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Related Concept Videos

Genomics02:02

Genomics

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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...
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Sanger Sequencing01:57

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DNA sequencing is a fundamental technique that is routinely used in the biological sciences. This method can be applied to a range of questions at different scales - from the sequencing of a cloned DNA fragment or the study of a mutation in a gene up to whole-genome sequencing. However, despite the widespread use of sequencing today, it was not until 1977 that Fredrick Sanger and his collaborators developed the chain-termination method to decode DNA sequences. It relies on the separation of a...
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Evolutionary Relationships through Genome Comparisons02:54

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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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Genome Annotation and Assembly03:36

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The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
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Multi-species Conserved Sequences02:51

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Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
Although the genome of each species varies greatly from each other, a few sequences are highly conserved. Such conserved...
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DNA Microarrays02:34

DNA Microarrays

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Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
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Related Experiment Video

Updated: May 10, 2025

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
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Learning and teaching biological data science in the Bioconductor community.

Jenny Drnevich1, Frederick J Tan2, Fabricio Almeida-Silva3,4

  • 1Roy J. Carver Biotechnology Center, University of Illinois Urbana-Champaign, Champaign, Illinois, United States of America.

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Summary
This summary is machine-generated.

This guide overviews Bioconductor project resources for biological data science training. It offers best practices for omics data analysis, aiding learners and educators in this data-intensive field.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Biological research generates vast amounts of data.
  • Effective training in biological data science is crucial.
  • The Bioconductor project offers open-source tools for omics data analysis.

Purpose of the Study:

  • To provide an overview of Bioconductor resources.
  • To highlight best practices for omics data analysis using Bioconductor.
  • To serve as a reference for biological data science education.

Main Methods:

  • Review of Bioconductor project documentation.
  • Identification of key resources for omics data analysis.
  • Compilation of best practices for using Bioconductor.

Main Results:

  • Bioconductor offers a comprehensive ecosystem of software and resources.
  • Specific tools and workflows facilitate various omics data analyses.
  • Best practices enhance reproducibility and efficiency in biological data science.

Conclusions:

  • Bioconductor is a vital resource for biological data science training.
  • Educators and learners can leverage Bioconductor for omics data analysis.
  • Adoption of Bioconductor best practices improves research outcomes.