The software, called BSArray, was developed by M. Habib Widyawan and Zenglu Li of the University of Georgia’s Institute of Plant Breeding, Genetics, and Genomics, together with Qijian Song of the USDA Agricultural Research Service’s Beltsville Agricultural Research Center. It packages a classical genetic technique known as bulked segregant analysis, or BSA, into an interactive R Shiny application, meaning that researchers can run the full analysis through a graphical interface rather than a command line. The developers designed it specifically for data generated on Illumina Infinium SNP genotyping arrays, the fixed-content genotyping platforms that many crop genetics labs already use routinely.

Bulked segregant analysis is an elegant shortcut in quantitative genetics. Instead of genotyping every individual in a breeding population and running a full linkage analysis, researchers create two bulks: one pooling the offspring that show the trait of interest most strongly, and one pooling those that show it least. Any genomic region that truly influences the trait will show a systematic shift in allele frequency between the two bulks, because the individuals in the high-trait bulk share, by selection, the parental chromosome segment carrying the favorable allele. Scanning the genome for such frequency differences points directly to the quantitative trait loci, or QTL, responsible. The approach trades some statistical power for enormous savings in cost and time, which is why it remains popular for early-stage gene discovery in crops.

Where sequencing-based BSA has attracted a rich ecosystem of automated pipelines, the array-based version has lagged behind. Illumina Infinium arrays produce fluorescence intensity measurements for each SNP, and converting those signals into reliable allele frequency estimates for bulks involves several judgment calls: how to normalize intensities, how to weight genotypes by their confidence, and how to filter out markers with poor performance. In practice, researchers have had to compare parental and bulk genotypes by hand, write custom filtering scripts, and assemble results in spreadsheets. Each manual step is an opportunity for error, and none of them scale well when a breeding program wants to analyze dozens of populations across multiple traits and seasons.

BSArray addresses this by implementing the full pipeline end to end. The application starts with the normalized intensity metrics from the array and estimates allele frequencies for each bulk directly from those signals. Its central innovation is a quality-weighted statistic, denoted W, which computes the allele frequency difference between bulks while integrating two pieces of information: the magnitude of the frequency difference itself and the confidence of the underlying genotype calls. Markers that show a large difference but rest on shaky genotype calls are discounted relative to markers where the signal is both large and reliable. This weighting scheme helps suppress false positives arising from noisy or poorly performing array probes, a persistent nuisance in array-based genotyping.

From the per-marker W statistic, the pipeline moves to a sliding window framework that scans the genome in overlapping segments. Rather than relying on a single noisy marker, the window approach asks whether a stretch of consecutive markers collectively shows an elevated frequency difference, which is the spatial signature of a genuine QTL. Significance is assessed against empirical genome-wide thresholds, so the user gets a statistically grounded call on which regions are putative QTL rather than an arbitrary visual cutoff. Once significant regions are identified, the pipeline automatically prioritizes the most informative candidate markers within them and annotates the nearby gene models, giving breeders a direct bridge from a statistical signal to the genes they might pursue for marker-assisted selection.

The developers demonstrated the tool using the SoySNP50K array, a 50,000-SNP platform developed for soybean, in three implementation scenarios that map neatly onto the realistic situations breeders encounter. In the first, BSArray detected a previously reported major-effect locus, confirming that the pipeline recovers known biology. In the second, it mapped a locus that had not previously been placed on the soybean genome, showing its value for genuinely new discovery. In the third, it detected a novel minor-effect locus within a QTL region that was already known, illustrating the tool’s ability to refine and dissect complex trait architecture rather than only finding the largest, most obvious signals. Together, the three cases cover the spectrum from validation to refinement to novel discovery.

The practical significance for crop improvement lies in speed and reproducibility. Marker-assisted selection, the practice of using DNA markers to guide breeding decisions, depends on having well-mapped QTL and reliable flanking markers. Every step that slows QTL discovery—manual genotype comparison, ad hoc scripting, irreproducible filtering—delays the delivery of markers that breeders can deploy in the field. By reducing manual intervention and computational requirements, BSArray offers what its developers describe as a rapid, reproducible, and user-friendly framework that serves as a practical and scalable alternative to sequencing-based approaches, particularly for laboratories whose primary genotyping investment is already in SNP arrays rather than whole-genome sequencing.

