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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.
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...
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...
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.
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...
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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Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved (Non-model) Organisms
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Comparing memory-efficient genome assemblers on stand-alone and cloud infrastructures.

Dimitrios Kleftogiannis1, Panos Kalnis, Vladimir B Bajic

  • 1Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.

Plos One
|October 3, 2013
PubMed
Summary
This summary is machine-generated.

Genome assembly using next-generation sequencing (NGS) requires significant memory. This study identifies memory-efficient genome assembly methods and strategies to reduce computational demands for processing NGS data.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Genome assembly is a fundamental bioinformatics challenge.
  • Next-generation sequencing (NGS) generates vast amounts of fragmented data.
  • Increasing data volumes necessitate efficient memory usage for genome assembly.

Purpose of the Study:

  • Compare memory-efficient genome assembly techniques.
  • Evaluate assembly quality, memory consumption, and execution time.
  • Identify minimum memory requirements for assembly programs.

Main Methods:

  • Comparative analysis of existing memory-efficient genome assembly methods.
  • Experimental evaluation on conventional multi-purpose computers.
  • Development of novel assembly strategies by combining existing methodologies.

Main Results:

  • Reasonable quality draft assemblies are achievable with limited memory using appropriate methods.
  • Minimum memory requirements for various assembly programs were determined.
  • Two general strategies were proposed to reduce memory footprint in short-read assembly.

Conclusions:

  • Selecting suitable assembly methods is crucial for efficient genome assembly on resource-constrained systems.
  • Cloud infrastructures offer potential for large-scale genome assembly.
  • Understanding computational resource needs is vital for optimizing assembly processes.