GigaScience最新文献

{"title":"Image segmentation of treated and untreated tumor spheroids by fully convolutional networks.","authors":"Matthias Streller, Soňa Michlíková, Willy Ciecior, Katharina Lönnecke, Leoni A Kunz-Schughart, Steffen Lange, Anja Voss-Böhme","doi":"10.1093/gigascience/giaf027","DOIUrl":"https://doi.org/10.1093/gigascience/giaf027","url":null,"abstract":"<p><strong>Background: </strong>Multicellular tumor spheroids (MCTS) are advanced cell culture systems for assessing the impact of combinatorial radio(chemo)therapy as they exhibit therapeutically relevant in vivo-like characteristics from 3-dimensional cell-cell and cell-matrix interactions to radial pathophysiological gradients. State-of-the-art assays quantify long-term curative endpoints based on collected brightfield image time series from large treated spheroid populations per irradiation dose and treatment arm. This analyses require laborious spheroid segmentation of up to 100,000 images per treatment arm to extract relevant structural information from the images (e.g., diameter, area, volume, and circularity). While several image analysis algorithms are available for spheroid segmentation, they all focus on compact MCTS with a clearly distinguishable outer rim throughout growth. However, they often fail for the common case of treated MCTS, which may partly be detached and destroyed and are usually obscured by dead cell debris.</p><p><strong>Results: </strong>To address these issues, we successfully train 2 fully convolutional networks, UNet and HRNet, and optimize their hyperparameters to develop an automatic segmentation for both untreated and treated MCTS. We extensively test the automatic segmentation on larger, independent datasets and observe high accuracy for most images with Jaccard indices around 90%. For cases with lower accuracy, we demonstrate that the deviation is comparable to the interobserver variability. We also test against previously published datasets and spheroid segmentations.</p><p><strong>Conclusions: </strong>The developed automatic segmentation can not only be used directly but also integrated into existing spheroid analysis pipelines and tools. This facilitates the analysis of 3-dimensional spheroid assay experiments and contributes to the reproducibility and standardization of this preclinical in vitro model.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":"14 ","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12056507/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144005631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Long-read metagenomic sequencing negates inferred loss of cytosine methylation in Myxosporea (Cnidaria: Myxozoa).","authors":"Antonio Starcevic, Rayline T A Figueredo, Juliana Naldoni, Lincoln L Corrêa, Beth Okamura, Edson A Adriano, Paul F Long","doi":"10.1093/gigascience/giaf014","DOIUrl":"10.1093/gigascience/giaf014","url":null,"abstract":"<p><p>Oxford-Nanopore PromethION sequencing is a PCR-free method that retains epigenetic markers and provides direct quantitative information about DNA methylation. Using this long-read sequencing technology, we successfully assembled 5 myxozoan genomes free from discernible host DNA contamination, surpassing previous studies in both quality and completeness. Genome assembly revealed DNA methylation patterns within myxozoan genomes, particularly in GC-rich regions within gene bodies. The findings not only refute the notion of myxozoans lacking DNA methylation capability but also offer a new perspective on gene regulation in these parasites. The high-quality genome assemblies lay a solid foundation for future research on myxozoans, including new strategies to control these commercially significant fish pathogens.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":"14 ","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11905887/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143624339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unlocking the power of AI for phenotyping fruit morphology in Arabidopsis.","authors":"Kieran Atkins, Gina A Garzón-Martínez, Andrew Lloyd, John H Doonan, Chuan Lu","doi":"10.1093/gigascience/giae123","DOIUrl":"10.1093/gigascience/giae123","url":null,"abstract":"<p><p>Deep learning can revolutionise high-throughput image-based phenotyping by automating the measurement of complex traits, a task that is often labour-intensive, time-consuming, and prone to human error. However, its precision and adaptability in accurately phenotyping organ-level traits, such as fruit morphology, remain to be fully evaluated. Establishing the links between phenotypic and genotypic variation is essential for uncovering the genetic basis of traits and can also provide an orthologous test of pipeline effectiveness. In this study, we assess the efficacy of deep learning for measuring variation in fruit morphology in Arabidopsis using images from a multiparent advanced generation intercross (MAGIC) mapping family. We trained an instance segmentation model and developed a pipeline to phenotype Arabidopsis fruit morphology, based on the model outputs. Our model achieved strong performance with an average precision of 88.0% for detection and 55.9% for segmentation. Quantitative trait locus analysis of the derived phenotypic metrics of the MAGIC population identified significant loci associated with fruit morphology. This analysis, based on automated phenotyping of 332,194 individual fruits, underscores the capability of deep learning as a robust tool for phenotyping large populations. Our pipeline for quantifying pod morphological traits is scalable and provides high-quality phenotype data, facilitating genetic analysis and gene discovery, as well as advancing crop breeding research.