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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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RNA-seq03:21

RNA-seq

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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. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
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Genome Annotation and Assembly03:36

Genome Annotation and Assembly

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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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Genomics02:02

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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Cloud-based bioinformatics workflow platform for large-scale next-generation sequencing analyses.

Bo Liu1, Ravi K Madduri2, Borja Sotomayor3

  • 1NEC Labs China, Beijing 100084, China.

Journal of Biomedical Informatics
|January 28, 2014
PubMed
Summary
This summary is machine-generated.

Researchers face challenges with large genome data. This study introduces a cloud-based bioinformatics platform for automated, scalable next-generation sequencing analysis, improving data management and tool accessibility.

Keywords:
BioinformaticsCloud computingGalaxyScientific workflowSequencing analyses

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • The increasing volume of genomic data presents significant storage, processing, and accessibility challenges for biomedical researchers.
  • Existing methods for conducting sequencing analyses are often not dynamic or scalable enough to handle large-scale data deluge.

Purpose of the Study:

  • To propose a Cloud-based bioinformatics workflow platform designed for efficient and scalable execution of large-scale next-generation sequencing analyses.
  • To enhance existing workflow systems with robust data management, tool integration, and automated cloud deployment capabilities.

Main Methods:

  • Extension of the Galaxy workflow system with Globus Transfer for reliable large-data management.
  • Integration of domain-specific analysis tools and a cloud provisioning tool for auto-scaling via HTCondor scheduler.
  • Implementation of semantic verification tools for workflow correctness and Globus Provision for on-demand resource allocation.

Main Results:

  • The proposed platform enables reliable and highly scalable execution of sequencing analyses workflows in a fully automated manner.
  • Demonstrated feasibility through two bioinformatics workflow use cases and performance evaluations.
  • The platform offers on-demand resource allocation and pay-as-you-go pricing.

Conclusions:

  • The Cloud-based bioinformatics workflow platform effectively addresses the challenges of large-scale genome data analysis.
  • The integrated approach enhances data management, tool accessibility, and computational scalability for researchers.
  • The platform provides a reliable and convenient solution for modern sequencing analyses.