Bioinformatics research and applications : ... international symposium, ISBRA ... proceedings. ISBRA (Conference)最新文献

{"title":"Gene Regulatory Network Inference from Pseudotime-Ordered scRNA-seq Data via Time-Lagged Divergence Measures.","authors":"Lingling Zhang, Tong Si, Lucas Koch, Haijun Gong","doi":"10.1145/3774976.3774995","DOIUrl":"10.1145/3774976.3774995","url":null,"abstract":"<p><p>Inferring cell type-specific gene regulatory networks (GRNs) from time-series single-cell RNA sequencing (scRNA-seq) data is challenging due to sparse temporal resolution, high dimensionality, and inherent cellular heterogeneity. We present a novel integrative framework, called PseudoGRN, that unifies multiple pseudotime inference methods, different time-lagged divergence measures, non-redundant penalized network inference, and partial correlation analysis to reconstruct directed GRNs from time-series scRNA-seq data. Applying our method to the real-world scRNA-seq dataset, we demonstrate its superior performance over existing approaches, offering a robust and interpretable tool for uncovering dynamic regulatory mechanisms in single-cell systems.</p>","PeriodicalId":93167,"journal":{"name":"Bioinformatics research and applications : ... international symposium, ISBRA ... proceedings. ISBRA (Conference)","volume":"2025 ","pages":"111-115"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12756061/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145901780","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 Data Set of Paired Structural Segments Between Protein Data Bank and AlphaFold DB for Medium-Resolution Cryo-EM Density Maps: A Gap in Overall Structural Quality.","authors":"Thu Nguyen, Willy Wriggers, Jing He","doi":"10.1007/978-981-97-5087-0_5","DOIUrl":"10.1007/978-981-97-5087-0_5","url":null,"abstract":"<p><p>The recent publication of the AlphaFold Protein Structure Database (AlphaFold DB) offers an opportunity to revisit and refine many existing structural models in computational biology. We tested the hypothesis that models determined from less-than-ideal cryo-electron microscopy (cryo-EM) data could benefit from more recent AlphaFold predictions. Ideally, atomic structures in the Protein Data Bank (PDB) are directly solved from experimental densities with a resolution higher than 4 Å. However, medium-resolution (5-10 Å) cryo-EM maps are also increasingly deposited in the Electron Microscopy Data Bank (EMDB), many of which have associated atomic models of varying quality. It is difficult to solve atomic structures from such medium-resolution maps directly, so modeling approaches often rely on the indirect fitting of known templates. Therefore, we hypothesize that early structural interpretations of medium-resolution cryo-EM maps in the EMDB could benefit from potentially more reliable AlphaFold models derived later after more structural templates become available in the PDB. To study the utility of AlphaFold-predicted models, we conducted systematic mapping between the PDB and AlphaFold DB for structures derived from medium-resolution cryo-EM density maps. A dataset of 918 nonredundant pairs of structural segments was established. Using MolProbity, a structural validation method, we observed a significant difference in the distributions of MolProbity scores between paired structural segments in the PDB and AlphaFold DB. The structural segments in the AlphaFold DB exhibit a unimodal distribution, with an average of 0.96 MolProbity score, which is better than the average (1.98) of their corresponding segments in the PDB. The MolProbity scores of structural segments in the PDB vary significantly more than those in the AlphaFold DB and exhibit a bimodal distribution with a longer tail, indicating a wider range of model quality owing to the diverse structure fitting and refinement strategies used in the past.</p>","PeriodicalId":93167,"journal":{"name":"Bioinformatics research and applications : ... international symposium, ISBRA ... proceedings. ISBRA (Conference)","volume":"14956 ","pages":"52-63"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12774667/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145936675","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":"SEMQuant: Extending Sipros-Ensemble with Match-Between-Runs for Comprehensive Quantitative Metaproteomics.","authors":"Bailu Zhang, Shichao Feng, Manushi Parajuli, Yi Xiong, Chongle Pan, Xuan Guo","doi":"10.1007/978-981-97-5087-0_9","DOIUrl":"10.1007/978-981-97-5087-0_9","url":null,"abstract":"<p><p>Metaproteomics, utilizing high-throughput LC-MS, offers a profound understanding of microbial communities. Quantitative metaproteomics further enriches this understanding by measuring relative protein abundance and revealing dynamic changes under different conditions. However, the challenge of missing peptide quantification persists in metaproteomics analysis, particularly in data-dependent acquisition mode, where high-intensity precursors for MS2 scans are selected. To tackle this issue, the match-between-runs (MBR) technique is used to transfer peptides between LC-MS runs. Inspired by the benefits of MBR and the need for streamlined metaproteomics data analysis, we developed SEMQuant, an end-to-end software integrating Sipros-Ensemble's robust peptide identifications with IonQuant's MBR function. The experiments show that SEMQuant consistently obtains the highest or second highest number of quantified proteins with notable precision and accuracy. This demonstrates SEMQuant's effectiveness in conducting comprehensive and accurate quantitative metaproteomics analyses across diverse datasets and highlights its potential to propel advancements in microbial community studies. SEMQuant is freely available under the GNU GPL license at https://github.com/Biocomputing-Research-Group/SEMQuant.</p>","PeriodicalId":93167,"journal":{"name":"Bioinformatics research and applications : ... international symposium, ISBRA ... proceedings. ISBRA (Conference)","volume":"14956 ","pages":"102-115"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11507799/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142514466","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":"Dilated-DenseNet For Macromolecule Classification In Cryo-electron Tomography.","authors":"Shan Gao,&nbsp;Renmin Han,&nbsp;Xiangrui Zeng,&nbsp;Xuefeng Cui,&nbsp;Zhiyong Liu,&nbsp;Min Xu,&nbsp;Fa Zhang","doi":"10.1007/978-3-030-57821-3_8","DOIUrl":"https://doi.org/10.1007/978-3-030-57821-3_8","url":null,"abstract":"<p><p>Cryo-electron tomography (cryo-ET) combined with subtomogram averaging (STA) is a unique technique in revealing macromolecule structures in their near-native state. However, due to the macromolecular structural heterogeneity, low signal-to-noise-ratio (SNR) and anisotropic resolution in the tomogram, macromolecule classification, a critical step of STA, remains a great challenge. In this paper, we propose a novel convolution neural network, named 3D-Dilated-DenseNet, to improve the performance of macromolecule classification in STA. The proposed 3D-Dilated-DenseNet is challenged by the synthetic dataset in the SHREC contest and the experimental dataset, and compared with the SHREC-CNN (the state-of-the-art CNN model in the SHREC contest) and the baseline 3D-DenseNet. The results showed that 3D-Dilated-DenseNet significantly outperformed 3D-DenseNet but 3D-DenseNet is well above SHREC-CNN. Moreover, in order to further demonstrate the validity of dilated convolution in the classification task, we visualized the feature map of 3D-Dilated-DenseNet and 3D-DenseNet. Dilated convolution extracts a much more representative feature map.</p>","PeriodicalId":93167,"journal":{"name":"Bioinformatics research and applications : ... international symposium, ISBRA ... proceedings. ISBRA (Conference)","volume":"12304 ","pages":"82-94"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8046028/pdf/nihms-1675391.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38812019","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}