0.04 µL RNase inhibitor (Roche), 0.16 µL SuperScriptIII RT/ Platinum Taq (Life Technologies), and 130 1.4 µL pre-amplified sample (2.3). Each sample/buffer mixture is loaded into three separate inlets in 131 order to improve detection sensitivity. Each assay port was loaded with premixed PCR primers 132 diluted to 20 µM in 1X Loading buffer (Fluidigm) . Each Access Array mixes and carries out 2304 133 individual PCR reactions simultaneously. Reverse transcription was conducted on the Access Array 134 at 50°C for 30 min and PCR cycling conditions of: 95°C 2 min, then 50 cycles of 15s@94°C, 135 30s@50°C, 30s@72°C. The 2-Primer Amplicon Tagging Protocol from the manufacturer was used 136 (Fluidigm). 137
Following pan-viral group PCR on the Access Array, 10 µL of the amplicons, pooled by sample, are 138 collected from the original sample inlets. A second PCR reaction is then performed using 1:100 139 diluted amplicons to add Illumina sequence-specific adaptors and sample barcodes. All barcoded 140 samples are pooled and mixed with a 5% PhiX DNA spike in and sequenced on an Illumina MiSeq 141 instrument using v3 PE600 reagents. Custom sequencing primers are used, per Fluidigm 142 recommendation, in order to not sequence the linker sequences. Sequencing primers FL1 and FL2 143 (Fluidigm) are added to the Illumina sequencing primers, mixed, and replaced into the default primer 144 wells on the reagent cartridge. Samples are pooled such that there are 100,000-400,000 reads 145 obtained per sample. 146
Samples were first pre-amplified using SISPA (described in 2.3). Libraries were generated using the 148 NEBNext Ultra II DNA library prep kit (NEB) and dual indexed. They were then multiplexed, spiked 149 with 1% PhiX DNA, and run on a MiSeq with the goal of obtaining 3 million read pairs per sample. 150
MiSeq v3 PE600 reagents were used, according to the manufacturer's recommendation. 151
Data was analyzed with an internally-developed pipeline ("PAIDIRT"). Briefly, residual sequencing 153 primers are trimmed off based on homology and reads are trimmed for quality. Then, a further 26bp 154 is removed from each end to insure the pan-viral group PCR primers are removed. Sequences are 155 then aligned to a database of virus sequences using bowtie2 (Langmead and Salzberg, 2012) and 156 consensus sequences are generated. BLASTN (Altschul et al., 1997) is used to correct the taxonomic 157 assignment of each consensus sequences and obtain match quality information. The number of 158 supporting reads, length of consensus sequence, percent match to the reference, consensus sequence, 159 and taxonomy, including best match, are returned for each virus group. In order to call a result 160 positive, it must be supported by 10X the number of any reads in the non-template control, in order to 161 account for the phenomenon of index hopping. Further, it must have a match length of >80% the 162 length of the targeted region in order to discount short alignments that may occur. 163 164
Results 165
In order to evaluate the relative sensitivity of AA-PCR-Seq compared to shotgun sequencing, we 167 chose of a panel of five representative virus isolates (Tables 1 and 2 to collect at least three million read pairs per sample in order to sequence 8 samples per MiSeq run 175 with a moderate, but not excessive sequencing effort. We also performed conventional pan-viral 176 group PCR and qPCR on each of these viruses (Table 2) . To call samples positive for shotgun NGS, 177 there must be at least a 10-fold enrichment in classified reads in the sample, compared to a no-178 template control and at least 10 classified reads. For AA-PCR-Seq, the reads must generate a 179 consensus sequence that covers at least 80% of the projected size of the target amplicon and the 180 number of classified reads must similarly be 10-fold higher than the no-template control sample. 181
For AdV, HBoV, FluA, and MERS-CoV, AA-PCR-Seq was capable of detecting lower titers of virus 182 than shotgun sequencing. There was a 2-log increase in sensitivity for detecting AdV and influenza 183 virus, while there was a 1-log increase in sensitivity for detecting HBoV and MERS-CoV by AA-184 PCR-Seq compared to the ones by shotgun sequencing (Table 2) . RSV detection was equivalent 185 between the methods, however there are 10-100 fold more reads detected by AA-PCR-Seq compared 186 to shotgun sequencing, making the positive call more robust. As expected, the higher-volume 187 conventional pan-viral group PCR was able to detect lower starting titers for some viruses tested than 188 AA-PCR-Seq or shotgun sequencing (Table 2) . 189 190
