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Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens

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HIA: a genome mapper using hybrid index-based sequence alignment.

Jongpill Choi¹, Kiejung Park², Seong Beom Cho¹

¹Division of Bio-Medical Informatics, Center for Genome Science, Korea National Institute of Health, Osong, Korea.

Algorithms for Molecular Biology : AMB

|December 25, 2015

Summary

A new hybrid index combining hash tables and suffix arrays significantly speeds up next-generation sequencing (NGS) read alignment to the human genome. This tool efficiently maps massive datasets without sacrificing accuracy.

Keywords:

Hash table index Hybrid index Mapper NGS Sequence alignment Suffix array index

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Mapping Mammalian 3D Genome Interactions with Micro-C-XL

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Published on: November 3, 2023

Area of Science:

Bioinformatics
Genomics
Computational Biology

Background:

Next-generation sequencing (NGS) generates vast amounts of data, necessitating efficient alignment tools.
Current alignment tools struggle with the rapidly increasing scale of NGS datasets.
Faster, more sensitive, and accurate mapping tools are crucial for analyzing large-scale genomic data.

Purpose of the Study:

To develop a faster and more accurate alignment tool for next-generation sequencing (NGS) data.
To address the challenges posed by the increasing volume of genomic sequencing data.
To improve the efficiency of mapping sequencing reads to the human reference genome.

Main Methods:

Developed a hybrid index combining hash table and suffix array structures.
Utilized the hybrid index to identify candidate alignment regions (CARs) by finding matching regions (MRs).
Implemented an alignment strategy focusing on unmatched regions within CARs.

Main Results:

The hybrid index demonstrated significantly faster substring searching compared to suffix array methods alone.
Aligning only within identified CARs proved more efficient than aligning entire regions.
Benchmarking showed the developed tool (HIA) outperformed other aligners in mapping speed with comparable accuracy.

Conclusions:

A hybrid index of hash table and suffix array effectively accelerates NGS read mapping to the human genome.
The developed alignment tool is suitable for processing the massive datasets characteristic of modern NGS sequencing.
This approach offers a promising solution for efficient large-scale genomic data analysis.