the Earth Microbiome Project. EMPress provides novel functionality—including ordination integration and animations—alongside many standard tree visualization features, and thus simplifies exploratory analyses of many forms of ‘omic data.

scholarly journals Cerebrospinal fluid metabolomics identifies 19 brain-related phenotype associations

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Daniel J. Panyard ◽  
Kyeong Mo Kim ◽  
Burcu F. Darst ◽  
Yuetiva K. Deming ◽  
Xiaoyuan Zhong ◽  
...  
Keyword(s):  
Cerebrospinal Fluid ◽  
Drug Targets ◽  
Large Scale ◽  
Prediction Models ◽  
Genome Wide Association ◽  
Large Samples ◽  
Genome Wide ◽  
Metabolomic Data ◽  
Related Phenotype ◽  
Omic Data

AbstractThe study of metabolomics and disease has enabled the discovery of new risk factors, diagnostic markers, and drug targets. For neurological and psychiatric phenotypes, the cerebrospinal fluid (CSF) is of particular importance. However, the CSF metabolome is difficult to study on a large scale due to the relative complexity of the procedure needed to collect the fluid. Here, we present a metabolome-wide association study (MWAS), which uses genetic and metabolomic data to impute metabolites into large samples with genome-wide association summary statistics. We conduct a metabolome-wide, genome-wide association analysis with 338 CSF metabolites, identifying 16 genotype-metabolite associations (metabolite quantitative trait loci, or mQTLs). We then build prediction models for all available CSF metabolites and test for associations with 27 neurological and psychiatric phenotypes, identifying 19 significant CSF metabolite-phenotype associations. Our results demonstrate the feasibility of MWAS to study omic data in scarce sample types.

scholarly journals Nonparametric reconstruction of the dark energy equation of state from diverse data sets

2011 ◽  
Vol 84 (8) ◽  
Author(s):  
Tracy Holsclaw ◽  
Ujjaini Alam ◽  
Bruno Sansó ◽  
Herbie Lee ◽  
Katrin Heitmann ◽  
...  
Keyword(s):  
Dark Energy ◽  
Equation Of State ◽  
Energy Equation ◽  
Data Sets ◽  
Diverse Data

scholarly journals Future of Medical Research in Rare Diseases and Cancers: Shift from Pharma to Biotech and the Golden Age of Medical Advancement

2017 ◽  
Vol 6 (2) ◽  
pp. 12
Author(s):  
Abhith Pallegar
Keyword(s):  
Big Data ◽  
Medical Research ◽  
Rare Diseases ◽  
Network Effects ◽  
Medical Knowledge ◽  
Economic Cost ◽  
Medical Data ◽  
Data Sets ◽  
Leading Role ◽  
Diverse Data

The objective of the paper is to elucidate how interconnected biological systems can be better mapped and understood using the rapidly growing area of Big Data. We can harness network efficiencies by analyzing diverse medical data and probe how we can effectively lower the economic cost of finding cures for rare diseases. Most rare diseases are due to genetic abnormalities, many forms of cancers develop due to genetic mutations. Finding cures for rare diseases requires us to understand the biology and biological processes of the human body. In this paper, we explore what the historical shift of focus from pharmacology to biotechnology means for accelerating biomedical solutions. With biotechnology playing a leading role in the field of medical research, we explore how network efficiencies can be harnessed by strengthening the existing knowledge base. Studying rare or orphan diseases provides rich observable statistical data that can be leveraged for finding solutions. Network effects can be squeezed from working with diverse data sets that enables us to generate the highest quality medical knowledge with the fewest resources. This paper examines gene manipulation technologies like Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) that can prevent diseases of genetic variety. We further explore the role of the emerging field of Big Data in analyzing large quantities of medical data with the rapid growth of computing power and some of the network efficiencies gained from this endeavor. 

scholarly journals SpiderSeqR: an R package for crawling the web of high-throughput multi-omic data repositories for data-sets and annotation

2020 ◽  
Author(s):  
Anna M. Sozanska ◽  
Charles Fletcher ◽  
Dóra Bihary ◽  
Shamith A. Samarajiwa
Keyword(s):  
High Throughput ◽  
R Package ◽  
Data Reuse ◽  
Massively Parallel ◽  
Data Sets ◽  
Similar Data ◽  
Data Generation ◽  
Data Repositories ◽  
Public Data ◽  
Omic Data

