Pub Date : 2024-08-09DOI: 10.3389/fbinf.2024.1415078
Tim Muruvanda, Hugh Rand, James Pettengill, A. Pightling
Monitoring data submitted to the National Center for Biotechnology Information’s Pathogen Detection whole-genome sequence database, which includes the foodborne bacterial pathogens Listeria monocytogenes, Salmonella enterica, and Escherichia coli, has proven effective for detecting emerging outbreaks. As part of the submission process, new sequence data are typed using a whole-genome multi-locus sequence typing scheme and clustered with sequences already in the database. Publicly available text files contain the results of these analyses. However, contextualizing and interpreting this information is complex. We present the Rapid Intuitive Pathogen Surveillance (RIPS) tool, which shows the results of the NCBI Rapid Reports, along with appropriate metadata, in a graphical, interactive dashboard. RIPS makes the information in the Rapid Reports useful for real-time surveillance of genome sequence databases.
{"title":"RIPS (rapid intuitive pathogen surveillance): a tool for surveillance of genome sequence data from foodborne bacterial pathogens","authors":"Tim Muruvanda, Hugh Rand, James Pettengill, A. Pightling","doi":"10.3389/fbinf.2024.1415078","DOIUrl":"https://doi.org/10.3389/fbinf.2024.1415078","url":null,"abstract":"Monitoring data submitted to the National Center for Biotechnology Information’s Pathogen Detection whole-genome sequence database, which includes the foodborne bacterial pathogens Listeria monocytogenes, Salmonella enterica, and Escherichia coli, has proven effective for detecting emerging outbreaks. As part of the submission process, new sequence data are typed using a whole-genome multi-locus sequence typing scheme and clustered with sequences already in the database. Publicly available text files contain the results of these analyses. However, contextualizing and interpreting this information is complex. We present the Rapid Intuitive Pathogen Surveillance (RIPS) tool, which shows the results of the NCBI Rapid Reports, along with appropriate metadata, in a graphical, interactive dashboard. RIPS makes the information in the Rapid Reports useful for real-time surveillance of genome sequence databases.","PeriodicalId":507586,"journal":{"name":"Frontiers in Bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141921242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-09DOI: 10.3389/fbinf.2024.1470107
Huixiao Hong, Inimary Toby-Ogundeji, Robert J. Doerksen, Zhaohui Steve Qin
{"title":"Editorial: Big data and artificial intelligence for genomics and therapeutics – Proceedings of the 19th Annual Meeting of the MidSouth Computational Biology and Bioinformatics Society (MCBIOS)","authors":"Huixiao Hong, Inimary Toby-Ogundeji, Robert J. Doerksen, Zhaohui Steve Qin","doi":"10.3389/fbinf.2024.1470107","DOIUrl":"https://doi.org/10.3389/fbinf.2024.1470107","url":null,"abstract":"","PeriodicalId":507586,"journal":{"name":"Frontiers in Bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141923553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-09DOI: 10.3389/fbinf.2024.1428539
T. A. Ogunnupebi, G. Oduselu, O. F. Elebiju, O. Ajani, E. Adebiyi
In malaria management, insecticides play a crucial role in targeting disease vectors. Benzothiazole derivatives have also been reported to possess insecticidal properties, among several other properties they exhibit. The female Anopheles mosquito is responsible for transmitting the malaria parasite when infected. Anopheles gambiae (Ag) and Anopheles funestus (Af) are two of the most notable Anopheles species known to spread malaria in Nigeria. Trehalase is an enzyme that breaks down trehalose. Recent research has proposed it as a viable target for inhibition since it aids in flight and stress adaptation.This study aimed to investigate benzothiazole derivatives as potential inhibitors of trehalase of Anopheles funestus (AfTre) and Anopheles gambiae (AgTre) using toxicity profiling, molecular docking, and dynamic simulation for future insecticidal intervention. A total of 4,214 benzothiazole-based compounds were obtained from the PubChem database and subjected to screening against the 3D modelled structure of AfTre and AgTre. Compounds with some toxicity levels were optimised, and the obtained lead compounds were further investigated through molecular docking studies. Furthermore, the best hit was subjected to parameters such as RMSD, RMSF, SASA, Rg, and hydrogen bond to confirm its stability when in a complex with AfTre, and these parameters were compared to that of validamycin A (control ligand).The post-screening analysis showed binding affinities of −8.7 and −8.2 kcal/mol (compound 1), −8.2 and −7.4 kcal/mol (compound 2), compared to −6.3 and −5.1 kcal/mol (Validamycin A, a known inhibitor) against AfTre and AgTre, respectively. The molecular dynamics simulation showed that compound 1 (the best hit) had good stability when in complex with AfTre. These findings suggest that these best hits can serve as potential inhibitors for the development of novel insecticides in the control of malaria vectors.
