Environmental DNA, or eDNA, metabarcoding has transformed biodiversity monitoring over the past decade. Instead of catching and killing insects to identify them, scientists can now collect traces of genetic material that organisms shed in their environment and identify the species from those fragments. In pollinator research, the pioneering work of Thomsen and Sigsgaard in 2019 showed that flowers themselves can serve as a source of arthropod eDNA, allowing researchers to reconstruct pollination networks non-invasively. Since then, a growing number of studies have confirmed that eDNA collected from flowers can effectively reveal insect-plant interactions with high sensitivity, taxonomic breadth, and temporal resolution.

However, the method has faced persistent technical obstacles. One major problem is that flowers are not only visited by pollinators; they are also home to small insects such as thrips, aphids, and pollen beetles that may live on the flower and enter the sample as whole individuals. These flower-living organisms contribute orders of magnitude more DNA than the trace material left by transient pollinators. Because the polymerase chain reaction, or PCR, preferentially amplifies the most abundant DNA in a sample, the genetic signal of bees can be outcompeted by the overwhelming DNA of these abundant residents, producing false negatives. A second challenge lies in the universal arthropod primers themselves, which exhibit well-documented amplification bias toward certain taxonomic groups and often underrepresent bees in mixed floral community samples.

These biases are especially problematic for studies aiming to reconstruct plant-pollinator networks, because such inference requires extensive within-group taxonomic coverage rather than a focus on the most common arthropod taxa in general. The role of certain bee species may be underestimated, skewing network structure and leading to misleading conclusions for biodiversity assessments and conservation efforts. Despite bees being one of the most ecologically and economically important groups in pollination networks, no bee-specific metabarcoding primer had been available until now.

To close this gap, the research team targeted the mitochondrial cytochrome c oxidase subunit I gene, or COI, the most widely used DNA barcode for the animal kingdom. COI combines conserved primer-binding sites with sufficient sequence variation to discriminate between species, and it is well represented in public sequence databases such as BOLD and NCBI. BOLD currently contains approximately 112,000 publicly available bee sequences, of which roughly 108,000 correspond to the standard COI barcode region. The researchers downloaded COI sequences from flower-visiting arthropod families previously detected in flower wash samples, aligned them, and removed low-quality or suspect records, yielding a reference set representing 173,830 unique arthropod species, of which 2,394 were bees covering 39 percent of Austrian bee diversity.

From this alignment, the team identified conserved regions unique within bees, paying particular attention to avoiding shared consensus with non-target groups such as thrips and pollen beetles, which had previously dominated PCR output from flower wash samples. Using manual expert judgment alongside established primer design principles, they generated two forward and three reverse primer candidates with lengths of 20 to 30 base pairs, melting temperatures of 50 to 60 degrees Celsius, and expected amplicon sizes of roughly 200 to 400 base pairs. Degenerate bases were incorporated to account for sequence variation among bee taxa, and all candidates were checked for secondary structures such as hairpins and dimers. The selected pair, BeePrimeF and BeePrimeR, amplifies a short COI fragment of 198 base pairs, a length suitable for species-level resolution in most organismal groups and compatible with Illumina sequencing platforms.

Validation proceeded in two stages. In silico, the primers were screened against an alignment of 67,352 centroid sequences clustered from a total of 316,254 sequences retrieved from NCBI, using the R package PrimerMiner to calculate weighted penalty scores that account for mismatch position and type. Both primers showed high binding complementarity to bee sequences, while their affinity to non-target arthropod groups was substantially lower, with mismatches concentrated at the critical 3-prime ends where they strongly reduce amplification efficiency. In vitro, the primers were tested against DNA extracts from 48 bee specimens representing 38 species and 23 genera spanning all six bee families of Central Europe, alongside 34 non-target extracts including spiders, thrips, beetles, flies, bugs, butterflies, wasps, and ants. Thirty-three of the 38 bee extracts were successfully amplified, an 86.84 percent success rate, while only nine of the 34 non-target extracts yielded positive results, indicating a non-target exclusion rate of approximately 73.52 percent. Notably, no amplification occurred in groups known to be problematic in flower metabarcoding, such as thrips, pollen beetles, or aphids.

