PriSeT: Efficient De Novo Primer Discovery

Marie Hoffmann; Michael T. Monaghan; Knut Reinert

After having transformed the frequent k-mers into TKMerIDs, each k-mer it represents needs to satisfy a set of constraints which must hold independently (see constraint set C s in Table 2 ). All these constraint filters process 10 Concretely, we use the sdsl::bit_vector by Gog et al. (2014) combined with sdsl::rank_support_v5 and sdsl::select_support_mcl occupying at most 0.0625n, and 0.2n extra bits, respectively (Vigna, 2008; Clark, 1998) . CC-BY-NC-ND 4.0 International license author/funder. It is made available under a The copyright holder for this preprint (which was not peer-reviewed) is the . https://doi.org/10.1101/2020.04.06.027961 doi: bioRxiv preprint Figure 3 . Encoding of TKMerID locations. Relative to a reference R PriSeT utilizes a sdsl::bit_vector with rank1 and select1 support in O(1). In this example the reference from which the bit vector originates from, has k-mers starting at the indices 0, 2, 3, 6, and so forth. Note, that in practice, we omit storing trailing zeros and the length of the bit vector corresponds to the position of the last k-mer occurrence. When searching for possible k-mer combinations to produce forward and reverse primer pairs, we have to parse for each forward TKmerID at a position i the window [i + κmin + τmin : i + κmax + τmax].