{"title":"In silico studies of benzothiazole derivatives as potential inhibitors of Anopheles funestus and Anopheles gambiae trehalase","authors":"T. A. Ogunnupebi, G. Oduselu, O. F. Elebiju, O. Ajani, E. Adebiyi","doi":"10.3389/fbinf.2024.1428539","DOIUrl":"https://doi.org/10.3389/fbinf.2024.1428539","url":null,"abstract":"In malaria management, insecticides play a crucial role in targeting disease vectors. Benzothiazole derivatives have also been reported to possess insecticidal properties, among several other properties they exhibit. The female Anopheles mosquito is responsible for transmitting the malaria parasite when infected. Anopheles gambiae (Ag) and Anopheles funestus (Af) are two of the most notable Anopheles species known to spread malaria in Nigeria. Trehalase is an enzyme that breaks down trehalose. Recent research has proposed it as a viable target for inhibition since it aids in flight and stress adaptation.This study aimed to investigate benzothiazole derivatives as potential inhibitors of trehalase of Anopheles funestus (AfTre) and Anopheles gambiae (AgTre) using toxicity profiling, molecular docking, and dynamic simulation for future insecticidal intervention. A total of 4,214 benzothiazole-based compounds were obtained from the PubChem database and subjected to screening against the 3D modelled structure of AfTre and AgTre. Compounds with some toxicity levels were optimised, and the obtained lead compounds were further investigated through molecular docking studies. Furthermore, the best hit was subjected to parameters such as RMSD, RMSF, SASA, Rg, and hydrogen bond to confirm its stability when in a complex with AfTre, and these parameters were compared to that of validamycin A (control ligand).The post-screening analysis showed binding affinities of −8.7 and −8.2 kcal/mol (compound 1), −8.2 and −7.4 kcal/mol (compound 2), compared to −6.3 and −5.1 kcal/mol (Validamycin A, a known inhibitor) against AfTre and AgTre, respectively. The molecular dynamics simulation showed that compound 1 (the best hit) had good stability when in complex with AfTre. These findings suggest that these best hits can serve as potential inhibitors for the development of novel insecticides in the control of malaria vectors.","PeriodicalId":507586,"journal":{"name":"Frontiers in Bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141924896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-09DOI: 10.3389/fbinf.2024.1458237
S. Broschat, Shirley W. I. Siu, César de la Fuente-Nunez
{"title":"Editorial: Machine learning approaches to antimicrobials: discovery and resistance","authors":"S. Broschat, Shirley W. I. Siu, César de la Fuente-Nunez","doi":"10.3389/fbinf.2024.1458237","DOIUrl":"https://doi.org/10.3389/fbinf.2024.1458237","url":null,"abstract":"","PeriodicalId":507586,"journal":{"name":"Frontiers in Bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141921173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-26DOI: 10.3389/fbinf.2024.1411935
A. Aborode, Neeraj Kumar, Christopher Busayo Olowosoke, Tope Abraham Ibisanmi, Islamiyyah Ayoade, H. I. Umar, A. Jamiu, Basit Bolarinwa, Zainab Olapade, A. R. Idowu, Ibrahim O. Adelakun, I. A. Onifade, Benjamin Akangbe, Modesta Abacheng, O. O. Ikhimiukor, A. A. Awaji, R. Adesola
Introduction: This work utilizes predictive modeling in drug discovery to unravel potential candidate genes from Escherichia coli that are implicated in antimicrobial resistance; we subsequently target the gidB, MacB, and KatG genes with some compounds from plants with reported antibacterial potentials.Method: The resistance genes and plasmids were identified from 10 whole-genome sequence datasets of E. coli; forty two plant compounds were selected, and their 3D structures were retrieved and optimized for docking. The 3D crystal structures of KatG, MacB, and gidB were retrieved and prepared for molecular docking, molecular dynamics simulations, and ADMET profiling.Result: Hesperidin showed the least binding energy (kcal/mol) against KatG (−9.3), MacB (−10.7), and gidB (−6.7); additionally, good pharmacokinetic profiles and structure–dynamics integrity with their respective protein complexes were observed.Conclusion: Although these findings suggest hesperidin as a potential inhibitor against MacB, gidB, and KatG in E. coli, further validations through in vitro and in vivo experiments are needed. This research is expected to provide an alternative avenue for addressing existing antimicrobial resistances associated with E. coli’s MacB, gidB, and KatG.
