The standard method for rapidly detecting known human viruses in clinical samples is the (Flygare et al. 2016) . As a consequence, we have devel- 43 oped DisCVR, which is a fast, accurate and easy-to-use tool for detecting known human viruses 44 in clinical samples. 45 DisCVR employs an abundance-based method, which is a metagenomic approach for 46 rapidly profiling the organisms present in a sample. It works by creating a database of short nu-47 cleotide sequences (k-mers) from a large set of viral reference sequences, tagging the k-mers 48 taxonomically according to the viruses from which they came, screening each read in the HTS 49 dataset for the presence of virus k-mers, and organising a summary of the viruses present in the 50 sample via the tags. This approach makes data analysis very efficient, thereby minimizing the 51 computing effort required (Orton et al. 2016) . 52 Several existing tools utilize the abundance-based method to classify the reads in an 53 HTS dataset. NBC (Rosen et al. 2011 ) employs a naïve Bayesian classifier to assign a log-likeli-54 hood score to each read. This classifier is trained by using a set of unique profiles of 15 nucleo-55 tide k-mers from microbial genomes, and then allows users to upload the dataset to a web site 56 and obtain a summary of results listing the best taxonomic match for each read. Kraken (Wood & 57 Salzberg 2014) assigns each k-mer in the database to the last common ancestor of species hav-58 ing that k-mer, and then assigns each read to the taxon with the most matching k-mers. CoMeta 59 (Kawulok & Deorowicz 2015) creates a database of all k-mers for each rank in the taxonomic 60 tree, and then uses these databases to classify the reads at each rank. CLARK (Ounit et al. 61 2015) collects target-specific k-mer sets from reference genomes belonging to a certain taxo-62 nomic rank (e.g. genus), and then classifies reads at that rank. This approach reduces the data- the entire dataset and returns the relative proportions of organisms present by using k-mer sizes 70 of 30 and 50, based on the rationale that using two different k-mer sizes allows strain-level varia-71 tion to be captured more accurately. Taxonomer (Flygare et al. 2016 ) compares each read to 72 multiple reference databases, assigning it to a high-level taxonomic category on the basis of the 73 k-mer content of the read, and then uses exact k-mer matching to assign each read to a refer-74 ence by maximizing the total k-mer weight. This weight, which is a function of the k-mer count in 75 the reference and the database, provides a database-specific measure of how likely it is that a k-76 mer originated from a particular reference sequence. Here, we present DisCVR, a k-mer-based classification tool for detecting known human tion of a k-mer, which is lexicographically the smaller of a k-mer and its reverse complement.
For the purpose of this study, we define a virus k-mer as a k-mer that uniquely represents 115 a virus or set of related viruses, to the exclusion of the host. A shared k-mer is defined as a k-116 mer that is common to a virus and the host. By excluding shared k-mers, it is not necessary for 117 the user to remove host reads before using DisCVR, thus speeding up the overall processing 118 time. If k is small, many copies of shared k-mers are generated, and if k is large, many copies of 119 virus k-mers are found. Choosing the optimal k-mer size depends on balancing the advantages 120 of speed (small k) with those of specificity and sensitivity (large k). Furthermore, it is necessary 121 to reduce the number of low-complexity k-mers in the virus k-mer database, as these may be re-122 petitive in sequence and present in otherwise unrelated viruses. The filtering of low-complexity k-123 mers and the selection of the size of k is explained in Supplementary Section S1.
For constructing the virus k-mer databases, three comprehensive datasets of complete or 125 partial viral sequences were extracted from the NCBI taxonomy database. The first, the human 126 hemorrhagic virus dataset (shortened below to "hemorrhagic dataset"), contained 33,367 se- based short read aligner that is particularly tolerant of mismatches. In each case, the output is a 6 graph showing the depth and coverage of k-mers or sequence reads across the reference ge-173 nome and a summary of statistics for the mapping results (Fig. 3) . 