AbstractMore than three decades ago, the microarray revolution brought about high-throughput data generation capability to biology and medicine. Subsequently, the emergence of massively parallel sequencing technologies led to many big-data initiatives such as the human genome project and the encyclopedia of DNA elements (ENCODE) project. These, in combination with cheaper, faster massively parallel DNA sequencing capabilities, have democratised multi-omic (genomic, transcriptomic, translatomic and epigenomic) data generation leading to a data deluge in bio-medicine. While some of these data-sets are trapped in inaccessible silos, the vast majority of these data-sets are stored in public data resources and controlled access data repositories, enabling their wider use (or misuse). Currently, most peer reviewed publications require the deposition of the data-set associated with a study under consideration in one of these public data repositories. However, clunky and difficult to use interfaces, subpar or incomplete annotation prevent discovering, searching and filtering of these multi-omic data and hinder their re-purposing in other use cases. In addition, the proliferation of multitude of different data repositories, with partially redundant storage of similar data are yet another obstacle to their continued usefulness. Similarly, interfaces where annotation is spread across multiple web pages, use of accession identifiers with ambiguous and multiple interpretations and lack of good curation make these data-sets difficult to use. We have produced SpiderSeqR, an R package, whose main features include the integration between NCBI GEO and SRA databases, enabling an integrated unified search of SRA and GEO data-sets and associated annotations, conversion between database accessions, as well as convenient filtering of results and saving past queries for future use. All of the above features aim to promote data reuse to facilitate making new discoveries and maximising the potential of existing data-sets.Availabilityhttps://github.com/ss-lab-cancerunit/SpiderSeqR

scholarly journals Uncovering the mesendoderm gene regulatory network through multi-omic data integration

2020 ◽  
Author(s):  
Camden Jansen ◽  
Kitt D. Paraiso ◽  
Jeff J. Zhou ◽  
Ira L. Blitz ◽  
Margaret B. Fish ◽  
...  
Keyword(s):  
Cell Differentiation ◽  
Cell Fate ◽  
Data Sets ◽  
List Type ◽  
Rna Seq ◽  
Data Types ◽  
Cell Fate Decisions ◽  
Gene Regulatory ◽  
Genome Scale ◽  
Omic Data

SummaryMesendodermal specification is one of the earliest events in embryogenesis, where cells first acquire distinct identities. Cell differentiation is a highly regulated process that involves the function of numerous transcription factors (TFs) and signaling molecules, which can be described with gene regulatory networks (GRNs). Cell differentiation GRNs are difficult to build because existing mechanistic methods are low-throughput, and high-throughput methods tend to be non-mechanistic. Additionally, integrating highly dimensional data comprised of more than two data types is challenging. Here, we use linked self-organizing maps to combine ChIP-seq/ATAC-seq with temporal, spatial and perturbation RNA-seq data from Xenopus tropicalis mesendoderm development to build a high resolution genome scale mechanistic GRN. We recovered both known and previously unsuspected TF-DNA/TF-TF interactions and validated through reporter assays. Our analysis provides new insights into transcriptional regulation of early cell fate decisions and provides a general approach to building GRNs using highly-dimensional multi-omic data sets.HighlightsBuilt a generally applicable pipeline to creating GRNs using highly-dimensional multi-omic data setsPredicted new TF-DNA/TF-TF interactions during mesendoderm developmentGenerate the first genome scale GRN for vertebrate mesendoderm and expanded the core mesendodermal developmental network with high fidelityDeveloped a resource to visualize hundreds of RNA-seq and ChIP-seq data using 2D SOM metaclusters.

scholarly journals Linking Norms, Ratings, and Relations of Words and Concepts Across Multiple Language Varieties

2020 ◽  
Author(s):  
Annika Tjuka ◽  
Robert Forkel ◽  
Johann-Mattis List
Keyword(s):  
Web Application ◽  
Age Of Acquisition ◽  
Data Curation ◽  
Data Sets ◽  
Data Types ◽  
Word Meanings ◽  
Language Varieties ◽  
Diverse Data ◽  
Multiple Languages ◽  
Word Frequencies

