Frontiers in bioinformatics最新文献_第8页

Reconstructing diploid 3D chromatin structures from single cell Hi-C data with a polymer-based approach 用基于聚合物的方法从单细胞 Hi-C 数据中重建二倍体三维染色质结构

Frontiers in bioinformatics Pub Date : 2023-12-11 DOI: 10.3389/fbinf.2023.1284484

Jan Rothörl, M. Brems, Tim J. Stevens, Peter Virnau

引用次数: 0

Benchmarking software tools for trimming adapters and merging next-generation sequencing data for ancient DNA. 对用于修剪适配体和合并古 DNA 下一代测序数据的软件工具进行基准测试。

Frontiers in bioinformatics Pub Date : 2023-12-07 eCollection Date: 2023-01-01 DOI: 10.3389/fbinf.2023.1260486

Annette Lien, Leonardo Pestana Legori, Louis Kraft, Peter Wad Sackett, Gabriel Renaud

{"title":"Benchmarking software tools for trimming adapters and merging next-generation sequencing data for ancient DNA.","authors":"Annette Lien, Leonardo Pestana Legori, Louis Kraft, Peter Wad Sackett, Gabriel Renaud","doi":"10.3389/fbinf.2023.1260486","DOIUrl":"10.3389/fbinf.2023.1260486","url":null,"abstract":"Ancient DNA is highly degraded, resulting in very short sequences. Reads generated with modern high-throughput sequencing machines are generally longer than ancient DNA molecules, therefore the reads often contain some portion of the sequencing adaptors. It is crucial to remove those adaptors, as they can interfere with downstream analysis. Furthermore, overlapping portions when DNA has been read forward and backward (paired-end) can be merged to correct sequencing errors and improve read quality. Several tools have been developed for adapter trimming and read merging, however, no one has attempted to evaluate their accuracy and evaluate their potential impact on downstream analyses. Through the simulation of sequencing data, seven commonly used tools were analyzed in their ability to reconstruct ancient DNA sequences through read merging. The analyzed tools exhibit notable differences in their abilities to correct sequence errors and identify the correct read overlap, but the most substantial difference is observed in their ability to calculate quality scores for merged bases. Selecting the most appropriate tool for a given project depends on several factors, although some tools such as fastp have some shortcomings, whereas others like leeHom outperform the other tools in most aspects. While the choice of tool did not result in a measurable difference when analyzing population genetics using principal component analysis, it is important to note that downstream analyses that are sensitive to wrongly merged reads or that rely on quality scores can be significantly impacted by the choice of tool.","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"3 ","pages":"1260486"},"PeriodicalIF":0.0,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10733496/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138833352","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}

引用次数: 0

RCSB Protein Data Bank: visualizing groups of experimentally determined PDB structures alongside computed structure models of proteins RCSB 蛋白质数据库：可视化实验确定的 PDB 结构组和计算得出的蛋白质结构模型

Frontiers in bioinformatics Pub Date : 2023-12-04 DOI: 10.3389/fbinf.2023.1311287

J. Segura, Yana Rose, Chunxiao Bi, Jose M. Duarte, Stephen K. Burley, S. Bittrich

{"title":"RCSB Protein Data Bank: visualizing groups of experimentally determined PDB structures alongside computed structure models of proteins","authors":"J. Segura, Yana Rose, Chunxiao Bi, Jose M. Duarte, Stephen K. Burley, S. Bittrich","doi":"10.3389/fbinf.2023.1311287","DOIUrl":"https://doi.org/10.3389/fbinf.2023.1311287","url":null,"abstract":"Recent advances in Artificial Intelligence and Machine Learning (e.g., AlphaFold, RosettaFold, and ESMFold) enable prediction of three-dimensional (3D) protein structures from amino acid sequences alone at accuracies comparable to lower-resolution experimental methods. These tools have been employed to predict structures across entire proteomes and the results of large-scale metagenomic sequence studies, yielding an exponential increase in available biomolecular 3D structural information. Given the enormous volume of this newly computed biostructure data, there is an urgent need for robust tools to manage, search, cluster, and visualize large collections of structures. Equally important is the capability to efficiently summarize and visualize metadata, biological/biochemical annotations, and structural features, particularly when working with vast numbers of protein structures of both experimental origin from the Protein Data Bank (PDB) and computationally-predicted models. Moreover, researchers require advanced visualization techniques that support interactive exploration of multiple sequences and structural alignments. This paper introduces a suite of tools provided on the RCSB PDB research-focused web portal RCSB. org, tailor-made for efficient management, search, organization, and visualization of this burgeoning corpus of 3D macromolecular structure data.","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"6 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138603602","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}

引用次数: 0

Real-time open-source FLIM analysis. 实时开源 FLIM 分析。

Frontiers in bioinformatics Pub Date : 2023-11-30 eCollection Date: 2023-01-01 DOI: 10.3389/fbinf.2023.1286983

