GigaScience最新文献

Background: High-throughput phenotyping is addressing the current bottleneck in phenotyping within breeding programs. Imaging tools are becoming the primary resource for improving the efficiency of phenotyping processes and providing large datasets for genomic selection approaches. The advent of AI brings new advantages by enhancing phenotyping methods using imaging, making them more accessible to breeding programs. In this context, we have developed an open Python workflow for analyzing morphology, colour and morphometric traits using AI, which can be applied to fruits and other plant organs.

Results: The workflow was implemented in almond (Prunus dulcis (Mill.) D. A. Webb), a species where breeding efficiency is critical due to its long breeding cycle. Over 25,000 kernels, more than 20,000 nuts, and over 600 individuals were phenotyped, making this the largest morphological study conducted in almond so far. The best segmentation and reconstruction approaches achieved error rates below 1%. Weight and area variables enabled accurate estimation of kernel thickness, with a root mean squared error (RMSE) of 0.47. Fifty-five heritable morphological, morphometric and colour traits were identified, highlighting their potential as target traits in breeding programs.

Conclusion: The proposed workflow demonstrated robust performance across diverse datasets and being effective with limited training data for fine-tuning. Its compatibility with the output of AI-based labelling tools allows users to fully leverage the advantages of these technologies-reducing manual effort, accelerating dataset preparation, and streamlining the fine-tuning process of segmentation models. This flexibility enhances the scalability and practical applicability of the workflow in real-world phenotyping scenarios, especially in the context of breeding programs.

Background: Praying mantises, members of the order Mantodea, play important roles in agriculture, medicine, bionics, and entertainment. However, the scarcity of genomic resources has hindered extensive studies on mantis evolution and behaviour.

Results: Here, we present the chromosome-scale reference genomes of five mantis species: the European mantis (Mantis religiosa), Chinese mantis (Tenodera sinensis), triangle dead leaf mantis (Deroplatys truncata), orchid mantis (Hymenopus coronatus), and metallic mantis (Metallyticus violacea). The assembled genome sizes range ∼2.3-4.2 Gb, with contig N50 size 1-109 Mb and 85-99% of sequence anchored to chromosomes. The annotated protein-coding gene number ranges 17,804-19,017, with BUSCO complete rate 96.7-98.4%. We found that transposable element expansion is the major force governing genome size in Mantodea, and suggest that translocations between the X chromosome and an autosome have occurred in the lineage of the family Mantidae. In addition, we found the lineage of M. violacea has accumulated fewer substitutions than the lineages of other mantises. Furthermore, our genome-wide analyses showed that D. truncata is sister to H. coronatus than M. religiosa and T. sinensis, helps resolve the phylogenic controversies of Deroplatys genus.

Conclusions: The high-quality genome assemblies of the five mantises provide a valuable resource for evolution studies of Mantodea and genetic improvement and breeding of beneficial biological control agents.

Background: Scientific research relies on well-structured, standardized data; however, much of it is stored in formats such as free-text lab notebooks, non-standardized spreadsheets, or data repositories. This lack of structure challenges interoperability, making data integration, validation, and reuse difficult.

Findings: LinkML (Linked Data Modeling Language) is an open framework that simplifies the process of authoring, validating, and sharing data. LinkML can describe a range of data structures, from flat, list-based models to complex, interrelated, and normalized models that utilize polymorphism and compound inheritance. It offers an approachable syntax that is not tied to any one technical architecture and can be integrated seamlessly with many existing frameworks. The LinkML syntax provides a standard way to describe schemas, classes, and relationships, allowing modelers to build well-defined, stable, and optionally ontology-aligned data structures. Once defined, LinkML schemas may be imported into other LinkML schemas. These key features make LinkML an accessible platform for interdisciplinary collaboration and a reliable way to define and share data semantics.

Conclusions: LinkML helps reduce heterogeneity, complexity, and the proliferation of single-use data models while simultaneously enabling compliance with FAIR data standards. LinkML has seen increasing adoption in various fields, including biology, chemistry, biomedicine, microbiome research, finance, electrical engineering, transportation, and commercial software development. In short, LinkML makes implicit models explicitly computable and allows data to be standardized at its origin. LinkML documentation and code are available at linkml.io.

Background: Structural variants (SVs) are genomic differences ≥50 bp in length. They remain challenging to detect even with long sequence reads, and the sources of these difficulties are not well quantified.

