Abstract
Insect Olfactory Receptors (ORs) are diverse family of membrane protein receptors responsible for most of the insect olfactory perception and communication, and hence they are of utmost importance for developing repellents or pesticides. Accurate gene prediction of insect ORs from newly sequenced genomes is an important but challenging task. We have developed a dedicated webserver, 'insectOR', to predict and validate insect OR genes using multiple gene prediction algorithms, accompanied by relevant validations. It is possible to employ this server nearly automatically and perform rapid prediction of the OR gene loci from thousands of OR-protein-to-genome alignments, resolve gene boundaries for tandem OR genes and refine them further to provide more complete OR gene models. InsectOR outperformed the popular genome annotation pipelines (MAKER and NCBI eukaryotic genome annotation) in terms of overall sensitivity at base, exon and locus level, when tested on two distantly related insect genomes. It displayed more than 95% nucleotide level precision in both tests. Finally, given the same input data and parameters, InsectOR missed less than 2% gene loci, in contrast to 55% loci missed by MAKER for Drosophila melanogaster. The webserver is freely available on the web at http://caps.ncbs.res.in/insectOR/ and the basic package can be downloaded from https://github.com/sdk15/insectOR for local use. This tool will allow biologists to perform quick preliminary identification of insect olfactory receptor genes from newly sequenced genomes and also assist in their further detailed annotation. Its usage can also be extended to other divergent gene families.
Highlights
Insect biology has been studied extensively over the years for human benefit–to collect honey, pollinate crops, ward off pests, etc
InsectOR was more sensitive in predicting genes at base, exon and locus level as compared to MAKER and NCBI gene annotation
Insect Olfactory Receptors (ORs) genes are diverse across various taxonomical categories and these are hard to detect for general genome annotation pipelines, which tend to wrongly predict fused tandem OR gene models
Summary
Insect biology has been studied extensively over the years for human benefit–to collect honey, pollinate crops, ward off pests, etc. These diverse species are being used as model organisms for modern experiments to understand their (and in-turn our own) biology in intricate details. Advent of Generation Sequencing (NGS) technologies has given us powers to study this vast diversity at genomic level [1]. Identification of insect olfactory receptors from genomic data
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