The decisive test came with field samples. The researchers applied BeePrime to eDNA extracted from 48 flower-wash samples collected in Austrian flower strips in 2024 and compared the results directly with the universal arthropod primer pair fwhF2/fwhR2n, one of the most widely recognized primers available. BeePrime detected bee DNA in 35 of the 48 samples, compared with only 17 for the universal primer, raising per-sample detection from 35.4 percent to 72.9 percent. At the species level, the universal primer identified 14 bee-positive samples, while BeePrime detected 87, a sixfold increase. Individual floral samples typically yielded multiple bee species rather than single detections, with a mean of 1.8 species-level detections per sample and a maximum of 6. Across the entire dataset, the universal primer recovered just six distinct bee species, whereas BeePrime detected 27.

The improvement in sequencing efficiency was equally striking. Amplifications with the universal primer produced only very low proportions of bee reads, with a median of 0.03 percent, in many cases approaching zero. BeePrime yielded a median of 15 percent and a mean of approximately 30 percent bee reads per sample, a difference confirmed as highly significant by a Wilcoxon rank-sum test. Thrips, which dominated the genus-level dataset with almost 85 percent of all reads under the universal primer, were reduced to roughly 30 percent under BeePrime, while wild bees rose from only a few percent to nearly 50 percent of reads. Together with honeybees, bees represented more than 60 percent of the total relative read proportion with the new primer pair.

Every bee taxon detected with BeePrime had previously been recorded at the same sites using traditional hand-net sampling and morphological identification, corroborating the molecular assignments. The authors caution, however, that empirical performance has so far been demonstrated only for common bee taxa in Central European agricultural landscapes, and that local testing is advised before applying the primer pair in other biogeographic regions. Several Apidae species, including some carpenter bees and long-horned bees, did not amplify successfully despite acceptable primer fit, so underdetected lineages should not automatically be interpreted as absent in ecological network analyses. The researchers also emphasize that read numbers do not translate directly into bee abundance, since DNA deposition depends on body size, body hairiness, and behavior. Nevertheless, by overcoming a key methodological bottleneck in pollinator eDNA metabarcoding, BeePrime substantially strengthens the molecular toolkit available for monitoring bee diversity and resolving plant-pollinator networks, marking a major step toward establishing floral eDNA as a routine approach in ecological monitoring and conservation.

Subject of Research: Development and validation of a bee-specific COI metabarcoding primer pair for non-invasive monitoring of plant-pollinator interactions

Article Title: A bee-specific metabarcoding primer pair for non-invasive assessments of plant–pollinator interactions

Article References: Gurten, S., Sint, D., Traugott, M., & Rennstam Rubbmark, O. R. (2026). A bee-specific metabarcoding primer pair for non-invasive assessments of plant–pollinator interactions. Metabarcoding and Metagenomics, 10, Article e183708. https://doi.org/10.3897/mbmg.10.183708

Image Credits: AI Generated

DOI: 10.3897/mbmg.10.183708

Keywords: bees, environmental DNA, metabarcoding, pollinators, primer design, COI barcode, plant-pollinator networks, biodiversity monitoring, bee-specific, primer, pair, non-invasive

The stakes are considerable. Globally, dugong numbers have fallen by roughly 20 percent over the past ninety years, and some regional populations face outright extirpation. The species is listed as Vulnerable on the IUCN Red List, with certain subpopulations, such as those around the Nansei Islands of Japan and along the East African coast, classified as Critically Endangered, and the New Caledonia subpopulation listed as Endangered. Habitat loss, marine pollution, and bycatch mortality all contribute to the decline, and the animal’s naturally low fecundity makes recovery slow even when pressures ease.

Traditional monitoring methods struggle with an animal like the dugong. In the Red Sea, which hosts the third largest dugong population in the world, most knowledge has come from aerial and surface observations, photo identification, analysis of grazing trails left in seagrass meadows, and interviews with fishermen. Because dugongs are timid and spend limited time near the surface, visual surveys are inefficient and prone to non-detection error, meaning animals can be present but simply missed. Environmental DNA techniques promise to sidestep these problems by detecting the genetic material organisms leave behind in water, mucus, feces, and sloughed cells.