{"title":"Predictive identification and design of potent inhibitors targeting resistance-inducing candidate genes from E. coli whole-genome sequences","authors":"A. Aborode, Neeraj Kumar, Christopher Busayo Olowosoke, Tope Abraham Ibisanmi, Islamiyyah Ayoade, H. I. Umar, A. Jamiu, Basit Bolarinwa, Zainab Olapade, A. R. Idowu, Ibrahim O. Adelakun, I. A. Onifade, Benjamin Akangbe, Modesta Abacheng, O. O. Ikhimiukor, A. A. Awaji, R. Adesola","doi":"10.3389/fbinf.2024.1411935","DOIUrl":"https://doi.org/10.3389/fbinf.2024.1411935","url":null,"abstract":"Introduction: This work utilizes predictive modeling in drug discovery to unravel potential candidate genes from Escherichia coli that are implicated in antimicrobial resistance; we subsequently target the gidB, MacB, and KatG genes with some compounds from plants with reported antibacterial potentials.Method: The resistance genes and plasmids were identified from 10 whole-genome sequence datasets of E. coli; forty two plant compounds were selected, and their 3D structures were retrieved and optimized for docking. The 3D crystal structures of KatG, MacB, and gidB were retrieved and prepared for molecular docking, molecular dynamics simulations, and ADMET profiling.Result: Hesperidin showed the least binding energy (kcal/mol) against KatG (−9.3), MacB (−10.7), and gidB (−6.7); additionally, good pharmacokinetic profiles and structure–dynamics integrity with their respective protein complexes were observed.Conclusion: Although these findings suggest hesperidin as a potential inhibitor against MacB, gidB, and KatG in E. coli, further validations through in vitro and in vivo experiments are needed. This research is expected to provide an alternative avenue for addressing existing antimicrobial resistances associated with E. coli’s MacB, gidB, and KatG.","PeriodicalId":507586,"journal":{"name":"Frontiers in Bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141801132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-15DOI: 10.3389/fbinf.2024.1403826
Jiquan Shen, Xuanhui Guo, Hanwen Bai, Junwei Luo
The identification of cancer subtypes plays a very important role in the field of medicine. Accurate identification of cancer subtypes is helpful for both cancer treatment and prognosis Currently, most methods for cancer subtype identification are based on single-omics data, such as gene expression data. However, multi-omics data can show various characteristics about cancer, which also can improve the accuracy of cancer subtype identification. Therefore, how to extract features from multi-omics data for cancer subtype identification is the main challenge currently faced by researchers. In this paper, we propose a cancer subtype identification method named CAEM-GBDT, which takes gene expression data, miRNA expression data, and DNA methylation data as input, and adopts convolutional autoencoder network to identify cancer subtypes. Through a convolutional encoder layer, the method performs feature extraction on the input data. Within the convolutional encoder layer, a convolutional self-attention module is embedded to recognize higher-level representations of the multi-omics data. The extracted high-level representations from the convolutional encoder are then concatenated with the input to the decoder. The GBDT (Gradient Boosting Decision Tree) is utilized for cancer subtype identification. In the experiments, we compare CAEM-GBDT with existing cancer subtype identifying methods. Experimental results demonstrate that the proposed CAEM-GBDT outperforms other methods. The source code is available from GitHub at https://github.com/gxh-1/CAEM-GBDT.git.
