General Strategies for Glycan Microarray Data Processing and Analysis

2022 ◽  
pp. 67-87
Author(s):  
J. Sebastian Temme ◽  
Jeffrey C. Gildersleeve
Keyword(s):  
Data Processing ◽  
Microarray Data ◽  
Glycan Microarray

scholarly journals Workflows for microarray data processing in the Kepler environment

2012 ◽  
Vol 13 (1) ◽  
pp. 102 ◽  
Author(s):  
Thomas Stropp ◽  
Timothy McPhillips ◽  
Bertram Ludäscher ◽  
Mark Bieda
Keyword(s):  
Data Processing ◽  
Microarray Data

scholarly journals Tools for generating and analyzing glycan microarray data

2020 ◽  
Vol 16 ◽  
pp. 2260-2271
Author(s):  
Akul Y Mehta ◽  
Jamie Heimburg-Molinaro ◽  
Richard D Cummings
Keyword(s):  
Microarray Data ◽  
Automated Analysis ◽  
Experimental Methods ◽  
Vital Role ◽  
Data Repositories ◽  
Glycan Microarray ◽  
Manual Analysis ◽  
Analysis Tools ◽  
Biological Polymers ◽  
Informatics Tools

Glycans are one of the major biological polymers found in the mammalian body. They play a vital role in a number of physiologic and pathologic conditions. Glycan microarrays allow a plethora of information to be obtained on protein–glycan binding interactions. In this review, we describe the intricacies of the generation of glycan microarray data and the experimental methods for studying binding. We highlight the importance of this knowledge before moving on to the data analysis. We then highlight a number of tools for the analysis of glycan microarray data such as data repositories, data visualization and manual analysis tools, automated analysis tools and structural informatics tools.

scholarly journals NMPP: a user-customized NimbleGen microarray data processing pipeline

2006 ◽  
Vol 22 (23) ◽  
pp. 2955-2957 ◽  
Author(s):  
X. Wang ◽  
H. He ◽  
L. Li ◽  
R. Chen ◽  
X. W. Deng ◽  
...  
Keyword(s):  
Data Processing ◽  
Microarray Data ◽  
Processing Pipeline

Comparative Analysis between Soft and Tools in Microarray Data Processing

2013 ◽  
Author(s):  
Ouafae Kaissi ◽  
Ahmed Moussa ◽  
Brigitte Vannier ◽  
Abdellatif Ghacham
Keyword(s):  
Comparative Analysis ◽  
Data Processing ◽  
Microarray Data

A Generic Concept for Large-scale Microarray Analysis Dedicated to Medical Diagnostics

2006 ◽  
Vol 45 (02) ◽  
pp. 146-152 ◽  
Author(s):  
F. Weninger ◽  
S. Merk ◽  
A. Kohlmann ◽  
T. Haferlach ◽  
M. Dugas
Keyword(s):  
Data Processing ◽  
Clinical Response ◽  
Microarray Analysis ◽  
Microarray Data ◽  
Classification Accuracy ◽  
Large Scale ◽  
Medical Diagnostics ◽  
Microarray Experiments ◽  
Generic Concept ◽  
Response Variables

Summary Background: The development of diagnostic procedures based on microarray analysis confronts the bioinformatician and the biomedical researcher with a variety of challenges. Microarrays generate a huge amount of data. There are many, not yet clearly defined, data processing steps and many clinical response variables which may not match gene expression patterns. Objectives: To design a generic concept for large-scale microarray experiments dedicated to medical diagnostics; to create a system capable of handling several 1000 microarrays per analysis and more than 100 clinical response variables; to design a standardized workflow for quality control, data calibration, identification of differentially expressed genes and estimation of classification accuracy; and to provide a user-friendly interface for clinical researchers with respect to biomedical interpretation. Methods: We designed a database structure suitable for the storage of microarray data and analysis results. We applied statistical procedures to identify differential genes and developed a technique to estimate classification accuracy of gene patterns with confidence intervals. Results: We implemented a Gene Analysis Management System (GAMS) based on this concept, using MySQL for data storage, R/Bioconductor for analysis and PHP for a web-based front-end for the exploration of microarray data and analysis results. This system was utilized with large data sets from several medical disciplines, mainly from oncology (~ 2000 micro-arrays). Conclusions: A systematic approach is necessary for the analysis of microarray experiments in a medical diagnostics setting to get comprehensible results. Due to the complexity of the analysis, data processing (by bioinformaticians) and interactive exploration of results (by biomedical experts) should be separated.

scholarly journals GlyMDB: Glycan Microarray Database and analysis toolset

2019 ◽  
Vol 36 (8) ◽  
pp. 2438-2442 ◽  
Author(s):  
Yiwei Cao ◽  
Sang-Jun Park ◽  
Akul Y Mehta ◽  
Richard D Cummings ◽  
Wonpil Im
Keyword(s):  
Fluorescence Intensity ◽  
Microarray Data ◽  
Motif Discovery ◽  
Binding Proteins ◽  
Biological Activities ◽  
Supplementary Information ◽  
Binding Motif ◽  
Structural Determinants ◽  
Glycan Microarray ◽  
Microarray Database

Abstract Motivation Glycan microarrays are capable of illuminating the interactions of glycan-binding proteins (GBPs) against hundreds of defined glycan structures, and have revolutionized the investigations of protein–carbohydrate interactions underlying numerous critical biological activities. However, it is difficult to interpret microarray data and identify structural determinants promoting glycan binding to glycan-binding proteins due to the ambiguity in microarray fluorescence intensity and complexity in branched glycan structures. To facilitate analysis of glycan microarray data alongside protein structure, we have built the Glycan Microarray Database (GlyMDB), a web-based resource including a searchable database of glycan microarray samples and a toolset for data/structure analysis. Results The current GlyMDB provides data visualization and glycan-binding motif discovery for 5203 glycan microarray samples collected from the Consortium for Functional Glycomics. The unique feature of GlyMDB is to link microarray data to PDB structures. The GlyMDB provides different options for database query, and allows users to upload their microarray data for analysis. After search or upload is complete, users can choose the criterion for binder versus non-binder classification. They can view the signal intensity graph including the binder/non-binder threshold followed by a list of glycan-binding motifs. One can also compare the fluorescence intensity data from two different microarray samples. A protein sequence-based search is performed using BLAST to match microarray data with all available PDB structures containing glycans. The glycan ligand information is displayed, and links are provided for structural visualization and redirection to other modules in GlycanStructure.ORG for further investigation of glycan-binding sites and glycan structures. Availability and implementation http://www.glycanstructure.org/glymdb. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Which to use? - microarray data analysis in input and output data processing

2004 ◽  
Vol 4 (2) ◽  
pp. 56-72 ◽  
Author(s):  
Qingwei Zhang ◽  
Rie Ushijima ◽  
Takatoshi Kawai ◽  
Hiroshi Tanaka
Keyword(s):  
Data Analysis ◽  
Data Processing ◽  
Microarray Data ◽  
Microarray Data Analysis ◽  
Output Data ◽  
Input And Output
Sign in / Sign up

Export Citation Format

Share Document