Figure 2

Overview of the NEXT-RNAi workflow. NEXT-RNAi requires a defined set of input files in FASTA or tab-delimited formats. First, the program filters the input target sequences for six (default) or more contiguous CAN repeats and for other regions of low complexity (for example, simple nucleotide repeats) using mdust. Sequences are then 'diced' to generate all possible siRNA sequences with a default length of 19 nucleotides (nt) and an offset of 1 nucleotide. Subsequently, each siRNA is mapped to a user-defined off-target database (for example, the whole transcriptome) with Bowtie [37] to determine its specificity. The specificity is set to one if the siRNA targets a single gene or to zero otherwise. In the next step, the predicted efficiency of each 19-nucleotide siRNA is computed. Two methods can be selected, the 'rational' method according to Reynolds et al. [9] and the 'weighted' method according to Shah et al. [12], assigning each siRNA an efficiency score between 0 and 100. Optionally, the seed complement frequency for each siRNA can be computed for any FASTA file provided (for example, a file containing 3' UTR sequences). siRNAs that did not pass the low-complexity filters, show perfect homology to multiple target genes or do not meet the user-defined cutoffs for efficiency or seed complement frequency are excluded from the queried target sequences. Remaining sequences are used as templates for primer design (with Primer3 [36]) for long dsRNAs or are directly subjected to the final ranking for the design of siRNAs. Designs are ranked by (i) their predicted specificity and (ii) their predicted efficiency and, in the case of siRNA designs, (iii) their calculated seed complement frequency. Sequences can also be evaluated for additional features, such as homology to unintended transcripts, or SNP and UTR contents. Final designs can be visualized using GBrowse [40]. All results are presented in a comprehensive HTML report and are also exported to text files.