Browse
Submit Data
Databases
API
Help

Dataset Information

22 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

A strategy for extracting and analyzing large-scale quantitative epistatic interaction data.

ABSTRACT: Recently, approaches have been developed for high-throughput identification of synthetic sick/lethal gene pairs. However, these are only a specific example of the broader phenomenon of epistasis, wherein the presence of one mutation modulates the phenotype of another. We present analysis techniques for generating high-confidence quantitative epistasis scores from measurements made using synthetic genetic array and epistatic miniarray profile (E-MAP) technology, as well as several tools for higher-level analysis of the resulting data that are greatly enhanced by the quantitative score and detection of alleviating interactions.

SUBMITTER: Collins SR

PROVIDER: S-EPMC1779568 | biostudies-literature | 2006

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

A strategy for extracting and analyzing large-scale quantitative epistatic interaction data.

Collins Sean R SR Schuldiner Maya M Krogan Nevan J NJ Weissman Jonathan S JS

Genome biology 20060101 7

Recently, approaches have been developed for high-throughput identification of synthetic sick/lethal gene pairs. However, these are only a specific example of the broader phenomenon of epistasis, wherein the presence of one mutation modulates the phenotype of another. We present analysis techniques for generating high-confidence quantitative epistasis scores from measurements made using synthetic genetic array and epistatic miniarray profile (E-MAP) technology, as well as several tools for highe ...[more]

PMID: 16859555

Similar Datasets

A strategy for sensitive, large scale quantitative metabolomics.

Project description:Metabolite profiling has been a valuable asset in the study of metabolism in health and disease. However, current platforms have different limiting factors, such as labor intensive sample preparations, low detection limits, slow scan speeds, intensive method optimization for each metabolite, and the inability to measure both positively and negatively charged ions in single experiments. Therefore, a novel metabolomics protocol could advance metabolomics studies. Amide-based hydrophilic chromatography enables polar metabolite analysis without any chemical derivatization. High resolution MS using the Q-Exactive (QE-MS) has improved ion optics, increased scan speeds (256 msec at resolution 70,000), and has the capability of carrying out positive/negative switching. Using a cold methanol extraction strategy, and coupling an amide column with QE-MS enables robust detection of 168 targeted polar metabolites and thousands of additional features simultaneously. Data processing is carried out with commercially available software in a highly efficient way, and unknown features extracted from the mass spectra can be queried in databases.

| S-EPMC4109060 | biostudies-literature

Analyzing gender inequality through large-scale Facebook advertising data.

Project description:Online social media are information resources that can have a transformative power in society. While the Web was envisioned as an equalizing force that allows everyone to access information, the digital divide prevents large amounts of people from being present online. Online social media, in particular, are prone to gender inequality, an important issue given the link between social media use and employment. Understanding gender inequality in social media is a challenging task due to the necessity of data sources that can provide large-scale measurements across multiple countries. Here, we show how the Facebook Gender Divide (FGD), a metric based on aggregated statistics of more than 1.4 billion users in 217 countries, explains various aspects of worldwide gender inequality. Our analysis shows that the FGD encodes gender equality indices in education, health, and economic opportunity. We find gender differences in network externalities that suggest that using social media has an added value for women. Furthermore, we find that low values of the FGD are associated with increases in economic gender equality. Our results suggest that online social networks, while suffering evident gender imbalance, may lower the barriers that women have to access to informational resources and help to narrow the economic gender gap.

| S-EPMC6142225 | biostudies-literature

Analyzing large scale genomic data on the cloud with Sparkhit.

