On the optimal trimming of high-throughput mRNA sequence data

Area of Science:

• Genomics
• Evolutionary Biology
• Bioinformatics

Background:

• High-throughput sequencing technologies enable deep understanding of genome-level processes and genotype-phenotype links.
• mRNA sequencing (mRNA-Seq) is crucial for functional biology and adaptation studies, often comparing transcriptomes across tissues or phenotypes.
• Data quality is paramount, necessitating careful quality trimming of sequence reads due to the error-prone nature of high-throughput sequencing compared to Sanger sequencing.

Purpose of the Study:

• To provide general guidelines for optimal quality trimming in mRNA-Seq studies.
• To determine the ideal strength of quality trimming based on empirical data.
• To address the lack of standardized trimming protocols in bioinformatics pipelines.

Main Methods:

• Empirical analysis of sequence data from mRNA-Seq studies.
• Evaluation of various quality trimming strengths.
• Assessment of trimming impact across a wide variety of metrics.

Main Results:

• Aggressive quality trimming is common but not always optimal.
• Gentle trimming, specifically removing nucleotides with Phred scores <2 or <5, is recommended.
• This optimized trimming strategy benefits most mRNA-Seq studies.

Conclusions:

• Establishing optimal quality trimming parameters is essential for reliable mRNA-Seq data analysis.
• A less aggressive trimming approach (Phred score <2 or <5) preserves valuable data while ensuring quality.
• These recommendations can improve the accuracy and reproducibility of functional genomics and adaptation research.