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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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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.
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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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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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Genomic DNA in Eukaryotes00:58

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Eukaryotes have large genomes compared to prokaryotes. To fit their genomes into a cell, eukaryotic DNA is packaged extraordinarily tightly inside the nucleus. To achieve this, DNA is tightly wound around proteins called histones, which are packaged into nucleosomes that are joined by linker DNA and coil into chromatin fibers. Additional fibrous proteins further compact the chromatin, which is recognizable as chromosomes during certain phases of cell division.
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Genome Modeling System: A Knowledge Management Platform for Genomics.

Malachi Griffith1, Obi L Griffith2, Scott M Smith3

  • 1The Genome Institute, Washington University in St. Louis, St. Louis, Missouri, United States of America; Department of Genetics, Washington University School of Medicine, St. Louis, Missouri, United States of America.

Plos Computational Biology
|July 10, 2015
PubMed
Summary
This summary is machine-generated.

The Genome Modeling System (GMS) enables large-scale, automated genome analysis with robust data management. This system integrates reproducible pipelines and collaborative bioinformatics development for enhanced genomic research.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Genomic data analysis requires scalable and reproducible pipelines.
  • Managing complex datasets and fostering collaboration in bioinformatics is challenging.

Purpose of the Study:

  • To introduce the Genome Modeling System (GMS), an analysis information management system.
  • To enable automated genome analysis pipelines at a massive scale.
  • To integrate ad-hoc analysis with rigorous, reproducible pipelines.

Main Methods:

  • Development of the Genome Modeling System (GMS) framework.
  • Implementation of detailed sample and data tracking.
  • Creation of reliable and repeatable analysis pipelines.
  • Integration of whole genome, exome, and transcriptome sequencing data analysis.

Main Results:

  • The GMS facilitates massive-scale automated genome analysis.
  • It provides a platform for collaborative bioinformatics development.
  • Demonstrated integrated analysis of multi-omic data from breast cancer cell lines.

Conclusions:

  • The GMS enhances the efficiency and reproducibility of genomic data analysis.
  • It supports both individual researchers and large teams in bioinformatics.
  • The system promotes systematic integration of analysis methods.