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Compressed Sparse FM-Index: Fast Sequence Alignment Using Large K-Steps.

Ruben Langarita, Adria Armejach, Javier Setoain

IEEE/ACM Transactions on Computational Biology and Bioinformatics

|August 6, 2020

Summary

This summary is machine-generated.

We introduce COFI, a Compressed FM-Index, to overcome memory limitations in genomic sequence searching. COFI enables efficient large k-step searches on human genomes, improving performance on modern hardware.

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Area of Science:

Bioinformatics
Computational Genomics
Data Structures

Background:

The FM-index is crucial for exact sequence searching in large reference genomes.
Existing FM-index algorithms suffer from irregular memory access, leading to memory-bound performance issues.
Current implementations face throughput-memory trade-offs, limiting the use of large k-steps.

Purpose of the Study:

To address the memory limitations of FM-index implementations for large k-steps.
To propose and evaluate COFI (COmpressed FM-Index) for enhanced genomic sequence searching.
To demonstrate COFI's efficiency on human genome data and modern computing architectures.

Main Methods:

Analysis of FM-index memory access patterns and throughput-memory trade-offs.
Development of COFI, a compressed FM-index designed for large k-steps.
Evaluation of a COFI-based algorithm on Knights Landing (KNL) and Skylake (SKX) systems.

Main Results:

COFI enables a 15-step FM-index using under 16 GB for a 3 Gbp human genome.
Average speed-ups of 1.46x on KNL and 1.39x on SKX were achieved compared to a state-of-the-art implementation.
COFI effectively overcomes memory constraints for large k-step FM-index applications.

Conclusions:

COFI significantly improves the efficiency of FM-index based sequence searching by reducing memory requirements.
The proposed compressed FM-index offers substantial performance gains on modern hardware architectures.
COFI represents a practical advancement for large-scale genomic data analysis.