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":"14 ","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11816797/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143407006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Genomic and transcriptomic analyses of Heteropoda venatoria reveal the expansion of P450 family for starvation resistance in spiders.","authors":"Guoqing Zhang, Yiru Wang, Hongcen Jiang, Yi Wang","doi":"10.1093/gigascience/giaf019","DOIUrl":"10.1093/gigascience/giaf019","url":null,"abstract":"<p><strong>Background: </strong>Research on the mechanism of starvation resistance can help reveal how animals adjust their physiology and behavior to adapt to the uncertainty of food resources. A low metabolic rate is a significant characteristic of spider physiological activity and can increase spider starvation resistance and adapt to complex ecological environments.</p><p><strong>Results: </strong>We sequenced the genome of Heteropoda venatoria and discovered significant expansions in gene families related to lipid metabolism, such as cytochrome P450 and steroid hormone biosynthesis genes, through comparative genomic analysis. We also systematically analyzed the gene expression characteristics of H. venatoria at different starvation resistance stages and reported that the fat body plays a crucial role during starvation in spiders. This study indicates that during the early stages of starvation, H. venatoria relies on glucose metabolism to meet its energy demands. In the middle stage, gene expression stabilizes, whereas in the late stage of starvation, pathways for fatty acid metabolism and protein degradation are significantly activated, and autophagy is increased, serving as a survival strategy under extreme starvation. Notably, analysis of expanded P450 gene families revealed that H. venatoria has many duplicated CYP3 clan genes that are highly expressed in the fat body, which may help maintain a low-energy metabolic state, allowing H. venatoria to endure longer periods of starvation. We also observed that the motifs of P450 families in H. venatoria are less conserved than those in insects are, which may be related to the greater polymorphism of spider genomes.</p><p><strong>Conclusions: </strong>This research not only provides important genetic and transcriptomic evidence for understanding the starvation mechanisms of spiders but also offers new insights into the adaptive evolution of arthropods.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":"14 ","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11927401/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143673819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The telomere-to-telomere genome of flowering cherry (Prunus campanulata) reveals genomic evolution of the subgenus Cerasus.","authors":"Dongyue Jiang, Yingang Li, Fei Zhuge, Qi Zhou, Wenjin Zong, Xinhong Liu, Xin Shen","doi":"10.1093/gigascience/giaf009","DOIUrl":"10.1093/gigascience/giaf009","url":null,"abstract":"<p><strong>Background: </strong>Prunus campanulata, a species of ornamental cherry, holds significant genetic and horticultural value. Despite the availability of various cherry genomes, a fully resolved telomere-to-telomere (T2T) assembly for this species has been lacking. Recent advancements in long-read sequencing technologies have made it possible to generate gap-free genome assemblies, providing comprehensive insights into genomic structures that were previously inaccessible.</p><p><strong>Findings: </strong>We present the first T2T genome assembly for P. campanulata \"Lianmeiren\" (v2.0), achieved through the integration of PacBio HiFi, ultra-long Oxford Nanopore Technologies, Illumina, and Hi-C sequencing. The assembly resulted in a highly contiguous genome with a total size of 266.23 Mb and a contig N50 of 31.6 Mb. The genome exhibits remarkable completeness (98.9% BUSCO) and high accuracy (quality value of 48.75). Additionally, 13 telomeres and putative centromere regions were successfully identified across the 8 pseudochromosomes. Comparative analysis with the previous v1.0 assembly revealed 336,943 single nucleotide polymorphisms, 107,521 indels, and 1,413 structural variations, along with the annotation of 1,402 new genes.