Project description:Motivation:The increasing amount of next-generation sequencing data poses a fundamental challenge on large scale genomic analytics. Existing tools use different distributed computational platforms to scale-out bioinformatics workloads. However, the scalability of these tools is not efficient. Moreover, they have heavy run time overheads when pre-processing large amounts of data. To address these limitations, we have developed Sparkhit: a distributed bioinformatics framework built on top of the Apache Spark platform. Results:Sparkhit integrates a variety of analytical methods. It is implemented in the Spark extended MapReduce model. It runs 92-157 times faster than MetaSpark on metagenomic fragment recruitment and 18-32 times faster than Crossbow on data pre-processing. We analyzed 100 terabytes of data across four genomic projects in the cloud in 21?h, which includes the run times of cluster deployment and data downloading. Furthermore, our application on the entire Human Microbiome Project shotgun sequencing data was completed in 2?h, presenting an approach to easily associate large amounts of public datasets with reference data. Availability and implementation:Sparkhit is freely available at: https://rhinempi.github.io/sparkhit/. Contact:asczyrba@cebitec.uni-bielefeld.de. Supplementary information:Supplementary data are available at Bioinformatics online.

| S-EPMC5925781 | biostudies-literature

ODT FLOW: Extracting, analyzing, and sharing multi-source multi-scale human mobility.

Project description:In response to the soaring needs of human mobility data, especially during disaster events such as the COVID-19 pandemic, and the associated big data challenges, we develop a scalable online platform for extracting, analyzing, and sharing multi-source multi-scale human mobility flows. Within the platform, an origin-destination-time (ODT) data model is proposed to work with scalable query engines to handle heterogenous mobility data in large volumes with extensive spatial coverage, which allows for efficient extraction, query, and aggregation of billion-level origin-destination (OD) flows in parallel at the server-side. An interactive spatial web portal, ODT Flow Explorer, is developed to allow users to explore multi-source mobility datasets with user-defined spatiotemporal scales. To promote reproducibility and replicability, we further develop ODT Flow REST APIs that provide researchers with the flexibility to access the data programmatically via workflows, codes, and programs. Demonstrations are provided to illustrate the potential of the APIs integrating with scientific workflows and with the Jupyter Notebook environment. We believe the platform coupled with the derived multi-scale mobility data can assist human mobility monitoring and analysis during disaster events such as the ongoing COVID-19 pandemic and benefit both scientific communities and the general public in understanding human mobility dynamics.

| S-EPMC8341631 | biostudies-literature

Identifying main effects and epistatic interactions from large-scale SNP data via adaptive group Lasso.

Project description:BACKGROUND: Single nucleotide polymorphism (SNP) based association studies aim at identifying SNPs associated with phenotypes, for example, complex diseases. The associated SNPs may influence the disease risk individually (main effects) or behave jointly (epistatic interactions). For the analysis of high throughput data, the main difficulty is that the number of SNPs far exceeds the number of samples. This difficulty is amplified when identifying interactions. RESULTS: In this paper, we propose an Adaptive Group Lasso (AGL) model for large-scale association studies. Our model enables us to analyze SNPs and their interactions simultaneously. We achieve this by introducing a sparsity constraint in our model based on the fact that only a small fraction of SNPs is disease-associated. In order to reduce the number of false positive findings, we develop an adaptive reweighting scheme to enhance sparsity. In addition, our method treats SNPs and their interactions as factors, and identifies them in a grouped manner. Thus, it is flexible to analyze various disease models, especially for interaction detection. However, due to the intensive computation when millions of interaction terms needs to be searched in the model fitting, our method needs to combined with some filtering methods when applied to genome-wide data for detecting interactions. CONCLUSION: By using a wide range of simulated datasets and a real dataset from WTCCC, we demonstrate the advantages of our method.

| S-EPMC3203332 | biostudies-literature

Identifying Beneficial Learning Behaviors from Large-Scale Interaction Data

Project description:Understanding the effect of learning behavior is fundamental to improving learning outcomes. In this paper, we perform a behavioral analysis based on data from a large high-stakes exam preparation platform. By measuring the importance of a set of candidate learning behaviors in predicting final exam outcomes, we identify a suite of beneficial behaviors. In particular, we find that breadth (wide coverage of content per week) and intensity together with consistency (frequent and equal-length practice for a limited period) are most predictive of final exam success rate, among eleven studied behaviors.

| S-EPMC7334710 | biostudies-literature

Extracting quantitative genetic interaction phenotypes from matrix combinatorial RNAi.

