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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.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features....
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Sanger Sequencing01:57

Sanger Sequencing

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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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Multi-species Conserved Sequences02:51

Multi-species Conserved Sequences

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Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
Although the genome of each species varies greatly from each other, a few sequences are highly conserved. Such conserved...
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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.
Challenges of the Maxam-Gilbert Method
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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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Collection and Extraction of Saliva DNA for Next Generation Sequencing
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SCA-NGS: Secure compression algorithm for next generation sequencing data using genetic operators and block sorting.

Muhammad Sardaraz1, Muhammad Tahir1

  • 1Department of Computer Science, Faculty of Information Sciences & Technology, COMSATS University Islamabad, Attock Campus, Attock, Punjab, Pakistan.

Science Progress
|June 18, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel lossless algorithm for compressing and encrypting sequencing data, addressing storage and security challenges. The method enhances data security and compression efficiency, outperforming existing techniques.

Keywords:
NGS datadata compressionencryptiongenetic algorithm

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomic Data Science

Background:

  • Advancements in sequencing technologies have led to a dramatic increase in genomic data volume.
  • Managing large-scale sequencing data presents significant challenges in storage, transfer, and processing.
  • Ensuring data security, including confidentiality, integrity, and authenticity, is crucial for genomic research.

Purpose of the Study:

  • To develop a novel lossless, reference-free algorithm for secure compression of sequencing data.
  • To integrate data compression and encryption to enhance data security parameters.
  • To evaluate the performance of the proposed algorithm against existing state-of-the-art methods.

Main Methods:

  • A novel lossless, reference-free algorithm was developed.
  • The algorithm incorporates a preprocessing step before applying a general-purpose compression library.
  • A genetic algorithm was employed for data encryption.

Main Results:

  • Experimental validation on benchmark datasets demonstrated the algorithm's effectiveness.
  • The proposed method achieved superior compression ratios and execution times compared to existing techniques.
  • Comparative analysis confirmed the enhanced security and efficiency of the novel approach.

Conclusions:

  • The developed algorithm offers an effective solution for secure and efficient compression of large-scale sequencing data.
  • Integrating compression with encryption provides a robust approach to address data security concerns in genomics.
  • The findings suggest a promising direction for managing and protecting sensitive genomic information.