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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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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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RNA-seq03:21

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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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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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Updated: Mar 8, 2026

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies
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Next-generation sequencing: big data meets high performance computing.

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This summary is machine-generated.

Next-generation sequencing generates massive genomic data for personalized medicine. Efficient big data algorithms are crucial for leveraging this high-throughput technology effectively.

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Area of Science:

  • Genomics
  • Medical Research
  • Bioinformatics

Background:

  • Next-generation sequencing (NGS) technology has revolutionized medical and genomic research.
  • High-throughput sequencing now produces terabytes of data from billions of DNA/RNA fragments per run.
  • Decreasing costs (approx. $1000/genome) enable large-scale population studies.

Purpose of the Study:

  • To highlight the impact of NGS on medical and genomic research.
  • To emphasize the need for advanced big data algorithms.
  • To underscore the importance of efficient implementation on high-performance computing systems.

Main Methods:

  • Analysis of high-throughput sequencing data.
  • Review of applications in personalized cancer treatment and precision medicine.
  • Discussion of computational challenges and requirements.

Main Results:

  • NGS enables massive data generation for applications like personalized medicine.
  • Reduced sequencing costs make population-scale genomic projects feasible.
  • Effective utilization of NGS data necessitates sophisticated big data algorithms.

Conclusions:

  • The advancement of NGS offers unprecedented opportunities in healthcare and genomics.
  • Developing efficient big data algorithms is essential for realizing the full potential of NGS.
  • High-performance computing is critical for processing and analyzing large-scale genomic datasets.