We used a set of de-identified clinical respiratory specimens to evaluate the performance of PCR-Seq in a real-world setting. Each of these specimens was previously tested and found qPCR-193 positive for one or more for viral agents. In total, the specimens were positive for a total of 112 194 respiratory viruses by qPCR, with Ct values varying from 18 to 39 (Table 1) . 195 We tested each of these clinical specimens by AA-PCR-Seq, conventional pan-viral group PCR, and 196 shotgun NGS. Results are shown as percent agreement with qPCR testing ( Figure 1A ). Conventional 197 pan-viral group PCR had 83% concordance to qPCR, AA-PCR-Seq had 79% concordance, and 198 shotgun NGS had the lowest rate of agreement (59%) ( Figure 1A ). The same trend was seen for the 199 various viruses detected, with shotgun NGS being the least sensitive ( Figure 1B ). Grouping the 200 samples by Ct, we observed that the AA-PCR-Seq and conventional pan-viral group PCR 201 concordance rate begins to decline only beyond Ct range of 32 ( Figure 1C ). There is 100% agreement 202 between AA-PCR-Seq and conventional pan-viral group PCR at Ct ≤ 29 ( Figure 1C ). Importantly, 203 AA-PCR-Seq was able to detect more qPCR-confirmed targets than shotgun NGS where the Ct was 204 above 29. Above Ct 29, shotgun NGS detects 10/52 (19%), compared to 30/52 (58%) by AA-PCR-205 Seq. 206
In order to test the capacity of AA-PCR-Seq to detect divergent viruses, we assembled a set of rectal 208 and nasal swabs from various bat species which had been collected and tested as part of prior 209 zoonotic surveillance studies. This sample set was comprised of 34 old world and new world bats 210 from more than 21 species, from Kenya, the Democratic Republic of Congo, Democratic Republic of 211 Georgia, and Guatemala (Table 3) . Using AA-PCR-Seq, we identified 55 viruses from 4 virus 212 families and 9 viral genera. There was an adenovirus with 95% identity based on the amplicon 213 sequence to its nearest neighbor, 23 alphacoronaviruses with identities ranging from 80-100%, 6 214 betacoronaviruses with identities from 83-99%, 4 gammacoronaviruses with identities from 92-215 100%, 3 deltacoronaviruses with identities from 87-93%, 12 dependoparvoviruses with identities 216 from 84-100%, one protoparvovirus with 85% identity, 4 morbiliviruses with identities from 83-92%, 217 1 rubulavirus with 92% identity to their nearest neighbors ( A sensitive and high throughput identification system for a broad range of viruses is needed in cases 223 of unknown viral disease outbreaks in humans or animals. A major challenge is that viruses lack a 224 shared universal phylogenetic marker such as 16S rRNA for bacteria or 18S rRNA for eukaryotes. 225 AA-PCR-Seq provides a solution to identify a wide range of known and potentially novel viruses 226 from large, complex sample sets. Using a respiratory virus panel as a proof of concept, we evaluated 227 AA-PCR-Seq as a comprehensive virus detection and discovery platform, comparing its relative 228 sensitivity to shotgun NGS as well as to qPCR and conventional pan-viral group PCR. Finally, we 229 used AA-PCR-Seq to test a large set of human clinical samples as well as samples from bats, and we 230 were able to detect a wide array of viruses with varying degrees of homology to known species. 231
Shotgun NGS, PCR/qPCR, and AA-PCR-Seq are all useful and valid tools in virus detection and 232 discovery, each with certain strengths and weaknesses. Next-generation sequencing allows for 233 detection of a very broad array of pathogens, but it lacks the sensitivity, scalability, and low cost of 234 PCR. On the other hand, though relatively inexpensive, sensitive, and straightforward, PCR strategies 235 can be too narrowly targeted to identify a large panel of viruses or novel viruses. Unlike qPCR, 236 consensus degenerate pan-viral group PCR provides broad specificity to amplify a broad range of 237 known viruses as well as distantly related virus species. AA-PCR-Seq builds upon the strength of 238 both NGS and pan-viral group PCR, using microfluidics to run dozens of these assays in parallel on 239 dozens of samples. NGS allows analysis of these thousands of amplicons in a fraction of the time, 240 effort, and cost it otherwise would take to purify and sequence individual amplicons using gels and a 241 capillary sequencer. 242