Project description:BackgroundSystematic measurement of genetic interactions by combinatorial RNAi (co-RNAi) is a powerful tool for mapping functional modules and discovering components. It also provides insights into the role of epistasis on the way from genotype to phenotype. The interpretation of co-RNAi data requires computational and statistical analysis in order to detect interactions reliably and sensitively.ResultsWe present a comprehensive approach to the analysis of univariate phenotype measurements, such as cell growth. The method is based on a quantitative model and is demonstrated on two example Drosophila cell culture data sets. We discuss adjustments for technical variability, data quality assessment, model parameter fitting and fit diagnostics, choice of scale, and assessment of statistical significance.ConclusionsAs a result, we obtain quantitative genetic interactions and interaction networks reflecting known biological relationships between target genes. The reliable extraction of presence, absence, and strength of interactions provides insights into molecular mechanisms.

| S-EPMC3230910 | biostudies-literature

CausalR: extracting mechanistic sense from genome scale data.

Project description:SummaryUtilization of causal interaction data enables mechanistic rather than descriptive interpretation of genome-scale data. Here we present CausalR, the first open source causal network analysis platform. Implemented functions enable regulator prediction and network reconstruction, with network and annotation files created for visualization in Cytoscape. False positives are limited using the introduced Sequential Causal Analysis of Networks approach.Availability and implementationCausalR is implemented in R, parallelized, and is available from Bioconductor.Contactglyn.x.bradley@gsk.com.Supplementary informationSupplementary data are available at Bioinformatics online.

| S-EPMC5870775 | biostudies-literature

Quantitative Missense Variant Effect Prediction Using Large-Scale Mutagenesis Data.

Project description:Large datasets describing the quantitative effects of mutations on protein function are becoming increasingly available. Here, we leverage these datasets to develop Envision, which predicts the magnitude of a missense variant's molecular effect. Envision combines 21,026 variant effect measurements from nine large-scale experimental mutagenesis datasets, a hitherto untapped training resource, with a supervised, stochastic gradient boosting learning algorithm. Envision outperforms other missense variant effect predictors both on large-scale mutagenesis data and on an independent test dataset comprising 2,312 TP53 variants whose effects were measured using a low-throughput approach. This dataset was never used for hyperparameter tuning or model training and thus serves as an independent validation set. Envision prediction accuracy is also more consistent across amino acids than other predictors. Finally, we demonstrate that Envision's performance improves as more large-scale mutagenesis data are incorporated. We precompute Envision predictions for every possible single amino acid variant in human, mouse, frog, zebrafish, fruit fly, worm, and yeast proteomes (https://envision.gs.washington.edu/).

| S-EPMC5799033 | biostudies-literature

Evaluating epistatic interaction signals in complex traits using quantitative traits.

Project description:Rheumatoid arthritis (RA) is a complex, chronic inflammatory disease implicated to have several plausible candidate loci; however, these may not account for all the genetic variations underlying RA. Common disorders are hypothesized to be highly complex with interaction among genes and other risk factors playing a major role in the disease process. This complexity is further magnified because such interactions may be with or without a strong independent effect and are thus difficult to detect using traditional statistical methodologies. The main challenge to analyze such gene x gene and gene x environment interaction is attributed to a phenomenon referred to as the "curse of dimensionality." Several combinatorial methodologies have been proposed to tackle this analytical challenge. Because quantitative traits underlie complex phenotypes and contain more information on the trait variation within genotypes than qualitative dichotomy, analyzing quantitative traits correlated with the affection status is a more powerful tool for mapping such trait genes. Recently, a generalized multifactor dimensionality reduction method was proposed that allows for adjustment for discrete and quantitative traits and can be used to analyze qualitative and quantitative phenotypes in a population based study design.In this report, we evaluate the efficiency of the generalized multifactor dimensionality reduction statistical suite to decipher small interacting factors that contribute to RA disease pathogenesis.

| S-EPMC2795985 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data