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Related Concept Videos

Next-generation Sequencing03:00

Next-generation Sequencing

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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.
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Sanger Sequencing01:57

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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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Maxam-Gilbert Sequencing01:05

Maxam-Gilbert Sequencing

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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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Genome Annotation and Assembly03:36

Genome Annotation and Assembly

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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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RNA-seq03:21

RNA-seq

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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
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Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

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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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Novel Sequence Discovery by Subtractive Genomics
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Novel Sequence Discovery by Subtractive Genomics

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A new algorithm for "the LCS problem" with application in compressing genome resequencing data.

Richard Beal1, Tazin Afrin2, Aliya Farheen2

  • 1Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV, USA. r.beal@computer.org.

BMC Genomics
|August 25, 2016
PubMed
Summary
This summary is machine-generated.

A new algorithm for the longest common subsequence (LCS) problem significantly improves genome compression. This method achieves a compression ratio of 360, outperforming existing state-of-the-art techniques for genome resequencing data.

Keywords:
BiologyCompressionGenome resequencingLCSLPFLongest common subsequenceLongest previous factor

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G2-seq: A High Throughput Sequencing-based Technique for Identifying Late Replicating Regions of the Genome
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Area of Science:

  • Computer Science
  • Bioinformatics
  • Genomics

Background:

  • The longest common subsequence (LCS) problem is fundamental in computer science.
  • LCS algorithms underpin current leading reference-based compression methods for genome resequencing data.

Purpose of the Study:

  • To introduce a novel algorithm for solving the LCS problem.
  • To develop an improved reference-based compression scheme for genomic data, motivated by the new LCS algorithm.

Main Methods:

  • A new LCS algorithm utilizing generalized suffix trees to identify common substrings.
  • Construction of a directed acyclic graph (DAG) from maximal common substrings to find the LCS.
  • Development of a compression scheme based on LCS components, not the full LCS.

Main Results:

  • The proposed compression scheme reduced the Homo sapiens genome size from over 3 GB to approximately 8.5 MB.
  • Achieved an exceptional compression ratio of 360, significantly surpassing previous state-of-the-art ratios (157 and 171).

Conclusions:

  • A novel LCS algorithm and a highly effective reference-based compression scheme for genome data have been presented.
  • The new method demonstrates superior performance compared to existing state-of-the-art compression algorithms for genome resequencing.