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Related Experiment Video

Updated: Apr 7, 2026

Targeted DNA Methylation Analysis by Next-generation Sequencing

Targeted DNA Methylation Analysis by Next-generation Sequencing

Published on: February 24, 2015

BatAlign: an incremental method for accurate alignment of sequencing reads.

Jing-Quan Lim¹, Chandana Tennakoon², Peiyong Guan³

¹Department of Computer Science, National University of Singapore, Singapore 117417 Laboratory of Cancer Epigenome, Division of Medical Sciences, National Cancer Centre Singapore, Singapore 169610.

Nucleic Acids Research

|July 15, 2015

Summary

BatAlign improves read-alignment accuracy for structural variant (SV) detection. This algorithm enhances the identification of genetic variations from high-throughput data, leading to more precise SV calling.

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Last Updated: Apr 7, 2026

Targeted DNA Methylation Analysis by Next-generation Sequencing

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Published on: February 24, 2015

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Structural variations (SVs) are key drivers of genetic diversity.
Polymorphisms often compromise the accuracy of read alignments near structural variations.
Accurate read alignment is essential for reliable SV detection.

Purpose of the Study:

To develop and evaluate BatAlign, a novel algorithm for accurate read-alignment in the presence of polymorphisms.
To improve the detection of structural variations (SVs) using high-throughput sequencing data.

Main Methods:

BatAlign integrates 'Reverse-Alignment' and 'Deep-Scan' strategies.
The algorithm was tested on simulated datasets with various types of aberrations (mismatch, indel, paired-end, SV-spanning).
Performance was evaluated on real sequencing data.

Main Results:

BatAlign achieved the highest F-measures across diverse alignment datasets.
On real data, BatAlign recovered 4.3% more PCR-validated SVs.
BatAlign reduced SV callings by 73.3% while improving accuracy.

Conclusions:

BatAlign demonstrates superior accuracy in read-alignment, particularly near structural variations.
The algorithm effectively detects structural variations and other polymorphic variants using high-throughput data.
BatAlign offers a more precise and efficient approach to SV analysis.