Optimizing and benchmarking de novo transcriptome sequencing: from library preparation to assembly evaluation.

Area of Science:

• Comparative genomics
• Molecular biology
• Bioinformatics

Background:

• RNA sequencing (RNA-seq) offers cost-effective gene expression profiling, even for non-model organisms.
• Existing RNA-seq workflows have room for optimization to leverage advancing sequencing technologies.

Purpose of the Study:

• To optimize de novo RNA-seq workflow for enhanced transcriptome assembly.
• To develop and validate a new reference gene set for assessing transcriptome completeness.

Main Methods:

• Performed transcriptome sequencing on three embryonic stages of the Madagascar ground gecko (Paroedura picta) using Illumina platform.
• Developed a core vertebrate gene set (CVG) of 233 one-to-one orthologs for completeness assessment.
• Utilized computational pipelines CEGMA and BUSCO with CVG for evaluating de novo assembly accuracy.

Main Results:

• Shortened RNA fragmentation time improved insert size distribution for longer reads (>150 nt).
• CVG demonstrated higher accuracy and resolution in assessing transcriptome completeness compared to previous methods.
• Generated the most comprehensive transcript sequence set for the Madagascar ground gecko.

Conclusions:

• Optimized de novo RNA-seq workflow through coordination of library insert size and read length for improved assembly connectivity.
• The CVG approach is applicable for transcriptome and whole genome analyses across diverse species.