Journal of Computational Biology最新文献_第6页

Transcriptional Hubs Within Cliques in Ensemble Hi-C Chromatin Interaction Networks. Hi-C 染色质相互作用网络中小群内的转录枢纽

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2024-06-01 Epub Date: 2024-05-20 DOI: 10.1089/cmb.2024.0515

Gatis Melkus, Andrejs Sizovs, Peteris Rucevskis, Sandra Silina

{"title":"Transcriptional Hubs Within Cliques in Ensemble Hi-C Chromatin Interaction Networks.","authors":"Gatis Melkus, Andrejs Sizovs, Peteris Rucevskis, Sandra Silina","doi":"10.1089/cmb.2024.0515","DOIUrl":"10.1089/cmb.2024.0515","url":null,"abstract":"\u0000 Chromatin conformation capture technologies permit the study of chromatin spatial organization on a genome-wide scale at a variety of resolutions. Despite the increasing precision and resolution of high-throughput chromatin conformation capture (Hi-C) methods, it remains challenging to conclusively link transcriptional activity to spatial organizational phenomena. We have developed a clique-based approach for analyzing Hi-C data that helps identify chromosomal hotspots that feature considerable enrichment of chromatin annotations for transcriptional start sites and, building on previously published work, show that these chromosomal hotspots are not only significantly enriched in RNA polymerase II binding sites as identified by the ENCODE project, but also identify a noticeable increase in FANTOM5 and GTEx transcription within our identified cliques across a variety of tissue types. From the obtained data, we surmise that our cliques are a suitable method for identifying transcription factories in Hi-C data, and outline further extensions to the method that may make it useful for locating regions of increased transcriptional activity in datasets where in-depth expression or polymerase data may not be available.\u0000 ","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"589-596"},"PeriodicalIF":1.4,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141071020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhanced Compression of k-Mer Sets with Counters via de Bruijn Graphs. 通过 de Bruijn 图增强带有计数器的 k-Mer 集的压缩。

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2024-06-01 Epub Date: 2024-05-31 DOI: 10.1089/cmb.2024.0530

Enrico Rossignolo, Matteo Comin

引用次数: 0

Improved Denoising of Cryo-Electron Microscopy Micrographs with Simulation-Aware Pretraining. 利用仿真预训练改进冷冻电镜显微照片的去噪效果

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2024-06-01 Epub Date: 2024-05-28 DOI: 10.1089/cmb.2024.0513

Zhidong Yang, Hongjia Li, Dawei Zang, Renmin Han, Fa Zhang

{"title":"Improved Denoising of Cryo-Electron Microscopy Micrographs with Simulation-Aware Pretraining.","authors":"Zhidong Yang, Hongjia Li, Dawei Zang, Renmin Han, Fa Zhang","doi":"10.1089/cmb.2024.0513","DOIUrl":"10.1089/cmb.2024.0513","url":null,"abstract":"Cryo-electron microscopy (cryo-EM) has emerged as a potent technique for determining the structure and functionality of biological macromolecules. However, limited by the physical imaging conditions, such as low electron beam dose, micrographs in cryo-EM typically contend with an extremely low signal-to-noise ratio (SNR), impeding the efficiency and efficacy of subsequent analyses. Therefore, there is a growing demand for an efficient denoising algorithm designed for cryo-EM micrographs, aiming to enhance the quality of macromolecular analysis. However, owing to the absence of a comprehensive and well-defined dataset with ground truth images, supervised image denoising methods exhibit limited generalization when applied to experimental micrographs. To tackle this challenge, we introduce a simulation-aware image denoising (SaID) pretrained model designed to enhance the SNR of cryo-EM micrographs where the training is solely based on an accurately simulated dataset. First, we propose a parameter calibration algorithm for simulated dataset generation, aiming to align simulation parameters with those of experimental micrographs. Second, leveraging the accurately simulated dataset, we propose to train a deep general denoising model that can well generalize to real experimental cryo-EM micrographs. Comprehensive experimental results demonstrate that our pretrained denoising model achieves excellent denoising performance on experimental cryo-EM micrographs, significantly streamlining downstream analysis.","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"564-575"},"PeriodicalIF":1.4,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141161121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Boolean Network Models of Human Preimplantation Development. 人类胚胎植入前发育的布尔网络模型