</p><p><strong>Conclusions: </strong>This T2T genome assembly of P. campanulata \"Lianmeiren\" provides a critical reference for understanding the genetic architecture of the species. It enhances our ability to study structural variations, gene function, and evolutionary biology within the Prunus genus.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":"14 ","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11843098/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143472345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identifying candidate genetic variants for egg number by analyzing over 1,000 fully sequenced layers.","authors":"Aixin Ni, Henk Bovenhuis, Mario P L Calus, Yunlei Li, Jingwei Yuan, Yanyan Sun, Jilan Chen","doi":"10.1093/gigascience/giaf064","DOIUrl":"10.1093/gigascience/giaf064","url":null,"abstract":"<p><strong>Background: </strong>Egg production over a long laying cycle until 700 days of age is preferred for modern layer chicken breeding. It is influenced by the onset of laying, stability during the peak period, and persistence at late laying stages. Conventional single-single nucleotide polymorphism (SNP) association analyses have identified additive loci, but few studies have explored dominance effects or integrated multiomics data to investigate the genetic basis of egg production traits from the onset to 700 days of age. A full diallel cross of 1,004 chickens was·subjected to whole-genome sequencing. Transcriptome data from the ovary were available for a subset of 120 chickens. A genome-wide association study (GWAS) was conducted using an additive-dominance model for cumulative egg number and egg number at different stages. Expression quantitative trait loci (eQTL) mapping was applied to investigate associations between SNPs and gene expression. A transcriptome-wide association study (TWAS) was conducted to explore the associations between gene expression and egg production traits to identify candidate genes.</p><p><strong>Results: </strong>The additive-dominance model identified 5,892 significant SNPs, comprising 805 additive SNPs and 360 dominance SNPs shared between 2 or more traits. By integrating loci identified through GWAS with eQTL mapping, the expression level of 27 genes was associated with significant SNPs. Further integration with TWAS results revealed 4 novel candidate genes. For the loci with significant SNP effects, we found a positive but insignificant correlation between the ratios of dominance to additive effects and observed heterosis. Observed heterosis was positively correlated with heterosis predicted based on dominance effects and allele frequencies of all SNPs.</p><p><strong>Conclusions: </strong>We identified candidate genetic variants for egg production traits by analyzing 1,004 fully sequenced layers. Detection benefited from incorporating dominance into the GWAS model. Traits with higher heterosis tended to be more affected by genes with a dominant mode of action. Moreover, multiomics data allowed for the contribution to deciphering genetic mechanisms underlying egg production by establishing connections between genetic variants, gene expression, and egg number.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":"14 ","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12203006/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144316595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multiomics uncovers the epigenomic and transcriptomic response to viral and bacterial stimulation in turbot.","authors":"Oscar Aramburu, Belén Gómez-Pardo, Paula Rodríguez-Villamayor, Andrés Blanco-Hortas, Jesús Lamas, Pooran Dewari, Diego Perojil-Morata, Pierre Boudinot, Daniel J Macqueen, Carmen Bouza, Paulino Martínez","doi":"10.1093/gigascience/giaf077","DOIUrl":"10.1093/gigascience/giaf077","url":null,"abstract":"<p><strong>Background: </strong>Uncovering the epigenomic regulation of immune response is essential for a comprehensive understanding of host defense mechanisms, though it remains poorly investigated in farmed fish.</p><p><strong>Results: </strong>We report the first annotation of the response of turbot (Scophthalmus maximus) immune cells to viral (poly I:C) and bacterial (inactive Vibrio anguillarum) mimics, integrating RNA sequencing with assay for transposase-accessible chromatin (ATAC) sequencing (ATAC-seq) and chromatin immunoprecipitation sequencing (ChIP-seq) (H3K4me3, H3K27ac, and H3K27me3) data from head kidney (in vivo) and primary leukocyte cultures (in vitro) 24 hours after stimulation. Among the 8,797 differentially expressed genes (DEGs), we observed enrichment of transcriptional activation pathways in response to Vibrio and immune pathways-including interferon-stimulated genes-for poly I:C. We identified notable differences in chromatin accessibility (20,617 in vitro, 59,892 in vivo) and H3K4me3-bound regions (11,454 in vitro, 10,275 in vivo) between stimulations and controls. Overlap of DEGs with promoters showing differential accessibility or histone mark binding revealed significant coupling of the transcriptome and chromatin state. DEGs with activation marks in their promoters were enriched for similar functions to the global DEG set but not always, suggesting key regulatory genes being in a poised state. Active promoters and putative enhancers were enriched in specific transcription factor binding motifs, many common to viral and bacterial responses. An in-depth analysis of chromatin state surrounding key DEGs encoding transcription factors was also performed to understand turbot immune response.