SPADA Draft Documents

human genome, human microbiome, soil microbiome, etc. For both the exclusivity and 319 background databases, sequence quality is generally not an issue and it is recommended to 320 include partial sequences as well as complete genomes. A common practice is to check primers 321 for reactivity with all known organisms (such as the GenBank nr or nt databases) using a 322 program such as BLAST or primer BLAST to detect all off-target hits and amplicons. However, it 323 is not recommended to use such exhaustive databases during the design stage because the nt and 324 nr databases contain many sequences that have no possibility of ever occurring in the sample 325 matrix and thus, including such exhaustive sequences would provide restrictive design 326 constraints that are not valid and could result in a sub-optimal design. 330 conserved among variants of a given target. A multiple-sequence-alignment (MSA) algorithm 331 (e.g., CLUSAL, T-COFFEE, MAFFT, or MUSCLE ) is the traditional approach to identify such 332 conserved regions. However, MSA algorithms do not scale well (in terms of CPU and memory) 333 with either large numbers of sequences or with long sequence lengths. Even with modern cloud 334 computing resources, computing a large MSA can be intractable. In addition, pathogen DNA and 335 RNA sequences vary significantly in their number of bases, substitutions, insertions, and 336 deletions. When combined with the low complexity of nucleic acids (i.e. only 4 bases for nucleic 337 acids vs. 20 amino acids for proteins), it is particularly difficult to get the high-quality 338 alignments that are required to deduce the desired conserved regions. These limitations make it 339 essentially impossible to apply an MSA to large collections of bacterial genomes or highly 340 variable viral genomes (e.g., LCMV, CCHFV, Lassa virus, HPV, and HRV). Sequence 341 327 328 Target region selection 329 Traditionally, the first step in design is to find a region of the pathogen genome that is

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