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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.
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

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...
Comparing Mitochondrial, Chloroplast, and Prokaryotic Genomes02:16

Comparing Mitochondrial, Chloroplast, and Prokaryotic Genomes

The present-day mitochondrial and chloroplast genomes have retained some of the characteristics of their ancestral prokaryotes and also have acquired new attributes during their evolution within eukaryotic cells. Like prokaryotic genomes, mitochondrial and chloroplast genomes neither bind with histone-like proteins nor show complex packaging into chromosome-like structures, as observed in eukaryotes. Unlike mitotic cell divisions observed in eukaryotic cells, mitochondria and chloroplasts...
Genomics02:02

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...
Next-generation Sequencing03:00

Next-generation Sequencing

The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features.
Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...

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Updated: Jun 4, 2026

Metagenomic Analysis of Silage
08:43

Metagenomic Analysis of Silage

Published on: January 13, 2017

A comparative analysis of parallel computing approaches for genome assembly.

Munib Ahmed1, Ishfaq Ahmad, Samee Ullah Khan

  • 1Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX 76019, USA. munib.ahmed@mavs.uta.edu

Interdisciplinary Sciences, Computational Life Sciences
|March 4, 2011
PubMed
Summary
This summary is machine-generated.

Genomic data assembly is computationally intensive. This study analyzes parallel algorithms to find the most efficient ones for varying genomic data sizes and complexities, improving bioinformatics workflows.

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Last Updated: Jun 4, 2026

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08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • The rapid growth of sequenced genomic data outpaces computational analysis capabilities.
  • Genome assembly is a computationally intensive bioinformatics process, often requiring weeks of compute time.
  • Existing parallel assembly algorithms lack thorough analysis using parallel computing metrics.

Purpose of the Study:

  • To investigate the scalability and efficiency of parallel genome assembly algorithms.
  • To establish the relationship between genomic data characteristics (size, repetition) and optimal parallel assembly algorithm selection.
  • To provide a comparative analysis of widely used parallel genome assembly approaches.

Main Methods:

  • Comparative analysis of selected parallel genome assembly algorithms.
  • Evaluation based on parallel computing metrics.
  • Assessment across diverse genomic datasets varying in size and repetition degree.

Main Results:

  • Identification of performance variations among parallel assembly algorithms based on data characteristics.
  • Quantification of scalability and efficiency for different algorithms.
  • Establishment of correlations between data complexity and algorithm performance.

Conclusions:

  • Parallel algorithms offer significant speedups for genome assembly.
  • Algorithm choice critically depends on genomic data size and repetition.
  • Further research is needed to optimize parallel genome assembly for diverse biological datasets.