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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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[Bioinformatics and Next -generation Sequencing].

A Krejčí, P Müller, B Vojtěšek

    Klinicka Onkologie : Casopis Ceske a Slovenske Onkologicke Spolecnosti
    |September 17, 2015
    PubMed
    Summary

    Next-generation sequencing (NGS) generates massive data, requiring bioinformatics methods for analysis. This review covers essential NGS bioinformatics for cancer research and highlights common data processing challenges.

    Area of Science:

    • Genomics
    • Bioinformatics
    • Computational Biology

    Context:

    • Next-generation sequencing (NGS) is a powerful research tool increasingly used in clinical settings.
    • NGS technologies generate substantial volumes of data essential for biological insights.
    • The interpretation of complex genomic data necessitates advanced computational approaches.

    Purpose:

    • To introduce fundamental bioinformatics methods for next-generation sequencing in oncological research.
    • To discuss common challenges encountered during the processing and interpretation of NGS data in cancer studies.

    Summary:

    • This review provides an overview of essential bioinformatics techniques crucial for analyzing next-generation sequencing data in cancer research.
    • It details common computational methods used to process and interpret vast datasets generated by modern sequencers.

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  • The article also addresses prevalent issues that complicate data analysis and result interpretation in the field of oncology.

    • Impact:

    • Facilitates a better understanding of the computational requirements for NGS in cancer research.
    • Aids researchers and clinicians in navigating the complexities of bioinformatics for oncological applications.
    • Highlights areas for improvement in data processing and interpretation methodologies for NGS in oncology.