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2024-06-01 Epub Date: 2024-05-29 DOI: 10.1089/cmb.2024.0517

Mathieu Bolteau, Lokmane Chebouba, Laurent David, Jérémie Bourdon, Carito Guziolowski

{"title":"Boolean Network Models of Human Preimplantation Development.","authors":"Mathieu Bolteau, Lokmane Chebouba, Laurent David, Jérémie Bourdon, Carito Guziolowski","doi":"10.1089/cmb.2024.0517","DOIUrl":"10.1089/cmb.2024.0517","url":null,"abstract":"\u0000 Single-cell transcriptomic studies of differentiating systems allow meaningful understanding, especially in human embryonic development and cell fate determination. We present an innovative method aimed at modeling these intricate processes by leveraging scRNAseq data from various human developmental stages. Our implemented method identifies pseudo-perturbations, since actual perturbations are unavailable due to ethical and technical constraints. By integrating these pseudo-perturbations with prior knowledge of gene interactions, our framework generates stage-specific Boolean networks (BNs). We apply our method to medium and late trophectoderm developmental stages and identify 20 pseudo-perturbations required to infer BNs. The resulting BN families delineate distinct regulatory mechanisms, enabling the differentiation between these developmental stages. We show that our program outperforms existing pseudo-perturbation identification tool. Our framework contributes to comprehending human developmental processes and holds potential applicability to diverse developmental stages and other research scenarios.\u0000 ","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"513-523"},"PeriodicalIF":1.4,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141179790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Visual Recalibration and Gating Enhancement Network for Radiology Report Generation. 用于生成放射报告的视觉重新校准和选通增强网络。

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2024-06-01 Epub Date: 2024-06-05 DOI: 10.1089/cmb.2024.0514

Xiaodi Hou, Guoming Sang, Zhi Liu, Xiaobo Li, Yijia Zhang

引用次数: 0

A Branch-and-Bound Algorithm for the Molecular Ordered Covering Problem. 分子有序覆盖问题的分支与边界算法

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2024-06-01 Epub Date: 2024-05-22 DOI: 10.1089/cmb.2024.0522

Michael Souza, Nilton Maia, Rômulo S Marques, Carlile Lavor

{"title":"A Branch-and-Bound Algorithm for the Molecular Ordered Covering Problem.","authors":"Michael Souza, Nilton Maia, Rômulo S Marques, Carlile Lavor","doi":"10.1089/cmb.2024.0522","DOIUrl":"10.1089/cmb.2024.0522","url":null,"abstract":"The Discretizable Molecular Distance Geometry Problem (DMDGP) plays a key role in the construction of three-dimensional molecular structures from interatomic distances acquired through nuclear magnetic resonance (NMR) spectroscopy, with the primary objective of validating a sequence of distance constraints related to NMR data. This article addresses the escalating complexity of the DMDGP encountered with larger and more flexible molecules by introducing a novel strategy via the Molecular Ordered Covering Problem, which optimizes the ordering of distance constraints to improve computational efficiency in DMDGP resolution. This approach utilizes a specialized Branch-and-Bound (BB) algorithm, tested on both synthetic and actual protein structures from the protein data bank. Our analysis demonstrates the efficacy of the previously proposed greedy heuristic in managing complex molecular scenarios, highlighting the BB algorithm's utility as a validation mechanism. This research contributes to ongoing efforts in molecular structure analysis, with possible implications for areas such as protein folding, drug design, and molecular modeling.","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"475-485"},"PeriodicalIF":1.4,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141076140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Special Issue, Part I 19th International Symposium on Bioinformatics Research and Applications (ISBRA 2023). 特刊，第 I 部分第 19 届生物信息学研究与应用国际研讨会（ISBRA 2023）。

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2024-06-01 Epub Date: 2024-06-13 DOI: 10.1089/cmb.2024.0636

