An efficient transcriptome analysis pipeline to accelerate venom peptide discovery and characterisation

Area of Science:

• * Venomics and bioinformatics
• * Marine biology and toxicology

Background:

• * Transcriptome sequencing is crucial for understanding venom chemical diversity.
• * Analyzing large venom gland transcriptome datasets requires efficient methods.

Purpose of the Study:

• * To optimize an analysis pipeline for cone snail venom gland transcriptomes.
• * To improve the accuracy and efficiency of toxin identification and classification.

Main Methods:

• * Combined ConoSorter with sequence architecture-based elimination.
• * Utilized BLAST for similarity searching.
• * Applied the pipeline to reanalyze three diverse cone snail transcriptomes.

Main Results:

• * The optimized pipeline achieved results comparable to published studies with significantly less manual intervention.
• * Discovered novel toxin sequences in *Conus geographus* and *Conus miles*.
• * Identified misclassified sequences in *Conus marmoreus* and *Conus geographus* transcriptomes.

Conclusions:

• * The developed method enhances toxin detection in venomous animals without increasing analysis time.
• * The pipeline is adaptable for analyzing transcriptomes from various venomous species.
• * Improved accuracy in sequence identification and classification aids venom research.