The problem CoMeDA addresses begins with the nature of sequencing data itself. When researchers sequence amplicons from a microbial community, the read counts they obtain are inherently compositional: the abundance of each taxon is only meaningful relative to the others, because sequencing depth constrains the total. A species that appears to double in abundance may simply have declined less than its neighbors. Ignoring this compositional structure, the authors note, can produce spurious results in downstream analyses, a pitfall that has been documented repeatedly in the compositional data analysis literature yet is still not consistently handled in widely used microbiome platforms.

Compounding the issue is the batch effect problem. Multi-cohort studies, which combine samples processed at different times, in different laboratories, or on different sequencing platforms, frequently carry technical variation that can masquerade as biological signal. If a disease cohort happens to be sequenced in a different batch than the healthy controls, apparent differences in microbial composition may reflect the sequencing run rather than the disease. Batch effects, if not properly addressed, can confound the very signals researchers are trying to detect. Despite the availability of multiple microbiome analysis platforms, the authors argue, these two analytical considerations have not always been incorporated together as central, user-accessible components of a workflow.

CoMeDA treats the compositional structure of metabarcoding data and batch effects as coordinated components of one analytical pipeline rather than as optional add-ons. The platform accepts both raw FASTQ files and pre-classified taxa tables, making it compatible with short-read and long-read sequencing technologies alike. That flexibility matters as long-read platforms such as PacBio gain traction in microbiome research, since many existing tools were built around short-read data. For the crucial first steps of quality control and taxonomic assignment, users can start from raw reads or jump in with an already classified table.

At the statistical core of the platform is the centered log-ratio transformation, applied as the default normalization strategy. CLR transformation is a standard technique in compositional data analysis: it converts relative abundance data into log-ratios relative to the geometric mean of all taxa in a sample, placing the data in a space where ordinary statistical operations are more defensible. Before the transformation can be applied, however, the platform must contend with zeros, the many taxa that were simply not observed in a given sample. CoMeDA performs zero handling through probabilistic estimation, replacing true zeros with small estimated values so that log-ratios remain computable without distorting the underlying structure.

Once the data are normalized, CoMeDA applies automated batch effect correction that removes technical variation while preserving biological signals. The balance is delicate: overcorrection can erase genuine differences between conditions, while undercorrection leaves artifacts in place. The authors evaluated the behavior of this automated strategy using simulated datasets and a mock community with known ground truth, allowing them to assess how well the correction distinguished technical noise from real biology. They also used these benchmarks to measure the contribution of individual analytical components and the accuracy of taxonomic classification within the pipeline.

Downstream, the platform offers a suite of analyses that all operate within the same compositional framework. Diversity assessment uses compositionally appropriate metrics, and significance testing draws on established methods including PERMANOVA, PERMDISP, and ANOSIM, alongside principal coordinates analysis for visualization. Differential abundance analysis incorporates effect size estimation, and the platform supports approaches such as linear discriminant analysis effect size, robust centered log-ratio methods, and interquartile log-ratio transformations. Co-occurrence network inference and functional prediction, the latter covering both bacterial and fungal communities with reference to resources such as the Kyoto Encyclopedia of Genes and Genomes, round out the workflow. Because every step follows the same compositional logic, the results are internally consistent in a way that pieced-together pipelines often are not.

One of the platform’s more distinctive features is the split-CLR transformation, designed for cross-dataset analyses. When researchers want to combine datasets with different compositional structures, for example to run cross-kingdom correlation analyses linking bacteria and fungi, or paired-condition analyses within the same subjects, directly merging the data can introduce spurious correlations. Split-CLR addresses this by transforming each dataset appropriately before integration, reducing the artificial associations that arise from naive combination. This capability is particularly relevant to studies of polymicrobial diseases, where bacterial-fungal interactions may be clinically important but statistically treacherous to assess.

To demonstrate the platform in practice, the authors reanalyzed three published datasets spanning different study designs and sequencing technologies. A multi-cohort gout dataset allowed them to test batch correction across cohorts, and the analysis recovered biologically coherent patterns while highlighting additional findings supported by the literature, including distinctions related to hyperuricemia. A pediatric Crohn’s disease dataset covering both bacterial and fungal communities exercised the cross-kingdom analysis capabilities. Finally, a PacBio long-read periodontitis dataset confirmed compatibility with long-read data. Across all three, CoMeDA recovered patterns consistent with the original publications while surfacing additional literature-supported findings, suggesting the integrated workflow can add value without discarding established results.

