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Related Experiment Video

Updated: Mar 26, 2026

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

Long Read Alignment with Parallel MapReduce Cloud Platform.

Ahmed Abdulhakim Al-Absi¹, Dae-Ki Kang²

¹Department of Ubiquitous IT, Graduate School, Dongseo University, 47 Jurye-ro, Sasang-gu, Busan 47011, Republic of Korea.

Biomed Research International

|February 4, 2016

Summary

This summary is machine-generated.

This study introduces a new cloud platform, BWASW-PMR, for efficiently aligning long genomic sequences. It significantly speeds up analysis and reduces execution time compared to existing methods.

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Mapping Genome-wide Accessible Chromatin in Primary Human T Lymphocytes by ATAC-Seq

Mapping Genome-wide Accessible Chromatin in Primary Human T Lymphocytes by ATAC-Seq

Published on: November 13, 2017

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Next-Generation Sequencing (NGS) generates large genomic datasets requiring efficient analysis.
Existing cloud-based gene sequencing tools often focus on short reads and use serial Hadoop MapReduce, creating computational bottlenecks.
Long sequence alignment is crucial for decoding complex genomes.

Purpose of the Study:

To develop an efficient cloud platform for long genomic sequence alignment.
To address the limitations of existing Hadoop MapReduce frameworks in handling large-scale genomic data.
To improve the speed and reduce the execution time of genomic sequence alignment.

Main Methods:

Introduction of Burrows-Wheeler Aligner's Smith-Waterman Alignment on Parallel MapReduce (BWASW-PMR) platform.
Adoption of the accurate Burrows-Wheeler Aligner-Single-Read (BWA-SW) algorithm for long sequence alignment.
Development of a custom MapReduce platform with parallel execution and Smith-Waterman algorithm optimization.

Main Results:

BWASW-PMR achieved an average speed-up of 6.7 compared to Bwasw-Cloud.
An average reduction of 30% in the map phase makespan was observed.
Optimization of the Smith-Waterman algorithm reduced execution time by 91.8%.

Conclusions:

BWASW-PMR demonstrates significant efficiency for aligning long genomic sequences on cloud platforms.
The parallel MapReduce strategy and algorithm optimization enhance computational performance.
This platform offers a viable solution for analyzing large-scale genomic data from NGS technologies.