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Related Concept Videos

Genome Annotation and Assembly03:36

Genome Annotation and Assembly

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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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
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Genomics

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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Enhancing the usability and performance of structured association mapping algorithms using automation,

Ross E Curtis1, Anuj Goyal, Eric P Xing

  • 1Joint Carnegie Mellon - University of Pittsburgh PhD Program in Computational Biology, Carnegie Mellon University, Pittsburgh, PA 15213, USA.

BMC Genetics
|April 5, 2012
PubMed
Summary

Auto-SAM simplifies complex genetic analysis by automating structured association mapping. This tool empowers geneticists to discover gene networks and population structures, enhancing disease-gene association studies.

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Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Structured association mapping is a powerful technique for identifying genetic variations linked to diseases.
  • Current algorithms are often command-line based, requiring specialized expertise and significant effort, which limits their adoption by geneticists.
  • Geneticists frequently opt for less sophisticated methods due to the complexity of advanced association mapping tools.

Purpose of the Study:

  • To enhance the accessibility of structured association mapping for geneticists.
  • To develop an automated system for running complex genetic association algorithms.
  • To provide tools for gene-network discovery and population structure analysis.

Main Methods:

  • Development of an automatic processing system named Auto-SAM.
  • Implementation of parallelization to enable automatic execution of algorithms.
  • Integration of algorithms for gene-network discovery and population structure identification.
  • Inclusion of popular association mapping algorithms alongside five structured association mapping algorithms.

Main Results:

  • Auto-SAM successfully automates structured association mapping, making advanced techniques more accessible.
  • The system facilitates the discovery of gene networks and the analysis of population structure.
  • Auto-SAM supports a range of association mapping algorithms, increasing analytical flexibility.

Conclusions:

  • Auto-SAM is integrated into GenAMap, a user-friendly desktop visualization tool.
  • Both Auto-SAM and GenAMap are developed in JAVA, ensuring cross-platform compatibility.
  • Binaries for GenAMap are available for download, providing a practical solution for genetic research.