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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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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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Collection and Extraction of Saliva DNA for Next Generation Sequencing
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A distributed system for fast alignment of next-generation sequencing data.

Jaydeep K Srimani1, Po-Yen Wu1, John H Phan1

  • 1Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, 30332, USA.

IEEE International Conference on Bioinformatics and Biomedicine Workshops. IEEE International Conference on Bioinformatics and Biomedicine
|August 19, 2016
PubMed
Summary
This summary is machine-generated.

We created a scalable distributed computing system using Berkeley Open Interface for Network Computing (BOINC) for fast and accurate next-generation sequencing (NGS) data alignment. This system optimizes NGS analysis and efficiently handles large datasets, offering significant speed-up.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Next-generation sequencing (NGS) offers high sensitivity for gene expression analysis, surpassing traditional microarray technology.
  • Large NGS datasets demand substantial computational resources for sequence alignment.
  • Understanding the impact of alignment parameters on NGS systems is crucial for accurate analysis.

Purpose of the Study:

  • To develop a scalable distributed computing system using Berkeley Open Interface for Network Computing (BOINC) for efficient NGS data alignment.
  • To validate the system's performance through speed-up analysis and parameter optimization.
  • To compare gene expression levels derived from NGS data with microarray data.

Main Methods:

  • Implementation of a distributed computing system leveraging BOINC for parallel processing of NGS data.
  • Validation through timing tests to quantify speed-up across multiple computers.
  • Optimization of alignment parameters using simulated NGS data.
  • Comparative analysis of gene expression levels between NGS and microarray data.

Main Results:

  • The distributed alignment system demonstrated approximately linear speed-up with increasing computational resources.
  • The system efficiently distributed sequence data and aggregated alignment results from multiple clients.
  • Optimal alignment parameters were identified for accurate gene expression profiling.

Conclusions:

  • The developed BOINC-based distributed system provides a scalable and efficient solution for next-generation sequencing data alignment.
  • This approach accelerates NGS data analysis and facilitates the optimization of alignment parameters.
  • The system enables accurate gene expression quantification, comparable to microarray analysis.