Accessibility is a stated design goal. The platform features an interactive web interface and requires no programming expertise, lowering the barrier for clinical researchers and experimentalists who lack bioinformatics support. The open-source release, published under a Creative Commons Attribution 4.0 license, includes the Docker image for local deployment, an option likely to appeal to institutions handling sensitive clinical data. As microbiome studies grow in scale and increasingly pool cohorts across laboratories, tools that treat compositionality and batch structure as first-class citizens, rather than afterthoughts, may become essential infrastructure. CoMeDA’s authors position their platform as a step in that direction: a compositionally coherent workflow in which normalization, batch correction, and downstream analysis follow a single, consistent analytical logic from raw reads to biological interpretation.

Subject of Research: An integrated web platform for compositional analysis and batch correction of metabarcoding data

Article Title: CoMeDA: an integrated platform for compositional analysis of metabarcoding data with batch correction

Article References: Lee, C.-Y., & Chang, T.-H. (2026). CoMeDA: an integrated platform for compositional analysis of metabarcoding data with batch correction. BMC Bioinformatics. https://doi.org/10.1186/s12859-026-06705-8

Image Credits: AI Generated

DOI: 10.1186/s12859-026-06705-8

Keywords: metabarcoding, microbiome, compositional data analysis, batch effect correction, 16S rRNA, ITS sequencing, CLR transformation, differential abundance, co-occurrence networks, cross-kingdom analysis, bioinformatics software, long-read sequencing

The significance of reference mapping in single-cell genomics is difficult to overstate. Large-scale integrated atlases, such as the Human Lung Cell Atlas and other tissue references accepted by the Human Cell Atlas consortium, have become central to understanding variation between cells and to establishing consensus on cell-type nomenclature. The standard way to exploit these resources is transfer learning: a researcher takes a freshly sequenced dataset, the query, and projects it onto a prebuilt reference model, inheriting the reference’s cell-type labels and biological context in much the same way that genomics researchers align new reads to the reference genome. But until now, doing this required fluency in Python, familiarity with complex machine learning pipelines and, increasingly, access to graphics processing units. Even for those with the expertise, assembling the necessary software environment is time-consuming and error-prone.

Existing tools have chipped away at this barrier without removing it. Azimuth offers a graphical interface for projecting new data onto atlases, but it restricts users to mapping methods from Seurat v4 and lacks collaborative features, preventing free, no-code comparison of different mapping approaches. FASTGenomics once provided access to the Human Lung Cell Atlas but is no longer freely available. The scGPT Hub and CellTypist offer graphical routes to reference mapping and cell-type annotation respectively, yet both carry caveats: scGPT is a foundation model pretrained on diverse datasets and fine-tuned on the user’s data, which can introduce biases and residual batch effects from unrelated datasets, and CellTypist alone cannot remove batch effects, so a separate integration method is still required. Foundation models have also been shown to perform worse than scVI-style variational inference methods for integration tasks while being more computationally expensive to fine-tune.

ArchMap was built around four guiding principles: maximizing accessibility, protecting user data privacy, ensuring utility, quality and reproducibility, and enabling collaboration. Its cloud-based setup eliminates software installations and dependency headaches entirely, and it grants automatic access to CELL×GENE Annotate for downstream exploration, effectively democratizing the full analysis pipeline from upload to visualization. Because researchers typically use the platform early in data processing, when privacy concerns are highest, all user data are encrypted during upload, download and storage on the team’s Google Cloud implementation, and projects deleted by users are automatically purged from cloud storage after three days. Expert users who prefer full control can host the web application themselves or run the mapping pipeline locally using provided tutorials, at an estimated cost of around 200 euros per month in cloud services for multiple daily mappings.

