Tisza et al. 2026 Supplemental Tables S1-S25 (bioRxiv media-1.xlsx)

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• 3 protocols
• 15 evaluations
• 105 results
• 7 evidence claims

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• Evidence summary: DNA pathogen identification workflows: Summary Most workflow comparisons in the reviewed evidence use mock communities, constructed metagenomes or simulated sequences. Two compare workflows on patient specimens, and in both the reference standard draws partly on sequencing: in Hu et al. 2026 only the culture comparison is independent of the sequencing results, and in Huang et al. 2026 the reference was built mainly from BALF mNGS and the conventional tests that are themselves scored. Both are author-reported and have co-authors from sequencing companies (Genskey Medical Technology for Hu; Geneplus-Beijing, which supplies the targeted assay's probes, for Huang). For the Karius plasma cell-free DNA assay, the developers report 93.7% (59 of 63) agreement with initial blood culture in a sepsis-alert cohort, and blood culture does not detect every infection. A prospective study in children and young adults with high-risk leukaemia, with Karius taking part in sample processing and analysis, found every blood-culture organism in 81.3% (65 of 80) of evaluable bloodstream infection episodes on the day of or after onset and in 51.9% (42 of 81) during the three days before; 82.7% (134 of 162) of control samples from patients without fever or infection had no bacterium or yeast detected. In 62 bronchoalveolar lavage specimens (Hu et al. 2026), a short-read MGI workflow and a host-depleted nanopore workflow both had 95.2% positive agreement with culture but 44.6% and 46.4% negative agreement, because they found organisms culture did not; against a composite reference that adds organisms found by both workflows, positive agreement was 93.3% and 90.7%. In 122 intensive-care patients with pneumonia-associated sepsis (Huang et al. 2026), a targeted probe-capture assay on plasma had 63.46% sensitivity against the reference diagnosis, against 35.58% for untargeted blood mNGS, 49.04% for conventional tests and 9.62% for blood culture, with specificity of 44.44% and 61.11% for the two blood sequencing assays. In mock communities (Portik et al. 2022), Kraken2 and Bracken called 44 to 204 absent species at a 0.001% read threshold (precision 0.05 to 0.21), while long-read MEGAN-LR and BugSeq-V2 called 0 to 2; the first author is a PacBio employee. In seven pathogen-free libraries scored against one shared virus database by the EsViritu developers (preprint), the median number of false virus species was 0 for EsViritu, Centrifuger and Sylph, 6 for Kraken2 and 18 for Metabuli. For M. tuberculosis reads in constructed metagenomes (Hall and Coin 2024), kraken with its standard database classified 0.07 to 0.74 of the M. tuberculosis reads as M. tuberculosis, against 0.97 to 0.995 with a Mycobacterium database. Two papers by the developers of trained classifiers compare them with the baseline tools, on simulated or isolate-derived data rather than clinical specimens. On simulated 1,500-nt genome fragments (Mock et al. 2022, BERTax), Kraken2 and minimap2 assigned a superkingdom to 76.11% and 75.59% of fragments, with 99.82% and 99.95% accuracy on the fragments they assigned, when relatives were in the reference, and to 21.56% and 19.88% with 90.71% and 94.42% accuracy when each fragment's genus was absent from it; BERTax assigned every fragment, with 94.78% and 88.95% accuracy in the two settings. On in silico mixtures of nanopore isolate reads from species absent from the reference (Peres da Silva et al. 2024, MetageNN), MetageNN assigned a genus to 93.26% of reads on average across 30 mixtures, against 64.90% for Kraken2 and 53.06% for MEGAN-LR (on minimap2 alignments); MMseqs2, a reference-based tool not in the baseline, assigned 85.49%. These shares count reads assigned, not reads assigned correctly. Each study used its own reference database and reporting rules, and none of these results is clinical validation. (amp-20261007-dna-pathogens-source: result amp-20261007-dna-pathogens-result; dna-pathogen-20261009-source-portik2022: Table 4; dna-pathogen-20261009-source-hall2024: Tables 5-8; dna-pathogen-20261011-wolf2026-source: Tables 2 and 3; dna-pathogen-20261011-hu2026-source: Table 2; dna-pathogen-20261011-huang2026-source: Table 2; rna-pathogen-20261011-tisza2026-source-supp: Supplemental Table S6; dna-pathogen-20261012-bertax-source: Materials and Methods 'Preparing Data for Fine-Tuning' (1,500-nt fragments; closely and distantly related selection); dna-pathogen-20261012-bertax-source-si: SI Table S2 (superkingdom acc. and prop.); dna-pathogen-20261012-metagenn-source: Results 'Read classification results' paragraph 1 and the pseudo-mock 'Experimental setup' paragraph)