The research team, drawn from King Abdullah University of Science and Technology in Saudi Arabia, Curtin University in Australia, eDNA Frontiers, Red Sea Global, the University of Milano-Bicocca, and the MaRHE Center in the Maldives, began by screening three candidate mitochondrial regions: the control region, the 12S rRNA gene, and the 16S rRNA gene. Eight candidate primer sets were designed in silico, four from the 12S region, three from the 16S region, and one from the control region. The designers required at least five variant positions within the primer annealing regions compared with 103 marine mammal mitogenomes, including manatees, seals, and dozens of whale and dolphin species, with stringent selection for at least one discriminatory mismatch at the 3-prime terminus of the primer.

That terminal mismatch matters because the final bases of a primer largely determine whether DNA polymerase will extend the reaction. A mismatch at the 3-prime end effectively blocks amplification of non-target DNA, giving the assay its specificity. The team also kept amplicons short, between 87 and 232 base pairs, because eDNA in aquatic systems is typically fragmented and degraded, and only small templates amplify reliably. After in vitro testing of the five most promising primer sets, one assay, designated Ddu_16S_07, emerged as the winner. It amplifies an 87 base pair fragment of the mitochondrial 16S rRNA gene using a dugong-specific reverse primer paired with a universal marine-vertebrate forward primer, and it relies on inexpensive SYBR Green chemistry rather than costly fluorescent probes.

The performance figures are impressive for a non-probe assay. Using synthetic DNA standards, the researchers modeled a limit of detection of 2.88 copies per reaction, meaning the assay can flag dugong DNA at extraordinarily low concentrations, and a limit of quantification of 66 copies per reaction, the threshold at which copy numbers can be measured reliably. The standard curve showed strong linearity, with an R-squared of 0.985 and an amplification slope of -3.28, corresponding to an efficiency of 101.78 percent, comfortably within the accepted 90 to 110 percent range for qPCR. In silico testing against hundreds of reference sequences found no binding to non-target species with four or fewer mismatches; the only predicted cross-binding at five mismatches was to Steller’s sea cow, which has been extinct since the eighteenth century.

Empirical validation backed up the computational predictions. The assay amplified dugong DNA from tissue samples and from water collected in the dugong exhibit at SeaWorld Abu Dhabi, while showing no amplification for six sympatric cetacean species tested in the laboratory, including Risso’s dolphin, spinner dolphin, Indian Ocean humpback dolphin, pantropical spotted dolphin, Indo-Pacific bottlenose dolphin, and humpback whale. Positive amplicons were confirmed by melt curve analysis, fragment sizing, and Sanger sequencing. The team also ran inhibition tests, spiking environmental water and seagrass samples with a known quantity of synthetic DNA. Neat samples showed a cycle delay of three or more cycles and incomplete amplification, but when samples were diluted tenfold, all replicates amplified, indicating that inhibitors are present in Red Sea eDNA matrices and that running samples both neat and diluted is best practice.

The real-world test involved 40 environmental samples: 28 seawater samples and 11 seagrass roller samples collected along the Saudi Arabian Red Sea coast, plus the aquarium water sample. Water was collected with Niskin bottles at one meter depth and filtered on site through 1.2 micrometer cellulose acetate cartridges. The roller samples were an experimental twist: because dugongs graze on seagrass, the team used sterile miniature paint rollers, wielded by snorkelers or an extendable handle from the boat, to swab seagrass surfaces in the hope that grazing activity would leave detectable DNA on the plants. Two field water samples produced positive amplification, each in two of three technical replicates, with melt temperatures consistent with the expected product. The aquarium sample amplified in all neat replicates. Notably, none of the seagrass roller samples amplified, despite being paired with two of the positive water sites.

The roller result is a cautionary tale about eDNA substrates. The authors suggest that substrate-specific differences in how DNA accumulates, persists, and is recovered likely explain the discrepancy: the dugong signal may simply not have been present at the exact swabbed spot, or plant material and surface debris may have diluted it. They propose that future roller sampling could be improved by increasing replicate numbers, covering broader areas of seagrass beds, or targeting sites showing fresh feeding trails. Similarly, the limited field detection rate probably reflects low and spatially patchy eDNA concentrations, and the authors note that filtering larger water volumes and sampling at greater depths, where dugongs are more active, could raise detection probabilities for this rare species.

