GigaScience最新文献

{"title":"CODARFE: Unlocking the prediction of continuous environmental variables based on microbiome.","authors":"Murilo Caminotto Barbosa, João Fernando Marques da Silva, Leonardo Cardoso Alves, Robert D Finn, Alexandre Rossi Paschoal","doi":"10.1093/gigascience/giaf055","DOIUrl":"https://doi.org/10.1093/gigascience/giaf055","url":null,"abstract":"<p><strong>Background: </strong>Despite the surge in microbiome data acquisition, there is a limited availability of tools capable of effectively analyzing it and identifying correlations between taxonomic compositions and continuous environmental factors. Furthermore, existing tools also do not predict the environmental factors in new samples, underscoring the pressing need for innovative solutions to enhance our understanding of microbiome dynamics and fulfill the prediction gap. Here we introduce CODARFE, a novel tool for sparse compositional microbiome predictor selection and prediction of continuous environmental factors.</p><p><strong>Results: </strong>We tested CODARFE against 4 state-of-the-art tools in 2 experiments. First, CODARFE outperformed predictor selection in 21 of 24 databases in terms of correlation. Second, among all the tools, CODARFE achieved the highest number of previously identified bacteria linked to environmental factors for human data-that is, at least 7% more. We also tested CODARFE in a cross-study, using the same biome but under different external effects, using a model trained on 1 dataset to predict environmental factors on another dataset, achieving 11% of mean absolute percentage error. Finally, CODARFE is available in 5 formats, including a Windows version with a graphical interface, to installable source code for Linux servers and an embedded Jupyter notebook available at MGnify.</p><p><strong>Conclusions: </strong>Our findings underscore the robustness and broad applicability of CODARFE across diverse fields, even under varying experimental conditions. Additionally, the ability to predict outcomes in new samples allows for the generation of new insights in previously unexplored contexts, providing researchers with a versatile tool.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":"14 ","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144474816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction to: Habitat suitability maps for Australian flora and fauna under CMIP6 climate scenarios.","authors":"","doi":"10.1093/gigascience/giae031","DOIUrl":"10.1093/gigascience/giae031","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/PMC11880536/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143556459","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":"TransHLA: a Hybrid Transformer model for HLA-presented epitope detection.","authors":"Tianchi Lu, Xueying Wang, Wan Nie, Miaozhe Huo, Shuaicheng Li","doi":"10.1093/gigascience/giaf008","DOIUrl":"10.1093/gigascience/giaf008","url":null,"abstract":"<p><strong>Background: </strong>Precise prediction of epitope presentation on human leukocyte antigen (HLA) molecules is crucial for advancing vaccine development and immunotherapy. Conventional HLA-peptide binding affinity prediction tools often focus on specific alleles and lack a universal approach for comprehensive HLA site analysis. This limitation hinders efficient filtering of invalid peptide segments.</p><p><strong>Results: </strong>We introduce TransHLA, a pioneering tool designed for epitope prediction across all HLA alleles, integrating Transformer and Residue CNN architectures. TransHLA utilizes the ESM2 large language model for sequence and structure embeddings, achieving high predictive accuracy. For HLA class I, it reaches an accuracy of 84.72% and an area under the curve (AUC) of 91.95% on IEDB test data. For HLA class II, it achieves 79.94% accuracy and an AUC of 88.14%. Our case studies using datasets like CEDAR and VDJdb demonstrate that TransHLA surpasses existing models in specificity and sensitivity for identifying immunogenic epitopes and neoepitopes.</p><p><strong>Conclusions: </strong>TransHLA significantly enhances vaccine design and immunotherapy by efficiently identifying broadly reactive peptides. Our resources, including data and code, are publicly accessible at https://github.com/SkywalkerLuke/TransHLA.