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Related Concept Videos

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.
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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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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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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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Sequences01:29

Sequences

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Sequences are fundamental mathematical objects consisting of ordered lists of numbers that follow a specific rule or pattern. Sequences are critical in various mathematical concepts, including calculus, series, and number theory. They can model real-world phenomena such as population growth, financial investments, and physical processes like the diminishing height of a bouncing ball.Each number in a sequence is referred to as a term. Typically, the terms are denoted as a1, a2, a3,…, where...
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Downsampling01:20

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When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
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Novel Sequence Discovery by Subtractive Genomics
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SeqCompress: an algorithm for biological sequence compression.

Muhammad Sardaraz1, Muhammad Tahir2, Ataul Aziz Ikram3

  • 1Department of Computing and Technology, Iqra University, Islamabad, Pakistan.

Genomics
|September 1, 2014
PubMed
Summary
This summary is machine-generated.

Next Generation Sequencing generates massive data, increasing storage costs. SeqCompress, a new lossless DNA sequence compression algorithm, offers superior compression gains over existing methods.

Keywords:
CompressionDNAGenome sequencesNGS technologies

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Next Generation Sequencing (NGS) technologies generate vast amounts of biological sequence data.
  • The cost of storing and managing this massive data presents a significant challenge in genomics research.
  • Increasing DNA sequencing rates are outpacing advancements in disk storage capacity.

Purpose of the Study:

  • To address the challenge of biological sequence data storage.
  • To develop an efficient bioinformatics tool for compressing large DNA sequence datasets.
  • To present a novel lossless data compression algorithm for DNA sequences.

Main Methods:

  • Developed SeqCompress, a DNA sequence compression algorithm.
  • The algorithm employs a statistical model and arithmetic coding for lossless compression.
  • Compared SeqCompress performance against existing specialized biological sequence compression tools.

Main Results:

  • SeqCompress effectively addresses the space complexity of biological sequences.
  • Experimental results demonstrate a superior compression gain for SeqCompress.
  • The proposed algorithm shows better performance compared to other current algorithms.

Conclusions:

  • SeqCompress offers an efficient solution for DNA sequence data compression.
  • The algorithm contributes to managing the growing data storage demands in genomics.
  • SeqCompress provides a valuable tool for bioinformatics research dealing with large datasets.