GigaScience最新文献

Background: Lettuce, an important member of the Asteraceae family, is a globally cultivated cash vegetable crop. With a highly complex genome (∼2.5 Gb; 2n = 18) rich in repeat sequences, current lettuce reference genomes exhibit thousands of gaps, impeding a comprehensive understanding of the lettuce genome.

Findings: Here, we present a near-complete gapless reference genome for cutting lettuce with high transformability, using long-read PacBio HiFi and Nanopore sequencing data. In comparison to stem lettuce genome, we identify 127,681 structural variations (SVs, present in 0.41 Gb of sequence), reflecting the divergence of leafy and stem lettuce. Interestingly, these SVs are related to transposons and DNA methylation states. Furthermore, we identify 4,612 whole-genome triplication genes exhibiting high expression levels associated with low DNA methylation levels and high N6-methyladenosine RNA modifications. DNA methylation changes are also associated with activation of genes involved in callus formation.

Conclusions: Our gapless lettuce genome assembly, an unprecedented achievement in the Asteraceae family, establishes a solid foundation for functional genomics, epigenomics, and crop breeding and sheds new light on understanding the complexity of gene regulation associated with the dynamics of DNA and RNA epigenetics in genome evolution.

Background: As biological data increase, we need additional infrastructure to share them and promote interoperability. While major effort has been put into sharing data, relatively less emphasis is placed on sharing metadata. Yet, sharing metadata is also important and in some ways has a wider scope than sharing data themselves.

Results: Here, we present PEPhub, an approach to improve sharing and interoperability of biological metadata. PEPhub provides an API, natural-language search, and user-friendly web-based sharing and editing of sample metadata tables. We used PEPhub to process more than 100,000 published biological research projects and index them with fast semantic natural-language search. PEPhub thus provides a fast and user-friendly way to finding existing biological research data or to share new data.

Availability: https://pephub.databio.org.

Background: Chiridota heheva is a cosmopolitan holothurian well adapted to diverse deep-sea ecosystems, especially chemosynthetic environments. Besides high hydrostatic pressure and limited light, high concentrations of metal ions also represent harsh conditions in hydrothermal environments. Few holothurian species can live in such extreme conditions. Therefore, it is valuable to elucidate the adaptive genetic mechanisms of C. heheva in hydrothermal environments.

Findings: Herein, we report a high-quality reference genome assembly of C. heheva from the Kairei vent, which is the first chromosome-level genome of Apodida. The chromosome-level genome size was 1.43 Gb, with a scaffold N50 of 53.24 Mb and BUSCO completeness score of 94.5%. Contig sequences were clustered, ordered, and assembled into 19 natural chromosomes. Comparative genome analysis found that the expanded gene families and positively selected genes of C. heheva were involved in the DNA damage repair process. The expanded gene families and the unique genes contributed to maintaining iron homeostasis in an iron-enriched environment. The positively selected gene RFC2 with 10 positively selected sites played an essential role in DNA repair under extreme environments.

Conclusions: This first chromosome-level genome assembly of C. heheva reveals the hydrothermal adaptation of holothurians. As the first chromosome-level genome of order Apodida, this genome will provide the resource for investigating the evolution of class Holothuroidea.

Background: Structural variants (SVs) are genomic polymorphisms defined by their length (>50 bp). The usual types of SVs are deletions, insertions, translocations, inversions, and copy number variants. SV detection and genotyping is fundamental given the role of SVs in phenomena such as phenotypic variation and evolutionary events. Thus, methods to identify SVs using long-read sequencing data have been recently developed.

Findings: We present an accurate and efficient algorithm to predict germline SVs from long-read sequencing data. The algorithm starts collecting evidence (signatures) of SVs from read alignments. Then, signatures are clustered based on a Euclidean graph with coordinates calculated from lengths and genomic positions. Clustering is performed by the DBSCAN algorithm, which provides the advantage of delimiting clusters with high resolution. Clusters are transformed into SVs and a Bayesian model allows to precisely genotype SVs based on their supporting evidence. This algorithm is integrated into the single sample variants detector of the Next Generation Sequencing Experience Platform, which facilitates the integration with other functionalities for genomics analysis. We performed multiple benchmark experiments, including simulation and real data, representing different genome profiles, sequencing technologies (PacBio HiFi, ONT), and read depths.

Conclusion: The results show that our approach outperformed state-of-the-art tools on germline SV calling and genotyping, especially at low depths, and in error-prone repetitive regions. We believe this work significantly contributes to the development of bioinformatic strategies to maximize the use of long-read sequencing technologies.

Over the past decade, deep learning (DL) research in computer vision has been growing rapidly, with many advances in DL-based image analysis methods for biomedical problems. In this work, we introduce MMV_Im2Im, a new open-source Python package for image-to-image transformation in bioimaging applications. MMV_Im2Im is designed with a generic image-to-image transformation framework that can be used for a wide range of tasks, including semantic segmentation, instance segmentation, image restoration, image generation, and so on. Our implementation takes advantage of state-of-the-art machine learning engineering techniques, allowing researchers to focus on their research without worrying about engineering details. We demonstrate the effectiveness of MMV_Im2Im on more than 10 different biomedical problems, showcasing its general potentials and applicabilities. For computational biomedical researchers, MMV_Im2Im provides a starting point for developing new biomedical image analysis or machine learning algorithms, where they can either reuse the code in this package or fork and extend this package to facilitate the development of new methods. Experimental biomedical researchers can benefit from this work by gaining a comprehensive view of the image-to-image transformation concept through diversified examples and use cases. We hope this work can give the community inspirations on how DL-based image-to-image transformation can be integrated into the assay development process, enabling new biomedical studies that cannot be done only with traditional experimental assays. To help researchers get started, we have provided source code, documentation, and tutorials for MMV_Im2Im at [https://github.com/MMV-Lab/mmv_im2im] under MIT license.

