Pub Date : 2026-02-08DOI: 10.1186/s12859-026-06393-4
Yi Zhao, Xuteng Ye, Jun Cheng, Li Yin, Danyu Shen, Daolong Dou, Jinding Liu
Yams (Dioscorea spp.) are a highly important class of horticultural crops, serving as a staple food for millions of people in Africa and contributing significantly to food security. They are also widely cultivated in East Asia as medicinal herbs, bringing substantial economic incomes. Diverse omics data play a pivotal role in advancing yam research and breeding. However, these data are often scattered, lacking in systematic organization and analysis, which underscores the need for centralized and comprehensive data management. In view of this, we gathered extensive omics data and developed the Yam Omics Database (YamOmics; https://biotec.njau.edu.cn/yamdb). The database currently offers a vast and diverse range of omics data, covering genomic, transcriptomic and plastomic data from 41 distinct yam species, along with detailed records of genomic variants from 935 germplasms, and gene expression profiles from 191 samples. Additionally, the database features thorough annotations, encompassing aspects like genome synteny, ortholog groups, signaling pathways, gene families and protein interactions. To support yam basic biology and breeding research, it is also equipped with a suite of user-friendly online tools, including PCR primer design, CRISPR design, expression analysis, enrichment analysis, and phylogenetic inference among Dioscorea accessions tools.
{"title":"YamOmics: a comprehensive data resource on yam multi-omics.","authors":"Yi Zhao, Xuteng Ye, Jun Cheng, Li Yin, Danyu Shen, Daolong Dou, Jinding Liu","doi":"10.1186/s12859-026-06393-4","DOIUrl":"https://doi.org/10.1186/s12859-026-06393-4","url":null,"abstract":"<p><p>Yams (Dioscorea spp.) are a highly important class of horticultural crops, serving as a staple food for millions of people in Africa and contributing significantly to food security. They are also widely cultivated in East Asia as medicinal herbs, bringing substantial economic incomes. Diverse omics data play a pivotal role in advancing yam research and breeding. However, these data are often scattered, lacking in systematic organization and analysis, which underscores the need for centralized and comprehensive data management. In view of this, we gathered extensive omics data and developed the Yam Omics Database (YamOmics; https://biotec.njau.edu.cn/yamdb). The database currently offers a vast and diverse range of omics data, covering genomic, transcriptomic and plastomic data from 41 distinct yam species, along with detailed records of genomic variants from 935 germplasms, and gene expression profiles from 191 samples. Additionally, the database features thorough annotations, encompassing aspects like genome synteny, ortholog groups, signaling pathways, gene families and protein interactions. To support yam basic biology and breeding research, it is also equipped with a suite of user-friendly online tools, including PCR primer design, CRISPR design, expression analysis, enrichment analysis, and phylogenetic inference among Dioscorea accessions tools.</p>","PeriodicalId":8958,"journal":{"name":"BMC Bioinformatics","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146140965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-08DOI: 10.1186/s12859-026-06384-5
Xuan Wang, Zhengao Mo, Fuwei Li, Fa Zhang, Xiaohua Wan
Background: Cryo-electron microscopy (cryo-EM) has emerged as a powerful technique for high-resolution structural determination of macromolecules. However, accurately classifying single-particle cryo-EM images remains challenging, especially when dealing with deformed particles. In traditional 2D classification methods, clustering algorithms are used for classification. This assumption leads to some deformed particles being misclassified in 2D images, which adversely affects downstream tasks. To address this challenge, we propose a point cloud-based deformation measurement model that integrates a Variational Autoencoder (VAE) with a heuristic point cloud matching algorithm to calculate particle deformation values.
Results: This model enables the identification and removal of particles with large deformations. Our experiments on simulated and real cryo-EM datasets, including Tobacco Mosaic Virus (TMV) and mixed capsids of MS2 virions (MS2). The model achieves robust classification (F1: 0.85-0.88) while preserving 93-95% of structural details, and can effectively filter out deformed particles after 2D classification.
Conclusion: The model identifies and removes deformed or misclassified particles to improve classification quality. It serves as a data-filtering post-processing step following 2D classification. By improving the quality of particle datasets, it enhances the reliability of subsequent analysis in cryo-EM.