</p><p><strong>Conclusions: </strong>This multiomics investigation provides an improved understanding of the epigenomic basis of turbot immune response to mimics of viral and bacterial stimuli, offering novel functional genomic information that provides a valuable resource for exploring immune regulation in flatfish.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":"14 ","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12263217/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144642307","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"eNRSA: a faster and more powerful approach for nascent transcriptome analysis.","authors":"Jing Wang, Hua-Chang Chen, Scott W Hiebert, Quanhu Sheng, William P Tansey, Yu Shyr, Qi Liu","doi":"10.1093/gigascience/giaf071","DOIUrl":"10.1093/gigascience/giaf071","url":null,"abstract":"<p><p>Nascent RNA sequencing tracks primary transcriptional events, making it crucial for studying the immediate regulatory changes of genes and enhancers in response to both endogenous and exogenous stimuli. NRSA is a widely used tool for analyzing nascent transcriptomic data, enabling quantification of transcriptional changes at proximal promoters and gene bodies, estimation of pausing indices, identifying active enhancers, and establishing enhancer-target gene relationships. To improve its functionality and broaden its applicability to diverse organisms and complex study designs, we have developed an enhanced version, eNRSA. Key advancements include adaptive selection of major transcripts, support for any organism with known gene structures, compatibility with complex study designs, and identification of alternative transcription start and termination sites, as well as transcription readthrough events. Additionally, eNRSA achieves a ∼20-fold increase in analysis speed while significantly reducing memory usage. These enhancements make eNRSA a faster, more versatile, and more powerful tool for nascent transcriptome analysis. eNRSA is freely available at https://bioinfo.vanderbilt.edu/eNRSA/.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":"14 ","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12231571/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144559883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction to: \"UDE DIATOMS in the Wild 2024\": a new image dataset of freshwater diatoms for training deep learning models.","authors":"","doi":"10.1093/gigascience/giaf043","DOIUrl":"10.1093/gigascience/giaf043","url":null,"abstract":"","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":"14 ","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11943468/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143729536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Benchmarking short-read metagenomics tools for removing host contamination.","authors":"Yunyun Gao, Hao Luo, Hujie Lyu, Haifei Yang, Salsabeel Yousuf, Shi Huang, Yong-Xin Liu","doi":"10.1093/gigascience/giaf004","DOIUrl":"10.1093/gigascience/giaf004","url":null,"abstract":"<p><strong>Background: </strong>The rapid evolution of metagenomic sequencing technology offers remarkable opportunities to explore the intricate roles of microbiome in host health and disease, as well as to uncover the unknown structure and functions of microbial communities. However, the swift accumulation of metagenomic data poses substantial challenges for data analysis. Contamination from host DNA can substantially compromise result accuracy and increase additional computational resources by including nontarget sequences.</p><p><strong>Results: </strong>In this study, we assessed the impact of computational host DNA decontamination on downstream analyses, highlighting its importance in producing accurate results efficiently. We also evaluated the performance of conventional tools like KneadData, Bowtie2, BWA, KMCP, Kraken2, and KrakenUniq, each offering unique advantages for different applications. Furthermore, we highlighted the importance of an accurate host reference genome, noting that its absence negatively affected the decontamination performance across all tools.</p><p><strong>Conclusions: </strong>Our findings underscore the need for careful selection of decontamination tools and reference genomes to enhance the accuracy of metagenomic analyses. These insights provide valuable guidance for improving the reliability and reproducibility of microbiome research.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":"14 ","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11878760/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143556458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}