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Published on: April 11, 2016
Computational characterisation of cancer molecular profiles derived using next generation sequencing
Urszula Oleksiewicz1, Katarzyna Tomczak2, Jakub Woropaj3
1Laboratory of Gene Therapy, Department of Cancer Immunology, The Greater Poland Cancer Centre, Poznan, Poland ; Department of Cancer Immunology and Diagnostics, Chair of Medical Biotechnology, Poznan University of Medical Sciences, Poznan, Poland ; These authors contributed equally to this paper.
Analyzing cancer genetics involves processing complex next-generation sequencing (NGS) data. This guide details NGS data quality control, processing, and analysis tools for oncogenomic datasets.
Area of Science:
- Oncology
- Genomics
- Bioinformatics
Background:
- Cancer genetics research relies on identifying somatic genetic aberrations for malignant transformation.
- Next-generation sequencing (NGS) generates vast amounts of data on genomic alterations in cancer patients.
- Analyzing raw NGS reads presents significant algorithmic and computational challenges.
Purpose of the Study:
- To outline common steps for quality control and processing of NGS data in cancer research.
- To highlight methodological challenges and solutions for extracting biological information from NGS data.
- To guide researchers in selecting appropriate bioinformatics tools for oncogenomic data analysis.
Main Methods:
- Quality control and preprocessing of raw sequencing reads.
- Alignment of NGS reads to reference genomes.
- Bioinformatic analysis for detecting mutations, copy number variations, and gene expression changes.
- Software and infrastructure for integrated oncogenomic data analysis.
Main Results:
- Common steps for NGS data quality control and processing are detailed.
- Importance of accurate read alignment and challenges in data interpretation are discussed.
- Comprehensive lists of available software tools for oncogenomic data analysis are provided.
Conclusions:
- Effective analysis of NGS data is crucial for understanding cancer genetics.
- Choosing the right bioinformatics tools is essential for accurate oncogenomic insights.
- This article serves as a guide for navigating the complexities of cancer genomics data analysis.
