SPADA Docs

stage because the nt and nr databases contain many sequences that have no possibility of ever 346 occurring in the sample matrix and thus, including such exhaustive sequences would provide 347 restrictive design constraints that are not valid and could result in a sub-optimal design.

348 349

350

4.3.4 Target region selection

Traditionally, the first step in design is to find a region of the pathogen genome that is 351 conserved among variants of a given target. A multiple-sequence-alignment (MSA) algorithm 352 (e.g., CLUSAL, T-COFFEE, MAFFT, or MUSCLE ) is the traditional approach to identify such 353 conserved regions. However, MSA algorithms do not scale well (in terms of CPU and memory) 354 with either large numbers of sequences or with long sequence lengths. Even with modern cloud 355 computing resources, computing a large MSA can be intractable. In addition, pathogen DNA and 356 RNA sequences vary significantly in their number of bases, substitutions, insertions, and 357 deletions. When combined with the low complexity of nucleic acids (i.e. only 4 bases for nucleic 358 acids vs. 20 amino acids for proteins), it is particularly difficult to get the high-quality 359 alignments that are required to deduce the desired conserved regions. These limitations make it 360 essentially impossible to apply an MSA to large collections of bacterial genomes or highly 361 variable viral genomes (e.g., LCMV, CCHFV, Lassa virus, HPV, and HRV). Sequence 362 alignments of the final design region, however, are helpful for displaying the variations present 363 and provide a helpful reality check after a design region is discovered with a k-mer approach. 364 Thus, we do recommend using an MSA that is restricted to the design region of interest, but not 365 for the entire genome. 366 A superior approach for determining the optimal design region(s) is to analyze targets using 367 k-mers (i.e. substrings of length k, usually 14-25, depending on the application; the rationale is 368

20