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Augmented Interval List: a novel data structure for efficient genomic interval search.

Jianglin Feng¹, Aakrosh Ratan^1,2,3, Nathan C Sheffield^1,2,3,4

¹Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA.

Bioinformatics (Oxford, England)

|June 1, 2019

Summary

This summary is machine-generated.

We developed the Augmented Interval List (AIList), a new data structure for efficiently searching genomic interval data. AIList offers faster query times and reduced memory usage compared to existing methods, improving scalability for large genomic datasets.

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Genomic data is commonly stored as intervals, necessitating efficient interval comparison for analysis.
The increasing volume of genomic data requires scalable search methods.

Purpose of the Study:

To introduce a novel data structure, the Augmented Interval List (AIList), for efficient interval intersection queries.
To improve the performance and scalability of fundamental genomic analysis operations.

Main Methods:

The AIList is constructed by sorting intervals by start coordinate, decomposing into flattened sublists, and augmenting with running maximum end coordinates.
Querying involves enumerating intersections between a query interval and the interval set R.

Main Results:

AIList query time complexity is O(log2N+n+m).
AIList demonstrated 5-18 times faster performance than standard methods like BEDTools on real genomic datasets.
AIList achieved 4-60% lower memory usage compared to other methods for large datasets.

Conclusions:

The AIList data structure provides a significant improvement for fundamental interval search operations in genomics.
AIList offers a highly scalable solution for analyzing large-scale genomic interval data.