Evaluating the Effectiveness of Various Small RNA Alignment Techniques in Transcriptomic Analysis by Examining Different Sources of Variability Through a Multi-Alignment Approach
DNA and RNA nucleotide sequences are ubiquitous in all biological cells, serving as both a comprehensive library of capabilities for the cells and as an impressive regulatory system to control cellular function. The multi-alignment framework (MAF) provided in this study offers a user-friendly platfo...
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MDPI AG
2025-06-01
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| author | Xinwei Zhao Eberhard Korsching |
| author_facet | Xinwei Zhao Eberhard Korsching |
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| description | DNA and RNA nucleotide sequences are ubiquitous in all biological cells, serving as both a comprehensive library of capabilities for the cells and as an impressive regulatory system to control cellular function. The multi-alignment framework (MAF) provided in this study offers a user-friendly platform for sequence alignment and quantification. It is adaptable to various research needs and can incorporate different tools and parameters for in-depth analysis, especially in low read rate scenarios. This framework can be used to compare results from different alignment programs and algorithms on the same dataset, allowing for a comprehensive analysis of subtle to significant differences. This concept is demonstrated in a small RNA case study. MAF is specifically designed for the Linux platform, commonly used in bioinformatics. Its script structure streamlines processing steps, saving time when repeating procedures with various datasets. While the focus is on microRNA analysis, the templates provided can be adapted for all transcriptomic and genomic analyses. The template structure allows for flexible integration of pre- and post-processing steps. MicroRNA analysis indicates that STAR and Bowtie2 alignment programs are more effective than BBMap. Combining STAR with the Salmon quantifier or, with some limitations, the Samtools quantification, appears to be the most reliable approach. This method is ideal for scientists who want to thoroughly analyze their alignment results to ensure quality. The detailed microRNA analysis demonstrates the quality of three alignment and two quantification methods, offering guidance on assessing result quality and reducing false positives. |
| format | Article |
| id | doaj-art-e3619ce8263d45d8969b603bc9c32861 |
| institution | Kabale University |
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| language | English |
| publishDate | 2025-06-01 |
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| spelling | doaj-art-e3619ce8263d45d8969b603bc9c328612025-08-20T03:29:44ZengMDPI AGMethods and Protocols2409-92792025-06-01836510.3390/mps8030065Evaluating the Effectiveness of Various Small RNA Alignment Techniques in Transcriptomic Analysis by Examining Different Sources of Variability Through a Multi-Alignment ApproachXinwei Zhao0Eberhard Korsching1CCSR Group, Institute of Bioinformatics, University Hospital of Münster (UKM), University of Münster, DE 48149 Münster, GermanyCCSR Group, Institute of Bioinformatics, University Hospital of Münster (UKM), University of Münster, DE 48149 Münster, GermanyDNA and RNA nucleotide sequences are ubiquitous in all biological cells, serving as both a comprehensive library of capabilities for the cells and as an impressive regulatory system to control cellular function. The multi-alignment framework (MAF) provided in this study offers a user-friendly platform for sequence alignment and quantification. It is adaptable to various research needs and can incorporate different tools and parameters for in-depth analysis, especially in low read rate scenarios. This framework can be used to compare results from different alignment programs and algorithms on the same dataset, allowing for a comprehensive analysis of subtle to significant differences. This concept is demonstrated in a small RNA case study. MAF is specifically designed for the Linux platform, commonly used in bioinformatics. Its script structure streamlines processing steps, saving time when repeating procedures with various datasets. While the focus is on microRNA analysis, the templates provided can be adapted for all transcriptomic and genomic analyses. The template structure allows for flexible integration of pre- and post-processing steps. MicroRNA analysis indicates that STAR and Bowtie2 alignment programs are more effective than BBMap. Combining STAR with the Salmon quantifier or, with some limitations, the Samtools quantification, appears to be the most reliable approach. This method is ideal for scientists who want to thoroughly analyze their alignment results to ensure quality. The detailed microRNA analysis demonstrates the quality of three alignment and two quantification methods, offering guidance on assessing result quality and reducing false positives.https://www.mdpi.com/2409-9279/8/3/65sequence alignmentalignment variabilityread quantificationtranscriptome sequencesgenome sequencesdifferential analysis |
| spellingShingle | Xinwei Zhao Eberhard Korsching Evaluating the Effectiveness of Various Small RNA Alignment Techniques in Transcriptomic Analysis by Examining Different Sources of Variability Through a Multi-Alignment Approach Methods and Protocols sequence alignment alignment variability read quantification transcriptome sequences genome sequences differential analysis |
| title | Evaluating the Effectiveness of Various Small RNA Alignment Techniques in Transcriptomic Analysis by Examining Different Sources of Variability Through a Multi-Alignment Approach |
| title_full | Evaluating the Effectiveness of Various Small RNA Alignment Techniques in Transcriptomic Analysis by Examining Different Sources of Variability Through a Multi-Alignment Approach |
| title_fullStr | Evaluating the Effectiveness of Various Small RNA Alignment Techniques in Transcriptomic Analysis by Examining Different Sources of Variability Through a Multi-Alignment Approach |
| title_full_unstemmed | Evaluating the Effectiveness of Various Small RNA Alignment Techniques in Transcriptomic Analysis by Examining Different Sources of Variability Through a Multi-Alignment Approach |
| title_short | Evaluating the Effectiveness of Various Small RNA Alignment Techniques in Transcriptomic Analysis by Examining Different Sources of Variability Through a Multi-Alignment Approach |
| title_sort | evaluating the effectiveness of various small rna alignment techniques in transcriptomic analysis by examining different sources of variability through a multi alignment approach |
| topic | sequence alignment alignment variability read quantification transcriptome sequences genome sequences differential analysis |
| url | https://www.mdpi.com/2409-9279/8/3/65 |
| work_keys_str_mv | AT xinweizhao evaluatingtheeffectivenessofvarioussmallrnaalignmenttechniquesintranscriptomicanalysisbyexaminingdifferentsourcesofvariabilitythroughamultialignmentapproach AT eberhardkorsching evaluatingtheeffectivenessofvarioussmallrnaalignmenttechniquesintranscriptomicanalysisbyexaminingdifferentsourcesofvariabilitythroughamultialignmentapproach |