GigaScience最新文献

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.

Magnetic resonance imaging (MRI) preprocessing is a critical step for neuroimaging analysis. However, the computational cost of MRI preprocessing pipelines is a major bottleneck for large cohort studies and some clinical applications. While high-performance computing and, more recently, deep learning have been adopted to accelerate the computations, these techniques require costly hardware and are not accessible to all researchers. Therefore, it is important to understand the performance bottlenecks of MRI preprocessing pipelines to improve their performance. Using the Intel VTune profiler, we characterized the bottlenecks of several commonly used MRI preprocessing pipelines from the Advanced Normalization Tools (ANTs), FMRIB Software Library, and FreeSurfer toolboxes. We found few functions contributed to most of the CPU time and that linear interpolation was the largest contributor. Data access was also a substantial bottleneck. We identified a bug in the Insight Segmentation and Registration Toolkit library that impacts the performance of the ANTs pipeline in single precision and a potential issue with the OpenMP scaling in FreeSurfer recon-all. Our results provide a reference for future efforts to optimize MRI preprocessing pipelines.

Osteogenic differentiation is crucial in normal bone formation and pathological calcification, such as calcific aortic valve disease (CAVD). Understanding the proteomic and transcriptomic landscapes underlying this differentiation can unveil potential therapeutic targets for CAVD. In this study, we employed RNA sequencing transcriptomics and proteomics on a timsTOF Pro platform to explore the multiomics profiles of valve interstitial cells (VICs) and osteoblasts during osteogenic differentiation. For proteomics, we utilized 3 data acquisition/analysis techniques: data-dependent acquisition (DDA)-parallel accumulation serial fragmentation (PASEF) and data-independent acquisition (DIA)-PASEF with a classic library-based (DIA) and machine learning-based library-free search (DIA-ML). Using RNA sequencing data as a biological reference, we compared these 3 analytical techniques in the context of actual biological experiments. We use this comprehensive dataset to reveal distinct proteomic and transcriptomic profiles between VICs and osteoblasts, highlighting specific biological processes in their osteogenic differentiation pathways. The study identified potential therapeutic targets specific for VICs osteogenic differentiation in CAVD, including the MAOA and ERK1/2 pathway. From a technical perspective, we found that DIA-based methods demonstrate even higher superiority against DDA for more sophisticated human primary cell cultures than it was shown before on HeLa samples. While the classic library-based DIA approach has proved to be a gold standard for shotgun proteomics research, the DIA-ML offers significant advantages with a relatively minor compromise in data reliability, making it the method of choice for routine proteomics.

Background: We investigate which procedure selects the most trustworthy predictive model to explain the effect of an intervention and support decision-making.

Methods: We study a large variety of model selection procedures in practical settings: finite samples settings and without a theoretical assumption of well-specified models. Beyond standard cross-validation or internal validation procedures, we also study elaborate causal risks. These build proxies of the causal error using "nuisance" reweighting to compute it on the observed data. We evaluate whether empirically estimated nuisances, which are necessarily noisy, add noise to model selection and compare different metrics for causal model selection in an extensive empirical study based on a simulation and 3 health care datasets based on real covariates.

Results: Among all metrics, the mean squared error, classically used to evaluate predictive modes, is worse. Reweighting it with a propensity score does not bring much improvement in most cases. On average, the $Rtext{-risk}$, which uses as nuisances a model of mean outcome and propensity scores, leads to the best performances. Nuisance corrections are best estimated with flexible estimators such as a super learner.

Conclusions: When predictive models are used to explain the effect of an intervention, they must be evaluated with different procedures than standard predictive settings, using the $Rtext{-risk}$ from causal inference.

Background: The Hong Kong orchid tree Bauhinia × blakeana Dunn has long been proposed to be a sterile interspecific hybrid exhibiting flower heterosis when compared to its likely parental species, Bauhinia purpurea L. and Bauhinia variegata L. Here, we report comparative genomic and transcriptomic analyses of the 3 Bauhinia species.

Findings: We generated chromosome-level assemblies for the parental species and applied a trio-binning approach to construct a haplotype-resolved telomere-to-telomere (T2T) genome for B. blakeana. Comparative chloroplast genome analysis confirmed B. purpurea as the maternal parent. Transcriptome profiling of flower tissues highlighted a closer resemblance of B. blakeana to its maternal parent. Differential gene expression analyses revealed distinct expression patterns among the 3 species, particularly in biosynthetic and metabolic processes. To investigate the genetic basis of flower heterosis observed in B. blakeana, we focused on gene expression patterns within pigment biosynthesis-related pathways. High-parent dominance and overdominance expression patterns were observed, particularly in genes associated with carotenoid biosynthesis. Additionally, allele-specific expression analysis revealed a balanced contribution of maternal and paternal alleles in shaping the gene expression patterns in B. blakeana.

Conclusions: Our study offers valuable insights into the genome architecture of hybrid B. blakeana, establishing a comprehensive genomic and transcriptomic resource for future functional genetics research within the Bauhinia genus. It also serves as a model for exploring the characteristics of hybrid species using T2T haplotype-resolved genomes, providing a novel approach to understanding genetic interactions and evolutionary mechanisms in complex genomes with high heterozygosity.