{"title":"Point cloud deformation modeling for particle selection following cryo-EM 2D classification.","authors":"Xuan Wang, Zhengao Mo, Fuwei Li, Fa Zhang, Xiaohua Wan","doi":"10.1186/s12859-026-06384-5","DOIUrl":"https://doi.org/10.1186/s12859-026-06384-5","url":null,"abstract":"<p><strong>Background: </strong>Cryo-electron microscopy (cryo-EM) has emerged as a powerful technique for high-resolution structural determination of macromolecules. However, accurately classifying single-particle cryo-EM images remains challenging, especially when dealing with deformed particles. In traditional 2D classification methods, clustering algorithms are used for classification. This assumption leads to some deformed particles being misclassified in 2D images, which adversely affects downstream tasks. To address this challenge, we propose a point cloud-based deformation measurement model that integrates a Variational Autoencoder (VAE) with a heuristic point cloud matching algorithm to calculate particle deformation values.</p><p><strong>Results: </strong>This model enables the identification and removal of particles with large deformations. Our experiments on simulated and real cryo-EM datasets, including Tobacco Mosaic Virus (TMV) and mixed capsids of MS2 virions (MS2). The model achieves robust classification (F1: 0.85-0.88) while preserving 93-95% of structural details, and can effectively filter out deformed particles after 2D classification.</p><p><strong>Conclusion: </strong>The model identifies and removes deformed or misclassified particles to improve classification quality. It serves as a data-filtering post-processing step following 2D classification. By improving the quality of particle datasets, it enhances the reliability of subsequent analysis in cryo-EM.</p>","PeriodicalId":8958,"journal":{"name":"BMC Bioinformatics","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146140967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-04DOI: 10.1186/s12859-026-06376-5
Niccolò Bianchi, Armel E J L Lefebvre, Katherine J Wolstencroft, Marco Spruit
{"title":"Design and evaluation of semantically-valid negative samples integration techniques for scalable semi-automated drug repurposing prediction pipelines in rare disease research.","authors":"Niccolò Bianchi, Armel E J L Lefebvre, Katherine J Wolstencroft, Marco Spruit","doi":"10.1186/s12859-026-06376-5","DOIUrl":"https://doi.org/10.1186/s12859-026-06376-5","url":null,"abstract":"","PeriodicalId":8958,"journal":{"name":"BMC Bioinformatics","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146117714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-04DOI: 10.1186/s12859-026-06387-2
Hamdiye Uzuner, Felix Wiegand, Sven Schrinner, David Lähnemann, Dirk Schadendorf, Johannes Köster
{"title":"Virus variant quantification with Orthanq.","authors":"Hamdiye Uzuner, Felix Wiegand, Sven Schrinner, David Lähnemann, Dirk Schadendorf, Johannes Köster","doi":"10.1186/s12859-026-06387-2","DOIUrl":"https://doi.org/10.1186/s12859-026-06387-2","url":null,"abstract":"","PeriodicalId":8958,"journal":{"name":"BMC Bioinformatics","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146117655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-04DOI: 10.1186/s12859-026-06382-7
Muhaiminur Hossain, Anik Mojumder, S M Mahbubur Rashid, Abul Bashar Mir Md Khademul Islam
{"title":"ChromAcS: an automated and flexible GUI for end-to-end reproducible ATAC-seq analysis across multiple species.","authors":"Muhaiminur Hossain, Anik Mojumder, S M Mahbubur Rashid, Abul Bashar Mir Md Khademul Islam","doi":"10.1186/s12859-026-06382-7","DOIUrl":"https://doi.org/10.1186/s12859-026-06382-7","url":null,"abstract":"","PeriodicalId":8958,"journal":{"name":"BMC Bioinformatics","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146117725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-03DOI: 10.1186/s12859-026-06394-3
Simo Inkala, Michele Fratello, Giusy Del Giudice, Giorgia Migliaccio, Angela Serra, Dario Greco, Antonio Federico
{"title":"MUUMI: an R package for statistical and network-based meta-analysis for multi-omics data integration.","authors":"Simo Inkala, Michele Fratello, Giusy Del Giudice, Giorgia Migliaccio, Angela Serra, Dario Greco, Antonio Federico","doi":"10.1186/s12859-026-06394-3","DOIUrl":"https://doi.org/10.1186/s12859-026-06394-3","url":null,"abstract":"","PeriodicalId":8958,"journal":{"name":"BMC Bioinformatics","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146112147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-02DOI: 10.1186/s12859-026-06377-4
Yongqing Zhang, Le Chen, Hong Luo, Tianhao Li, Shuwen Xiong, Zixuan Wang, Quan Zou, Wenqian Zhang
{"title":"Contrastive learning in both structure and function spaces improve drug-target interaction prediction.","authors":"Yongqing Zhang, Le Chen, Hong Luo, Tianhao Li, Shuwen Xiong, Zixuan Wang, Quan Zou, Wenqian Zhang","doi":"10.1186/s12859-026-06377-4","DOIUrl":"https://doi.org/10.1186/s12859-026-06377-4","url":null,"abstract":"","PeriodicalId":8958,"journal":{"name":"BMC Bioinformatics","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146103696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-30DOI: 10.1186/s12859-026-06386-3
Thi Van Nguyen, Van Hoan Do, Vu-Linh Nguyen
{"title":"Srnc: semi-supervised learning for robust novel cell-type identification in single cell RNA sequencing data.","authors":"Thi Van Nguyen, Van Hoan Do, Vu-Linh Nguyen","doi":"10.1186/s12859-026-06386-3","DOIUrl":"https://doi.org/10.1186/s12859-026-06386-3","url":null,"abstract":"","PeriodicalId":8958,"journal":{"name":"BMC Bioinformatics","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146092036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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