Frontiers in bioinformatics最新文献

{"title":"Molecular docking and molecular dynamic simulation studies to identify potential terpenes against Internalin A protein of <i>Listeria monocytogenes</i>.","authors":"Deepasree K, Subhashree Venugopal","doi":"10.3389/fbinf.2024.1463750","DOIUrl":"10.3389/fbinf.2024.1463750","url":null,"abstract":"<p><strong>Introduction: </strong>Ever since the outbreak of listeriosis and other related illnesses caused by the dreadful pathogen <i>Listeria monocytogenes</i>, the lives of immunocompromised individuals have been at risk.</p><p><strong>Objectives and methods: </strong>The main goal of this study is to comprehend the potential of terpenes, a major class of secondary metabolites in inhibiting one of the disease-causing protein Internalin A (InlA) of the pathogen via <i>in silico</i> approaches.</p><p><strong>Results: </strong>The best binding affinity value of -9.5 kcal/mol was observed for Bipinnatin and Epispongiadiol according to the molecular docking studies. The compounds were further subjected to ADMET and biological activity estimation which confirmed their good pharmacokinetic properties and antibacterial activity.</p><p><strong>Discussion: </strong>Molecular dynamic simulation for a timescale of 100 ns finally revealed Epispongiadiol to be a promising drug-like compound that could possibly pave the way to the treatment of this disease.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"4 ","pages":"1463750"},"PeriodicalIF":2.8,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11412924/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142302476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PhIP-Seq: methods, applications and challenges.","authors":"Ziru Huang, Samarappuli Mudiyanselage Savini Gunarathne, Wenwen Liu, Yuwei Zhou, Yuqing Jiang, Shiqi Li, Jian Huang","doi":"10.3389/fbinf.2024.1424202","DOIUrl":"https://doi.org/10.3389/fbinf.2024.1424202","url":null,"abstract":"<p><p>Phage-immunoprecipitation sequencing (PhIP-Seq) technology is an innovative, high-throughput antibody detection method. It enables comprehensive analysis of individual antibody profiles. This technology shows great potential, particularly in exploring disease mechanisms and immune responses. Currently, PhIP-Seq has been successfully applied in various fields, such as the exploration of biomarkers for autoimmune diseases, vaccine development, and allergen detection. A variety of bioinformatics tools have facilitated the development of this process. However, PhIP-Seq technology still faces many challenges and has room for improvement. Here, we review the methods, applications, and challenges of PhIP-Seq and discuss its future directions in immunological research and clinical applications. With continuous progress and optimization, PhIP-Seq is expected to play an even more important role in future biomedical research, providing new ideas and methods for disease prevention, diagnosis, and treatment.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"4 ","pages":"1424202"},"PeriodicalIF":2.8,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11408297/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142302500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rvisdiff: An R package for interactive visualization of differential expression.","authors":"David Barrios, Carlos Prieto","doi":"10.3389/fbinf.2024.1349205","DOIUrl":"https://doi.org/10.3389/fbinf.2024.1349205","url":null,"abstract":"<p><p>Rvisdiff is an R/Bioconductor package that generates an interactive interface for the interpretation of differential expression results. It creates a local web page that enables the exploration of statistical analysis results through the generation of auto-analytical visualizations. Users can explore the differential expression results and the source expression data interactively in the same view. As input, the package supports the results of popular differential expression packages such as DESeq2, edgeR, and limma. As output, the package generates a local HTML page that can be easily viewed in a web browser. Rvisdiff is freely available at https://bioconductor.org/packages/Rvisdiff/.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"4 ","pages":"1349205"},"PeriodicalIF":2.8,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11402892/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142302501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"<i>Rhizobium etli</i> CFN42 and <i>Sinorhizobium meliloti</i> 1021 bioinformatic transcriptional regulatory networks from culture and symbiosis.","authors":"Hermenegildo Taboada-Castro, Alfredo José Hernández-Álvarez, Juan Miguel Escorcia-Rodríguez, Julio Augusto Freyre-González, Edgardo Galán-Vásquez, Sergio Encarnación-Guevara","doi":"10.3389/fbinf.2024.1419274","DOIUrl":"https://doi.org/10.3389/fbinf.2024.1419274","url":null,"abstract":"<p><p><i>Rhizobium etli</i> CFN42 proteome-transcriptome mixed data of exponential growth and nitrogen-fixing bacteroids, as well as <i>Sinorhizobium meliloti</i> 1021 transcriptome data of growth and nitrogen-fixing bacteroids, were integrated into transcriptional regulatory networks (TRNs). The one-step construction network consisted of a matrix-clustering analysis of matrices of the gene profile and all matrices of the transcription factors (TFs) of their genome. The networks were constructed with the prediction of regulatory network application of the RhizoBindingSites database (http://rhizobindingsites.ccg.unam.mx/). The deduced free-living <i>Rhizobium etli</i> network contained 1,146 genes, including 380 TFs and 12 sigma factors. In addition, the bacteroid <i>R. etli</i> CFN42 network contained 884 genes, where 364 were TFs, and 12 were sigma factors, whereas the deduced free-living <i>Sinorhizobium meliloti</i> 1021 network contained 643 genes, where 259 were TFs and seven were sigma factors, and the bacteroid <i>Sinorhizobium meliloti</i> 1021 network contained 357 genes, where 210 were TFs and six were sigma factors. The similarity of these deduced condition-dependent networks and the biological <i>E. coli</i> and <i>B. subtilis</i> independent condition networks segregates from the random Erdös-Rényi networks. Deduced networks showed a low average clustering coefficient. They were not scale-free, showing a gradually diminishing hierarchy of TFs in contrast to the hierarchy role of the sigma factor <i>rpoD</i> in the <i>E. coli</i> K12 network. For rhizobia networks, partitioning the genome in the chromosome, chromids, and plasmids, where essential genes are distributed, and the symbiotic ability that is mostly coded in plasmids, may alter the structure of these deduced condition-dependent networks. It provides potential TF gen-target relationship data for constructing regulons, which are the basic units of a TRN.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"4 ","pages":"1419274"},"PeriodicalIF":2.8,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11387232/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142302475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design principles for molecular animation.","authors":"Stuart G Jantzen, Gaël McGill, Jodie Jenkinson","doi":"10.3389/fbinf.2024.1353807","DOIUrl":"10.3389/fbinf.2024.1353807","url":null,"abstract":"<p><p>Molecular visualization is a powerful way to represent the complex structure of molecules and their higher order assemblies, as well as the dynamics of their interactions. Although conventions for depicting static molecular structures and complexes are now well established and guide the viewer's attention to specific aspects of structure and function, little attention and design classification has been devoted to how molecular motion is depicted. As we continue to probe and discover how molecules move - including their internal flexibility, conformational changes and dynamic associations with binding partners and environments - we are faced with difficult design challenges that are relevant to molecular visualizations both for the scientific community and students of cell and molecular biology. To facilitate these design decisions, we have identified twelve molecular animation design principles that are important to consider when creating molecular animations. Many of these principles pertain to misconceptions that students have primarily regarding the agency of molecules, while others are derived from visual treatments frequently observed in molecular animations that may promote misconceptions. For each principle, we have created a pair of molecular animations that exemplify the principle by depicting the same content in the presence and absence of that design approach. Although not intended to be prescriptive, we hope this set of design principles can be used by the scientific, education, and scientific visualization communities to facilitate and improve the pedagogical effectiveness of molecular animation.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"4 ","pages":"1353807"},"PeriodicalIF":2.8,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11371733/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142134659","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A layout framework for genome-wide multiple sequence alignment graphs.","authors":"Jeremias Schebera, Dirk Zeckzer, Daniel Wiegreffe","doi":"10.3389/fbinf.2024.1358374","DOIUrl":"10.3389/fbinf.2024.1358374","url":null,"abstract":"<p><p>Sequence alignments are often used to analyze genomic data. However, such alignments are often only calculated and compared on small sequence intervals for analysis purposes. When comparing longer sequences, these are usually divided into shorter sequence intervals for better alignment results. This usually means that the order context of the original sequence is lost. To prevent this, it is possible to use a graph structure to represent the order of the original sequence on the alignment blocks. The visualization of these graph structures can provide insights into the structural variations of genomes in a semi-global context. In this paper, we propose a new graph drawing framework for representing gMSA data. We produce a hierarchical graph layout that supports the comparative analysis of genomes. Based on a reference, the differences and similarities of the different genome orders are visualized. In this work, we present a complete graph drawing framework for gMSA graphs together with the respective algorithms for each of the steps. Additionally, we provide a prototype and an example data set for analyzing gMSA graphs. Based on this data set, we demonstrate the functionalities of the framework using two examples.