The cost logic is an important part of the appeal. Whole-genome resequencing of bulks delivers dense marker coverage but carries substantial per-sample expense and generates data volumes that demand serious computational infrastructure. SNP arrays, by contrast, offer a fixed, curated marker set at a fraction of the cost, and for many crop species the array density is more than sufficient for bulked segregant analysis, which needs enough markers to detect regional allele frequency shifts rather than the causal variant itself. A tool that makes array-based BSA as turnkey as its sequencing-based counterparts could therefore broaden access to QTL discovery for smaller breeding programs, national agricultural research systems, and university labs in developing countries—settings where the soybean work’s funding context, including the United Soybean Board and support for an Indonesian doctoral researcher through the Fulbright FIRST program, reflects the international reach of modern crop genomics.

BSArray is published as open-access software in BMC Bioinformatics, with earlier versions tested by collaborators including Francismar C. Marcelino-Guimarães of the Brazilian Agricultural Research Corporation. The design choices—an interactive web-style interface, automated quality weighting, empirical significance thresholds, and built-in gene annotation—reflect a broader trend in bioinformatics toward tools that embed expert judgment into software so that domain scientists can perform analyses that once required a dedicated computational specialist. For a field racing to develop climate-resilient, disease-resistant crop varieties faster than pathogens and weather can evolve, shaving weeks off the gene-discovery pipeline is not a trivial convenience. It is the difference between a breeding cycle that waits on analysis and one that moves at the speed of the growing season.

Subject of Research: Automated bulked segregant analysis pipeline for QTL discovery using SNP genotyping arrays

Article Title: BSArray: An automated bulked segregant analysis pipeline for QTL discovery using Illumina Infinium SNP genotyping array

Article References: Widyawan, M. H., Song, Q., & Li, Z. (2026). BSArray: An automated bulked segregant analysis pipeline for QTL discovery using Illumina Infinium SNP genotyping array. BMC Bioinformatics. https://doi.org/10.1186/s12859-026-06670-2

Image Credits: AI Generated

DOI: 10.1186/s12859-026-06670-2

Keywords: bulked segregant analysis, QTL discovery, Illumina Infinium, SNP array, soybean, SoySNP50K, marker-assisted selection, R Shiny, bioinformatics pipeline, quantitative trait loci, crop breeding, genetic mapping

The motivation behind Polaris reflects a well-documented problem in evidence synthesis. Literature reviews are essential across disciplines: in the health sciences they track rapidly evolving methods of human care, in geography they have shaped frameworks from economic geography to the geography of poverty, and in interdisciplinary fields such as climate science they assess impacts on ecosystems and social inequalities. There are at least fourteen recognized review typologies, ranging from rapid and scoping reviews to full systematic reviews with meta-analysis. Yet conducting any of them well is time-consuming, particularly during bibliographic database searches, supplementary searches, and the review of each retrieved manuscript for inclusion or exclusion. Existing software such as Covidence and Rayyan helps with screening and selection, while tools like ASySD, Deduklick, BibexPy, and the Systematic Review Accelerator handle deduplication and bibliographic processing. Polaris deliberately targets a different stage: retrieval itself, the point where bias and errors most easily creep in.

Bias in literature searching can arise from the researcher’s own decisions, including selective choices of databases, filters, and search terms. When the systematic process behind a review is not fully disclosed, reproducibility suffers. Errors, meanwhile, can stem from technical and procedural limitations during data retrieval: incomplete metadata, missing author information, export restrictions, or, in the case of Google Scholar, the requirement to visit each paper’s website individually to download metadata. To combat these problems, the developers grounded Polaris in established methodological guidance. The tool follows the eight-stage model of literature searching described by Cooper and colleagues, and it aligns with the PRISMA framework, the widely adopted set of twenty-seven recommendations for reporting systematic reviews and meta-analyses. By standardizing retrieval, automating exports, preserving query logs, and improving the auditability of searches, Polaris aims to reduce potential bias and errors before screening even begins.

Technically, Polaris 1.0 is implemented in R, requiring version 4.5.1 or higher, and built on packages including httr, lite, dplyr, stringr, purrr, xml2, zip, and httr2. The architecture has two layers. The front end is a graphical user interface built with the R Shiny package, where users configure search parameters and monitor progress in real time. The back end is a processing engine with an additional asynchronous execution layer to support long-running searches, organized into modular components for software environment configuration, directory and file handling, database-specific retrieval functions, and the core engine. Users supply their queries in a simple TXT file, select the desired databases, and the software accesses each provider through its official API, retrieves the results, generates harmonized metadata, and saves each query’s output as a Research Information System (RIS) file compatible with Mendeley, Zotero, and EndNote.