The ROC curve (Fig. 4) (Table S2 ). The ROC curves of DisCVR and the 215 other programs (Fig. 4) did not differ significantly from each other, and had overlapping confi-216 dence intervals. Kraken and KrakenHLL had identical curves. Kraken and CLARK rated as 217 slightly more sensitive but less specific than DisCVR as a result of HCoV NL63 being the top hit 218 in sample 1D3 and the second hit in DisCVR (Table 1 and Table S2 ). The top hit in DisCVR was 219 HRV-A, which was the second hit in Kraken and CLARK but was not detected using RT-PCR. It 
The RT-PCR assay was limited by the range of viruses that it could detect, by its depend-232 ence on sequence conservation, and consequently also by its potential to identify infections by 233 multiple viruses. Consequently, the false positive results were assessed using the validation 234 module (Table 2) , and the false negative results were investigated by examining the second hits 235 recorded by DisCVR (Table 1 ). In most false positive cases, the validation module showed that 236 there were multiple reads mapping to several regions of the reference genome, thus confirming 237 the presence of the viruses identified even though they had not been detected by RT-PCR. (Table 1 and Table S2 ). In addition, the virus identified by RT-PCR was 249 detected as the second hit in 10 samples (1B5, 1D3, 1E5, 1G1, 1F7, 1F8 sessing the sensitivity and specificity of DisCVR at 79% and 100%, respectively (Fig. 4) .
The threshold of 850 k-mers was also used in the analysis of the Nigerian datasets 
Using HTS in diagnostic settings offers many advantages, including the ability to sequence 273 pathogen genomes both individually and as communities. However, the uptake of HTS in such 274 settings has been slow, due partly to the cost, turnover time and bioinformatic demands of this 275 technology. We developed DisCVR to help address these challenges. DisCVR is a fast, accurate 276 program for detecting viruses from HTS data using the increasingly exploited approach of k-mer 277 classification. It offers the advantage of a non-targeted approach and also enables typing below 278 the species level (e.g. subtype, serotype, genotype or strain). Unlike other tools for detecting 279 9 viruses from HTS data, DisCVR is easy to use in diagnostic settings through the graphical user 280 interface, requires no bioinformatic expertise, and can be used on the Windows operating sys-281 tems that are commonly used in diagnostic laboratories. The basic output is easy to interpret, 282 and the advanced output provides more detailed statistics and a validation capability.
DisCVR was designed for detecting known viruses and cannot be used to discover novel vi-284 ruses. Indeed, the paper on the Nigerian patients (Stremlau et al. 2015) reported novel 285 rhabdoviruses in healthy patients using a metagenomic approach, and these were not detected 286 by DisCVR. However, metagenomics requires bioinformatic infrastructure and expertise at levels 287 that are not commonly available in diagnostic laboratories. Nonetheless, DisCVR enables the 288 detection of 148 pathogenic human viruses using one of the three implemented datasets (the 289 pathogenic dataset), and more using the others. This represents a greater than ten-fold increase 290 in target species over multiplex RT-PCR. Moreover, the number of viruses incorporated into the 291 DisCVR databases is flexible, and can also be expanded by building custom databases.
In the datasets from respiratory tract infections, DisCVR had high sensitivity and specificity 293 levels but did not identify all the viruses detected by RT-PCR when the threshold of ≥850 k-mers 294 was used. This threshold may be set by the user and was calculated for the respiratory dataset 295 for which we had paired RT-PCR and HTS data. As more datasets with paired information be-296 come available, it will be possible to tune the threshold more accurately to specific sample types 297 and sizes. Further efforts could also be made to calibrate DisCVR from artificially constructed 298 communities of viruses in various proportions.
Finally, DisCVR is configured as a human viral diagnostic tool, but could be readily expanded 300 to include non-viral human pathogens and pathogens with non-human hosts by using the cus-301 tom-build scripts in the DisCVR distribution. 