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Searching and Indexing Genomic Databases via Kernelization.

Travis Gagie¹, Simon J Puglisi¹

¹Helsinki Institute for Information Technology (HIIT) and Department of Computer Science, University of Helsinki , Helsinki , Finland.

Frontiers in Bioengineering and Biotechnology

|February 25, 2015

Summary

This summary is machine-generated.

Genomic databases are growing rapidly. This paper reviews a 20-year-old idea to efficiently search these large datasets by indexing only genomic differences from a reference, connecting it to kernelization in parameterized complexity.

Keywords:

approximate pattern matching data compression genomic databases indexing kernelization random-access reading string algorithms

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

The proliferation of genomic data from advanced sequencing necessitates efficient search and indexing strategies.
Leveraging similarities between genomes is crucial for managing large-scale genomic databases.

Purpose of the Study:

To survey the historical development of indexing genomic databases by focusing on differences from a reference genome.
To explore the connection between this indexing approach and kernelization in parameterized complexity theory.

Main Methods:

Literature review of 20 years of research on indexing genomic databases.
Conceptual analysis linking difference-based indexing to kernelization algorithms.

Main Results:

The concept of indexing genomic differences has evolved over two decades.
A theoretical link is established between difference-based genomic indexing and kernelization techniques.

Conclusions:

Efficiently searching vast genomic datasets can be achieved by indexing variations from a reference genome.
Parameterized complexity, specifically kernelization, offers a theoretical framework for understanding and potentially optimizing these indexing methods.