• Evidence summary: RNA pathogen detection workflows: Summary The single-assay results are a UCSF respiratory RNA metagenomic assay at 93.6% (103 of 110) sensitivity against original clinical respiratory panel testing, as reported by its developers, and the developers' own prospective validation of an Oxford nanopore workflow on 344 nasopharyngeal swabs (preprint). With thresholds fixed on a separate derivation set, the nanopore workflow detected 51% (133 of 260) of PCR-positive targets in the samples passing quality control, from 85% for RSV to 19% for rhinovirus/enterovirus, and 99.8% of PCR-negative targets were not detected; criteria chosen after the validation raised sensitivity to 58%, and the authors attributed all nine false positives to barcode crossover. Its swabs were selected mostly as PCR-positive at Ct 30 or below, with about one negative per eight positives, and reads were matched only to the 23 panel pathogens, so it does not test detection outside the panel. Three comparisons of several workflows add proxy evidence. On 88 nasal washings scored against 1144 respiratory virus PCR results (Carbo et al. 2022), all five classifiers detected 22 or 23 of the 24 PCR-positive results at species level at a read-count cut-off chosen on the same data, but 31% to 58% of their positive calls were PCR-negative. When 13 European clinical virology laboratories each ran their own pipeline on the same 13 clinical datasets (de Vries et al. 2021, preprint), 10 to 13 samples were correctly called positive, and the three pipelines that found every sample reported 6, 2 and 0 additional PCR-negative viruses. In the CAMI II blood metagenome challenge (Meyer et al. 2022), 4 of 10 submissions listed the causal virus. In a developer benchmark of five virus profilers on one shared database (Tisza et al. 2026, preprint), the model-estimated probability of reporting the correct family for reads from seven divergent viruses at 10 read pairs was 0.974 for EsViritu and 0.9998 for Metabuli but 0.045 for Kraken2, 0.012 for Centrifuger and 0.0009 for Sylph at the authors' headline settings, and Metabuli reported a median of 18 false species in seven pathogen-free libraries against 0 for EsViritu. None of this is clinical validation: the cohorts are small or single-site, reference databases and reporting rules differ, and the benchmark data are mostly synthetic. (amp-20261007-rna-pathogens-source: result amp-20261007-rna-pathogens-result; rna-pathogen-20261009-source-carbo2022-supp: Supplementary Table 1; rna-pathogen-20261009-source-devries2021-supp: Supplementary Tables 2 and 4; rna-pathogen-20261009-source-meyer2022-supp: Supplementary Table 39; articles: Methods and Results; rna-pathogen-20261011-sanderson2026-source: Results 'Validation Set: Diagnostic performance', 'Performance by pathogen'; rna-pathogen-20261011-tisza2026-source-supp: Supplemental Tables S2 and S6)
• Relevance of False positives in seven pathogen-free libraries (Tisza et al. 2026 Table S6) to "Select a DNA pathogen-identification workflow for diagnostic testing": Assessed by False positives in seven pathogen-free libraries (Tisza et al. 2026 Table S6) (rna-pathogen-20261011-tisza2026-source-supp: Supplemental Table S6)
• Relevance of False positives in seven pathogen-free libraries (Tisza et al. 2026 Table S6) to "Select an RNA pathogen-detection workflow for diagnostic testing": Assessed by False positives in seven pathogen-free libraries (Tisza et al. 2026 Table S6) (rna-pathogen-20261011-tisza2026-source-supp: Supplemental Table S6)
• Relevance of Mutated virus mock-community profiling against ground truth (Tisza et al. 2026 Table S9) to "Select an RNA pathogen-detection workflow for diagnostic testing": Assessed by Mutated virus mock-community profiling against ground truth (Tisza et al. 2026 Table S9) (rna-pathogen-20261011-tisza2026-source-supp: Supplemental Table S9)
• Relevance of Positive-control virus detection by read depth (Tisza et al. 2026 Table S2) to "Select a DNA pathogen-identification workflow for diagnostic testing": Assessed by Positive-control virus detection by read depth (Tisza et al. 2026 Table S2) (rna-pathogen-20261011-tisza2026-source-supp: Supplemental Table S2)
• Relevance of Positive-control virus detection by read depth (Tisza et al. 2026 Table S2) to "Select an RNA pathogen-detection workflow for diagnostic testing": Assessed by Positive-control virus detection by read depth (Tisza et al. 2026 Table S2) (rna-pathogen-20261011-tisza2026-source-supp: Supplemental Table S2)

Models, benchmarks and other records

Evidence

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