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"4 ","pages":"1358374"},"PeriodicalIF":2.8,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11362851/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142115616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A hybrid approach for predicting transcription factors.","authors":"Sumeet Patiyal, Palak Tiwari, Mohit Ghai, Aman Dhapola, Anjali Dhall, Gajendra P S Raghava","doi":"10.3389/fbinf.2024.1425419","DOIUrl":"10.3389/fbinf.2024.1425419","url":null,"abstract":"<p><p>Transcription factors are essential DNA-binding proteins that regulate the transcription rate of several genes and control the expression of genes inside a cell. The prediction of transcription factors with high precision is important for understanding biological processes such as cell differentiation, intracellular signaling, and cell-cycle control. In this study, we developed a hybrid method that combines alignment-based and alignment-free methods for predicting transcription factors with higher accuracy. All models have been trained, tested, and evaluated on a large dataset that contains 19,406 transcription factors and 523,560 non-transcription factor protein sequences. To avoid biases in evaluation, the datasets were divided into training and validation/independent datasets, where 80% of the data was used for training, and the remaining 20% was used for external validation. In the case of alignment-free methods, models were developed using machine learning techniques and the composition-based features of a protein. Our best alignment-free model obtained an AUC of 0.97 on an independent dataset. In the case of the alignment-based method, we used BLAST at different cut-offs to predict the transcription factors. Although the alignment-based method demonstrated excellent performance, it was unable to cover all transcription factors due to instances of no hits. To combine the strengths of both methods, we developed a hybrid method that combines alignment-free and alignment-based methods. In the hybrid method, we added the scores of the alignment-free and alignment-based methods and achieved a maximum AUC of 0.99 on the independent dataset. The method proposed in this study performs better than existing methods. We incorporated the best models in the webserver/Python Package Index/standalone package of \"TransFacPred\" (https://webs.iiitd.edu.in/raghava/transfacpred).</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"4 ","pages":"1425419"},"PeriodicalIF":2.8,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11306938/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141908534","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AUTO-TUNE: selecting the distance threshold for inferring HIV transmission clusters.","authors":"Steven Weaver, Vanessa M Dávila Conn, Daniel Ji, Hannah Verdonk, Santiago Ávila-Ríos, Andrew J Leigh Brown, Joel O Wertheim, Sergei L Kosakovsky Pond","doi":"10.3389/fbinf.2024.1400003","DOIUrl":"10.3389/fbinf.2024.1400003","url":null,"abstract":"<p><p>Molecular surveillance of viral pathogens and inference of transmission networks from genomic data play an increasingly important role in public health efforts, especially for HIV-1. For many methods, the genetic distance threshold used to connect sequences in the transmission network is a key parameter informing the properties of inferred networks. Using a distance threshold that is too high can result in a network with many spurious links, making it difficult to interpret. Conversely, a distance threshold that is too low can result in a network with too few links, which may not capture key insights into clusters of public health concern. Published research using the HIV-TRACE software package frequently uses the default threshold of 0.015 substitutions/site for HIV pol gene sequences, but in many cases, investigators heuristically select other threshold parameters to better capture the underlying dynamics of the epidemic they are studying. Here, we present a general heuristic scoring approach for tuning a distance threshold adaptively, which seeks to prevent the formation of giant clusters. We prioritize the ratio of the sizes of the largest and the second largest cluster, maximizing the number of clusters present in the network. We apply our scoring heuristic to outbreaks with different characteristics, such as