Under the hood, ArchMap’s mapping engine is scArches, a transfer learning framework that extends conditional neural-network-based integration methods to new query data. The platform currently supports atlases built with scVI, scANVI and scPoli, with scVI and scANVI consistently ranking among the top-performing integration methods in independent benchmarks. The number of training epochs for mapping was fixed at 100 after systematic benchmarking across healthy and diseased query datasets showed that reconstruction loss plateaus and most integration metrics stop improving beyond that point. Crucially, the platform does not retrain reference models itself, because doing so would void the extensive performance evaluations and community-based annotations that consortium-approved atlases have already undergone.

Once a query is mapped, ArchMap offers a choice of pretrained classifiers for cell-type label transfer, including out-of-the-box XGBoost and K-nearest neighbors implementations as well as the native scANVI and scPoli classifiers. Using 80-20 train-test splits, the team found that KNN performed best across all atlases and label granularities. The platform also computes mapping quality metrics that go beyond what existing tools provide. Euclidean uncertainty, derived from a weighted KNN classifier over the reference embedding, flags cells whose transferred labels are unreliable, with a suggested cutoff of 0.5 above which cells are labeled unknown. A second metric, Mahalanobis uncertainty, accounts for correlations between latent features by rescaling the embedding, and the team recommends it for identifying disease-associated cell states. A presence score, computed by diffusing weighted-neighbor counts through the reference graph with a random walk with restart, quantifies which reference cell states are actually represented in the query, helping users judge whether a given atlas is suitable for their data.

To demonstrate the workflow, the team mapped a single-cell dataset from patients with idiopathic pulmonary fibrosis onto the Human Lung Cell Atlas. Mapping quality was high, with 96.85 percent gene overlap and full preservation of Leiden clusters, scoring five out of five. Just under 54 percent of query cells found an anchor, a reference cell in a similar biological state, while 8.99 percent of cells carried uncertainty scores above 0.5 and were classified as unknown, a pattern the authors note is expected when mapping diseased tissue onto a healthy reference. Examining the results in the integrated CELL×GENE viewer, they observed striking variability in uncertainty scores among adventitial fibroblasts, a cell type implicated in pulmonary fibrosis, and found that high-uncertainty cells were strongly associated with diseased donor labels.

Differential expression analysis within the same interface sharpened the biological story. Low-uncertainty adventitial fibroblasts reflected homeostatic processes, whereas high-uncertainty cells displayed fibrotic signatures, including elevated expression of CTHRC1, a well-known marker of fibrotic fibroblasts, while low-uncertainty cells showed higher SERPINF1 expression. The result shows that ArchMap can surface disease-associated cell states and candidate driver genes directly within its interface, without any local analysis. The authors are careful to add a caution: high uncertainty alone is not proof of a novel or diseased state, since residual batch effects or low-quality cells with few genes or high mitochondrial content can also inflate the score, so high-uncertainty cells should be validated manually, for example through differential expression testing.

The platform is also designed as a community repository. Atlas builders can upload their own references, which are initially marked as in revision while automated quality checks benchmark their integration method against scVI, scPoli with and without prototype learning, and principal component analysis using scIB metrics. Atlases that pass review can be published publicly or kept private, and compatibility with the scvi-hub repository extends coverage to atlases without formal publications, provided they include a preprint or documented model evaluation. Collaboration features allow secure sharing of projects within or across teams and institutions, and joint annotation through CELL×GENE. Current server limits cap each mapping at 200,000 cells, though users can split larger queries into batches and concatenate results with provided Colab notebooks, an approach the benchmarks show yields identical performance to mapping everything at once.

Looking ahead, the developers see ArchMap growing alongside the atlas ecosystem itself. As new generations of atlases incorporate multimodal data from CITE-seq and spatial transcriptomics, the platform is expected to extend toward multimodal reference mapping using frameworks such as TotalVI and MultiMIL. The team also envisions support for multiple lineage atlases, allowing users to map onto specific subsets of a reference, and the evolution of its repository functions into an open-source atlas version control system as existing atlases are updated. For now, the message to the community is simple: a wet-lab biologist with a properly formatted h5ad file, raw counts, gene identifiers and a batch key can go from raw query to annotated, visualized, quality-checked mapping in a browser session. In a field where the gap between data generation and biological interpretation has often been bridged only by computational specialists, ArchMap aims to make the reference atlas as routine a tool as the reference genome became for genomics.