Background: Next-generation sequencing has created many new technological challenges in organizing and distributing genomics datasets, which now can routinely reach petabyte scales. Coupled with data-hungry artificial intelligence and machine learning applications, findable, accessible, interoperable, and reusable genomics datasets have never been more valuable. While major archives like the Genomics Data Commons, Sequence Reads Archive, and European Genome-Phenome Archive have improved researchers' ability to share and reuse data, and general-purpose repositories such as Zenodo and Figshare provide valuable platforms for research data publication, the diversity of genomics research precludes any one-size-fits-all approach. In many cases, bespoke solutions are required, and despite funding agencies and journals increasingly mandating reusable data practices, researchers still lack the technical support needed to meet the multifaceted challenges of data reuse.

Findings: Overture bridges this gap by providing open-source software for building and deploying customizable genomics data platforms. Its architecture consists of modular microservices, each of which is generalized with narrow responsibilities that together combine to create complete data management systems. These systems enable researchers to organize, share, and explore their genomics data at any scale. Through Overture, researchers can connect their data to both humans and machines, fostering reproducibility and enabling new insights through controlled data sharing and reuse.

Conclusions: By making these tools freely available, we can accelerate the development of reliable genomic data management across the research community quickly, flexibly, and at multiple scales. Overture is an open-source project licensed under AGPLv3.0 with all source code publicly available from https://github.com/overture-stack and documentation on development, deployment, and usage available from www.overture.bio.

Background: Electric eels evolved remarkable electric organs that enable them to instantaneously discharge hundreds of volts for predation, defense, and communication. However, the absence of a high-quality reference genome has extremely constrained the studies of electric eels in various aspects.

Results: Using high-depth, multiplatform sequencing data, we successfully assembled the first telomere-to-telomere high-quality reference genome of Electrophorus electricus, which has a genome size of 833.43 Mb and comprises 26 chromosomes. Multiple evaluations, including N50 statistics (30.38 Mb), BUSCO scores (97.30%), and mapping ratio of short-insert sequencing data (99.91%), demonstrate the high contiguity and completeness of the electric eel genome assembly we obtained. Genome annotation predicted 396.63 Mb repetitive sequences and 20,992 protein-coding genes. Furthermore, evolutionary analyses indicate that Gymnotiformes, which the electric eel belongs to, has a closer relationship with Characiformes than Siluriformes and diverged from Characiformes 95.00 million years ago. Pairwise sequentially Markovian coalescent analysis found a sharply decreased trend of the population size of E. electricus over the past few hundred thousand years. Furthermore, many regulatory factors related to neurotransmitters and classical signaling pathways during embryonic development were significantly expanded, potentially contributing to the generation of high-voltage electricity.

Conclusions: This study not only provided the first high-quality telomere-to-telomere reference genome of E. electricus but also greatly enhanced our understanding of electric eels.

Background: An unprecedented amount of personal health data, with the potential to revolutionize precision medicine, is generated at health care institutions worldwide. The exploitation of such data using artificial intelligence (AI) relies on the ability to combine heterogeneous, multicentric, multimodal, and multiparametric data, as well as thoughtful representation of knowledge and data availability. Despite these possibilities, significant methodological challenges and ethicolegal constraints still impede the real-world implementation of data models.

Technical details: The EuCanImage is an international consortium aimed at developing AI algorithms for precision medicine in oncology and enabling secondary use of the data based on necessary ethical approvals. The use of well-defined clinical data standards to allow interoperability was a central element within the initiative. The consortium is focused on 3 different cancer types and addresses 7 unmet clinical needs. We have conceived and implemented an innovative process to capture clinical data from hospitals, transform it into the newly developed EuCanImage data models, and then store the standardized data in permanent repositories. This new workflow combines recognized software (REDCap for data capture), data standards (FHIR for data structuring), and an existing repository (EGA for permanent data storage and sharing), with newly developed custom tools for data transformation and quality control purposes (ETL pipeline, QC scripts) to complement the gaps.

Conclusion: This article synthesizes our experience and procedures for health care data interoperability, standardization, and reproducibility.

The Earth BioGenome Project has the extremely ambitious goal of generating, at scale, high-quality reference genomes across the entire Tree of Life. Currently in its first phase, the project is targeting family-level representatives and is progressing rapidly. Here we outline recommended standards and considerations in sample acquisition and processing for those involved in biodiverse reference genome creation. These standards and recommendations will evolve with advances in related processes. Additionally, we discuss the challenges raised by the ambitions for later phases of the project, highlighting topics related to sample collection and processing that require further development.

Background: Truly understanding the cancer biology of heterogeneous tumors in precision medicine requires capturing the complexities of multiple omics levels and the spatial heterogeneity of cancer tissue. Techniques like mass spectrometry imaging (MSI) and spatial transcriptomics (ST) achieve this by spatially detecting metabolites and RNA but are often applied to serial sections. To fully leverage the advantage of such multi-omics data, the individual measurements need to be integrated into 1 dataset.

Results: We present the Multi-Omics Imaging Integration Toolset (MIIT), a Python framework for integrating spatially resolved multi-omics data. A key component of MIIT's integration is the registration of serial sections for which we developed a nonrigid registration algorithm, GreedyFHist. We validated GreedyFHist on 244 images from fresh-frozen serial sections, achieving state-of-the-art performance. As a proof of concept, we used MIIT to integrate ST and MSI data from prostate tissue samples and assessed the correlation of a gene signature for citrate-spermine secretion derived from ST with metabolic measurements from MSI.

Conclusion: MIIT is a highly accurate, customizable, open-source framework for integrating spatial omics technologies performed on different serial sections.