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Target‐site mutations and detoxification gene overexpression are two major mechanisms conferring insecticide resistance. Molecular assays applied to detect these resistance genetic markers are time‐consuming and with high false‐positive rates. RNA‐Seq data contains information on the variations within expressed genomic regions and expression of detoxification genes. However, there is no corresponding method to detect resistance markers at present. Here, we collected 66 reported resistance mutations of four insecticide targets (AChE, VGSC, RyR, and nAChR) from 82 insect species. Next, we obtained 403 sequences of the four target genes and 12,665 sequences of three kinds of detoxification genes including P450s, GSTs, and CCEs. Then, we developed a Perl program, FastD, to detect target‐site mutations and overexpressed detoxification genes from RNA‐Seq data and constructed a web server for FastD (http://www.insect-genome.com/fastd). The estimation of FastD on simulated RNA‐Seq data showed high sensitivity and specificity. We applied FastD to detect resistant markers in 15 populations of six insects, Plutella xylostella, Aphis gossypii, Anopheles arabiensis, Musca domestica, Leptinotarsa decemlineata and Apis mellifera. Results showed that 11 RyR mutations in P. xylostella, one nAChR mutation in A. gossypii, one VGSC mutation in A. arabiensis and five VGSC mutations in M. domestica were found to be with frequency difference >40% between resistant and susceptible populations including previously confirmed mutations G4946E in RyR, R81T in nAChR and L1014F in VGSC. And 49 detoxification genes were found to be overexpressed in resistant populations compared with susceptible populations including previously confirmed detoxification genes CYP6BG1, CYP6CY22, CYP6CY13, CYP6P3, CYP6M2, CYP6P4 and CYP4G16. The candidate target‐site mutations and detoxification genes were worth further validation. Resistance estimates according to confirmed markers were consistent with population phenotypes, confirming the reliability of this program in predicting population resistance at omics‐level., We developed a program called FastD to detect the insecticide target‐site mutations and overexpressed detoxification genes from RNA‐Seq data and constructed a corresponding web server for FastD (http://www.insect-genome.com/fastd) in this article.

Insect chitinases participate in numerous physiological processes such as nutrition, parasitism, morphogenesis and immunity. These properties make chitinases good targets for pest control. Rice striped stem borer (SSB), Chilo suppressalis Walker, is one of the most destructive pests of rice causing huge yield losses. In our previous work, we reported the identification of 12 SSB chitinase (CsCht) genes, and studied the functions of CsCht1 to 4. Here, we have extended our study to investigate the expression patterns and functions of CsCht5 to 12. All eight chitinase genes displayed distinct temporospatial expression profiles. We looked at the effect of knocking down each gene at the developmental stage where highest expression was observed. Knocking down CsCht5, CsCht6 and CsCht8 resulted in high mortality and delayed development. Although silencing CsCht7, CsCht9, CsCht10, CsCht11 and CsCht12 had no apparent effect on development, knocking down CsCht10 in SSB individuals that were simultaneously treated with Beauveria bassiana (Bb84) led to higher mortality rates and quicker death, suggesting CsCht10 has an essential role in protecting SSB from exogenous microorganisms. In summary, we elucidated the functions of eight SSB chitinase genes and found that CsCht10 could be a good candidate for pest control.