Subject of Research: A no-code web platform for reference-based mapping and annotation of single-cell transcriptomics datasets

Article Title: ArchMap is a web-based platform for reference-based analysis of single-cell datasets

Article References: Lotfollahi, M., Bright, C., Skorobogat, R., Dehkordi, M. M., George, X., Richter, S., Shitov, V. A., Topalova, A., Melcher, H., Nussbaumer, N., Fomm, A., Luecken, M. D., & Theis, F. J. (2026). ArchMap is a web-based platform for reference-based analysis of single-cell datasets. Nature Genetics. https://doi.org/10.1038/s41588-026-02756-y

Image Credits: AI Generated

DOI: 10.1038/s41588-026-02756-y

Keywords: single-cell genomics, reference mapping, ArchMap, Human Cell Atlas, transfer learning, cell-type annotation, scArches, scVI, machine learning, bioinformatics software, CELLxGENE, idiopathic pulmonary fibrosis

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 platform, called lncRNA Seeker Hub, was developed by Pok-Son Kim of Kookmin University, Klaus Heese of Hanyang University’s Graduate School of Biomedical Science and Engineering, and Arne Kutzner of Hanyang University’s Department of Information Systems. Writing in the open-access journal BMC Bioinformatics, the team describes a system that aggregates RNA sequencing evidence across entire cohorts of samples and then lets researchers manually reconstruct transcript structures that automated assemblers cannot reliably recover. The work was supported by a grant from the National Research Foundation of Korea, funded by the Ministry of Science and ICT, and the software’s case studies focus on two of the most famous non-coding RNA loci in human biology: MALAT1 and PCA3.

The core problem the team attacks is statistical and computational at once. When a gene is expressed weakly, any single RNA-seq sample may show only sparse local coverage along the genome, and the reads that do map across splice junctions, the points where RNA segments are stitched together after introns are removed, may be too few to convince an assembler that a genuine transcript exists there. Individual samples also differ from one another in unpredictable ways, so the splicing pattern supported by one patient’s tissue may barely register in another’s. Yet when many samples are pooled, these faint, distributed signals can become reproducible and interpretable. Automated assembly tools, which typically operate sample by sample or merge results only crudely, are poorly suited to exploit that cohort-level pattern. Genome browsers that researchers routinely rely on, including IGV, JBrowse, and the UCSC Genome Browser, offer superb visualization but, according to the authors, lack integrated support for cohort-level aggregation and the kind of hands-on transcript reconstruction their workflow demands.

lncRNA Seeker Hub fills that gap with an architecture that reflects modern software engineering as much as genomics. At its heart sits a high-performance backend written in Rust, a systems programming language prized for speed and memory safety. The backend performs targeted aggregation of BAM files, the standard binary format in which aligned sequencing reads are stored, pulling only the genomic regions of interest rather than loading entire chromosomes. To keep interactive exploration responsive, the system employs binary caching, so that previously computed aggregations can be served instantly on repeat queries. On top of this engine runs an interactive visualization interface built with Bokeh, a Python-based plotting library designed for browser-delivered, data-dense graphics. The combination means a researcher can zoom into a specific gene locus across dozens or hundreds of samples and see aggregated evidence render quickly enough to support genuine, exploratory analysis rather than batch-and-wait processing.

What makes the platform distinctive is not merely speed but the way it synchronizes different layers of evidence. In a single view, a researcher can examine read coverage along the genome, the splice junctions supported by split reads, and the individual reads themselves, all aligned and all drawn from the aggregated cohort rather than from one sample at a time. This synchronized exploration is precisely what weak-lncRNA discovery requires. A candidate exon may show barely detectable coverage in any single individual, yet appear consistently, sample after sample, once the cohort is pooled. A splice junction may lack the read support an assembler demands in isolation, but reveal itself as a recurring pattern when evidence is aggregated. The software then supports manual transcript reconstruction: researchers can build, inspect, and refine candidate transcript structures directly from that aggregated evidence, effectively acting as expert curators of their own data.

