scholarly journals Enabling Data Analysis on High-Throughput Data in Large Data Depository Using Web-Based Analysis Platform - A Case Study on Integrating QUEST with GenePattern in Epigenetics Research

2009 ◽  
Author(s):  
Terry Camerlengo ◽  
Hatice Gulcin Ozer ◽  
Pearlly Yan ◽  
Jeffrey Parvin ◽  
Tim Huang ◽  
...  
Keyword(s):  
Data Analysis ◽  
High Throughput ◽  
Large Data ◽  
Web Based ◽  
High Throughput Data ◽  
Analysis Platform

scholarly journals ReactomeFIViz: the Reactome FI Cytoscape app for pathway and network-based data analysis

F1000Research ◽  
2014 ◽  
Vol 3 ◽  
pp. 146 ◽  
Author(s):  
Guanming Wu ◽  
Eric Dawson ◽  
Adrian Duong ◽  
Robin Haw ◽  
Lincoln Stein
Keyword(s):  
Experimental Data ◽  
Data Analysis ◽  
Graphical Models ◽  
High Throughput ◽  
Interaction Network ◽  
Large Data ◽  
Relevant Information ◽  
Data Sets ◽  
Data Types ◽  
Biological Studies

High-throughput experiments are routinely performed in modern biological studies. However, extracting meaningful results from massive experimental data sets is a challenging task for biologists. Projecting data onto pathway and network contexts is a powerful way to unravel patterns embedded in seemingly scattered large data sets and assist knowledge discovery related to cancer and other complex diseases. We have developed a Cytoscape app called “ReactomeFIViz”, which utilizes a highly reliable gene functional interaction network and human curated pathways from Reactome and other pathway databases. This app provides a suite of features to assist biologists in performing pathway- and network-based data analysis in a biologically intuitive and user-friendly way. Biologists can use this app to uncover network and pathway patterns related to their studies, search for gene signatures from gene expression data sets, reveal pathways significantly enriched by genes in a list, and integrate multiple genomic data types into a pathway context using probabilistic graphical models. We believe our app will give researchers substantial power to analyze intrinsically noisy high-throughput experimental data to find biologically relevant information.

scholarly journals Unraveling molecular mechanism underlying biomaterial and stem cells interaction during cell fate commitment using high throughput data analysis

2020 ◽  
Author(s):  
Erfan Sharifi ◽  
Niusha Khazaei ◽  
Nicholas Kieran ◽  
Sahel Jahangiri Esfahani ◽  
Abdulshakour Mohammadnia ◽  
...  
Keyword(s):  
Stem Cells ◽  
Data Analysis ◽  
Molecular Mechanism ◽  
Cell Fate ◽  
High Throughput ◽  
High Throughput Data ◽  
High Throughput Data Analysis

scholarly journals eRNA: a graphic user interface-based tool optimized for large data analysis from high-throughput RNA sequencing

BMC Genomics ◽  
2014 ◽  
Vol 15 (1) ◽  
pp. 176 ◽  
Author(s):  
Tiezheng Yuan ◽  
Xiaoyi Huang ◽  
Rachel L Dittmar ◽  
Meijun Du ◽  
Manish Kohli ◽  
...  
Keyword(s):  
Data Analysis ◽  
User Interface ◽  
Rna Sequencing ◽  
High Throughput ◽  
Large Data ◽  
Graphic User Interface ◽  
Large Data Analysis

High-Throughput Data Analysis of Proteomic Mass Spectra on the SwissBioGrid

2011 ◽  
pp. 228-244
Author(s):  
Andreas Quandt ◽  
Sergio Maffioletti ◽  
Cesare Pautasso ◽  
Heinz Stockinger ◽  
Frederique Lisacek
Keyword(s):  
Mass Spectrometry ◽  
Data Analysis ◽  
High Throughput ◽  
Protein Function ◽  
Mass Spectra ◽  
High Throughput Data ◽  
Analysis Process ◽  
Speed Up ◽  
Grid Infrastructures ◽  
High Throughput Data Analysis

