A Fast and Scalable Workflow for SNPs Detection in Genome Sequences Using Hadoop Map-Reduce.

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Next-Generation Sequencing (NGS) generates vast biological data, posing computational challenges.
• Accurate and efficient detection of single nucleotide polymorphisms (SNPs) is crucial for genomic analysis.
• Existing SNP detection algorithms often suffer from high computational costs, memory requirements, and accuracy limitations.

Purpose of the Study:

• To develop a fast and scalable workflow for improved SNP detection in genome sequences.
• To integrate the Bowtie aligner with a Hadoop-based Heap SNP caller for enhanced performance.
• To validate the proposed workflow using benchmark datasets and compare its efficiency against existing methods.

Main Methods:

• Developed a novel workflow integrating Bowtie aligner with Hadoop-based Heap SNP caller.
• Utilized benchmark datasets from NCBI and DDBJ DRA for validation.
• Performed comparative analysis against established aligners (Bowtie, BWA) and SNP callers (GATK, FaSD, SparkGA, Halvade, Heap).

Main Results:

• The proposed workflow demonstrated superior performance over existing frameworks in SNP detection.
• Achieved an average of 22.46% more efficient F-score and 99.80% consistent accuracy.
• Showcased a comparatively higher mean accuracy of 0.21% and performed SNP mining for region identification.

Conclusions:

• The integrated Bowtie and Hadoop-based Heap workflow offers a significant advancement in fast and scalable SNP detection.
• The method provides improved accuracy and efficiency for analyzing large-scale genomic data from NGS.
• Future work includes graphical visualization of mined SNPs and memory requirement optimization.