The published case studies illustrate the approach at the MALAT1 and PCA3 loci. MALAT1, or metastasis-associated lung adenocarcinoma transcript 1, is one of the most abundant and intensively studied lncRNAs in mammals, involved in regulating gene expression in the cell nucleus and strongly implicated in cancer metastasis. PCA3 is a prostate-specific non-coding RNA used as a biomarker in prostate cancer diagnostics, notable because it is expressed at very low levels compared with most coding genes. At both loci, the authors report that lncRNA Seeker Hub enabled the detection of candidate cohort-supported transcriptional structures and splice-graph heterogeneity, meaning variability in how the splice junctions connect exons across samples, that they characterize as not readily identifiable using automated assembly or single-sample visualization. In other words, at even these well-trodden genomic addresses, the cohort-level view surfaced structural detail that mainstream tools had been leaving on the table.

Performance matters as much as discovery in a tool meant for daily research use, and the team benchmarked the platform across representative genes spanning a wide range of read-depth regimes, including extreme local coverage of the kind encountered at hyper-expressed loci such as MALAT1. The benchmarks show predictable latency and memory scaling, meaning that response times and memory consumption grow in a controlled, anticipatable way as the amount of sequencing data at a locus increases. That predictability is more than an engineering nicety. Researchers exploring weak transcriptional signals need to know that panning across a gene with enormous depth of coverage will not crash the session or freeze the interface, and the benchmarking suggests the Rust-and-caching design delivers exactly that stability even under the punishing local coverage that some of the most biologically interesting regions produce.

The broader significance of the work lies in a quiet tension running through modern genomics. Automated pipelines are indispensable, and no one proposes abandoning them; they process thousands of samples with consistency and scale no human can match. But they encode assumptions, and those assumptions are calibrated to the signals that dominate most datasets: well-expressed, cleanly spliced, protein-coding transcripts. Non-coding RNAs, with their low abundance, tissue specificity, and splicing variability, sit precisely at the margins where those assumptions break. By combining cohort-level aggregation with expert-driven, manual reconstruction, lncRNA Seeker Hub represents a hybrid model of discovery in which the machine does the heavy lifting of data handling and the human contributes pattern recognition and biological judgment. The authors position the platform, accordingly, as a practical framework for cohort-level RNA-seq exploration and manual transcript validation rather than as a replacement for assembly pipelines.

For the lncRNA field specifically, the implications could be considerable. Thousands of long non-coding RNAs have been catalogued, but annotations remain incomplete and uneven, and many candidate transcripts in public databases rest on thin or inconsistent evidence. Tools that can marshal the collective weight of large sequencing cohorts, from population genomics projects to cancer specimen collections, offer a route to confirming which of these faint transcriptional whispers are real, reproducible biology and which are noise. If weak lncRNAs regulating cancer pathways, immune responses, or developmental programs have been hiding below the detection threshold of automated assembly, platforms like this one give researchers a credible way to bring them into view, and potentially into the clinic, since molecules like PCA3 already demonstrate that non-coding transcripts can serve as disease biomarkers.

The software arrives as open-access research, citable under a permanent digital object identifier in BMC Bioinformatics, and the published version represents peer-reviewed, accepted work. Whether lncRNA Seeker Hub becomes a standard fixture in transcriptomics laboratories will depend on adoption and on how the community balances automated throughput with manual curation. But the message of the paper is clear and likely to resonate well beyond bioinformatics: some of the genome’s most important signals are individually too faint for machines to trust, yet collectively unmistakable when the right tools make them visible. Sometimes the next discovery is not hidden in the data so much as hidden in the aggregation of the data, waiting for a browser built to show it.

Subject of Research: Cohort-level RNA-seq visualization software for discovering weakly expressed long non-coding RNAs

Article Title: lncRNA Seeker Hub: cohort-level RNA-seq visualization and manual transcript reconstruction for weak lncRNA discovery

Article References: Kim, P.-S., Heese, K., & Kutzner, A. (2026). lncRNA Seeker Hub: cohort-level RNA-seq visualization and manual transcript reconstruction for weak lncRNA discovery. BMC Bioinformatics. https://doi.org/10.1186/s12859-026-06671-1

Image Credits: AI Generated

DOI: 10.1186/s12859-026-06671-1

Keywords: lncRNA, RNA-seq, transcript discovery, bioinformatics software, genome visualization, splice junctions, MALAT1, PCA3, cohort analysis, Rust programming, BAM aggregation, non-coding RNA

