Described in the Biodiversity Data Journal, MycorrhizaTracer is a bioinformatic pipeline that takes raw Sanger chromatograms and delivers classified, functionally annotated taxa with minimal human intervention. It was built with fungi and plants in mind, particularly the fungal internal transcribed spacer (ITS) region and the plant markers ITS2 and rbcL, but its developers say it can be adapted to essentially any group of organisms simply by swapping in an appropriate reference database and primers. The software is written in Python and released under an MIT licence through GitHub, making it free for any laboratory to install, inspect and modify.

The pipeline’s architecture follows five main steps: consensus calling, taxonomic classification, clustering, salvaging and functional annotation. In the consensus stage, forward and reverse reads for each sample are loaded and quality-trimmed using PHRED scores together with chromatogram peak heights, evaluated through a sliding window that strips low-quality regions from the ends of each read. Reads that pass the filters are aligned with Biopython’s pairwise aligner, and a consensus sequence is generated: matching bases are kept with the higher quality value, while disagreements between the two strands are recorded as ambiguous Ns. Samples for which neither read survives quality control are discarded outright, a deliberate design choice meant to keep unreliable data from contaminating downstream results.

Classification then proceeds by BLASTn search against a user-specified database, most commonly UNITE for fungal ITS sequences. Hits are filtered by alignment length and coverage thresholds, and a taxonomic assignment is only accepted when the top hits, defined as those within 0.5 percent identity of the best match, agree with one another. Where they disagree, the assignment is truncated to the last rank shared by all candidates, and further minimum-identity cutoffs constrain how deep the label can go, with 97 percent identity required for species-level calls. Sequences matched against UNITE also receive the database’s species hypothesis numbers, a convention that supports reproducible comparisons across studies. Anything that cannot be named at species level is routed onward to clustering.

The clustering module applies strict quality and length filters, uses ITSx to confirm that a full ITS region is present, and then groups the remaining sequences into operational taxonomic units with VSEARCH at a default 97 percent identity threshold. These de novo clusters, labelled denovo_X, represent lineages that exist in the samples but not in the reference database, which is a common situation for fungi given how many species remain formally undescribed. An optional salvaging step then gives poor-quality sequences a second chance: they are searched against the OTUs already identified in the study with a lenient bitscore-based match, but a rescued sequence is only accepted if another sample from the same predefined salvage group, such as a soil core or a site, has independently been assigned to that same OTU. That guard rail is designed to stop sequencing artefacts from inflating local diversity estimates.

The final optional stage bridges taxonomy and ecology. Fungal taxa resolved to genus or species level are run through FUNGuild, a widely used annotation tool that assigns trophic mode and lifestyle, distinguishing for example ectomycorrhizal symbionts from wood-decaying saprotrophs or pathogens. The pipeline also accepts user-supplied regional species lists so that classifications remain ecologically plausible, although the authors caution that such lists can accidentally filter out newly arriving or poorly documented species. Every threshold in the workflow, from quality windows to identity cutoffs, is exposed as a command-line argument, so users can tune behaviour without touching the code.

To demonstrate performance, the team ran the pipeline on a real-world dataset: 2,357 ectomycorrhizal root tips collected from 63 soil plots across England through the UK Department for Environment, Food and Rural Affairs’ Natural Capital and Ecosystem Assessment programme. The samples generated 14,134 chromatograms spanning three target regions and deliberately included both pristine and poor-quality traces. On average, 88 percent of targets yielded a consensus sequence, and of the samples that could be classified, 62 percent of fungal ITS sequences reached species level and 79 percent reached genus level, with somewhat lower figures for the plant markers. In total the run resolved 677 fungal OTUs, 19 plant OTUs from ITS2 and 15 from rbcL.

The efficiency numbers are striking. The entire dataset, all 2,357 samples and 14,134 chromatograms, was processed through the core pipeline with clustering and salvaging enabled in under 20 minutes on an ordinary consumer laptop, a MacBook Air with 16 gigabytes of memory, using six CPU cores. No high-performance computing facility is required, which matters enormously for field-based and resource-limited research groups in the very settings where Sanger sequencing remains most popular. Salvage success proved sensitive to how groups are defined: 122 of 257 poor-quality fungal samples were rescued when the whole country was treated as one group, compared with 58 when each of the 63 sites stood alone, illustrating the trade-off between recovering rare taxa and avoiding artefacts.