regional or temporal variability, and demonstrate the utility of using the scoring mechanism's suggested distance threshold to identify clusters exhibiting risk factors that would have otherwise been more difficult to identify. For example, while we found that a 0.015 substitutions/site distance threshold is typical for US-like epidemics, recent outbreaks like the CRF07_BC subtype among men who have sex with men (MSM) in China have been found to have a lower optimal threshold of 0.005 to better capture the transition from injected drug use (IDU) to MSM as the primary risk factor. Alternatively, in communities surrounding Lake Victoria in Uganda, where there has been sustained heterosexual transmission for many years, we found that a larger distance threshold is necessary to capture a more risk factor-diverse population with sparse sampling over a longer period of time. Such identification may allow for more informed intervention action by respective public health officials.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"4 ","pages":"1400003"},"PeriodicalIF":2.8,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11289888/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141861844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A systematic overview of single-cell transcriptomics databases, their use cases, and limitations.","authors":"Mahnoor N Gondal, Saad Ur Rehman Shah, Arul M Chinnaiyan, Marcin Cieslik","doi":"10.3389/fbinf.2024.1417428","DOIUrl":"10.3389/fbinf.2024.1417428","url":null,"abstract":"<p><p>Rapid advancements in high-throughput single-cell RNA-seq (scRNA-seq) technologies and experimental protocols have led to the generation of vast amounts of transcriptomic data that populates several online databases and repositories. Here, we systematically examined large-scale scRNA-seq databases, categorizing them based on their scope and purpose such as general, tissue-specific databases, disease-specific databases, cancer-focused databases, and cell type-focused databases. Next, we discuss the technical and methodological challenges associated with curating large-scale scRNA-seq databases, along with current computational solutions. We argue that understanding scRNA-seq databases, including their limitations and assumptions, is crucial for effectively utilizing this data to make robust discoveries and identify novel biological insights. Such platforms can help bridge the gap between computational and wet lab scientists through user-friendly web-based interfaces needed for democratizing access to single-cell data. These platforms would facilitate interdisciplinary research, enabling researchers from various disciplines to collaborate effectively. This review underscores the importance of leveraging computational approaches to unravel the complexities of single-cell data and offers a promising direction for future research in the field.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"4 ","pages":"1417428"},"PeriodicalIF":2.8,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11260681/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141749912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DNA structural features and variability of complete MHC locus sequences.","authors":"Trudy M Wassenaar, Terry Harville, Jonathan Chastain, Visanu Wanchai, David W Ussery","doi":"10.3389/fbinf.2024.1392613","DOIUrl":"10.3389/fbinf.2024.1392613","url":null,"abstract":"<p><p>The major histocompatibility (MHC) locus, also known as the Human Leukocyte Antigen (HLA) genes, is located on the short arm of chromosome 6, and contains three regions (Class I, Class II and Class III). This 5 Mbp locus is one of the most variable regions of the human genome, yet it also encodes a set of highly conserved and important proteins related to immunological response. Genetic variations in this region are responsible for more diseases than in the entire rest of the human genome. However, information on local structural features of the DNA is largely ignored. With recent advances in long-read sequencing technology, it is now becoming possible to sequence the entire 5 Mbp MHC locus, producing complete diploid haplotypes of the whole region. Here, we describe structural maps based on the complete sequences from six different homozygous HLA cell lines. We find long-range structural variability in the different sequences for DNA stacking energy, position preference and curvature, variation in repeats, as well as more local changes in regions forming open chromatin structures, likely to influence gene expression levels. These structural maps can be useful in visualizing large scale structural variation across HLA types, in particular when this can be complemented with epigenetic signals.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"4 ","pages":"1392613"},"PeriodicalIF":2.8,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11251971/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141636053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}