Evaluating the Effectiveness of Various Small RNA Alignment Techniques in Transcriptomic Analysis by Examining Different Sources of Variability Through a Multi-Alignment Approach

Abstract

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.

Keywords

• sequence alignment
• alignment variability
• read quantification
• transcriptome sequences
• genome sequences
• differential analysis