Psychologists and linguists have collected a great diversity of data for word and concept properties. In psychology, many studies accumulate norms and ratings such as word frequencies or age-of-acquisition often for a large number of words. Linguistics, on the other hand, provides valuable insights into relations of word meanings. We present a collection of those data sets for norms, ratings, and relations that cover different languages: ‘NoRaRe.’ To enable a comparison between the diverse data types, we established workflows that facilitate the expansion of the database. A web application allows convenient access to the data (https://digling.org/norare/). Furthermore, a software API ensures consistent data curation by providing tests to validate the data sets. The NoRaRe collection is linked to the database curated by the Concepticon project (https://concepticon.clld.org) which offers a reference catalog of unified concept sets. The link between words in the data sets and the Concepticon concept sets makes a cross-linguistic comparison possible. In three case studies, we test the validity of our approach, the accuracy of our workflow, and the applicability of our database. The results indicate that the NoRaRe database can be applied for the study of word properties across multiple languages. The data can be used by psychologists and linguists to benefit from the knowledge rooted in both research disciplines.

scholarly journals KnowEnG: a knowledge engine for genomics

2015 ◽  
Vol 22 (6) ◽  
pp. 1115-1119 ◽  
Author(s):  
Saurabh Sinha ◽  
Jun Song ◽  
Richard Weinshilboum ◽  
Victor Jongeneel ◽  
Jiawei Han
Keyword(s):  
Big Data ◽  
National Institutes Of Health ◽  
Knowledge Network ◽  
Data Sets ◽  
Network Mining ◽  
Construction Of Knowledge ◽  
Community Data ◽  
Science Framework ◽  
The University ◽  
Big Data Computing

Abstract We describe here the vision, motivations, and research plans of the National Institutes of Health Center for Excellence in Big Data Computing at the University of Illinois, Urbana-Champaign. The Center is organized around the construction of “Knowledge Engine for Genomics” (KnowEnG), an E-science framework for genomics where biomedical scientists will have access to powerful methods of data mining, network mining, and machine learning to extract knowledge out of genomics data. The scientist will come to KnowEnG with their own data sets in the form of spreadsheets and ask KnowEnG to analyze those data sets in the light of a massive knowledge base of community data sets called the “Knowledge Network” that will be at the heart of the system. The Center is undertaking discovery projects aimed at testing the utility of KnowEnG for transforming big data to knowledge. These projects span a broad range of biological enquiry, from pharmacogenomics (in collaboration with Mayo Clinic) to transcriptomics of human behavior.

Integrating Diverse Data Sets to Assess the Risks of Airborne Pollutants

1989 ◽  
pp. 3-22 ◽  
Author(s):  
R. O. McClellan ◽  
R. G. Cuddihy ◽  
W. C. Griffith ◽  
J. L. Mauderly
Keyword(s):  
Data Sets ◽  
Airborne Pollutants ◽  
Diverse Data

Semantic-Based Indexing Approaches for Medical Document Clustering Using Cognitive Search

2019 ◽  
pp. 41-64
Author(s):  
Logeswari Shanmugam ◽  
Premalatha K.
Keyword(s):  
Text Processing ◽  
Relevant Information ◽  
Biomedical Literature ◽  
Data Sets ◽  
Original Text ◽  
Biomedical Knowledge ◽  
Text Data ◽  
Natural Language Text ◽  
Diverse Data ◽  
Medical Document

Biomedical literature is the primary repository of biomedical knowledge in which PubMed is the most absolute database for collecting, organizing and analyzing textual knowledge. The high dimensionality of the natural language text makes the text data quite noisy and sparse in the vector space. Hence, the data preprocessing and feature selection are important processes for the text processing issues. Ontologies select the meaningful terms semantically associated with the concepts from a document to reduce the dimensionality of the original text. In this chapter, semantic-based indexing approaches are proposed with cognitive search which makes use of domain ontology to extract relevant information from big and diverse data sets for users.

Sign in / Sign up

Export Citation Format

Share Document