The toolkit, called mSigSDK, was developed by Aaron Ge and colleagues at the National Cancer Institute’s Division of Cancer Epidemiology and Genetics, working with collaborators at the University of Maryland School of Medicine and Brazil’s National Laboratory of Scientific Computing. Rather than building yet another signature-fitting algorithm, the team took a different approach: mSigSDK is an orchestration layer, a JavaScript-based software development kit that loads into a tested desktop browser through a single dynamic import, with no installation whatsoever. Once loaded, it can run analyses entirely on the user’s own device, meaning that sensitive genomic data — the mutation spectra and mutation annotation format rows that describe a patient’s tumor — never have to leave the computer they sit on.

At the heart of the problem mSigSDK addresses is a quiet but consequential inconsistency in the field. Several mature packages for mutational signature analysis exist, most prominently written in R and Python, and each has its own conventions for input data, its own fitting algorithms, and its own quirks. The choice of fitting tool can meaningfully affect the results a researcher obtains, which makes triangulating across multiple tools highly desirable. Yet doing so has been operationally expensive: a scientist would need to install and maintain several environments, convert data between formats, and manually reconcile outputs. Worse, no standard machine-readable format existed to capture the parameters of an analysis, the evidence supporting a review, and the provenance of the results — the kind of documentation that makes a computational finding reproducible and trustworthy.

mSigSDK tackles this by wrapping four established analysis tools — SigProfilerAssignment, MuSiCal, deconstructSigs, and sigminer — behind a uniform adapter layer. Through that layer, the kit orchestrates the tools so they can be run side by side on the same data without the user touching their individual installation requirements or input formats. The SDK also adds its own browser-native non-negative least squares fitting, a standard mathematical approach for estimating how much each known reference signature contributed to a tumor’s observed mutation pattern, as well as non-negative matrix factorization-based exploratory extraction, which allows researchers to discover novel signatures rather than only testing against a fixed catalogue such as the COSMIC reference set.

The engineering achievement here is substantial. Running R and Python code inside a browser is possible thanks to technologies like WebR and Pyodide, which compile those language runtimes to WebAssembly, but mSigSDK goes further by making the whole experience seamless. A zero-install demonstration reached a fully rendered local report in roughly 1.82 seconds. The kit emits configurable, rule-based review evidence — essentially automated documentation of why an analysis reached its conclusions — and serializes portable, schema-validated reports with built-in visualization and export. That means an analysis performed on one machine can be packaged as a machine-readable file, checked against a formal schema, and shared or archived with its full provenance intact.

Privacy is a central design consideration. Because analysis runs locally, mutation spectra and mutation annotation format rows stay on the user’s device by default. The authors are transparent about one caveat: optional reference-context helpers may transmit genomic coordinates to public endpoints unless a strict-local mode is enabled. This kind of explicit, switchable data-flow control is increasingly important as genomics moves into clinical settings where patient data cannot simply be uploaded to a remote server. A browser-native tool that keeps raw data on-device while still offering state-of-the-art analysis could lower the barrier for hospitals and research groups working under strict data governance rules.

The validation work behind the paper is unusually thorough. The team tested each of the four adapters against the corresponding package running locally on 38 single-base substitution spectra from lung adenocarcinoma samples in the Pan-Cancer Analysis of Whole Genomes project. Three of the four adapters reproduced their package’s local execution exactly, and the fourth matched to floating-point precision. The kit’s data converter matched SigProfilerMatrixGenerator version 1.3.6 exactly for single-base substitution spectra in both 96-channel and 1,536-channel formats, for double-base substitution spectra in the 78-channel format, and for the 83-channel small insertion-deletion classification, provided that repeat and microhomology annotations derived from SigProfilerMatrixGenerator were supplied to mSigSDK. In other words, running these tools in the browser yields the same numbers as running them on a desktop installation.