Under the standardized validation framework proposed by Thalinger and colleagues, which grades eDNA assays from Level 1 (Incomplete) to Level 5 (Operational), the dugong assay meets the criteria for Level 4 (Substantial), reflecting demonstrated sensitivity plus expanded in vitro and field testing. Reaching Level 5 would require statistical estimation of detection probabilities and exhaustive assessment of environmental variables affecting performance. Even so, the assay fills a genuine gap: no species-specific qPCR assay for Dugong dugon had previously been published, although comparable assays exist for manatees, the dugong’s closest living relatives. The authors report that their assay performs comparably to a validated probe-based manatee assay, and its modeled detection limit is actually lower, though the two studies used different estimation approaches. For conservationists working across the Indo-Pacific, where dugong distribution data are often decades old or nonexistent, a cheap, scalable, probe-free test that can confirm the presence of dugongs from a few liters of seawater could transform monitoring, enabling managers to detect population change, target protection measures, and evaluate whether interventions are actually working before it is too late for the species’ most imperiled populations.

Subject of Research: Development of a species-specific environmental DNA qPCR assay for detecting the threatened dugong

Article Title: Development of a species-specific qPCR assay for the detection of the threatened dugong (Dugong dugon)

Article References: Mousavi-Derazmahalleh, M., Lukehurst, S. S., Hardenstine, R. S., Peverley, G., Dawkins, K. L., O’Toole, K. A., Asher, J. M., Boksmati, T., Richards, Z. T., Valsecchi, E., & Berumen, M. L. (2026). Development of a species-specific qPCR assay for the detection of the threatened dugong (Dugong dugon). Frontiers in Mammal Science, 5, Article 1915420. https://doi.org/10.3389/fmamm.2026.1915420

Image Credits: AI Generated

DOI: 10.3389/fmamm.2026.1915420

Keywords: dugong, environmental DNA, qPCR, eDNA biomonitoring, Red Sea, marine mammal conservation, mitochondrial 16S rRNA, species-specific assay, seagrass, IUCN Vulnerable species, non-invasive monitoring, primer design

The original AssayBLAST was built around a simple but powerful idea: use the well-established BLAST search engine to compare candidate primers and probes against large sequence databases, and thereby predict how they will perform in a polymerase chain reaction or related amplification assay. This approach allows researchers to screen oligonucleotides computationally against thousands or even millions of potential template sequences, flagging those that might bind to off-target sites or fail to amplify the organisms they were designed to detect. For multi-parameter assays, which combine many primer and probe pairs in a single reaction to detect numerous targets simultaneously, such computational pre-screening is not merely convenient; it is essential for keeping development costs and failure rates under control.

The new version addresses one of the most subtle and consequential failure modes in multiplex assay design: the spatial relationship between paired oligonucleotides. In a functioning amplification reaction, a forward and reverse primer must bind to opposite strands of the template DNA in the correct orientation and within a suitable distance of each other for the polymerase to generate a product. A primer pair that individually looks perfect can be rendered useless if the two primers bind to the same strand, or if their binding sites are too far apart or oriented incorrectly. AssayBLAST v2 introduces an integrated strand and proximity check that validates corresponding oligonucleotides against these geometric requirements, ensuring that predicted amplification events reflect biologically plausible configurations rather than coincidental sequence matches.

This check matters because earlier approaches could, in principle, report a match between a primer and a genomic sequence without verifying whether that match could actually support amplification. In a multiplex assay targeting dozens of pathogens or resistance genes at once, such false positives in the computational analysis can mislead researchers about the expected specificity and coverage of their design. By explicitly evaluating orientation and spacing, the updated software produces a more faithful picture of what the reaction will actually do. The developers describe this as enabling precise validation of corresponding oligonucleotides, and the improvement is backed by a comparative evaluation against the previous version of AssayBLAST, giving users a quantified sense of how much the analysis has matured.

Beyond geometry, the second major addition concerns the outcome of the reaction itself. AssayBLAST v2 does not stop at identifying where oligonucleotides bind; it uses the predicted interactions among all oligonucleotides in the assay to determine the theoretical amplification outcomes. In other words, the software constructs a computational benchmark of which template molecules should be amplified, which should be missed, and how the various primer and probe pairs in a multiplex panel are expected to behave together. This predicted outcome profile then serves as a reference point for downstream wet-laboratory validation, allowing experimentalists to compare observed assay performance against theoretical expectations and to correlate discrepancies with specific oligonucleotide interactions.

