Genomic Diversity
Genomic Diversity - Related Videos
09:51
Enhanced Genome Editing with Cas9 Ribonucleoprotein in Diverse Cells and Organisms
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Cited by 45 •
2018
Utilizing a preassembled Cas9 ribonucleoprotein complex (RNP) is a powerful method for precise, efficient genome editing. Here, we highlight its utility across a broad range of cells and organisms, including primary human cells and both classic and emerging model organisms.
06:40
Plant Diversity - Concepts
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2019
From Water to Land Kingdom Plantae first appeared about 410 million years ago as green algae transitioned from water to land. Though challenging, this transition benefited early colonizers in several ways. Initially, most living organisms (including plants and animals) were ocean dwelling, making aquatic environments crowded and highly competitive. In contrast, land was a relatively uncolonized environment with ample resources and little to no predators or competitors. Terrestrial environments...
05:49
Community Diversity - Concepts
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2019
Populations do not live in isolation; thus, every population interacts with others in certain ways. These interactions give rise to a network of populations. Hence, an ecological community is composed of such population networks of various species interacting with each other within the same area. These biological, or biotic, components may also closely interact with non-living, or abiotic, components, forming an ecosystem. Ecosystems may be as small as the microbial communities inside the human...
13:04
Community Diversity - Student Protocol
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2019
Measuring Biodiversity in Areas with Different Disturbance Regimes ExpandNOTE: In this experiment, you will compare the biodiversity in a less disturbed area against a more highly disturbed area. HYPOTHESES: The experimental hypothesis could be that a less disturbed area will contain a greater diversity of species than a highly disturbed area. The null hypothesis could be that there will be no difference in diversity between less disturbed and highly disturbed areas. To begin, divide into...
12:39
A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
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Cited by 6 •
2012
Our Bayesian Change Point (BCP) algorithm builds on state-of-the-art advances in modeling change-points via Hidden Markov Models and applies them to chromatin immunoprecipitation sequencing (ChIPseq) data analysis. BCP performs well in both broad and punctate data types, but excels in accurately identifying robust, reproducible islands of diffuse histone enrichment.
FAQs
Genomic diversity can increase through several complementary processes. Mutation introduces variation in DNA sequences, while recombination redistributes existing alleles. Chromosome rearrangements change genome structure, and gene flow transfers alleles among populations. Together, these mechanisms expand or redistribute heritable variation, giving evolution and biological adaptation more potential to occur.
It matters because variation provides differences on which selection and environmental change can act. If populations contain different alleles or genome structures, their responses to changing conditions may differ. Studying those patterns helps connect genomic variation with adaptation, evolutionary change, and the biological consequences of climate shifts.
Gene flow redistributes alleles between populations, so it can alter the amount and pattern of variation observed across groups. That makes it important when interpreting population structure and reconstructing evolutionary relationships. In a biological study, detecting differences or similarities among populations therefore requires considering whether alleles have moved between them.
Researchers compare DNA sequences and genome structures across individuals, populations, or species to characterize genomic differences and similarities. They can use the resulting patterns to assess population structure and reconstruct evolutionary relationships. The same measurements also provide evidence about genetic health and responses to environmental change, connecting genomic data with broader biological questions.
These analyses can identify population structure, assess genetic health, and show how variation is distributed among groups. That information supports conservation planning by helping researchers evaluate biological differences within or between populations. It also provides a way to monitor how genetic resources may relate to adaptation and responses to environmental change.
Genomic diversity is relevant because disease research and studies of emerging pathogens examine biological variation and change. Comparing genomic patterns can place these questions within an evolutionary context, while related analyses can also address responses to selection and environmental shifts. This connects genome-level evidence with pathogen emergence and changing biological conditions.