{"title":"CAEM-GBDT: a cancer subtype identifying method using multi-omics data and convolutional autoencoder network","authors":"Jiquan Shen, Xuanhui Guo, Hanwen Bai, Junwei Luo","doi":"10.3389/fbinf.2024.1403826","DOIUrl":"https://doi.org/10.3389/fbinf.2024.1403826","url":null,"abstract":"The identification of cancer subtypes plays a very important role in the field of medicine. Accurate identification of cancer subtypes is helpful for both cancer treatment and prognosis Currently, most methods for cancer subtype identification are based on single-omics data, such as gene expression data. However, multi-omics data can show various characteristics about cancer, which also can improve the accuracy of cancer subtype identification. Therefore, how to extract features from multi-omics data for cancer subtype identification is the main challenge currently faced by researchers. In this paper, we propose a cancer subtype identification method named CAEM-GBDT, which takes gene expression data, miRNA expression data, and DNA methylation data as input, and adopts convolutional autoencoder network to identify cancer subtypes. Through a convolutional encoder layer, the method performs feature extraction on the input data. Within the convolutional encoder layer, a convolutional self-attention module is embedded to recognize higher-level representations of the multi-omics data. The extracted high-level representations from the convolutional encoder are then concatenated with the input to the decoder. The GBDT (Gradient Boosting Decision Tree) is utilized for cancer subtype identification. In the experiments, we compare CAEM-GBDT with existing cancer subtype identifying methods. Experimental results demonstrate that the proposed CAEM-GBDT outperforms other methods. The source code is available from GitHub at https://github.com/gxh-1/CAEM-GBDT.git.","PeriodicalId":507586,"journal":{"name":"Frontiers in Bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141649028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-08DOI: 10.3389/fbinf.2024.1365200
S. S. Patil, Steven A. Roberts, A. Gebremedhin
Cancer is a heterogeneous disease that results from genetic alteration of cell cycle and proliferation controls. Identifying mutations that drive cancer, understanding cancer type specificities, and delineating how driver mutations interact with each other to establish disease is vital for identifying therapeutic vulnerabilities. Such cancer specific patterns and gene co-occurrences can be identified by studying tumor genome sequences, and networks have proven effective in uncovering relationships between sequences. We present two network-based approaches to identify driver gene patterns among tumor samples. The first approach relies on analysis using the Directed Weighted All Nearest Neighbors (DiWANN) model, which is a variant of sequence similarity network, and the second approach uses bipartite network analysis. A data reduction framework was implemented to extract the minimal relevant information for the sequence similarity network analysis, where a transformed reference sequence is generated for constructing the driver gene network. This data reduction process combined with the efficiency of the DiWANN network model, greatly lowered the computational cost (in terms of execution time and memory usage) of generating the networks enabling us to work at a much larger scale than previously possible. The DiWANN network helped us identify cancer types in which samples were more closely connected to each other suggesting they are less heterogeneous and potentially susceptible to a common drug. The bipartite network analysis provided insight into gene associations and co-occurrences. We identified genes that were broadly mutated in multiple cancer types and mutations exclusive to only a few. Additionally, weighted one-mode gene projections of the bipartite networks revealed a pattern of occurrence of driver genes in different cancers. Our study demonstrates that network-based approaches can be an effective tool in cancer genomics. The analysis identifies co-occurring and exclusive driver genes and mutations for specific cancer types, providing a better understanding of the driver genes that lead to tumor initiation and evolution.
