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

We used viral-ngs to assemble genomes of all viruses previously detected in these samples or identified by metagenomic analyses, including the LASV genomes from the 2018 Lassa fever outbreak in Nigeria and the EBOV genomes from the dilution series. For each virus we taxonomically filtered reads against many available sequences for that virus (Supplementary Table 10 ). We used one representative genome to scaffold the de novo assembled contigs (Supplementary Table 3, Supplementary Table 5, Supplementary Table 7) . We set the parameters assembly min length fraction of reference and assembly min unambig to 0.01 for all assemblies. We took the fraction of the genome assembled to be the number of base calls we could make in the assembly divided by the length of the reference genome used for scaffolding. To calculate per-base read depth, we aligned depleted reads from viral-ngs to the same reference genome that we used for scaffolding. We did this alignment with BWA 86 through the align and plot coverage function of viral-ngs with the following parameters: -m 50000 --excludeDuplicates --aligner options '-k 12 -B 2 -O 3' --minScoreToFilter 60. We counted the number of aligned reads (unique viral reads) using SAMtools 85 with samtools view -F 1024, and calculated enrichment of unique viral content by comparing number of aligned reads before and after capture. viralngs removes PCR duplicate reads with Picard based on alignments, allowing us to measure unique content. We excluded samples where one or more conditions had less than 100,000 raw reads for reasons of comparability. Excluded samples are highlighted in red in Supplementary Table 3 .