Microarrays (Genomics)

An ordered assembly of nucleic acids immobilized on a solid support, often glass (like a microscope slide).

Probes are short pieces of single-stranded DNA.

They are oligonucleotides immobilized on the surface of the array.

Thousands of probes with the same sequence.

Gene expression (transcriptomics).

SNP genotyping (SNP arrays).

Structural variant detection (array CGH).

Next Generation Sequencing (NGS) protocols like RNA-Seq, WES (Whole Exome Sequencing), and WGS (Whole Genome Sequencing).

No, not widely used in the clinic yet.

Spotted arrays.

Printed arrays.

Array Comparative Genomic Hybridisation (aCGH).

Validation of results obtained from gene expression arrays.

The complete set of RNA transcripts produced by the genome.

To discover the expression levels of all genes in a sample.

To determine which genes are expressed at different levels between two sample types.

The biology of your samples by analyzing gene expression patterns.

It can classify samples based on their gene expression profiles.

It helps predict the class or type of a sample based on its gene expression levels.

Lots of copies of the same probe in a spot

Each spot gives the relative expression for one transcript

Detects all known transcripts in one sample

Expression analysis involves measuring gene expression levels in samples.

It is important for understanding gene function and identifying disease mechanisms.

Normalisation is the process of adjusting gene expression data to account for technical variations.

Its significance lies in ensuring comparability across samples for accurate analysis.

Hierarchical clustering is a statistical method used to group genes or samples based on expression profiles.

It is used in genomics to identify patterns and relationships in gene expression data, aiding in the discovery of co-regulated genes.

This helps researchers make sense of complex datasets.

Gene filtering involves selecting a subset of genes based on specific criteria.

It is necessary to reduce data complexity and focus on the most relevant genes for further analysis.

Statistical tests determine the significance of observed differences in gene expression between groups.

Their purpose is to validate findings and ensure results are not due to random chance.

This is critical for drawing reliable conclusions from the data.

Generating a gene list involves compiling genes that meet specific criteria from the analysis.

This list is utilized for downstream analyses, such as pathway enrichment and biological interpretation.

Biological interpretation involves linking gene expression data to biological processes and pathways.

It is important because it provides context to the data, helping researchers understand the biological implications and potential applications of their findings.

Clustering organizes data with similar patterns into classes.

Objects within a class are more similar to each other than to objects outside the class.

Data repositories maximize the utility of microarray experiments by allowing users to share data.

They enable researchers to use other people's data, enhancing collaborative research.

If users provide the Minimum Information About a Microarray Experiment (MIAME), it becomes easier to compare results.

ArrayExpress - run by EBI (European Bioinformatics Institute)

GEO - run by (Gene Expression Omnibus), NCBI (National Center for Biotechnology Information, USA)

qPCR, also known as RT-PCR, is a technique used to confirm microarray results by quantifying gene expression levels.

It involves amplifying the DNA of a gene of interest and comparing it to a housekeeping gene for normalization.

The assessment can be done at the transcript level, measuring DNA, RNA, and protein levels through the use of reverse transcriptase.

RT-PCR can be made quantitative by counting the number of copies of amplified DNA present.

This counting is achieved by using fluorescent molecules, often referred to as “tags,” which allow for the detection of the amplified DNA.

Include a dye in the PCR reaction mix that fluoresces when it binds to double-stranded DNA, such as an intercalating dye like SYBR Green.

Alternatively, label a probe in the PCR that only fluoresces when it is incorporated into the PCR product, such as TaqMan.

qPCR is used to independently confirm differences in RNA levels between samples.

Probe binding can be noisy, and differences detected may not be real, especially for small differences (less than 2-fold).

While RNA-Seq provides a more accurate measure of RNA transcript abundance and is more reproducible over a wider range of concentrations, it is generally more expensive.

At a low score (EPclin < 3), endocrine therapy (ET) alone is sufficient for treatment.

At higher scores, combination therapy (ET+C) is clearly beneficial compared to ET alone.

This test ensures that only patients who will benefit from chemotherapy receive it, optimizing treatment plans.

DFRI stands for distant recurrence-free interval.

SNP microarrays enable Genome-wide Association Studies by allowing the genotyping of large numbers of SNPs in a large number of subjects.

This is achieved by using microarrays that hybridize with genomic DNA adjacent to SNPs rather than RNA transcripts.

The SNP is then extended by one base that is fluorescently labeled and detected using a high-definition scanner.

A spot contains many copies of the same single-stranded oligonucleotide, referred to as a “probe.”

Each probe is designed for genotyping one specific SNP.

SNP microarrays consist of a grid of spots, each containing probes specific to a particular SNP.

When genomic DNA is applied to the microarray, it hybridizes with the complementary probe at each spot.

The hybridized DNA is then extended by one fluorescently labeled base, allowing detection via a high-definition scanner, which identifies the presence or absence of specific SNPs based on the fluorescence signals.

Each spot on a genotyping microarray provides the genotype for one specific SNP.

Up to 5 million spots can be present per sample on the array, allowing for extensive analysis.

This technology enables genome-wide analysis, facilitating large-scale genetic studies.

Software translates the three different color signals from each probe into corresponding genotypes.

A limited number of SNPs (fewer than 50) are typically reviewed manually to ensure accuracy.

The majority of SNPs are processed automatically by the software, streamlining the genotyping process.

Array-CGH is a genomic hybridization technique used to detect copy number variations (CNVs) across the genome.

It involves comparing the genomic DNA of a test sample to a reference sample to identify deletions or duplications of DNA segments.

This method is crucial for understanding genetic disorders, cancer, and other diseases by providing insights into genomic alterations.

Structural variants (SV) refer to large alterations in the genome, such as inversions, translocations, or large deletions and duplications.

Copy number variants (CNV) are a specific type of structural variant that involves changes in the number of copies of a particular DNA segment, leading to either gains (duplications) or losses (deletions) of genetic material.

While all CNVs are considered structural variants, not all structural variants are CNVs; SVs encompass a broader range of genetic changes.

aCGH is a molecular cytogenetic technique used to detect and quantify genomic copy number variations (CNVs) across the genome.

It involves comparing labeled DNA from a test sample to a reference sample, allowing for the identification of deletions, duplications, and other structural variations.

Key applications of aCGH include cancer research, prenatal testing, and the study of genetic disorders, providing insights into genomic imbalances associated with various diseases.