{"title":"Network analysis of driver genes in human cancers","authors":"S. S. Patil, Steven A. Roberts, A. Gebremedhin","doi":"10.3389/fbinf.2024.1365200","DOIUrl":"https://doi.org/10.3389/fbinf.2024.1365200","url":null,"abstract":"Cancer is a heterogeneous disease that results from genetic alteration of cell cycle and proliferation controls. Identifying mutations that drive cancer, understanding cancer type specificities, and delineating how driver mutations interact with each other to establish disease is vital for identifying therapeutic vulnerabilities. Such cancer specific patterns and gene co-occurrences can be identified by studying tumor genome sequences, and networks have proven effective in uncovering relationships between sequences. We present two network-based approaches to identify driver gene patterns among tumor samples. The first approach relies on analysis using the Directed Weighted All Nearest Neighbors (DiWANN) model, which is a variant of sequence similarity network, and the second approach uses bipartite network analysis. A data reduction framework was implemented to extract the minimal relevant information for the sequence similarity network analysis, where a transformed reference sequence is generated for constructing the driver gene network. This data reduction process combined with the efficiency of the DiWANN network model, greatly lowered the computational cost (in terms of execution time and memory usage) of generating the networks enabling us to work at a much larger scale than previously possible. The DiWANN network helped us identify cancer types in which samples were more closely connected to each other suggesting they are less heterogeneous and potentially susceptible to a common drug. The bipartite network analysis provided insight into gene associations and co-occurrences. We identified genes that were broadly mutated in multiple cancer types and mutations exclusive to only a few. Additionally, weighted one-mode gene projections of the bipartite networks revealed a pattern of occurrence of driver genes in different cancers. Our study demonstrates that network-based approaches can be an effective tool in cancer genomics. The analysis identifies co-occurring and exclusive driver genes and mutations for specific cancer types, providing a better understanding of the driver genes that lead to tumor initiation and evolution.","PeriodicalId":507586,"journal":{"name":"Frontiers in Bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141669653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-16DOI: 10.3389/fbinf.2024.1336135
Fengyuan Huang, Robert S. Welner, Jake Y. Chen, Zongliang Yue
Background: Understanding how cells and tissues respond to stress factors and perturbations during disease processes is crucial for developing effective prevention, diagnosis, and treatment strategies. Single-cell RNA sequencing (scRNA-seq) enables high-resolution identification of cells and exploration of cell heterogeneity, shedding light on cell differentiation/maturation and functional differences. Recent advancements in multimodal sequencing technologies have focused on improving access to cell-specific subgroups for functional genomics analysis. To facilitate the functional annotation of cell groups and characterization of molecular mechanisms underlying cell trajectories, we introduce the Pathways, Annotated Gene Lists, and Gene Signatures Electronic Repository for Single-Cell Functional Genomics Analysis (PAGER-scFGA).Results: We have developed PAGER-scFGA, which integrates cell functional annotations and gene-set enrichment analysis into popular single-cell analysis pipelines such as Scanpy. Using differentially expressed genes (DEGs) from pairwise cell clusters, PAGER-scFGA infers cell functions through the enrichment of potential cell-marker genesets. Moreover, PAGER-scFGA provides pathways, annotated gene lists, and gene signatures (PAGs) enriched in specific cell subsets with tissue compositions and continuous transitions along cell trajectories. Additionally, PAGER-scFGA enables the construction of a gene subcellular map based on DEGs and allows examination of the gene functional compartments (GFCs) underlying cell maturation/differentiation. In a real-world case study of mouse natural killer (mNK) cells, PAGER-scFGA revealed two major stages of natural killer (NK) cells and three trajectories from the precursor stage to NK T-like mature stage within blood, spleen, and bone marrow tissues. As the trajectories progress to later stages, the DEGs exhibit greater divergence and variability. However, the DEGs in different trajectories still interact within a network during NK cell maturation. Notably, PAGER-scFGA unveiled cell cytotoxicity, exocytosis, and the response to interleukin (IL) signaling pathways and associated network models during the progression from precursor NK cells to mature NK cells.Conclusion: PAGER-scFGA enables in-depth exploration of functional insights and presents a comprehensive knowledge map of gene networks and GFCs, which can be utilized for future studies and hypothesis generation. It is expected to become an indispensable tool for inferring cell functions and detecting molecular mechanisms within cell trajectories in single-cell studies. The web app (accessible at https://au-singlecell.streamlit.app/) is publicly available.
