Whole Genome Expression
Whole Genome Expression - Related Videos
08:31
Isolation and Genome Analysis of Single Virions using 'Single Virus Genomics'
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Cited by 2 •
2013
Single Virus Genomics (SVG) is a method to isolate and amplify the genomes of single virons. Viral suspensions of a mixed assemblage are sorted using flow cytometry onto a microscope slide with discrete wells containing agarose, thereby capturing the virion and reducing genome shearing during downstream processing. Whole genome amplification is achieved using multiple displacement amplification (MDA) resulting in genomic material that is suitable for sequencing.
02:02
Genomics
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2020
Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
08:33
Labelling and Visualization of Mitochondrial Genome Expression Products in Baker's Yeast Saccharomyces cerevisiae
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2021
Baker’s yeast mitochondrial genome encodes eight polypeptides. The goal of the current protocol is to label all of them and subsequently visualize them as separate bands.
02:30
Genomic Imprinting and Inheritance
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2020
Diploid organisms inherit genetic material through chromosomes from both parents. Copies of the same gene are known as alleles. In most cases, both alleles are simultaneously expressed and allow various cellular processes to function optimally. If one of the alleles is missing or mutated, the expression of the other allele can compensate; however, this is not true for all genes. The expression of some genes depends on which parent passed the gene to the offspring, through a phenomenon known as...
08:31
Genome Editing
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2023
A well-established technique for modifying specific sequences in the genome is gene targeting by homologous recombination, but this method can be laborious and only works in certain organisms. Recent advances have led to the development of “genome editing”, which works by inducing double-strand breaks in DNA using engineered nuclease enzymes guided to target genomic sites by either proteins or RNAs that recognize specific sequences. When a cell attempts to repair this damage, mutations can be...
FAQs
Its value comes from examining patterns across many transcripts at once. Groups of genes that change together can point to biological pathways, altered cell states, or responses to disease and environmental conditions. This systems-level perspective helps researchers recognize coordinated activity that may be missed when they examine genes individually, while also generating hypotheses about how gene regulation operates.
Comparisons provide the basis for identifying genes associated with a particular biological state. Measuring transcript abundance across cells, tissues, conditions, or developmental stages can reveal which genes respond to change and which expression patterns remain associated with a specific context. The resulting differences help connect observed cellular activity with disease, development, or environmental response.
Expression profiles can identify coordinated changes that are consistent with regulatory relationships or pathway involvement. For example, a set of transcripts that shifts under a disease condition may indicate a biological process worth investigating. These measurements generate hypotheses about regulation, but the profile itself primarily describes RNA production patterns rather than establishing that one gene directly controls another.
A typical workflow begins by extracting RNA from the biological sample. The RNA is then converted into measurable signals using RNA sequencing or microarrays, after which transcript abundance is compared among the selected samples or conditions. Careful comparison is central to the workflow because the biological interpretation depends on relating expression patterns to cell type, tissue, developmental stage, or experimental context.
Researchers apply it when they need a broad assessment of cellular activity, such as studying disease-associated changes, environmental responses, developmental stages, or differences among cell states. The resulting profiles can support systems-level research, help identify candidate biomarkers, and evaluate whether an experimental or therapeutic intervention is associated with a changed biological response.
Researchers examine differences in transcript abundance and look for patterns that correspond to pathways, cell states, or biological responses. These patterns can reveal candidate genes and processes for further study, support biomarker discovery, or indicate whether an intervention affected cellular activity. Interpretation should remain connected to the comparison design because expression profiles provide evidence for hypotheses rather than a complete explanation of mechanism.