Results: We identified 35.4 Mb of low-complexity regions (LCRs) in GRCh38. Although these regions cover only 1.2% of the genome, they contain 69.1% of confident SVs in sample HG002. Across long-read SV callers, 77.3-91.3% of erroneous SV calls occur within LCRs, with error rates increasing with LCR length.

Conclusion: SVs are enriched and difficult to call in LCRs. Special care needs to be taken for calling and analyzing these variants.

Background: Safflower (Carthamus tinctorius L.) is a drought-resilient oilseed crop. Besides producing edible oil rich in oleic and linoleic acid, it is also used in biofuels, cosmetics, colouring dyes, pharmaceuticals and nutraceuticals. Despite its significant economic uses, availability of genetic and genomic resources in safflower are limited.

Results: We report an improved de novo genome assembly of safflower (Safflower_A2). A chromosome-level assembly of 1.15 Gb with telomeres and centromeric repeats, was constructed using PacBio HiFi reads, optical maps, Illumina short reads, and Hi-C sequencing. Safflower_A2 shows better contiguity, completeness, and high-quality annotation than previous assemblies. The assembly was further validated with the help of a single nucleotide polymorphism (SNP)-based linkage map. A genome-wide survey identified genes for comprehensive exploration of disease resistance in the safflower. Employing the de novo genome assembly as a reference, we used resequencing data of a global core-collection of 123 accessions to carry out a SNP-based genome-wide association study, which identified significant associations for several traits, their haplotypes of agronomic value, including seed oil content. Resequencing data was also applied for a pan-genome analysis which provided critical insights into genome diversity identifying an additional ∼11000 genes and their functional enrichment that will be useful for region-specific breeding lines.

Conclusion: Our study provides insights into the genomic architecture of safflower by leveraging an improved genome assembly and annotation. Additionally, resources including high-density linkage map, marker-trait associations, and pan-genome developed in this study provide valuable resources for use in breeding and crop improvement programs by the global research community.

High-throughput technologies now produce a wide array of omics data, from genomic and transcriptomic profiles to epigenomic and proteomic measurements. Integrating multiple omics layers measured on the same samples can reveal cross-layer molecular hubs that single-layer analyses miss. We present an unsupervised, multivariate random forest (MRF) framework with an inverse minimal depth (IMD) importance to prioritize shared biomarkers across omics. In each forest, one layer serves as a multivariate response and the other as predictors; IMD summarizes how early a predictor (or response MSRV) appears across trees, yielding interpretable, cross-layer feature rankings. We provide three IMD-based selection strategies and introduce an optional IMD power transform to enhance sensitivity to interaction signals. In extensive simulations spanning linear, nonlinear, and interaction regimes, our method matches SPLS/CCA under linear settings and outperforms them as nonlinearity increases, while adapted univariate ensemble learners (RF, GBM, XGBoost) underperform in the multivariate, unsupervised context. Applied to TCGA BRCA and COAD, MRF-IMD identifies genes, CpGs, and miRNAs enriched for cancer-relevant pathways and yields more robust survival stratification than linear integrators with matched model sizes. In a TCGA pan-cancer analysis, MRF-IMD features achieve higher ARI than alternatives and recover coherent tumor-type clusters; in ADNI, the integrative signature improves dementia-progression stratification over a published methylation risk score. Our scalable, interpretable MRF-IMD framework advances reliable multi-omics biomarker discovery when nonlinear, cross-layer dependencies matter.

Spatial 'omics techniques are indispensable for studying complex biological systems and for the discovery of spatial biomarkers. While several current matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) instruments are capable of localizing numerous metabolites at high spatial and spectral resolution, the majority of MSI data is acquired at the MS1 level only. Assigning molecular identities based on MS1 data presents significant analytical and computational challenges, as the inherent limitations of MS1 data preclude confident annotations beyond the sum formula level. To enable future advancements of computational lipid annotation tools, well-characterized benchmark - or ground truth - datasets are crucial, which exceed the scope of synthetic data or data derived from mimetic tissue models. To this end, we provide two sulfatide-centered, biology-driven magnetic resonance MSI (MR-MSI) datasets at different mass resolving powers that characterize lipids in a mouse model of human metachromatic dystrophy. This data includes an ultra-high-resolution (R ∼1,230,000) quantum cascade laser mid-infrared imaging-guided MR-MSI dataset that enables isotopic fine structure analysis and therefore enhances the level of confidence substantially. To highlight the usefulness of the data, we compared 118 manual sulfatide annotations with the number of decoy database-controlled sulfatide annotations performed in Metaspace (67 at FDR < 10%). Overall, our datasets can be used to benchmark annotation algorithms, validate spatial biomarker discovery pipelines, and serve as a reference for future studies that explore sulfatide metabolism and its spatial regulation.

