Capturing diverse microbial sequence with comprehensive and scalable probe design

Hayden C. Metsky; Katherine J. Siddle; Adrianne Gladden-Young; James Qu; David K. Yang; Patrick Brehio; Andrew Goldfarb; Anne Piantadosi; Shirlee Wohl; Amber Carter; Aaron E. Lin; Kayla G. Barnes; Damien C. Tully; Björn Corleis; Scott Hennigan; Giselle Barbosa-Lima; Yasmine R. Vieira; Lauren M. Paul; Amanda L. Tan; Kimberly F. Garcia; Leda A. Parham; Ikponmwonsa Odia; Philomena Eromon; Onikepe A. Folarin; Augustine Goba; Etienne Simon-Lorière; Lisa Hensley; Angel Balmaseda; Eva Harris; Douglas Kwon; Todd M. Allen; Jonathan A. Runstadler; Sandra Smole; Fernando A. Bozza; Thiago M. L. Souza; Sharon Isern; Scott F. Michael; Ivette Lorenzana; Lee Gehrke; Irene Bosch; Gregory Ebel; Donald Grant; Christian Happi; Daniel J. Park; Andreas Gnirke; Pardis C. Sabeti; Christian B. Matranga

In the plots showing how probe counts scale with the number of input genomes ( Fig. 1b and Supplementary Fig. 2) , the "Baseline" approach generates probes by tiling each input genome with a stride of 25 nt and removing exact duplicates. The "Clustering-based" approach generates candidate probes using a stride of 25 nt and deems two probes to be redundant if their longest common substring up to m mismatches (shown at m = 0 and m = 4) is at least 65 nt. It then constructs a graph in which vertices represent candidate probes and edges represent redundancy, and finds a probe set by approximating the smallest dominating set of this graph. For running this clustering-based approach, see the design naively.py executable in our implementation of CATCH. The CATCH approach generates candidate probes The copyright holder for this preprint (which was not peer-reviewed) is the author/funder. It . https://doi.org/10.1101/279570 doi: bioRxiv preprint across 5 replicates, with the input to each replicate being n genomes that were randomly selected with replacement. Again, shaded regions are 95% pointwise confidence bands.