The Zhao Bioinformatics Laboratory
pssRNAit: Designing Effective and Specific Plant RNAi siRNAs with Genome-wide Off-target Gene Assessment   
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About pssRNAit

Limitations of past methods: Gene silencing though RNA interference (RNAi) is a widely used molecular tool in plants and animals for functional genomics. The biogenesis of siRNA and its binding to the target for gene silencing is multi-step process of RNA interference (RNAi) pathways. Although a number of siRNA design tools have been developed, however, it is still challenging to design effective, specific and non-toxicity siRNAs against a target gene particularly for plants. The induction of RNAi in plant cell is mainly accomplished by expressing 300-1,200 bp Gene-Specific Sequence Tag (GST) that makes long dsRNA which process through Dicer-like (DCL) enzyme to generate small interfering RNAs (siRNAs) (1). However, siRNAs generating from GST can be a mixture of effective, non-effective, toxic, non-toxic, specific, and non-specific small RNAs, which poses a great challenge during gene silencing experiments in term specificity and effectiveness, and this still was not addressed.

Novelty: We present pssRNAit, a web server tool to design effective, specific and non-toxic siRNAs for plant RNAi. This tool implemented several innovative approaches based upon recent understanding of biological mechanism of RNAi pathways gain through our findings and literatures. For this, we have developed reliable computational models specific to each step of RNAi pathways and integrated these models which works like pathways to design siRNAs.

pssRNAit integrated several models and cDNA transcripts library.

  1. A new SVM model is use to design highly effective siRNA.
  2. Remove siRNA which contains toxic and non-specific sequence motifs (2).
  3. Our RISCbinder model is use to select those effective siRNA (antisense:sense) whose antisense strand have binding affinity with RISC machinery to execute gene silencing (3).
  4. Our psRNATarget tool is used to predict off-target genes of design siRNAs and to select more specific siRNAs (4).
  5. Our recent finding using high-throughput data of miRNAs showed several isomiRs are also generating along with canonical miRNA in order to increase the target specific gene silencing (5). Therefore, we are intelligently select pool of siRNAs to further increase the specificity of gene silencing. The basic principle is to select bunch of siRNAs which have common target gene but different off-targets.

Input/Output: The server front-end integrates simplified user-friendly interfaces to accept mRNA/cDNA sequence in FASTA format as input. The species name should be chosen to automatically load the cDNA/transcript libraries of this species for genome wide off-target assessment. The input interface also has options to remove siRNA containing toxic and non-specific sequence motifs. Upon submission, its backend pipeline that runs on linux cluster designs best siRNAs and gives the output result as table contains antisense and sense sequence of siRNA, alignment of siRNA binding with users’ sequence, silencing efficiency, number of off-targets and details through a link.

Expression of artificial-tasiRNA: We are proposing to fuse the sequence of siRNA pool to generate 300-1200 bp long Gene-Specific Sequence Tag (GST) and cloned into vector contains TAS gene (6). The expression of miRNA and its binding to the TAS gene triggers the DCL to cleave the GST at predefined cleavage site in phasing of 21 nucleotides and generate artificial-tasiRNAs. These artificial-tasiRNAs are same to the pool of siRNAs which will selectively silence the gene of interest without toxicity and off-target silencing.


  1. Hilson, P., Allemeersch, J., Altmann, T., Aubourg, S., Avon, A., Beynon, J., Bhalerao, R.P., Bitton, F., Caboche, M., Cannoot, B. et al. (2004) Versatile gene-specific sequence tags for Arabidopsis functional genomics: transcript profiling and reverse genetics applications. Genome research, 14, 2176-2189.
  2. Olejniczak, M., Galka, P. and Krzyzosiak, W.J. Sequence-non-specific effects of RNA interference triggers and microRNA regulators. Nucleic Acids Res, 38, 1-16.
  3. Ahmed, F., Ansari, H.R. and Raghava, G.P. (2009) Prediction of guide strand of microRNAs from its sequence and secondary structure. BMC Bioinformatics, 10, 105.
  4. Dai, X. and Zhao, P.X. (2011) psRNATarget: a plant small RNA target analysis server. Nucleic Acids Res, 39 Suppl 2, W155-159.
  5. Ahmed, F. and Zhao, P. (2011) A comprehensive analysis of isomirs and their targets using high-throughput sequencing data for Arabidopsis thaliana. Journal of Natural Science, Biology and Medicine, 2, 32.
  6. de la Luz Gutierrez-Nava, M., Aukerman, M.J., Sakai, H., Tingey, S.V. and Williams, R.W. (2008) Artificial trans-acting siRNAs confer consistent and effective gene silencing. Plant physiology, 147, 543-551.
   Funding by the National Science Foundation    Funding by the Oklahoma Center for the Advancement of Science & Technology    Additional funding by the Samuel Roberts Noble Foundation

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