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Related Experiment Video

Updated: Feb 13, 2026

Hybrid De Novo Genome Assembly for the Generation of Complete Genomes of Urinary Bacteria using Short- and Long-read Sequencing Technologies

Hybrid De Novo Genome Assembly for the Generation of Complete Genomes of Urinary Bacteria using Short- and Long-read Sequencing Technologies

Published on: August 20, 2021

Fast and efficient short read mapping based on a succinct hash index.

Haowen Zhang¹, Yuandong Chan², Kaichao Fan²

¹School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, 30332, GA, USA.

BMC Bioinformatics

|March 11, 2018

Summary

This summary is machine-generated.

We developed a novel succinct hash index for next-generation sequencing read mapping. This efficient data structure significantly improves mapping speed and reduces memory usage compared to existing tools.

Keywords:

Hash index Next-generation sequencing Read mapping Seed selection

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Next-generation sequencing (NGS) read mapping tools utilize various indexing techniques.
The selection of indexing data structures involves a trade-off between memory usage, mapping speed, and index construction time.

Purpose of the Study:

To introduce a novel, memory-efficient data structure for read mapping.
To develop a high-performance read mapper utilizing this new index.

Main Methods:

Implementation of a succinct hash index, a variant of the q-gram index, with a reduced memory footprint (3.5-5.3 GB for human genome).
Development of novel seed selection algorithms (group seeding, variable-length seeding).
Design of an efficient parallel construction algorithm for the index.
Creation of the Fast and Efficient read Mapper (FEM) using the succinct hash index.

Main Results:

The succinct hash index offers a feasible q-gram index implementation with low memory requirements.
FEM demonstrates scalability and superior performance in speed and memory footprint compared to state-of-the-art all-mappers.
FEM achieves significant speedups: an order of magnitude faster than Masai (single-thread) and two orders of magnitude faster (multi-thread), up to 2.8-fold faster than BitMapper, and an order of magnitude faster than BitMapper2 and Hobbes3.

Conclusions:

The succinct hash index represents a breakthrough in memory-efficient indexing for read mapping.
FEM provides a highly efficient and scalable solution for read mapping, outperforming existing methods.
FEM is available as open-source software for the research community.