Murray Patterson

引用次数: 0

Phylogenetic and Chemical Probing Information as Soft Constraints in RNA Secondary Structure Prediction. 系统发育和化学探测信息作为 RNA 二级结构预测的软约束。

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2024-06-01 DOI: 10.1089/cmb.2024.0519

Sarah von Löhneysen, Thomas Spicher, Yuliia Varenyk, Hua-Ting Yao, Ronny Lorenz, Ivo Hofacker, Peter F Stadler

{"title":"Phylogenetic and Chemical Probing Information as Soft Constraints in RNA Secondary Structure Prediction.","authors":"Sarah von Löhneysen, Thomas Spicher, Yuliia Varenyk, Hua-Ting Yao, Ronny Lorenz, Ivo Hofacker, Peter F Stadler","doi":"10.1089/cmb.2024.0519","DOIUrl":"https://doi.org/10.1089/cmb.2024.0519","url":null,"abstract":"Extrinsic, experimental information can be incorporated into thermodynamics-based RNA folding algorithms in the form of pseudo-energies. Evolutionary conservation of RNA secondary structure elements is detectable in alignments of phylogenetically related sequences and provides evidence for the presence of certain base pairs that can also be converted into pseudo-energy contributions. We show that the centroid base pairs computed from a consensus folding model such as RNAalifold result in a substantial improvement of the prediction accuracy for single sequences. Evidence for specific base pairs turns out to be more informative than a position-wise profile for the conservation of the pairing status. A comparison with chemical probing data, furthermore, strongly suggests that phylogenetic base pairing data are more informative than position-specific data on (un)pairedness as obtained from chemical probing experiments. In this context we demonstrate, in addition, that the conversion of signal from probing data into pseudo-energies is possible using thermodynamic structure predictions as a reference instead of known RNA structures.","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":"31 6","pages":"549-563"},"PeriodicalIF":1.4,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141468464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Comparative Study of Gene Co-Expression Thresholding Algorithms. 基因共表达阈值算法比较研究

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2024-06-01 Epub Date: 2024-05-23 DOI: 10.1089/cmb.2024.0509

Carissa Bleker, Stephen K Grady, Michael A Langston

引用次数: 0

A Fusion Learning Model Based on Deep Learning for Single-Cell RNA Sequencing Data Clustering. 基于深度学习的单细胞 RNA 测序数据聚类融合学习模型。

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2024-06-01 Epub Date: 2024-05-20 DOI: 10.1089/cmb.2024.0512

Tian-Jing Qiao, Feng Li, Sha-Sha Yuan, Ling-Yun Dai, Juan Wang

{"title":"A Fusion Learning Model Based on Deep Learning for Single-Cell RNA Sequencing Data Clustering.","authors":"Tian-Jing Qiao, Feng Li, Sha-Sha Yuan, Ling-Yun Dai, Juan Wang","doi":"10.1089/cmb.2024.0512","DOIUrl":"10.1089/cmb.2024.0512","url":null,"abstract":"\u0000 Single-cell RNA sequencing (scRNA-seq) technology provides a means for studying biology from a cellular perspective. The fundamental goal of scRNA-seq data analysis is to discriminate single-cell types using unsupervised clustering. Few single-cell clustering algorithms have taken into account both deep and surface information, despite the recent slew of suggestions. Consequently, this article constructs a fusion learning framework based on deep learning, namely scGASI. For learning a clustering similarity matrix, scGASI integrates data affinity recovery and deep feature embedding in a unified scheme based on various top feature sets. Next, scGASI learns the low-dimensional latent representation underlying the data using a graph autoencoder to mine the hidden information residing in the data. To efficiently merge the surface information from raw area and the deeper potential information from underlying area, we then construct a fusion learning model based on self-expression. scGASI uses this fusion learning model to learn the similarity matrix of an individual feature set as well as the clustering similarity matrix of all feature sets. Lastly, gene marker identification, visualization, and clustering are accomplished using the clustering similarity matrix. Extensive verification on actual data sets demonstrates that scGASI outperforms many widely used clustering techniques in terms of clustering accuracy.\u0000 ","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"576-588"},"PeriodicalIF":1.4,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140957730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0