背景：了解细胞和组织如何应对疾病过程中的应激因素和干扰，对于制定有效的预防、诊断和治疗策略至关重要。单细胞 RNA 测序（scRNA-seq）能够高分辨率地识别细胞并探索细胞异质性，从而揭示细胞分化/成熟和功能差异。多模态测序技术的最新进展主要集中在改善细胞特异性亚群的获取，以进行功能基因组学分析。为了促进细胞群的功能注释和细胞轨迹的分子机制表征，我们引入了用于单细胞功能基因组学分析的通路、注释基因列表和基因特征电子资源库（PAGER-scFGA）：我们开发了 PAGER-scFGA，它将细胞功能注释和基因组富集分析集成到了 Scanpy 等流行的单细胞分析管道中。PAGER-scFGA利用成对细胞簇的差异表达基因（DEGs），通过富集潜在的细胞标记基因组来推断细胞功能。此外，PAGER-scFGA 还提供了在特定细胞亚群中富集的通路、注释基因列表和基因特征 (PAG)，以及组织组成和细胞轨迹的连续转换。此外，PAGER-scFGA 还能根据 DEGs 构建基因亚细胞图谱，并检查细胞成熟/分化所依赖的基因功能区（GFCs）。在对小鼠自然杀伤（mNK）细胞的实际案例研究中，PAGER-scFGA 揭示了自然杀伤（NK）细胞的两个主要阶段，以及在血液、脾脏和骨髓组织中从前体阶段到类似 NK T 的成熟阶段的三个轨迹。随着轨迹向后期发展，DEGs 表现出更大的差异和变异性。然而，在 NK 细胞成熟过程中，不同轨迹的 DEGs 仍会在一个网络中相互作用。值得注意的是，PAGER-scFGA揭示了从前体NK细胞到成熟NK细胞过程中的细胞毒性、外吞作用和对白细胞介素（IL）信号通路的反应以及相关网络模型：结论：PAGER-scFGA 能够深入探讨功能性见解，并呈现基因网络和 GFC 的全面知识图谱，可用于未来的研究和假设生成。它有望成为单细胞研究中推断细胞功能和检测细胞轨迹中分子机制的不可或缺的工具。该网络应用程序（可通过 https://au-singlecell.streamlit.app/ 访问）已公开发布。
{"title":"PAGER-scFGA: unveiling cell functions and molecular mechanisms in cell trajectories through single-cell functional genomics analysis","authors":"Fengyuan Huang, Robert S. Welner, Jake Y. Chen, Zongliang Yue","doi":"10.3389/fbinf.2024.1336135","DOIUrl":"https://doi.org/10.3389/fbinf.2024.1336135","url":null,"abstract":"Background: Understanding how cells and tissues respond to stress factors and perturbations during disease processes is crucial for developing effective prevention, diagnosis, and treatment strategies. Single-cell RNA sequencing (scRNA-seq) enables high-resolution identification of cells and exploration of cell heterogeneity, shedding light on cell differentiation/maturation and functional differences. Recent advancements in multimodal sequencing technologies have focused on improving access to cell-specific subgroups for functional genomics analysis. To facilitate the functional annotation of cell groups and characterization of molecular mechanisms underlying cell trajectories, we introduce the Pathways, Annotated Gene Lists, and Gene Signatures Electronic Repository for Single-Cell Functional Genomics Analysis (PAGER-scFGA).Results: We have developed PAGER-scFGA, which integrates cell functional annotations and gene-set enrichment analysis into popular single-cell analysis pipelines such as Scanpy. Using differentially expressed genes (DEGs) from pairwise cell clusters, PAGER-scFGA infers cell functions through the enrichment of potential cell-marker genesets. Moreover, PAGER-scFGA provides pathways, annotated gene lists, and gene signatures (PAGs) enriched in specific cell subsets with tissue compositions and continuous transitions along cell trajectories. Additionally, PAGER-scFGA enables the construction of a gene subcellular map based on DEGs and allows examination of the gene functional compartments (GFCs) underlying cell maturation/differentiation. In a real-world case study of mouse natural killer (mNK) cells, PAGER-scFGA revealed two major stages of natural killer (NK) cells and three trajectories from the precursor stage to NK T-like mature stage within blood, spleen, and bone marrow tissues. As the trajectories progress to later stages, the DEGs exhibit greater divergence and variability. However, the DEGs in different trajectories still interact within a network during NK cell maturation. Notably, PAGER-scFGA unveiled cell cytotoxicity, exocytosis, and the response to interleukin (IL) signaling pathways and associated network models during the progression from precursor NK cells to mature NK cells.Conclusion: PAGER-scFGA enables in-depth exploration of functional insights and presents a comprehensive knowledge map of gene networks and GFCs, which can be utilized for future studies and hypothesis generation. It is expected to become an indispensable tool for inferring cell functions and detecting molecular mechanisms within cell trajectories in single-cell studies. The web app (accessible at https://au-singlecell.streamlit.app/) is publicly available.","PeriodicalId":507586,"journal":{"name":"Frontiers in Bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140694896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-11DOI: 10.3389/fbinf.2024.1356659
Hayet Belghit, Mariano Spivak, Manuel Dauchez, Marc Baaden, J. Jonquet-Prevoteau
Advances in simulations, combined with technological developments in high-performance computing, have made it possible to produce a physically accurate dynamic representation of complex biological systems involving millions to billions of atoms over increasingly long simulation times. The analysis of these computed simulations is crucial, involving the interpretation of structural and dynamic data to gain insights into the underlying biological processes. However, this analysis becomes increasingly challenging due to the complexity of the generated systems with a large number of individual runs, ranging from hundreds to thousands of trajectories. This massive increase in raw simulation data creates additional processing and visualization challenges. Effective visualization techniques play a vital role in facilitating the analysis and interpretation of molecular dynamics simulations. In this paper, we focus mainly on the techniques and tools that can be used for visualization of molecular dynamics simulations, among which we highlight the few approaches used specifically for this purpose, discussing their advantages and limitations, and addressing the future challenges of molecular dynamics visualization.
