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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

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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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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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Gene Evolution - Fast or Slow?02:05

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The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
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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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An Ultrahigh-throughput Microfluidic Platform for Single-cell Genome Sequencing
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Streamlined Genome Sequence Compression using Distributed Source Coding.

Shuang Wang1, Xiaoqian Jiang1, Feng Chen2

  • 1Division of Biomedical Informatics, University of California, San Diego, La Jolla, CA, USA.

Cancer Informatics
|December 19, 2014
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Summary
This summary is machine-generated.

This study introduces a new genome sequence compression algorithm for low-power sequencing devices. The efficient method uses distributed source coding for effective data reduction, outperforming existing algorithms.

Keywords:
distributed source codinggenome compressiongraphical model

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Area of Science:

  • Bioinformatics
  • Genomics
  • Data Compression

Background:

  • Miniaturized sequencing devices present unique challenges due to limited resources.
  • Existing genome compression algorithms often require significant computational power on the encoder side, making them unsuitable for these devices.

Purpose of the Study:

  • To develop a streamlined genome sequence compression algorithm tailored for resource-constrained sequencing devices.
  • To address the limitations of current compression techniques in low-power environments.

Main Methods:

  • Examined distributed source coding theory to design a low-complexity protocol.
  • Developed a customized reference-based genome compression protocol.
  • Adaptively employed syndrome coding or hash coding based on sequence variation for compressing subsequences of changing code length.

Main Results:

  • The proposed reference-based protocol demonstrated promising compression performance.
  • The method is suitable for low-complexity client-side encoding.
  • Experimental results indicate competitive performance compared to the state-of-the-art algorithm (GRS).

Conclusions:

  • The developed algorithm offers an effective solution for genome sequence compression on miniaturized devices.
  • The adaptive coding strategy efficiently handles variations in genome sequences.
  • This approach supports the advancement of portable and accessible genomic technologies.