Pan-viral group PCR assays are, by design, much more broadly-reactive than qPCR. Because of that 243 design choice, and the small sample volume afforded by the Access Array, some AA-PCR-Seq 244 assays were initially less sensitive compared to full-volume, traditional PCR. However, we were able 245 to compensate for much of the loss in sensitivity by incorporating a pre-amplification step, which 246 increases the concentration of total nucleic acid entering each AA-PCR-Seq reaction. Because of the 247 ability to test up to 48 samples concurrently using up to 48 viral assays we believe the sensitivity 248 level of AA-PCR-Seq is acceptable and within the Ct range of most clinical samples. In addition, the 249 assays included in AA-PCR-Seq are not limited to detecting known viruses. We demonstrate this 250 case with zoonotic virus testing where AA-PCR-Seq was able to detect 55 diverse and potentially 251 novel viruses in the bat panel without prior knowledge of their sequences. There are no existing 252 qPCR primers available for these viruses and many of them were not detectable by direct shotgun 253 sequencing.. 254 AA-PCR-Seq does not require multiplexing optimization, since it uses singelplex PCR assays in 255 individual reaction chambers. Further, the Access Array is modular so that it is simple to add or 256 replace assays. This is especially useful because pan-viral group PCR primers are updated and 257 improved as new viral sequences become available. In addition to the respiratory panel described in 258 this manuscript, we have also added a full complement of pan-viral group PCR assays for two main 259 syndrome diseases including gastroenteritis and encephalitis. Additional viruses were identified from 260 the bat sample set, including two astroviruses, four orthoreoviruses, and three rotaviruses. Work is 261 still being done to fully validate these expanded panels. 262
Finally, AA-PCR-Seq increases sample throughput while reducing reagent and sample usage. 263
Excluding capital costs, when run at capacity in screening large sample sets, the reagent cost of each 264 AA-PCR-Seq sample/assay pair is reduced 10-50-fold compared to conventional PCR and Sanger 265 sequencing. Furthermore, the microfluidic system requires a small volume of sample (<10 µL) to test 266 for the entire panel of viruses, whereas conventional pan-viral group PCR would require nearly 200 267 µL of sample. This is critical during unknown case investigations when sample volume is limited for 268 screening by a large panel of assays for differential testing. 269
We see AA-PCR-Seq as a strategy that complements PCR and shotgun NGS strategies. With 270 exponentially more sequencing effort (and the associated cost, time, and data analysis and storage 271 concerns), shotgun NGS should be able to identify all the viruses in this study and more. However, 272 labs that can afford routine NGS frequently rely on the MiSeq, which is a lower output instrument. United States. Emerging infectious diseases 19, 1978 -1980 Schroth, G.P., Tong, S., (2018) Wacharapluesadee, S., Duengkae, P., Rodpan, A., Kaewpom, T., Maneeorn, P., Kanchanasaka, B., 424 Yingsakmongkon, S., Sittidetboripat, N., Chareesaen, C., Khlangsap, N., Pidthong, A., Leadprathom, 425 K (23.8, 23.8, 27.1, 29.5, 29.9, 29.9, 30.1, 32, 33.2, 35.3), FluB (22.5, 29.8, 30 .
Paramyxoviridae HPIV1 (26.5, 34, 36) , HPIV2 (29.2, 38.6), HPIV3 (19, 21.8, 24, 24.5, 25, 26, 28.2, 29, 29, 30) Pneumoviridae RSV HMPV (20.9, 22.5, 22.5, 23, 23.8, 25, 25, 27.1, 28, 28, 28.6, 30, 29, 29, 30.2, 31, 31.9, 33, 33.8, 34, 36.6, 39.6) , RSVA (20.5, 21, 21, 22, 22, 23.5, 25, 25.5, 26, 27.5, 27.8, 31 .2), RSVB (21, 21, 22, 22.5, 23, 24.5, 25, 29, 30.5, 32.6, 38.5) , RSV (30, 36) Parvovirinae HBoV1
Picornaviridae HRV (24.5, 25.5, 26.5, 27, 28, 28, 28.5, 30, 30, 31, 31.5, 31.5, 32, 33, 33, 34, 34, 35, 36) 