Background: Rhododendron nivale subsp. boreale Philipson et M. N. Philipson is an alpine woody species with ornamental qualities that serve as the predominant species in mountainous scrub habitats found at an altitude of ∼4,200 m. As a high-altitude woody polyploid, this species may serve as a model to understand how plants adapt to alpine environments. Despite its ecological significance, the lack of genomic resources has hindered a comprehensive understanding of its evolutionary and adaptive characteristics in high-altitude mountainous environments.

Findings: We sequenced and assembled the genome of R. nivale subsp. boreale, an assembly of the first subgenus Rhododendron and the first high-altitude woody flowering tetraploid, contributing an important genomic resource for alpine woody flora. The assembly included 52 pseudochromosomes (scaffold N50 = 42.93 Mb; BUSCO = 98.8%; QV = 45.51; S-AQI = 98.69), which belonged to 4 haplotypes, harboring 127,810 predicted protein-coding genes. Conjoint k-mer analysis, collinearity assessment, and phylogenetic investigation corroborated autotetraploid identity. Comparative genomic analysis revealed that R. nivale subsp. boreale originated as a neopolyploid of R. nivale and underwent 2 rounds of ancient polyploidy events. Transcriptional expression analysis showed that differences in expression between alleles were common and randomly distributed in the genome. We identified extended gene families and signatures of positive selection that are involved not only in adaptation to the mountaintop ecosystem (response to stress and developmental regulation) but also in autotetraploid reproduction (meiotic stabilization). Additionally, the expression levels of the (group VII ethylene response factor transcription factors) ERF VIIs were significantly higher than the mean global gene expression. We suspect that these changes have enabled the success of this species at high altitudes.

Conclusions: We assembled the first high-altitude autopolyploid genome and achieved chromosome-level assembly within the subgenus Rhododendron. In addition, a high-altitude adaptation strategy of R. nivale subsp. boreale was reasonably speculated. This study provides valuable data for the exploration of alpine mountaintop adaptations and the correlation between extreme environments and species polyploidization.

Background: Linked-read sequencing technologies generate high-base quality short reads that contain extrapolative information on long-range DNA connectedness. These advantages of linked-read technologies are well known and have been demonstrated in many human genomic and metagenomic studies. However, existing linked-read analysis pipelines (e.g., Long Ranger) were primarily developed to process sequencing data from the human genome and are not suited for analyzing metagenomic sequencing data. Moreover, linked-read analysis pipelines are typically limited to 1 specific sequencing platform.

Findings: To address these limitations, we present the Linked-Read ToolKit (LRTK), a unified and versatile toolkit for platform agnostic processing of linked-read sequencing data from both human genome and metagenome. LRTK provides functions to perform linked-read simulation, barcode sequencing error correction, barcode-aware read alignment and metagenome assembly, reconstruction of long DNA fragments, taxonomic classification and quantification, and barcode-assisted genomic variant calling and phasing. LRTK has the ability to process multiple samples automatically and provides users with the option to generate reproducible reports during processing of raw sequencing data and at multiple checkpoints throughout downstream analysis. We applied LRTK on linked reads from simulation, mock community, and real datasets for both human genome and metagenome. We showcased LRTK's ability to generate comparative performance results from preceding benchmark studies and to report these results in publication-ready HTML document plots.

Conclusions: LRTK provides comprehensive and flexible modules along with an easy-to-use Python-based workflow for processing linked-read sequencing datasets, thereby filling the current gap in the field caused by platform-centric genome-specific linked-read data analysis tools.

Background: This study addresses the importance of precise referencing in 3-dimensional (3D) plant phenotyping, which is crucial for advancing plant breeding and improving crop production. Traditionally, reference data in plant phenotyping rely on invasive methods. Recent advancements in 3D sensing technologies offer the possibility to collect parameters that cannot be referenced by manual measurements. This work focuses on evaluating a 3D printed sugar beet plant model as a referencing tool.

Results: Fused deposition modeling has turned out to be a suitable 3D printing technique for creating reference objects in 3D plant phenotyping. Production deviations of the created reference model were in a low and acceptable range. We were able to achieve deviations ranging from -10 mm to +5 mm. In parallel, we demonstrated a high-dimensional stability of the reference model, reaching only ±4 mm deformation over the course of 1 year. Detailed print files, assembly descriptions, and benchmark parameters are provided, facilitating replication and benefiting the research community.

Conclusion: Consumer-grade 3D printing was utilized to create a stable and reproducible 3D reference model of a sugar beet plant, addressing challenges in referencing morphological parameters in 3D plant phenotyping. The reference model is applicable in 3 demonstrated use cases: evaluating and comparing 3D sensor systems, investigating the potential accuracy of parameter extraction algorithms, and continuously monitoring these algorithms in practical experiments in greenhouse and field experiments. Using this approach, it is possible to monitor the extraction of a nonverifiable parameter and create reference data. The process serves as a model for developing reference models for other agricultural crops.