The authors position MycorrhizaTracer alongside existing tools such as SangerFlow, SCATA, PipeBar and SangerAnalyzeR, and argue that, to their knowledge, it is the only pipeline carrying raw chromatograms all the way through to functional annotation. Competing pipelines typically stop at consensus generation, accept pre-processed FASTA files instead of trace files, or lack clustering and FUNGuild integration. The developers are equally candid about limitations: performance depends heavily on the quality of the incoming chromatograms, a single clustering threshold may not suit all fungal lineages given their variable rates of molecular diversification, and classification accuracy is ultimately bounded by the completeness of reference databases. They stress that expert judgement, microscopy and ecological knowledge remain essential companions to any automated workflow.

The broader message is that Sanger sequencing and high-throughput sequencing are complements rather than rivals. Short Illumina reads of roughly 550 base pairs capture only fragments of the full ITS region that reference databases like UNITE were built around, while long-read platforms overshoot those references entirely. Sanger reads of 600 to 1,000 base pairs, by contrast, match the databases almost perfectly, offering higher taxonomic resolution at modest cost. By removing the manual bottleneck that has long accompanied this technology, MycorrhizaTracer could let ecologists, conservationists and taxonomists turn thousands of field samples into named, ecologically annotated species lists within an afternoon, unlocking biodiversity data that has been sitting unread in trace files all along.

Subject of Research: An open-source bioinformatic pipeline for automated taxonomic classification of Sanger DNA barcodes from fungi and plants

Article Title: MycorrhizaTracer: A bioinformatic pipeline for fungi and plant classification of Sanger DNA sequences

Article References: Brekke, T., Weeks, T., Barber, R., Thomson, I., Gooda, R., Gargiulo, R., Delhaye, G., Andrew, C., Kowal, J., Bidartondo, M., & Suz, L. (2026). MycorrhizaTracer: A bioinformatic pipeline for fungi and plant classification of Sanger DNA sequences. Biodiversity Data Journal, 14, Article e198617. https://doi.org/10.3897/bdj.14.e198617

Image Credits: AI Generated

DOI: 10.3897/BDJ.14.e198617

Keywords: MycorrhizaTracer, Sanger sequencing, DNA barcoding, bioinformatics, mycorrhizal fungi, ITS region, taxonomic classification, BLAST, OTU clustering, FUNGuild, biodiversity, open-source software

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

The importance of haplotype structure is difficult to overstate in modern crop genetics. When a genome-wide association study flags a region linked to flowering time or disease resistance, the next question is almost always which accessions carry which allele combinations, whether those combinations translate into changes in the encoded protein, and which parental lines should be crossed to combine useful allelic diversity. A third question follows quickly: where should a diagnostic marker be placed so that breeders can track the desired version of the gene in a breeding population without sequencing every seedling. Each of these questions has traditionally demanded its own software. Alignment browsers display read-level evidence at a locus, variant databases catalog allele frequencies across a panel, and marker design tools work in isolation from both. The read counts supporting a variant call and the panel-wide pattern of alleles sit in separate views, and neither view leads naturally to haplotype classification or to primer design.

Hap-Browser consolidates these steps by reading precomputed pileups, the compressed records of sequencing reads aligned to a reference genome, and rendering an entire panel of accessions as a sample-by-position matrix. In practical terms, a researcher opens a gene of interest and immediately sees, for every accession in the panel, which nucleotide is present at every variant position within that gene. The application then groups accessions by haplotype over any set of positions the user selects, so that varieties sharing the same combination of alleles cluster together. Crucially, the tool also reports the amino-acid changes that follow from the nucleotide differences, translating synonymous changes, missense mutations, and premature stop codons into a functional interpretation of each haplotype class. This translation step is what turns a table of single-nucleotide polymorphisms into a hypothesis about which haplotype is likely to alter protein function and therefore which haplotype is worth pursuing in a breeding program.

The marker design capability is where the application departs most sharply from existing tools. From the same view that displays the haplotype matrix, Hap-Browser designs allele-specific KASP primers, the competitive PCR chemistry widely used in breeding laboratories to distinguish two alleles at a single position, as well as primers for insertion-deletion polymorphisms. The design step is variant-aware in a way that matters enormously for panel-scale breeding: the software checks every variant within the primer-binding regions across the entire panel, so that a candidate primer set that would fail, or produce misleading calls, on some accessions is flagged before the primers are ever ordered from a synthesis company. In large diversity panels, hidden polymorphisms under primer-binding sites are a notorious source of genotyping failure, and discovering the problem after ordering primers wastes both time and money. By surfacing those conflicts at design time, the tool shifts quality control to the earliest possible point in the marker development pipeline.

