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

CATCH's framework offers considerable flexibility in designing probes for various applications. For example, a user can customize the model of hybridization that CATCH uses to determine whether a candidate probe will hybridize to and capture a particular target sequence. Also, a user can design probe sets for capturing only a specified fraction of each target genome and, relatedly, for targeting regions of the genome that distinguish similar but distinct subtypes. CATCH also offers an option to blacklist sequences, e.g., highly abundant ribosomal RNA sequences, so that output probes are unlikely to capture them. CATCH can use locality-sensitive hashing 33, 34 , if desired, to reduce the number of candidate probes that are explored, improving runtime and memory usage on especially large numbers of input sequences. We implemented CATCH in a Python package that is publicly available at https://github.com/broadinstitute/catch. Figure 1 -Using CATCH for probe set design. (a) Sketch of CATCH's approach to probe design, shown with three datasets (typically, each is a taxon). For each dataset d, CATCH generates candidate probes by tiling across input genomes and, optionally, reduces the number of them using locality-sensitive hashing. Then, it determines a profile of where each candidate probe will hybridize (the genomes and regions within them) under a model with parameters θ d (see Supplementary Fig. 1b for details). Using these coverage profiles, it approximates the smallest collection of probes that fully captures all input genomes (described in text as s(d, θ d )). Given a constraint on the total number of probes (N) and a loss function over the θ d , it searches for optimal θ d . (b) Number of probes required to fully capture increasing numbers of HCV genomes. Approaches shown are simple tiling (gray), a clustering-based approach at two levels of stringency (red), and CATCH with three choices of parameter values specifying varying levels of stringency (blue). See Methods for details regarding parameter choices. Previous approaches for targeting viral diversity use clustering in probe set design. Shaded regions around each line are 95% pointwise confidence bands calculated across randomly sampled input genomes.