</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/PMC11878767/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143556462","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":"Cerebellocerebral connectivity predicts body mass index: a new open-source Python-based framework for connectome-based predictive modeling.","authors":"Tobias Bachmann, Karsten Mueller, Simon N A Kusnezow, Matthias L Schroeter, Paolo Piaggi, Christopher M Weise","doi":"10.1093/gigascience/giaf010","DOIUrl":"10.1093/gigascience/giaf010","url":null,"abstract":"<p><strong>Background: </strong>The cerebellum is one of the major central nervous structures consistently altered in obesity. Its role in higher cognitive function, parts of which are affected by obesity, is mediated through projections to and from the cerebral cortex. We therefore investigated the relationship between body mass index (BMI) and cerebellocerebral connectivity.</p><p><strong>Methods: </strong>We utilized the Human Connectome Project's Young Adults dataset, including functional magnetic resonance imaging (fMRI) and behavioral data, to perform connectome-based predictive modeling (CPM) restricted to cerebellocerebral connectivity of resting-state fMRI and task-based fMRI. We developed a Python-based open-source framework to perform CPM, a data-driven technique with built-in cross-validation to establish brain-behavior relationships. Significance was assessed with permutation analysis.</p><p><strong>Results: </strong>We found that (i) cerebellocerebral connectivity predicted BMI, (ii) task-general cerebellocerebral connectivity predicted BMI more reliably than resting-state fMRI and individual task-based fMRI separately, (iii) predictive networks derived this way overlapped with established functional brain networks (namely, frontoparietal networks, the somatomotor network, the salience network, and the default mode network), and (iv) we found there was an inverse overlap between networks predictive of BMI and networks predictive of cognitive measures adversely affected by overweight/obesity.</p><p><strong>Conclusions: </strong>Our results suggest obesity-specific alterations in cerebellocerebral connectivity, specifically with regard to task execution. With brain areas and brain networks relevant to task performance implicated, these alterations seem to reflect a neurobiological substrate for task performance adversely affected by obesity.</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/PMC11899596/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143614577","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":"Defining the limits of plant chemical space: challenges and estimations.","authors":"Chloe Engler Hart, Yojana Gadiya, Tobias Kind, Christoph A Krettler, Matthew Gaetz, Biswapriya B Misra, David Healey, August Allen, Viswa Colluru, Daniel Domingo-Fernández","doi":"10.1093/gigascience/giaf033","DOIUrl":"10.1093/gigascience/giaf033","url":null,"abstract":"<p><p>The plant kingdom, encompassing nearly 400,000 known species, produces an immense diversity of metabolites, including primary compounds essential for survival and secondary metabolites specialized for ecological interactions. These metabolites constitute a vast and complex phytochemical space with significant potential applications in medicine, agriculture, and biotechnology. However, much of this chemical diversity remains unexplored, as only a fraction of plant species has been studied comprehensively. In this work, we estimate the size of the plant chemical space by leveraging large-scale metabolomics and literature datasets. We begin by examining the known chemical space, which, while containing at most several hundred thousand unique compounds, remains sparsely covered. Using data from over 1,000 plant species, we apply various mass spectrometry-based approaches-a formula prediction model, a de novo prediction model, a combination of library search and de novo prediction, and MS2 clustering-to estimate the number of unique structures. Our methods suggest that the number of unique compounds in the metabolomics dataset alone may already surpass existing estimates of plant chemical diversity. Finally, we project these findings across the entire plant kingdom, estimating that the total plant chemical space likely spans millions, if not more, with most still unexplored.</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/PMC11970369/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143784433","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":"Chromosome-level genome assembly of Pinus massoniana provides insights into conifer adaptive evolution.","authors":"Hu Chen, Xinghu Qin, Yinghao Chen, Haoyu Zhang, Yuanheng Feng, Jianhui Tan, Xinhua Chen, La Hu, Junkang Xie, Jianbo Xie, Zhangqi Yang","doi":"10.1093/gigascience/giaf056","DOIUrl":"10.1093/gigascience/giaf056","url":null,"abstract":"<p><p>Pinus massoniana, a conifer of significant economic and ecological value in China, is renowned for its wide adaptability and oleoresin production. We sequenced and assembled the chromosomal-level P. massoniana genome, revealing 80,366 protein-coding genes and significant gene family expansions associated with