The practical implications of this benchmarking capability extend well beyond academic curiosity. Synthesizing primers and probes is a recurring expense in diagnostic development, and a poorly designed multiplex assay can burn through budgets in repeated rounds of synthesis, testing, and redesign. By catching suboptimal oligonucleotide combinations before synthesis, the software streamlines the assay development workflow and reduces the costs associated with suboptimal primer and probe synthesis. For laboratories developing diagnostic panels, particularly in fields such as infectious disease detection where panels must distinguish closely related organisms against a backdrop of host and environmental DNA, the difference between a well-screened and a poorly screened design can determine whether a product reaches the clinic at all.

The third pillar of the update is an adaptive optimization of BLAST parameters that responds dynamically to the size of the database being searched. BLAST searches are governed by numerous tunable parameters, including word size, scoring matrices, and statistical significance thresholds, and the settings that work well for a small custom database of target genomes may be poorly suited to a comprehensive repository containing billions of nucleotides. Fixed parameters force developers into a compromise: settings sensitive enough to detect weak but relevant off-target binding in large databases can become computationally expensive, while settings optimized for speed can miss biologically important interactions. The adaptive approach in AssayBLAST v2 scales its parameters with database size, improving both analytical sensitivity and computational performance simultaneously rather than trading one against the other.

This adaptive strategy reflects a broader trend in bioinformatics, where tools must increasingly operate across databases that grow at staggering rates. Sequence repositories double in size on timescales measured in years, and a screening tool with static assumptions about database scale risks becoming either obsolete or prohibitively slow. By building the scaling behavior directly into the search strategy, the AssayBLAST developers have future-proofed their tool to a degree, ensuring that the reliability of an in silico evaluation does not silently degrade as reference data expand. For users running routine re-evaluations of existing assay panels against updated databases, this consistency is a meaningful quality assurance benefit in its own right.

The team behind the software spans several Jena institutions, combining expertise in RNA bioinformatics, high-throughput analysis, and applied diagnostics. Tom Eulenfeld and Maximillian Collatz, who share first authorship, are based at Friedrich Schiller University Jena, with Collatz also affiliated with the university’s Bioinformatics Core Facility. Sascha D. Braun and Ralf Ehricht bring the diagnostic perspective from the Leibniz Institute of Photonic Technology, the InfectoGnostics Research Campus Jena, and associated translational research centers, including Jena University Hospital. This blend of computational and applied expertise is visible in the design of the update itself: the new features target precisely the points where computational predictions meet laboratory reality, from primer geometry to predicted amplification outcomes. The research was funded in part by the German Research Foundation under Germany’s Excellence Strategy, and the article is published open access under a Creative Commons Attribution license.

For the diagnostic and research communities that depend on molecular multi-parameter assays, the release of AssayBLAST v2 arrives at a moment when the stakes of in silico evaluation have never been higher. Multiplex panels now underpin syndromic testing for respiratory infections, sepsis diagnostics, food safety screening, and antimicrobial resistance surveillance, and each new panel multiplies the number of oligonucleotide interactions that must be anticipated and validated. A tool that can reliably predict amplification outcomes, verify the physical plausibility of primer binding, and adapt its search sensitivity to ever-growing databases addresses the central bottleneck in this workflow. The developers position the update as increasing the robustness and reliability of molecular diagnostics and research applications alike, and the comparative evaluation included in the publication offers users a transparent account of the gains. As computational screening continues to move from a supplementary check to a foundational step in assay design, tools of this kind are set to shape how quickly and how confidently new diagnostic panels reach the laboratory and, ultimately, the patient.

Subject of Research: In silico evaluation and design of molecular multi-parameter PCR assays using BLAST-based oligonucleotide analysis

Article Title: AssayBLAST v2: major update improving reliability and reporting of the in silico analysis of molecular multi-parameter assays

Article References: Eulenfeld, T., Collatz, M., Braun, S. D., & Ehricht, R. (2026). AssayBLAST v2: major update improving reliability and reporting of the in silico analysis of molecular multi-parameter assays. BMC Bioinformatics, 27(1), Article 205. https://doi.org/10.1186/s12859-026-06595-w

Image Credits: AI Generated

DOI: 10.1186/s12859-026-06595-w

Keywords: AssayBLAST, oligonucleotide design, molecular diagnostics, multiplex PCR, BLAST, in silico analysis, primer design, bioinformatics software, amplification prediction, diagnostic assay development, BMC Bioinformatics, computational biology