Brb Array Tools
Brb Array Tools - Related Videos
11:09
An Analytical Tool-box for Comprehensive Biochemical, Structural and Transcriptome Evaluation of Oral Biofilms Mediated by Mutans Streptococci
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Cited by 30 •
2011
Biofilms formed on tooth surfaces are highly complex and exposed to constant innate and exogenous environmental challenges, which modulate their architecture, physiology and transcriptome. We developed a toolbox to examine the composition, structural organization and gene expression of oral biofilms, which can be adapted to other areas of biofilm research.
09:16
Array Comparative Genomic Hybridization (Array CGH) for Detection of Genomic Copy Number Variants
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Cited by 7 •
2015
Array CGH for the detection of genomic copy number variants has replaced G-banded karyotype analysis. This paper describes the technology and its application in a diagnostic service laboratory.
19:40
Design and Use of Multiplexed Chemostat Arrays
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Cited by 108 •
2013
We developed and validated a small-footprint array of miniature chemostats built from readily available parts for low cost. Physiological and experimental evolution results were similar to larger volume chemostats. The ministat array provides a compact, inexpensive, and accessible platform for traditional chemostat experiments, functional genomics, and chemical screening applications.
09:27
DNA Microarrays: Sample Quality Control, Array Hybridization and Scanning
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Cited by 10 •
2011
We demonstrate the use of DNA microarrays for expression profiling of the nervous system. We describe RNA quality control, sample labeling, and array hybridization and scanning.
08:27
Multielectrode Array Recordings of the Vomeronasal Epithelium
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Cited by 14 •
2010
Multielectrode array (MEA) recordings provide a method for studying the electrical activity of large populations of neurons. Here, we present the details of a MEA preparation to record from the mouse vomeronasal epithelium while simultaneously stimulating the tissue.
FAQs
These stages prepare probe-level measurements for downstream interpretation. Quality assessment examines the reliability of the input data, normalization places measurements into a form suitable for comparison, and filtering focuses analysis on selected expression measurements. Together, they influence which genes or patterns enter statistical testing, so choices made before analysis can affect the biological conclusions drawn from the dataset.
Each method addresses a different analytical question. Class comparison identifies genes associated with predefined experimental groups, whereas class prediction evaluates patterns that distinguish those groups. Clustering searches for structure or subgroups without relying solely on predefined categories, and survival analysis examines relationships between expression patterns and clinical outcomes. Selecting the method depends on the study design and biological question.
Microarray experiments can produce large collections of individual probe measurements, but biological interpretation usually requires identifying genes, expression patterns, or signatures associated with a condition. BRB-ArrayTools supports this transition by combining data preparation with statistical analysis. The resulting patterns can be related to disease classification, treatment response, molecular subtypes, or gene-function relationships rather than remaining as uninterpreted measurement lists.
A typical workflow begins with quality assessment, followed by normalization and filtering of the expression data. Researchers then select an analysis suited to their question, such as class comparison, class prediction, clustering, or survival analysis. The software helps identify genes or patterns associated with groups or clinical outcomes, after which investigators can interpret the findings biologically and develop hypotheses for laboratory study.
Researchers can apply the suite when they need to examine gene-expression patterns across experimental groups or connect molecular measurements with clinical outcomes. Supported uses include studying disease classification, treatment response, molecular subtypes, and gene-function relationships. These applications allow investigators to evaluate genomic signatures and identify patterns that may help explain biological differences among samples or patient groups.
The analysis can identify genes, expression patterns, or genomic signatures associated with experimental categories or clinical outcomes. Such results may support evaluation of molecular subtypes, treatment-related responses, or relationships between expression and gene function. Because these findings generate hypotheses for further laboratory investigation, they provide a starting point for follow-up biological studies rather than replacing experimental validation.