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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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Genome Annotation and Assembly03:36

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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 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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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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Collection and Extraction of Saliva DNA for Next Generation Sequencing
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HIVE-hexagon: high-performance, parallelized sequence alignment for next-generation sequencing data analysis.

Luis Santana-Quintero1, Hayley Dingerdissen2, Jean Thierry-Mieg3

  • 1Center for Biologics Evaluation and Research, US Food and Drug Administration, Rockville, Maryland, United States of America.

Plos One
|June 12, 2014
PubMed
Summary
This summary is machine-generated.

The HIVE-hexagon DNA sequence aligner offers a novel algorithmic solution to accelerate Next-Generation Sequencing data analysis. This tool efficiently computes accurate alignments, addressing the significant computational challenges in bioinformatics pipelines.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Next-Generation Sequencing (NGS) generates massive datasets, posing significant computational challenges.
  • Sequence alignment is a critical but computationally intensive step in bioinformatics pipelines, consuming up to 90% of CPU time.
  • Existing methods struggle with the scale and complexity of modern genomic data.

Purpose of the Study:

  • To introduce HIVE-hexagon, a novel DNA sequence aligner designed for high-performance computing environments.
  • To present an algorithmic solution that addresses the computational bottleneck in processing large-scale sequencing data.
  • To improve the speed and accuracy of sequence alignment for Next-Generation Sequencing data.

Main Methods:

  • HIVE-hexagon utilizes a cloud-based High-performance Integrated Virtual Environment (HIVE) optimized for large data.
  • Novel approaches are implemented to exploit sequence space characteristics and CPU, RAM, and I/O architecture.
  • Key algorithmic components include non-redundification, sequence sorting, linearized dynamic programming with floating diagonals, and cross-similarity consideration.

Main Results:

  • HIVE-hexagon significantly reduces the computational time required for sequence alignment.
  • The aligner achieves accurate sequence alignments by employing optimized computational strategies.
  • The system effectively handles the storage and analysis of extra-large datasets.

Conclusions:

  • HIVE-hexagon provides an efficient and accurate solution for DNA sequence alignment in Next-Generation Sequencing data analysis.
  • The developed algorithm overcomes computational limitations, enabling faster and more scalable bioinformatics workflows.
  • This approach contributes to advancing the field of computational biology by optimizing critical data processing steps.