Meeting Abstract
The area of “venomics” has recently emerged as a growing field using combined transcriptomic and proteomic datasets to characterize toxin diversity in a variety of venomous taxa. Here we present Venomix, a bioinformatic pipeline written in the programming languages Python and R that follows widely accepted procedures for identifying and characterizing toxin-like genes from transcriptomic datasets. Venomix provides the user with several informative output files that can be used to characterize the potential function of these candidate toxins, compare relevant expression level values across toxin-gene candidates, evaluate amino acid conservation among functionally important residues in sequence alignments, and taxonomic and functional information in combination with tree reconstructions to further evaluate toxin gene candidates. We use Venomix to characterize the toxin-like diversity from venom gland transcriptomes for a cone snail (Chonus sponsalis), scorpion (Urodacus yaschenkoi), snake (Echis coloratus) and ant (Tetramorium bicarinatum). With the exception of T. bicarinatum the toxin diversity for each of these species were previously evaluated using lineage specific toxin gene datasets. Venomix expands beyond these lineage specific predictions identifying new candidate toxin groups and genes, identifying up to five times more toxin candidates when compared to the original study. Venomix quickly sorts, screens, and categorizes toxin-like transcripts from transcriptomic data, enabling researchers to focus on other aspects of toxin characterization beyond simply identification. Venomix is a python package available at: https://bitbucket.org/JasonMacrander/Venomix/ and is in a ready to use downloadable package.