Each database demands its own handling, and Polaris implements a dedicated retrieval workflow for every supported provider. PubMed is accessed through the NCBI E-utilities API in three steps: the esearch endpoint counts matching records and retrieves PubMed identifiers in batches of up to 300, the efetch endpoint collects abstracts in XML format parsed with the xml2 package, and the summary endpoint returns structured metadata including title, authors, journal, year, abstract, and DOI in JSON. No personal API key is needed, and a 0.4-second delay between requests keeps the tool compliant with PubMed policies. Scopus and ScienceDirect both rely on the Elsevier API, which requires an API key and an institutional token. Scopus queries are automatically wrapped in the TITLE-ABS-KEY field while preserving the user’s Boolean logic, and ScienceDirect searches title, abstract, keywords, and full text, with abstracts conveniently stored in the description field of the response. Semantic Scholar, which needs no key, uses a bulk search endpoint that supports Boolean expressions, with Polaris translating operators such as AND, OR, and NOT into the provider’s required syntax while preserving the logical structure of the original strategy.

A distinctive strength of the system is its three-level logging design. The first level feeds a real-time monitoring dashboard, updating users on execution status until each search completes. The second, a master log, records comprehensive metadata across all databases: the date and time each query ran, the source database, the query structure, the total number of papers retrieved, the exported RIS filename, and the search status. The third level maintains database-specific logs with the same structure. Together these logs document exactly which queries were executed, where, when, and with what results, making the entire retrieval stage auditable and reproducible. Polaris also handles Elsevier’s authentication errors gracefully: HTTP 401 and 403 errors terminate the affected request and prompt users to verify their credentials, while HTTP 429 rate-limiting responses trigger a temporary pause before retry, all displayed on the dashboard.

To demonstrate reliability, the team compared Polaris retrieval counts against the official web interfaces of PubMed, Scopus, and ScienceDirect across 60 queries, 20 per database, using identical search strings and equivalent fields. Agreement was assessed with linear regression, Pearson’s correlation, Bland–Altman analysis, and relative percentage differences. The results were striking: a Pearson correlation coefficient of r = 1.000 and a coefficient of determination R² = 1.000, with points closely following the identity line across searches ranging from a handful of records to several thousand. The Bland–Altman mean difference, or bias, was just −7.92 records, a negligible proportion for searches returning more than 5,000 results. Nearly all observations fell within the 95% limits of agreement. The mean relative difference was −0.15% and the median 0.00%, meaning most searches produced identical counts. PubMed and Scopus showed almost complete agreement, while ScienceDirect displayed a small number of larger but still limited deviations. A log-transformed Bland–Altman analysis estimated a bias of only −0.0007 log units, confirming that discrepancies do not grow with search size. The complete validation dataset and R scripts are publicly available on GitHub and archived on Zenodo.

The software does have limitations tied to each provider’s API. Without a key, PubMed caps results at 10,000 per query; ScienceDirect allows 6,000 under an API and institutional key; Scopus imposes no limit on broad retrievals; and Semantic Scholar is limited to roughly the first 300 results. Validation for Semantic Scholar was not possible because its webpage and API return different results, and for queries hitting a provider’s maximum, Polaris was considered to have correctly handled the constraint rather than achieving perfect agreement. Future versions plan to add front-end filters for year, field, and study design, along with support for the IEEE and SciELO databases to broaden coverage to engineering and Latin American literature.

Early applications suggest Polaris can scale to demanding real-world projects. In a critical review of outdoor workers and heat exposure in the United States, the software handled approximately 100,000 results drawn from multiple databases. In a systematic review of human attitudes and behavior toward snakes, queries using Boolean operators, parentheses, and asterisk truncation retrieved 21,734 records in preliminary results, demonstrating robust handling of complex syntax and special characters. Because repetitive tasks such as copying queries across platforms and manually exporting results are automated, and real-time monitoring catches interruptions during long runs, the developers argue that Polaris shifts literature retrieval from a manual, platform-dependent chore toward a standardized, traceable workflow. Its MIT-licensed R and Shiny code base makes it straightforward to adapt, extend, and reuse, potentially changing how evidence synthesis is conducted day to day in an era of ever-expanding publication volume.

Subject of Research: An automated, open-source software system for multi-database bibliographic literature retrieval supporting reproducible systematic reviews

Article Title: Polaris – an automated literature retrieval system

Article References: Polaris – an automated literature retrieval system. (n.d.). https://doi.org/10.1016/j.softx.2026.103024

Image Credits: AI Generated

DOI: 10.1016/j.softx.2026.103024

Keywords: Polaris, literature retrieval, systematic reviews, bibliographic databases, PRISMA, open-source software, R Shiny, PubMed, Scopus, ScienceDirect, Semantic Scholar, 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