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Genomic Revolution-Driven Cancer Research

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Abstract

In recent years of genomic era, continuous development of cutting-edge technologies has enormously increased the generation of genomic data and markedly transformed bioinformatic research. This plethora of genomic information accelerated the high-end digital storage capacity and computational efficiency. Now these developments and the availability of pinnacle amount of genomic data in the public database aid the potential application of genomics toward healthcare research in the context of clinical diagnosis and treatments for human disease. Along these lines, it is essential for the biomedical students and researchers to acquire up-to-date knowledge on the wide range of computational genomic tools and analysis methods involved in disease gene discovery. In this chapter, we provide an overview of the computational or downstream analysis of next-generation sequence (NGS) data and some guidance on its applications in clinical research with head and neck squamous cell carcinoma as an example. Overall, our aim is to give an accessible entry point to analyses of NGS data for identification of potential disease risk variants.

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Authors and Affiliations

  1. CSIR-National Environmental Engineering Research Institute, Hyderabad Zonal Centre, Hyderabad, India

    Meganathan P. Ramakodi

  2. Genetics and Bioinformatics, Dasman Diabetes Institute, Dasman, Kuwait

    Muthukrishnan Eaaswarkhanth

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  1. Meganathan P. Ramakodi
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  2. Muthukrishnan Eaaswarkhanth
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Corresponding author

Correspondence to Muthukrishnan Eaaswarkhanth .

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Editors and Affiliations

  1. Princess Al-Jawhara Center of Excellence in Research of Hereditary Disorders, Department of Genetic Medicine, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia

    Noor Ahmad Shaik

  2. Princess Dr. Najla Bint Saud Al-Saud Center for Excellence Research in Biotechnology, Department of Biological Sciences, King Abdulaziz University, Jeddah, Saudi Arabia

    Khalid Rehman Hakeem

  3. Princess Al-Jawhara Center of Excellence in Research of Hereditary Disorders, Department of Genetic Medicine, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia

    Babajan Banaganapalli

  4. Princess Al-Jawhara Center of Excellence in Research of Hereditary Disorders, Department of Genetic Medicine, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia

    Ramu Elango

Glossary

BAM

Binary Alignment Map, a compressed binary format for storing large nucleotide sequence alignments.

FASTQ

The text-based format for storing both a DNA sequence and its corresponding quality scores.

Paired-end

This sequencing procedure involves sequencing both the ends of the DNA fragments in a library and aligning the forward and reverse reads as read pairs.

Phred Q scores

The base calling converts the signals into actual sequence data with this quality scores.

Read

The WGS or WES procedure involves shearing DNA into hundreds of thousands of small fragments, and every single fragment is called a “read.”

Read depth

The average number of times that a given nucleotide in the genome has been read in a sequencing experiment. For instance, a 40× read depth means that each base is present in an average of 40 reads.

SAM

Sequence Alignment Map, a genetic format for storing large nucleotide sequence alignments.

Single-read

This sequencing procedure involves sequencing DNA from only one end.

TAB

The text-based tab-delimited file format.

VCF

Variant Calling Format, a text file format containing meta-information lines, a header line, and then data lines, each containing information about a position in the genome.

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Ramakodi, M.P., Eaaswarkhanth, M. (2019). Genomic Revolution-Driven Cancer Research. In: Shaik, N., Hakeem, K., Banaganapalli, B., Elango, R. (eds) Essentials of Bioinformatics, Volume II. Springer, Cham. https://doi.org/10.1007/978-3-030-18375-2_4

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