Proteomics is currently one of the most promising fields in bioinformatics as it provides important insights into the protein function of organisms. Mass spectrometry is one of the techniques to study the proteome, and several software tools exist for this purpose. The authors provide an extendable software platform called swissPIT that combines different existing tools and exploits Grid infrastructures to speed up the data analysis process for the proteomics pipeline.

scholarly journals Unraveling molecular mechanism underlying biomaterial and stem cells interaction during cell fate commitment using high throughput data analysis

Gene ◽  
2021 ◽  
pp. 146111
Author(s):  
Erfan Sharifi ◽  
Niusha Khazaei ◽  
Nicholas W. Kieran ◽  
Sahel Jahangiri Esfahani ◽  
Abdulshakour Mohammadnia ◽  
...  
Keyword(s):  
Stem Cells ◽  
Data Analysis ◽  
Molecular Mechanism ◽  
Cell Fate ◽  
High Throughput ◽  
High Throughput Data ◽  
High Throughput Data Analysis

scholarly journals NASQAR: A web-based platform for high-throughput sequencing data analysis and visualization

2019 ◽  
Author(s):  
Ayman Yousif ◽  
Nizar Drou ◽  
Jillian Rowe ◽  
Mohammed Khalfan ◽  
Kristin C Gunsalus
Keyword(s):  
New York ◽  
Data Analysis ◽  
Open Source ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
Web Applications ◽  
Rna Seq ◽  
Sequencing Data ◽  
Web Based ◽  
Link Type

AbstractBackgroundAs high-throughput sequencing applications continue to evolve, the rapid growth in quantity and variety of sequence-based data calls for the development of new software libraries and tools for data analysis and visualization. Often, effective use of these tools requires computational skills beyond those of many researchers. To ease this computational barrier, we have created a dynamic web-based platform, NASQAR (Nucleic Acid SeQuence Analysis Resource).ResultsNASQAR offers a collection of custom and publicly available open-source web applications that make extensive use of a variety of R packages to provide interactive data analysis and visualization. The platform is publicly accessible at http://nasqar.abudhabi.nyu.edu/. Open-source code is on GitHub at https://github.com/nasqar/NASQAR, and the system is also available as a Docker image at https://hub.docker.com/r/aymanm/nasqarall. NASQAR is a collaboration between the core bioinformatics teams of the NYU Abu Dhabi and NYU New York Centers for Genomics and Systems Biology.ConclusionsNASQAR empowers non-programming experts with a versatile and intuitive toolbox to easily and efficiently explore, analyze, and visualize their Transcriptomics data interactively. Popular tools for a variety of applications are currently available, including Transcriptome Data Preprocessing, RNA-seq Analysis (including Single-cell RNA-seq), Metagenomics, and Gene Enrichment.

scholarly journals Power and Stability Properties of Resampling-Based Multiple Testing Procedures with Applications to Gene Oncology Studies

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Dongmei Li ◽  
Timothy D. Dye
Keyword(s):  
Data Analysis ◽  
Sample Size ◽  
High Throughput ◽  
Rate Control ◽  
Multiple Testing ◽  
Testing Procedures ◽  
High Throughput Data ◽  
False Discovery ◽  
Multiple Testing Procedures ◽  
High Throughput Data Analysis

Resampling-based multiple testing procedures are widely used in genomic studies to identify differentially expressed genes and to conduct genome-wide association studies. However, the power and stability properties of these popular resampling-based multiple testing procedures have not been extensively evaluated. Our study focuses on investigating the power and stability of seven resampling-based multiple testing procedures frequently used in high-throughput data analysis for small sample size data through simulations and gene oncology examples. The bootstrap single-step minPprocedure and the bootstrap step-down minPprocedure perform the best among all tested procedures, when sample size is as small as 3 in each group and either familywise error rate or false discovery rate control is desired. When sample size increases to 12 and false discovery rate control is desired, the permutation maxTprocedure and the permutation minPprocedure perform best. Our results provide guidance for high-throughput data analysis when sample size is small.

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