InsectOR-Webserver for sensitive identification of insect olfactory receptor genes from non-model genomes

PLoS One. 2021 Jan 19;16(1):e0245324. doi: 10.1371/journal.pone.0245324. eCollection 2021.


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 and the basic package can be downloaded from 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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Animals
  • Drosophila melanogaster / genetics
  • Genome, Insect*
  • Receptors, Odorant / genetics*
  • User-Computer Interface*


  • Receptors, Odorant

Grant support

This work was funded by Shyama Prasad Mukherjee Fellowship from Council of Scientific and Industrial Research (CSIR), India and later Bridging Postdoctoral Fellowship from National Centre for Biological Sciences (Tata Institute of Fundamental Research), India for SDK; and JC Bose Fellowship (JC Bose fellowship (SB/S2/JC-071/2015) from Science and Engineering Research Board, India for RS. The authors used the infrastructural facilities of National Centre for Biological Sciences (NCBS). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.