{"title":"From complex data to clear insights: visualizing molecular dynamics trajectories","authors":"Hayet Belghit, Mariano Spivak, Manuel Dauchez, Marc Baaden, J. Jonquet-Prevoteau","doi":"10.3389/fbinf.2024.1356659","DOIUrl":"https://doi.org/10.3389/fbinf.2024.1356659","url":null,"abstract":"Advances in simulations, combined with technological developments in high-performance computing, have made it possible to produce a physically accurate dynamic representation of complex biological systems involving millions to billions of atoms over increasingly long simulation times. The analysis of these computed simulations is crucial, involving the interpretation of structural and dynamic data to gain insights into the underlying biological processes. However, this analysis becomes increasingly challenging due to the complexity of the generated systems with a large number of individual runs, ranging from hundreds to thousands of trajectories. This massive increase in raw simulation data creates additional processing and visualization challenges. Effective visualization techniques play a vital role in facilitating the analysis and interpretation of molecular dynamics simulations. In this paper, we focus mainly on the techniques and tools that can be used for visualization of molecular dynamics simulations, among which we highlight the few approaches used specifically for this purpose, discussing their advantages and limitations, and addressing the future challenges of molecular dynamics visualization.","PeriodicalId":507586,"journal":{"name":"Frontiers in Bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140715799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-08DOI: 10.3389/fbinf.2023.1286883
Szabolcs Éliás, Clemens Wrzodek, Charlotte M. Deane, Alain C. Tissot, Stefan Klostermann, Francesca Ros
Antibodies are generated with great diversity in nature resulting in a set of molecules, each optimized to bind a specific target. Taking advantage of their diversity and specificity, antibodies make up for a large part of recently developed biologic drugs. For therapeutic use antibodies need to fulfill several criteria to be safe and efficient. Polyspecific antibodies can bind structurally unrelated molecules in addition to their main target, which can lead to side effects and decreased efficacy in a therapeutic setting, for example via reduction of effective drug levels. Therefore, we created a neural-network-based model to predict polyspecificity of antibodies using the heavy chain variable region sequence as input. We devised a strategy for enriching antibodies from an immunization campaign either for antigen-specific or polyspecific binding properties, followed by generation of a large sequencing data set for training and cross-validation of the model. We identified important physico-chemical features influencing polyspecificity by investigating the behaviour of this model. This work is a machine-learning-based approach to polyspecificity prediction and, besides increasing our understanding of polyspecificity, it might contribute to therapeutic antibody development.
{"title":"Prediction of polyspecificity from antibody sequence data by machine learning","authors":"Szabolcs Éliás, Clemens Wrzodek, Charlotte M. Deane, Alain C. Tissot, Stefan Klostermann, Francesca Ros","doi":"10.3389/fbinf.2023.1286883","DOIUrl":"https://doi.org/10.3389/fbinf.2023.1286883","url":null,"abstract":"Antibodies are generated with great diversity in nature resulting in a set of molecules, each optimized to bind a specific target. Taking advantage of their diversity and specificity, antibodies make up for a large part of recently developed biologic drugs. For therapeutic use antibodies need to fulfill several criteria to be safe and efficient. Polyspecific antibodies can bind structurally unrelated molecules in addition to their main target, which can lead to side effects and decreased efficacy in a therapeutic setting, for example via reduction of effective drug levels. Therefore, we created a neural-network-based model to predict polyspecificity of antibodies using the heavy chain variable region sequence as input. We devised a strategy for enriching antibodies from an immunization campaign either for antigen-specific or polyspecific binding properties, followed by generation of a large sequencing data set for training and cross-validation of the model. We identified important physico-chemical features influencing polyspecificity by investigating the behaviour of this model. This work is a machine-learning-based approach to polyspecificity prediction and, besides increasing our understanding of polyspecificity, it might contribute to therapeutic antibody development.","PeriodicalId":507586,"journal":{"name":"Frontiers in Bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140731336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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