A Comprehensive Methodological Review of Major Developments in Bioinformatics Pipelines for Transcriptomic Data Analysis
Background: Recent advances in transcriptomics have provided new insights to analyze a wide range of biological data. RNA sequencing (RNA-Seq) is a common method used to study the complete set of RNA molecules (the transcriptome) in different cell types, genetic backgrounds, and environments. While...
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| Format: | Article |
| Language: | English |
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Shahid Beheshti University of Medical Sciences
2025-01-01
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| Series: | Novelty in Biomedicine |
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| Online Access: | https://journals.sbmu.ac.ir/nbm/article/view/45616 |
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| author | Awais ALi Syed Luqman Ali Abdelkarem Omneya |
| author_facet | Awais ALi Syed Luqman Ali Abdelkarem Omneya |
| author_sort | Awais ALi |
| collection | DOAJ |
| description | Background: Recent advances in transcriptomics have provided new insights to analyze a wide range of biological data. RNA sequencing (RNA-Seq) is a common method used to study the complete set of RNA molecules (the transcriptome) in different cell types, genetic backgrounds, and environments. While many computational tools exist for analyzing large RNA-Seq datasets, there is still a need to thoroughly compare methods used to separate mixed cell populations (deconvolution).
Materials and Methods: This review highlights recent software and database improvements for processing RNA-Seq data, including steps like matching sequences to the genome, reconstructing RNA molecules, and measuring RNA abundance.
Results: We examine how well different methods work under various experimental conditions and discuss important factors such as data quality, sequence alignment, data visualization, identifying gene expression differences, and data standardization. A novel approach is also introduced: an ensemble learning-based deconvolution method combining multiple techniques to improve accuracy, mitigate data contamination, and reduce errors. Our findings provide valuable guidance for using omics tools effectively and developing better analysis methods. This review offers detailed instructions for planning and evaluating Illumina sequencing experiments.
Conclusion: We cover basic concepts, RNA-Seq analysis steps, computational workflows, and potential difficulties. |
| format | Article |
| id | doaj-art-4db224181bbd4b04b5a9a17287711f43 |
| institution | Kabale University |
| issn | 2345-3907 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Shahid Beheshti University of Medical Sciences |
| record_format | Article |
| series | Novelty in Biomedicine |
| spelling | doaj-art-4db224181bbd4b04b5a9a17287711f432025-01-20T05:01:18ZengShahid Beheshti University of Medical SciencesNovelty in Biomedicine2345-39072025-01-0113110.22037/nbm.v13i1.4561635507A Comprehensive Methodological Review of Major Developments in Bioinformatics Pipelines for Transcriptomic Data AnalysisAwais ALi0Syed Luqman Ali1Abdelkarem Omneya21Department of Biochemistry, Abdul Wali Khan University Mardan, Madan 23200- Pakistan1Department of Biochemistry, Abdul Wali Khan University Mardan, Madan 23200- Pakistan2Department of Chemical Pathology, Medical Research Institute, Alexandria University, EgyptBackground: Recent advances in transcriptomics have provided new insights to analyze a wide range of biological data. RNA sequencing (RNA-Seq) is a common method used to study the complete set of RNA molecules (the transcriptome) in different cell types, genetic backgrounds, and environments. While many computational tools exist for analyzing large RNA-Seq datasets, there is still a need to thoroughly compare methods used to separate mixed cell populations (deconvolution). Materials and Methods: This review highlights recent software and database improvements for processing RNA-Seq data, including steps like matching sequences to the genome, reconstructing RNA molecules, and measuring RNA abundance. Results: We examine how well different methods work under various experimental conditions and discuss important factors such as data quality, sequence alignment, data visualization, identifying gene expression differences, and data standardization. A novel approach is also introduced: an ensemble learning-based deconvolution method combining multiple techniques to improve accuracy, mitigate data contamination, and reduce errors. Our findings provide valuable guidance for using omics tools effectively and developing better analysis methods. This review offers detailed instructions for planning and evaluating Illumina sequencing experiments. Conclusion: We cover basic concepts, RNA-Seq analysis steps, computational workflows, and potential difficulties.https://journals.sbmu.ac.ir/nbm/article/view/45616transcriptomicbioinformatics pipelinescontaminationilluminerna-seq analysis |
| spellingShingle | Awais ALi Syed Luqman Ali Abdelkarem Omneya A Comprehensive Methodological Review of Major Developments in Bioinformatics Pipelines for Transcriptomic Data Analysis Novelty in Biomedicine transcriptomic bioinformatics pipelines contamination illumine rna-seq analysis |
| title | A Comprehensive Methodological Review of Major Developments in Bioinformatics Pipelines for Transcriptomic Data Analysis |
| title_full | A Comprehensive Methodological Review of Major Developments in Bioinformatics Pipelines for Transcriptomic Data Analysis |
| title_fullStr | A Comprehensive Methodological Review of Major Developments in Bioinformatics Pipelines for Transcriptomic Data Analysis |
| title_full_unstemmed | A Comprehensive Methodological Review of Major Developments in Bioinformatics Pipelines for Transcriptomic Data Analysis |
| title_short | A Comprehensive Methodological Review of Major Developments in Bioinformatics Pipelines for Transcriptomic Data Analysis |
| title_sort | comprehensive methodological review of major developments in bioinformatics pipelines for transcriptomic data analysis |
| topic | transcriptomic bioinformatics pipelines contamination illumine rna-seq analysis |
| url | https://journals.sbmu.ac.ir/nbm/article/view/45616 |
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