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Review of alignment and SNP calling algorithms for next-generation sequencing data
1Biostatistics Group, Department of Genetics, Wroclaw University of Environmental and Life Sciences, Kożuchowska 7, 51-631, Wroclaw, Poland. magda.mielczarek@up.wroc.pl.
Journal of Applied Genetics
|June 10, 2015
Summary
Bioinformatics tools are essential for processing next-generation sequencing (NGS) data, including genome alignment and single nucleotide polymorphism (SNP) detection. This review covers current NGS data processing algorithms and available software for life sciences research.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Massive parallel sequencing, or next-generation sequencing (NGS), is pivotal in modern life sciences.
- Efficient processing of NGS data requires robust bioinformatics tools for critical tasks like genome alignment and SNP detection.
Purpose of the Study:
- To review the current development of algorithms for next-generation sequencing (NGS) data processing.
- To present an overview of available software for key NGS data analysis tasks.
Main Methods:
- Comparison of two main alignment algorithms: suffix tries (suffix array-based) and hash tables.
- Categorization of SNP and genotype callers into heuristic and probabilistic methods.
Main Results:
- Suffix array aligners offer memory efficiency and speed but lower accuracy compared to hash table-based aligners.
- Hash table algorithms are slower but more sensitive for sequence alignment.
- Diverse software solutions have emerged for NGS data processing, employing various algorithmic approaches.
Conclusions:
- The choice of aligner significantly impacts NGS pipeline efficiency and accuracy.
- Understanding different algorithmic strategies is crucial for selecting appropriate bioinformatics tools.
- This review provides a valuable resource for researchers navigating the landscape of NGS data processing software.
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