stress response and plant-pathogen interactions. Long-intron genes, which are predominantly presented in low-copy gene families, are strongly linked to the recent long terminal repeat burst in the Pinus genome. By reanalyzing population transcriptomic data, we identified genetic markers linked to oleoresin synthesis, including those within the CYP450 and TPS gene families. The results suggest that the genes of the resin terpene biosynthesis pathway can be activated in several cell types, and the oleoresin yield may depend on the rate-limiting enzymes. Using a multiomics algorithm, we identified several regulatory factors, including PmMYB4 and PmbZIP2, that interact with TPS and CYP450 genes, potentially playing a role in oleoresin production. This was further validated through molecular genetics analyses. We observed signatures of adaptive evolution in dispersed duplicates and horizontal gene transfer events that have contributed to the species adaptation. This study provides insights for further research into the evolutionary biology of conifers and lays the groundwork for genomic-assisted breeding and sustainable management of Masson pine.</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/PMC12122422/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144179673","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":"Artificial intelligence: the human response to approach the complexity of big data in biology.","authors":"Giovanni Melandri, Georges R-Radohery, Chloé Beaumont, Sara M de Cripan, Coralie Muller, Luca Piras, Maria Alcina Pereira, Andreia Ferreira Salvador, Xavier Domingo-Almenara, Marie Bolger, Sophie Colombié, Sylvain Prigent, Biotza Gutierrez Arechederra, Nuria Canela Canela, Pierre Pétriacq","doi":"10.1093/gigascience/giaf057","DOIUrl":"10.1093/gigascience/giaf057","url":null,"abstract":"<p><p>Since the late 2010s, artificial intelligence (AI), encompassing machine learning and propelled by deep learning, has transformed life science research. It has become a crucial tool for advancing the computational analysis of biological processes, the discovery of natural products, and the study of ecosystem dynamics. This review explores how the rapid increase in high-throughput omics data acquisition has driven the need for AI-based analysis in life sciences, with a particular focus on plant sciences, animal sciences, and microbiology. We highlight the role of omics-based predictive analytics in systems biology and innovative AI-based analytical approaches for gaining deeper insights into complex biological systems. Finally, we discuss the importance of FAIR (findable, accessible, interoperable, reusable) principles for omics data, as well as the future challenges and opportunities presented by the increasing use of AI in life sciences.</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/PMC12160488/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144274679","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":"Telomere-to-telomere genome of common bean (Phaseolus vulgaris L., YP4).","authors":"Yan Wang, Xiaopeng Hao, Chunhai Chen, Haigang Wang, Peng Gao, Xukui Yang, Xue Dong, Huibin Qin, Meng Li, Sen Hou, Jianbo Jian, Jianwu Chang, Jing Wu, Zhixin Mu","doi":"10.1093/gigascience/giaf001","DOIUrl":"10.1093/gigascience/giaf001","url":null,"abstract":"<p><strong>Background: </strong>Common bean is a significant grain legume in human diets. However, the lack of a complete reference genome for common beans has hindered efforts to improve agronomic cultivars.</p><p><strong>Findings: </strong>Herein, we present the first telomere-to-telomere (T2T) genome assembly of common bean (Phaseolus vulgaris L., YP4) using PacBio High-Fidelity reads, ONT ultra-long sequencing, and Hi-C technologies. The assembly resulted in a genome size of 560.30 Mb with an N50 of 55.11 Mb, exhibiting high completeness and accuracy (BUSCO score: 99.5%, quality value (QV): 54.86). The sequences were anchored into 11 chromosomes, with 20 of 22 telomeres identified, leading to the formation of 9 T2T pseudomolecules. Furthermore, we identified repetitive elements accounting for 61.20% of the genome and predicted 29,925 protein-coding genes. Phylogenetic analysis suggested an estimated divergence time of approximately 11.6 million years ago between P. vulgaris and Vigna angularis. Comparative genome analysis revealed the expanded gene families and variations between YP4 and G19833 associated with defense response.</p><p><strong>Conclusions: </strong>The T2T reference genome and genomic insights presented here are crucial for future genetic studies not only in common bean but also in other legumes.