With the increasing complexity and quantity of experimental and observational data, life scientists rely on programming to automate analyses, enhance reproducibility, and facilitate collaboration. Scripting languages like Python are often favored for their simplicity and flexibility, enabling researchers to focus primarily on high-level tasks. Compiled languages such as C++ and Rust offer greater efficiency, making them preferable for intensive or repeated computations. In educational settings, instructors may wish to teach both types of languages and thus may wish to translate content from one programming language to another. In research contexts, researchers may wish to implement their ideas in one language before translating the code to another. However, translating between programming languages requires significant effort, prompting our interest in using large language models (LLMs) for semi-automated code translation. This study explores the use of an LLM (GPT-4) to translate 559 short-form programming exercises from Python into C++, Rust, Julia, and JavaScript. We used three prompting strategies-instructions only, code only, or both combined-and compared the translated code's output against the Python code's output. Translation success differed considerably by prompting strategy, and at least one of the strategies tested was effective for nearly every exercise. The highest overall success rate occurred for Rust (99.5%), followed by JavaScript (98.9%), C++ (97.9%), and Julia (95.0%). Our findings demonstrate that LLMs can effectively translate small-scale programming exercises between languages, reducing the need for manual rewriting. To support education and research, we have manually translated all exercises that were not translated successfully through automation, and we have made our translations freely available.

Background: The world has witnessed a steady rise in neurological diseases, which represent a heterogeneous group of disorders characterized by complex pathogenesis involving disruptions at multiple molecular levels, including genomic, transcriptomic, proteomic, and metabolomic levels. These disorders, often caused by genetic mutations, metabolic imbalances, immune dysregulation, and environmental factors, pose significant challenges to global public health due to their high prevalence, mortality, and disability burden.

Results: The advent of high-throughput technologies, such as next-generation sequencing and mass spectrometry, has provided valuable insights into the underlying mechanisms of disease, especially the development of multi- and high-spatial-resolution omics technologies, enabling the interaction of multiple levels of biology and analysis of the complex molecular networks and pathophysiological processes.

Conclusions: This review provides a comprehensive analysis of the latest advancements in multi- and high-spatial-resolution omics, with a focus on their applications in precision diagnostics, biomarker discovery, and therapeutic target identification in brain diseases. The study also highlights the current challenges in the clinical implementation and discusses the future directions, with artificial intelligence being anticipated to enhance clinical translation and diagnostic accuracy significantly.

Background: Whole slide imaging (WSI) enables the digitisation of entire histological slides at high resolution, allowing pathologists and researchers to analyse tissue samples digitally rather than through traditional microscopy. This technology has become increasingly valuable in pathology for research, education, and clinical diagnostics. Endometrial biopsy is very common, often being undertaken to exclude non-cancerous disease. This means that most cases do not contain cancer, and the challenge is to accurately and efficiently exclude serious pathology rather than simply make a diagnosis of malignancy. A well-curated, expert-annotated, endometrial whole slide dataset covering a spread of cancer and non-cancer diagnoses will support machine learning applications in automated diagnosis, facilitate research into the pathology of endometrial cancer, and serve as an educational resource for medical professionals.

Results: We introduce a newly constructed, large-scale dataset of endometrial biopsies, comprising 2,909 whole slide images in iSyntax format, each accompanied by a corresponding annotation file in JSON format. Each whole slide image is labelled with a primary class label representing its final diagnosis and a sub-category label providing further details within that diagnostic class. These class labels are critical for machine learning applications, as they enable the development of AI models capable of distinguishing between different types of endometrial abnormalities, improving automated classification, and guiding clinical decision-making.

Conclusions: Constructing and curating a high-quality endometrial whole slide dataset requires significant effort to ensure accurate annotations, data integrity, and patient privacy protection. However, the availability of a well-annotated dataset with detailed class labels is crucial for advancing digital pathology. Such a resource can enhance diagnostic accuracy, support personalized treatment strategies, and ultimately improve outcomes for patients with endometrial cancer and other endometrial conditions.