But the benchmarks also revealed something the field should pay attention to. Across 2,700 synthetic spectra generated at three noise levels, the mean cosine similarity of estimated exposures exceeded 0.96 for all tools — meaning the overall mixture of signatures was recovered well. Yet the mean per-spectrum F1 score for identifying which signatures were genuinely active ranged from 0.532 to 0.924 at 10 percent noise, a striking spread. On real PCAWG data, all tools reconstructed the observed spectra well, with mean cosine similarities between 0.982 and 0.994, but they disagreed sharply on how many signatures were active per sample, calling anywhere from 4.13 to 17.39 active signatures. This confirms that the choice of fitting tool matters, and it makes the case for multi-tool triangulation all the more compelling.

Speed is another highlight. Warm-start refitting — re-estimating signature exposures when a small change is made to the input — took between 18.2 and 73.7 milliseconds on Windows and 22.2 to 44.6 milliseconds on macOS for 120 samples. For the heavier scenario of 300 samples against 40 reference signatures, refitting completed in 1.80 to 6.81 seconds on Windows and 2.12 to 5.89 seconds on macOS. Those are interactive timescales, fast enough for a researcher to adjust parameters and see updated results in real time, something that batch-oriented command-line workflows have never offered.

The broader significance of mSigSDK may lie less in any single benchmark than in what it signals about the future of scientific software. By combining multi-tool orchestration, exploratory signature extraction, rule-based review evidence, and schema-validated reporting in a zero-install browser environment, the kit lowers the cost of doing rigorous, reproducible, and privacy-preserving mutational signature analysis to nearly zero. Developed with support from the National Cancer Institute’s Intramural Research Program and released as open access, it invites a much wider community — from large consortia to individual labs in resource-limited settings — to interrogate the mutational histories written into cancer genomes. If the fingerprints in our DNA are a diary, mSigSDK hands more readers than ever the pen-light needed to read it.

Subject of Research: Browser-native computational analysis of cancer mutational signatures

Article Title: mSigSDK: browser-native computation of mutational signatures

Article References: Ge, A., Zhang, T., Martins, Y. C., Landi, M. T., Park, B., Chen, K., Balasubramanian, J., & Almeida, J. S. (2026). mSigSDK: browser-native computation of mutational signatures. BMC Bioinformatics. https://doi.org/10.1186/s12859-026-06681-z

Image Credits: AI Generated

DOI: 10.1186/s12859-026-06681-z

Keywords: mutational signatures, cancer genomics, JavaScript, browser-native computation, bioinformatics software, SigProfilerAssignment, MuSiCal, deconstructSigs, sigminer, non-negative least squares, PCAWG, reproducibility

A team of Austrian researchers believes they have a practical answer. In a study published in BMC Bioinformatics, Markus Steiner, Stephan Drothler, Jan P. Höpner, Roland Geisberger and Nadja Zaborsky, based at Paracelsus Medical University, the Salzburg Cancer Research Institute and Paris-Lodron University Salzburg, introduce Spatialgater, an open, web-based tool built in R Shiny that lets scientists select individual cells directly on a zoomable image of their tissue. Rather than forcing analysts to write custom scripts every time they want to isolate, say, a cluster of immune cells sitting at the edge of a tumor nest, Spatialgater allows them to draw polygon gates around cells of interest with a few clicks of a mouse — in situ, exactly where the cells actually live.

The problem the tool addresses is rooted in how spatial omics data are currently processed. The dominant data structure in the R ecosystem for this kind of work is the SpatialExperiment class, a container that stores molecular expression measurements alongside the spatial coordinates of each cell. Standard analysis workflows built on this framework typically proceed by clustering cells according to their expression of biomolecules — proteins, transcripts or other markers — and then subsetting or annotating cells based on those molecular groupings. What these workflows largely ignore is geography. A tumor cell and its lookalike elsewhere on the slide may share nearly identical expression profiles, yet occupy radically different microenvironments, one nestled against a blood vessel, the other buried in dense stroma. Clustering alone cannot tell them apart by location.

Computational biologists have developed patch-detection and neighborhood-analysis methods that identify recurring spatial patterns, such as regions where particular cell types consistently co-occur. These approaches are powerful for systematic surveys of tissue architecture, but they are fundamentally automated and population-level. None of them gives a researcher the simple, immediate ability to say: show me these cells, right here, and let me select exactly the ones I mean. That gap matters in practice. Pathologists and immunologists often spot something visually striking — an unusual accumulation of T cells at an invasive margin, a suspicious ring of macrophages around a necrotic core — and want to interrogate those specific cells without writing bespoke code or approximating their region of interest with crude coordinate filters.