The application also handles the reverse problem, assigning an unknown sample to a known haplotype class. Query sequences can be matched against the cataloged haplotypes using BLAST, the Basic Local Alignment Search Tool, allowing a breeder who has sequenced a new variety to determine immediately which haplotype group it belongs to and what that membership implies for the trait under study. This closes the loop between discovery and application: the same interface that reveals haplotype structure in the reference panel can classify new material against that structure, which is precisely the operation a breeding program performs when it screens germplasm for a version of a gene worth introgressing.

Performance considerations shaped the architecture in ways that will be visible to users. Because the pileups are precomputed rather than generated on the fly, a 200-accession panel is fetched and cached once when a gene is first opened. After that initial load, reclassifying haplotypes over a new set of selected positions and recomputing the display are effectively instantaneous. That responsiveness matters for exploratory analysis, where a researcher may want to try several different position sets within a gene, comparing how the haplotype groupings shift depending on whether the analysis is restricted to coding variants, expanded to promoter regions, or focused on a candidate causal polymorphism identified from a GWAS peak. A tool that forces a wait on every reclassification discourages exactly the kind of iterative reasoning that produces biological insight.

The demonstration dataset accompanying the paper covers 23 rice heading-date genes plus Sub1A, the well-known submergence-tolerance gene, evaluated across 200 accessions from the International Rice Research Institute panel. Heading-date genes are a natural showcase: flowering time in rice is controlled by a network of genes whose allelic variation underpins adaptation to different latitudes and growing seasons, making haplotype-level inspection genuinely informative for breeders. Sub1A, meanwhile, is one of the most successfully deployed genes in modern rice breeding, having been introgressed into numerous varieties to confer tolerance of complete submergence. Extending the demonstration set, the authors note, requires only editing a single tab-separated line and rerunning a Snakemake pipeline, the workflow management system that automates the precomputation steps. Nothing in the design of the software ties it to rice in its inputs, although the demonstration presented in the paper is confined to a single species.

That extensibility point deserves emphasis because it positions Hap-Browser as infrastructure rather than a one-off resource. Any crop species with a reference genome, a resequenced diversity panel, and standard annotation files in General Feature Format can be loaded by preparing the pileups through the pipeline and registering the gene of interest. The same logic applies to non-crop organisms: any research community maintaining a panel of resequenced individuals, whether studying natural variation in a model organism or cataloging variation in a livestock breed, faces the same gap between raw variant data and haplotype-level interpretation. The authors make the source code, the pipeline, and the demonstration data openly available, inviting other groups to adapt the system to their own organisms and to extend its functionality.

The broader context is the steady industrialization of genomics-assisted breeding. Marker-assisted selection, in which DNA markers stand in for laborious phenotyping, depends on a supply of reliable, well-validated diagnostic markers, and the rate at which such markers can be developed has been limited less by sequencing capacity than by the fragmented software chain connecting variant discovery to primer design. Tools that remove manual hand-offs from that chain compress the timeline from a GWAS hit to a working KASP assay from weeks to, potentially, an afternoon. The Jeonbuk National University team, whose work was supported by the Rural Development Administration of the Republic of Korea and the Jeonbuk RISE program, has effectively asked what a breeder actually needs to see at a candidate gene, and built the interface around that answer: the full panel of alleles, the haplotype groupings, the protein consequences, and the marker design, all in one session.

Whether Hap-Browser becomes a standard fixture in breeding laboratories will depend on adoption, but the problem it addresses is real and widely felt. As diversity panels grow from hundreds toward thousands of accessions, and as gene-level interpretation becomes the routine first step after any association study, the value of an interface that keeps panel-wide haplotype inspection and variant-aware marker design within a single view will only increase. For a discipline whose progress has often been gated by the friction between tools, the consolidation itself is the innovation.

Subject of Research: A web application for gene-level haplotype visualization and marker design in crop genomics

Article Title: Hap-Browser: a web application for gene-level haplotype visualization and marker design

Article References: Lee, H.-O., Kim, M., Jun, Y., Yang, C.-Y., Kwak, S.-H., & Mo, Y. (2026). Hap-Browser: a web application for gene-level haplotype visualization and marker design. BMC Bioinformatics. https://doi.org/10.1186/s12859-026-06651-5

Image Credits: AI Generated

DOI: 10.1186/s12859-026-06651-5

Keywords: haplotype analysis, crop breeding, KASP markers, rice genomics, genome browser, marker-assisted selection, variant visualization, GWAS, bioinformatics, resequencing, InDel markers, BLAST