Genome Classification
Genome Classification - 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.
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...
12:04
Mouse Genome Engineering Using Designer Nucleases
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Cited by 14 •
2014
Designer nucleases such as zinc finger nucleases (ZFNs) and transcription activator-like effector nucleases (TALENs) can be used to modify the genome of mouse preimplantation embryos by triggering both the nonhomologous end joining (NHEJ) and homologous recombination (HR) pathways. These advances enable the rapid generation of mice with precise genetic modifications.
11:59
Competitive Genomic Screens of Barcoded Yeast Libraries
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Cited by 44 •
2011
We have developed comprehensive, unbiased genome-wide screens to understand gene-drug and gene-environment interactions. Methods for screening these mutant collections are presented.
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...
FAQs
Shared features provide comparable evidence across genomes. Researchers can examine conserved regions or marker genes, compare gene content, and assess genome structure to determine whether samples show similar biological patterns. Adding evolutionary relationships helps place those observations in a broader framework, supporting more informed taxonomic or functional assignments.
Conserved marker genes offer a focused basis for comparison, whereas whole-genome analysis considers broader genome-wide patterns. The first approach can emphasize shared regions selected for comparison; the second can incorporate sequence features across the genome. Choosing between them affects how relatedness and categories are evaluated.
Alignment, similarity-search, clustering, and phylogenetic methods provide complementary ways to evaluate genome comparisons. Together, they can reveal matching sequence patterns, organize genomes into groups, and estimate evolutionary relatedness. The resulting evidence supports assignment to taxonomic or functional categories rather than relying on a single genome feature or comparison.
Genome structure adds information that sequence similarity alone may not capture. Examining how genetic material is organized, alongside gene content and shared sequences, gives a broader basis for comparing genomes. This matters when researchers need to interpret evolutionary relationships or separate taxonomic signals from evidence used to place genomes into functional categories.
A typical workflow begins by examining available genome sequences, conserved marker genes, gene content, or genome-wide patterns. Researchers then apply alignment, similarity-search, clustering, or phylogenetic methods to compare the data. The resulting relationships and shared features are used to assign taxonomic or functional categories and interpret the biological significance of the genomes.
In metagenomics, classification helps interpret environmental samples that may contain genomes from multiple organisms. Comparisons based on sequence features, marker genes, gene content, or whole-genome patterns can organize those data and support identification of biological groups. This provides a framework for studying biodiversity in environmental samples and connecting detected genomes with broader biological categories.
Comparing sequence features, gene content, and genome-wide patterns helps researchers distinguish closely related strains that may share many characteristics. Applying these comparisons across pathogen genomes can reveal variation and place different strains into meaningful groups. The resulting classifications support biological interpretation of pathogen diversity and contribute to genome-based diagnostic development.
Reference databases preserve classified genome information that can be used in later comparisons. As databases improve, researchers gain a stronger basis for interpreting newly analyzed genomes, environmental samples, and evolutionary relationships. This supports biological discovery by making genome patterns easier to relate to taxonomic or functional categories and can also improve genome-based diagnostics.