Spatialgater fills that gap with a deliberately simple interface. The tool renders cells as dots overlaid on a zoomable image of the tissue, so users can navigate the sample much as they would navigate a digital slide under a microscope. Drawing a polygon directly on the image selects every cell whose coordinates fall inside the drawn boundary. The selection is fully interactive: researchers can zoom in to single-cell resolution, refine a gate, add additional polygons and inspect the molecular profiles of the cells they have captured. Because the tool operates on SpatialExperiment objects, it slots naturally into existing R-based spatial omics pipelines rather than requiring researchers to export their data into an unfamiliar format.

Two features elevate the tool beyond a simple lasso. The first is an integrated k-nearest-neighbor function that can automatically extend a manually drawn gate across spatially similar microenvironments elsewhere in the tissue. If a researcher delineates a distinctive cellular neighborhood in one region, the algorithm can propagate that selection to comparable regions, dramatically reducing the manual labor of annotating large tissue sections while keeping the human judgment that defined the original gate. The second is traceability. Every polygon a user draws is recorded in a log file, creating an auditable record of exactly how each selection was made — a small but significant safeguard for reproducibility in a field where manual choices have traditionally gone undocumented.

Export and integration are equally considered. Selected cell identifiers can be written out as a standard CSV file for use in any downstream software, or saved directly back into the original SpatialExperiment object as a new logical column, meaning manual selections become first-class citizens in subsequent statistical analyses. This design choice reflects the authors’ core motivation: boosting interactivity while reducing the programming burden of image analysis. A researcher no longer needs to be a fluent R programmer to translate a visual observation into a computable cell set; the web interface handles the translation.

To demonstrate the tool in action, the team applied Spatialgater to a publicly available imaging mass cytometry dataset of breast cancer tissue, using it to characterize and compare T cells according to their spatial location within the tumor microenvironment. The demonstration speaks directly to one of the hottest questions in cancer immunology: how the precise positioning of immune cells — whether T cells are excluded from tumor nests, patrolling the invasive margin or dispersed through stroma — shapes antitumor immunity and predicts response to immunotherapy. A tool that makes spatially targeted cell selection fast and intuitive could accelerate exactly this kind of location-dependent immune analysis. The team also validated the package’s gating functionality using test data derived from a wild-type mouse, with the animal work approved by the Austrian Federal Ministry of Education, Science, and Research under approval number BMBWF 2023-0.644.528.

The significance of the work may lie less in any single algorithm than in what it signals about the maturing of spatial omics. The field’s first generation of tools focused on generating and processing data; the next generation is about making those data genuinely explorable by working biologists. By lowering the barrier between seeing something interesting in a tissue image and acting on it computationally, Spatialgater embodies a broader shift toward interactive, human-in-the-loop bioinformatics. The software is open access, published under a Creative Commons Attribution license, and funded by the Austrian Science Fund, WISS 2025 through the Cancer Cluster Salzburg, and the Province of Salzburg. For laboratories drowning in multiplexed images and struggling to connect visual insight with computational rigor, a free browser-based gate-drawing tool may prove to be one of those deceptively simple additions that changes daily practice.

Subject of Research: An interactive R Shiny web tool for spatially selecting and gating individual cells in spatial omics datasets.

Article Title: Spatialgater: an R Shiny webtool for in situ gating of cells in spatial omics experiments

Article References: Steiner, M., Drothler, S., Höpner, J. P., Geisberger, R., & Zaborsky, N. (2026). Spatialgater: an R Shiny webtool for in situ gating of cells in spatial omics experiments. BMC Bioinformatics. https://doi.org/10.1186/s12859-026-06620-y

Image Credits: AI Generated

DOI: 10.1186/s12859-026-06620-y

Keywords: Spatialgater, R Shiny, spatial omics, single cell gating, imaging mass cytometry, SpatialExperiment, multiplexed imaging, breast cancer, T cells, tumor microenvironment, bioinformatics software, Shiny