Kevin K D Tan, Mark A Tsuchida, Jenu V Chacko, Niklas A Gahm, Kevin W Eliceiri

{"title":"Real-time open-source FLIM analysis.","authors":"Kevin K D Tan, Mark A Tsuchida, Jenu V Chacko, Niklas A Gahm, Kevin W Eliceiri","doi":"10.3389/fbinf.2023.1286983","DOIUrl":"10.3389/fbinf.2023.1286983","url":null,"abstract":"Fluorescence lifetime imaging microscopy (FLIM) provides valuable quantitative insights into fluorophores' chemical microenvironment. Due to long computation times and the lack of accessible, open-source real-time analysis toolkits, traditional analysis of FLIM data, particularly with the widely used time-correlated single-photon counting (TCSPC) approach, typically occurs after acquisition. As a result, uncertainties about the quality of FLIM data persist even after collection, frequently necessitating the extension of imaging sessions. Unfortunately, prolonged sessions not only risk missing important biological events but also cause photobleaching and photodamage. We present the first open-source program designed for real-time FLIM analysis during specimen scanning to address these challenges. Our approach combines acquisition with real-time computational and visualization capabilities, allowing us to assess FLIM data quality on the fly. Our open-source real-time FLIM viewer, integrated as a Napari plugin, displays phasor analysis and rapid lifetime determination (RLD) results computed from real-time data transmitted by acquisition software such as the open-source Micro-Manager-based OpenScan package. Our method facilitates early identification of FLIM signatures and data quality assessment by providing preliminary analysis during acquisition. This not only speeds up the imaging process, but it is especially useful when imaging sensitive live biological samples.","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"3 ","pages":"1286983"},"PeriodicalIF":0.0,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10720713/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138813817","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}

引用次数: 0

Cluster analysis for localisation-based data sets: dos and don'ts when quantifying protein aggregates. 基于定位数据集的聚类分析：量化蛋白质聚集时的注意事项。

Frontiers in bioinformatics Pub Date : 2023-11-24 eCollection Date: 2023-01-01 DOI: 10.3389/fbinf.2023.1237551

Luca Panconi, Dylan M Owen, Juliette Griffié

引用次数: 0

The promises of large language models for protein design and modeling. 大语言模型在蛋白质设计和建模方面的前景。

IF 2.8

Frontiers in bioinformatics Pub Date : 2023-11-23 eCollection Date: 2023-01-01 DOI: 10.3389/fbinf.2023.1304099

Giorgio Valentini, Dario Malchiodi, Jessica Gliozzo, Marco Mesiti, Mauricio Soto-Gomez, Alberto Cabri, Justin Reese, Elena Casiraghi, Peter N Robinson

引用次数: 0

Blob-B-Gone: a lightweight framework for removing blob artifacts from 2D/3D MINFLUX single-particle tracking data. Blob-B-Gone：从二维/三维 MINFLUX 单粒子跟踪数据中去除 Blob 伪影的轻量级框架。

Frontiers in bioinformatics Pub Date : 2023-11-22 eCollection Date: 2023-01-01 DOI: 10.3389/fbinf.2023.1268899

Bela T L Vogler, Francesco Reina, Christian Eggeling

{"title":"Blob-B-Gone: a lightweight framework for removing blob artifacts from 2D/3D MINFLUX single-particle tracking data.","authors":"Bela T L Vogler, Francesco Reina, Christian Eggeling","doi":"10.3389/fbinf.2023.1268899","DOIUrl":"https://doi.org/10.3389/fbinf.2023.1268899","url":null,"abstract":"In this study, we introduce Blob-B-Gone, a lightweight framework to computationally differentiate and eventually remove dense isotropic localization accumulations (blobs) caused by artifactually immobilized particles in MINFLUX single-particle tracking (SPT) measurements. This approach uses purely geometrical features extracted from MINFLUX-detected single-particle trajectories, which are treated as point clouds of localizations. Employing k-means++ clustering, we perform single-shot separation of the feature space to rapidly extract blobs from the dataset without the need for training. We automatically annotate the resulting sub-sets and, finally, evaluate our results by means of principal component analysis (PCA), highlighting a clear separation in the feature space. We demonstrate our approach using two- and three-dimensional simulations of freely diffusing particles and blob artifacts based on parameters extracted from hand-labeled MINFLUX tracking data of fixed 23-nm bead samples and two-dimensional diffusing quantum dots on model lipid membranes. Applying Blob-B-Gone, we achieve a clear distinction between blob-like and other trajectories, represented in F1 scores of 0.998 (2D) and 1.0 (3D) as well as 0.995 (balanced) and 0.994 (imbalanced). This framework can be straightforwardly applied to similar situations, where discerning between blob and elongated time traces is desirable. Given a number of localizations sufficient to express geometric features, the method can operate on any generic point clouds presented to it, regardless of its origin.","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"3 ","pages":"1268899"},"PeriodicalIF":0.0,"publicationDate":"2023-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10704905/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138813815","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}

