Big Data Integration in Genomic Analysis: Applications to Genetically Modified Corn (Zea Mays)

This paper explores the integration of big data techniques into the genomic analysis pipeline, with an emphasis on methodological rigor and scalable computational frameworks. It presents a comprehensive, step-by-step approach to preprocessing raw FASTQ files using Biopython, NumPy, and Pandas for qu...

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Published inInternational journal of big data intelligence and applications Vol. 6; no. 1; pp. 1 - 30
Main Authors Segall, Richard S, Rajbhandari, Prasanna
Format Journal Article
LanguageEnglish
Published Dallas IGI Global 12.08.2025
Subjects
Big Data
Corn
Data integration
Genetic modification
Quality control
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ISSN2644-1675
2644-1683
DOI10.4018/IJBDIA.387389

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Abstract This paper explores the integration of big data techniques into the genomic analysis pipeline, with an emphasis on methodological rigor and scalable computational frameworks. It presents a comprehensive, step-by-step approach to preprocessing raw FASTQ files using Biopython, NumPy, and Pandas for quality control and visualization. Key metrics are visualized with Matplotlib and Seaborn software, providing insights into sample quality, contamination risks, and optimal trimming strategies. Portions of datasets containing over a million genomic values for genetically modified corn (Zea Mays) are used to illustrate practical challenges such as read variability and quality degradation, supporting the use of quality-based filtering. Related work on big data, genomics, and genomic tools and pipelines is also reviewed.
AbstractList This paper explores the integration of big data techniques into the genomic analysis pipeline, with an emphasis on methodological rigor and scalable computational frameworks. It presents a comprehensive, step-by-step approach to preprocessing raw FASTQ files using Biopython, NumPy, and Pandas for quality control and visualization. Key metrics are visualized with Matplotlib and Seaborn software, providing insights into sample quality, contamination risks, and optimal trimming strategies. Portions of datasets containing over a million genomic values for genetically modified corn (Zea Mays) are used to illustrate practical challenges such as read variability and quality degradation, supporting the use of quality-based filtering. Related work on big data, genomics, and genomic tools and pipelines is also reviewed.
Author Segall, Richard S
Rajbhandari, Prasanna
AuthorAffiliation Arkansas State University, USA
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  fullname: Rajbhandari, Prasanna
  organization: Arkansas State University, USA
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SubjectTerms Big Data
Corn
Data integration
Genetic modification
Quality control
Title Big Data Integration in Genomic Analysis: Applications to Genetically Modified Corn (Zea Mays)
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