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

Abstract

Please cite this article as: Prashanth, J.R., Lewis, R.J., An efficient transcriptome analysis pipeline to accelerate venom peptide discovery and characterisation, Toxicon (2015), doi: 10.1016/ j.toxicon.2015.09.012. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT AbstractTranscriptome sequencing is now widely adopted as an efficient means to study the chemical diversity of venoms. To improve the efficiency of analysis of these large datasets, we have optimised an analysis pipeline for cone snail venom gland transcriptomes. The pipeline combines ConoSorter with sequence architecture-based elimination and similarity searching using BLAST to improve the accuracy of sequence identification and classification, while reducing requirements for manual intervention. As a proof-of-concept, we used this approach reanalysed three previously published cone snail transcriptomes from diverse dietary groups. Our pipeline method generated similar results to the published studies with significantly less manual intervention. We additionally found undiscovered sequences in the piscovorous C. geographus and vermivorous C. miles and identified sequences in incorrect superfamilies in the molluscivorus C. marmoreus and C. geographus transcriptomes. Our results indicate that this method can improve toxin detection without extending analysis time. While this method was evaluated on cone snail transcriptomes it can be easily optimised to retrieve toxins from other venomous animals.

The role of defensive ecological interactions in the evolution of conotoxins

“…The transcriptome of Conus miles (Jin et al . ; Prashanth & Lewis ), another Rhizoconus species, also contained only αD‐conotoxins suggesting that this defensive recruitment is a subgenus‐wide adaptation.…”

“…The C. vexillum venom gland transcriptome was also dominated by the defensive αD‐conotoxins, with no A‐superfamily conotoxins detected, similar to another Rhizoconus subgenus member Conus miles (Jin et al . ; Prashanth & Lewis ). A number of other conotoxin classes were also identified in the C. vexillum transcriptome and validated by proteomics, including four new superfamilies, novel con‐ikot‐ikots, novel conantokins and previously discovered VxVIA (Jiang et al .…”

Deadly Proteomes: A Practical Guide to Proteotranscriptomics of Animal Venoms

“…The 454 platform had some advantages-for example, sequence read length was often sufficient to discern the complete sequence of many venom peptides without in silico assembly of the raw reads (though care needs to be taken to identify and correct errors). [74,90] At present, Illumina sequencing is the method of choice for sequencing venom-gland RNA as it delivers much higher coverage at reduced cost (compared to 454 sequencing). We typically aim for 20 million paired-end reads of 150 bp each.…”

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“…For many poorly studied taxonomic lineages (e.g. Cnidaria), similar techniques are being 628 used to evaluate venom diversity using bioinformatic pipelines for a particular species or taxonomic group 629 (Tan, Khan & Brusic, 2003;Reumont et al, 2014;Macrander, Brugler & Daly, 2015;Kaas & Craik, 630 2015; Prashanth & Lewis, 2015). Although these studies all take similar approaches to study diverse 631 venoms across animal lineages, a streamlined systematic pipeline does not exist for rapid identification of 632 candidate toxin genes from these datasets.…”