引用次数: 0

Development of an antimicrobial resistance plasmid transfer gene database for enteric bacteria 肠道细菌耐药质粒转移基因数据库的建立

Frontiers in bioinformatics Pub Date : 2023-11-14 DOI: 10.3389/fbinf.2023.1279359

Suad Algarni, Steven L. Foley, Hailin Tang, Shaohua Zhao, Dereje D. Gudeta, Bijay K. Khajanchi, Steven C. Ricke, Jing Han

{"title":"Development of an antimicrobial resistance plasmid transfer gene database for enteric bacteria","authors":"Suad Algarni, Steven L. Foley, Hailin Tang, Shaohua Zhao, Dereje D. Gudeta, Bijay K. Khajanchi, Steven C. Ricke, Jing Han","doi":"10.3389/fbinf.2023.1279359","DOIUrl":"https://doi.org/10.3389/fbinf.2023.1279359","url":null,"abstract":"Introduction: Type IV secretion systems (T4SSs) are integral parts of the conjugation process in enteric bacteria. These secretion systems are encoded within the transfer ( tra ) regions of plasmids, including those that harbor antimicrobial resistance (AMR) genes. The conjugal transfer of resistance plasmids can lead to the dissemination of AMR among bacterial populations. Methods: To facilitate the analyses of the conjugation-associated genes, transfer related genes associated with key groups of AMR plasmids were identified, extracted from GenBank and used to generate a plasmid transfer gene dataset that is part of the Virulence and Plasmid Transfer Factor Database at FDA, serving as the foundation for computational tools for the comparison of the conjugal transfer genes. To assess the genetic feature of the transfer gene database, genes/proteins of the same name (e.g., traI/ TraI) or predicted function (VirD4 ATPase homologs) were compared across the different plasmid types to assess sequence diversity. Two analyses tools, the Plasmid Transfer Factor Profile Assessment and Plasmid Transfer Factor Comparison tools, were developed to evaluate the transfer genes located on plasmids and to facilitate the comparison of plasmids from multiple sequence files. To assess the database and associated tools, plasmid, and whole genome sequencing (WGS) data were extracted from GenBank and previous WGS experiments in our lab and assessed using the analysis tools. Results: Overall, the plasmid transfer database and associated tools proved to be very useful for evaluating the different plasmid types, their association with T4SSs, and increased our understanding how conjugative plasmids contribute to the dissemination of AMR genes.","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"51 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134902965","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}

引用次数: 0

Editorial: Identification of phenotypically important genomic variants. 社论:表型上重要的基因组变异的鉴定。

Frontiers in bioinformatics Pub Date : 2023-11-10 eCollection Date: 2023-01-01 DOI: 10.3389/fbinf.2023.1328945

Elizabeth A Heron, Giorgio Valle, Anna Bernasconi

引用次数: 0

An algorithm for drug discovery based on deep learning with an example of developing a drug for the treatment of lung cancer 基于深度学习的药物发现算法，并以开发治疗肺癌的药物为例

Frontiers in bioinformatics Pub Date : 2023-11-09 DOI: 10.3389/fbinf.2023.1225149

Dmitrii K. Chebanov, Vsevolod A. Misyurin, Irina Zh. Shubina

{"title":"An algorithm for drug discovery based on deep learning with an example of developing a drug for the treatment of lung cancer","authors":"Dmitrii K. Chebanov, Vsevolod A. Misyurin, Irina Zh. Shubina","doi":"10.3389/fbinf.2023.1225149","DOIUrl":"https://doi.org/10.3389/fbinf.2023.1225149","url":null,"abstract":"In this study, we present an algorithmic framework integrated within the created software platform tailored for the discovery of novel small-molecule anti-tumor agents. Our approach was exemplified in the context of combatting lung cancer. In the initial phase, target identification for therapeutic intervention was accomplished. Leveraging deep learning, we scrutinized gene expression profiles, focusing on those associated with adverse clinical outcomes in lung cancer patients. Augmenting this, generative adversarial neural (GAN) networks were employed to amass additional patient data. This effort yielded a subset of genes definitively linked to unfavorable prognoses. We further employed deep learning to delineate genes capable of discriminating between normal and tumor tissues based on expression patterns. The remaining genes were earmarked as potential targets for precision lung cancer therapy. Subsequently, a dedicated module was formulated to predict the interactions between inhibitors and proteins. To achieve this, protein amino acid sequences and chemical compound formulations engaged in protein interactions were encoded into vectorized representations. Additionally, a deep learning-based component was developed to forecast IC 50 values through experimentation on cell lines. Virtual pre-clinical trials employing these inhibitors facilitated the selection of pertinent cell lines for subsequent laboratory assays. In summary, our study culminated in the derivation of several small-molecule formulas projected to bind selectively to specific proteins. This algorithmic platform holds promise in accelerating the identification and design of anti-tumor compounds, a critical pursuit in advancing targeted cancer therapies.","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":" 24","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135292756","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}

引用次数: 0