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Related Concept Videos

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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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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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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In the same year as the discovery of the Sanger sequencing method, another group of scientists, Allan Maxam and Walter Gilbert, demonstrated their chemical-cleavage method for DNA sequencing. The Maxam-Gilbert method relies on using different chemicals that can cleave the DNA sequence at specific sites, the separation of resulting DNA fragments of variable size using electrophoresis, and deciphering the DNA sequence from the resulting gel bands.
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Advancements in molecular biology have revolutionized the identification and characterization of bacteria, with multiple methods leveraging DNA sequencing for enhanced precision. As sequencing technologies improve and costs decline, these approaches are increasingly used in clinical, environmental, and evolutionary studies.Multilocus Sequence Typing (MLST) examines several housekeeping genes, essential chromosomal genes encoding cellular functions, to distinguish strains. Approximately...
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Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens
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A Sequence Distance Graph framework for genome assembly and analysis.

Luis Yanes1, Gonzalo Garcia Accinelli1, Jonathan Wright1

  • 1Earlham Institute, Norwich, Norfolk, NR4 7UZ, UK.

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|November 15, 2019
PubMed
Summary
This summary is machine-generated.

The Sequence Distance Graph (SDG) framework aids genome assembly by integrating various read types and graph formats. It offers a Python API for data analysis, enabling complex multi-stage pipelines for genomic research.

Keywords:
Genome graphgenome assembly

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Genome assembly relies on integrating diverse sequencing data and graph structures.
  • Existing tools often lack flexibility in handling different read types and graph formats.

Purpose of the Study:

  • To introduce the Sequence Distance Graph (SDG) framework for comprehensive genome assembly analysis.
  • To provide a flexible platform for mapping raw reads to graphs and performing downstream analyses.

Main Methods:

  • The SDG framework supports de Bruijn graphs and imports data via the Graphical Fragment Assembly (GFA) format.
  • It includes a Python API for navigating graphs, accessing mapped and raw read data, and enabling scripted analyses.
  • The framework allows for saving and loading workspaces, decoupling mapping from analysis for multi-stage pipelines.

Main Results:

  • Demonstrated scaffolding of a short-read graph using long reads.
  • Showcased navigation of paths within a heterozygous graph using simulated parent-offspring trio data.
  • The SDG framework successfully integrates raw reads and graph data for complex analyses.

Conclusions:

  • The SDG framework offers a robust and flexible solution for genome assembly and analysis.
  • Its design supports complex, multi-stage genomic pipelines and interactive data exploration.
  • SDG is freely available, promoting accessibility and further development in the field.