</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/PMC12077395/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144077126","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":"HVSeeker: a deep-learning-based method for identification of host and viral DNA sequences.","authors":"Abdullatif Al-Najim, Sven Hauns, Van Dinh Tran, Rolf Backofen, Omer S Alkhnbashi","doi":"10.1093/gigascience/giaf037","DOIUrl":"10.1093/gigascience/giaf037","url":null,"abstract":"<p><strong>Background: </strong>Bacteriophages are among the most abundant organisms on Earth, significantly impacting ecosystems and human society. The identification of viral sequences, especially novel ones, from mixed metagenomes is a critical first step in analyzing the viral components of host samples. This plays a key role in many downstream tasks. However, this is a challenging task due to their rapid evolution rate. The identification process typically involves two steps: distinguishing viral sequences from the host and identifying if they come from novel viral genomes. Traditional metagenomic techniques that rely on sequence similarity with known entities often fall short, especially when dealing with short or novel genomes. Meanwhile, deep learning has demonstrated its efficacy across various domains, including the bioinformatics field.</p><p><strong>Results: </strong>We have developed HVSeeker-a host/virus seeker method-based on deep learning to distinguish between bacterial and phage sequences. HVSeeker consists of two separate models: one analyzing DNA sequences and the other focusing on proteins. In addition to the robust architecture of HVSeeker, three distinct preprocessing methods were introduced to enhance the learning process: padding, contigs assembly, and sliding window. This method has shown promising results on sequences with various lengths, ranging from 200 to 1,500 base pairs. Tested on both NCBI and IMGVR databases, HVSeeker outperformed several methods from the literature such as Seeker, Rnn-VirSeeker, DeepVirFinder, and PPR-Meta. Moreover, when compared with other methods on benchmark datasets, HVSeeker has shown better performance, establishing its effectiveness in identifying unknown phage genomes.</p><p><strong>Conclusions: </strong>These results demonstrate the exceptional structure of HVSeeker, which encompasses both the preprocessing methods and the model design. The advancements provided by HVSeeker are significant for identifying viral genomes and developing new therapeutic approaches, such as phage therapy. Therefore, HVSeeker serves as an essential tool in prokaryotic and phage taxonomy, offering a crucial first step toward analyzing the host-viral component of samples by identifying the host and viral sequences in mixed metagenomes.</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/PMC12080225/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144077444","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":"Telomere-to-telomere genome and resequencing of 231 individuals reveal evolution, genomic footprints in Asian icefish, Protosalanx chinensis.","authors":"Yanfeng Zhou, Chenhe Wang, Binhu Wang, Dongpo Xu, Xizhao Zhang, You Ge, Shulun Jiang, Fujiang Tang, Chunhai Chen, Xuemei Li, Jianbo Jian, Yang You","doi":"10.1093/gigascience/giaf067","DOIUrl":"10.1093/gigascience/giaf067","url":null,"abstract":"<p><p>The Asian icefish, Protosalanx chinensis, has undergone extensive colonization in various waters across China for decades due to its ecological and physiological significance as well as its economic importance in the fishery resource. Here, we decoded a telomereto-telomere (T2T) genome for P. chinensis combining PacBio HiFi long reads and ultra-long ONT (nanopore) reads and Hi-C data. The telomere was identified in both ends of the contig/chromosome. The expanded gene associated with circadian entrainment suggests that P. chinensis may exhibit a high sensitivity to photoperiod. The contracted genes' immune-related families and DNA repair associated with positive selection in P. chinensis suggested the selection pressure during adaptive evolution. The population genetic analysis reported the genetic diversity and genomic footprints in 231 individuals from 7 different locations. The introduced highest alkalinity population (HRCL) exhibited higher values of inbreeding coefficients and clustered different from other groups suggested local environmental adaptation. Thus, the T2T genome and genetic variation can be valuable resources for genomic footprints in P. chinensis, shedding light on its evolution, comparative genomics, and the genetic differences between natural and introduced populations.</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/PMC12266836/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144649193","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}