Bioinformatics最新文献

{"title":"scPRAM accurately predicts single-cell gene expression perturbation response based on attention mechanism.","authors":"Qun Jiang, Shengquan Chen, Xiaoyang Chen, Rui Jiang","doi":"10.1093/bioinformatics/btae265","DOIUrl":"https://doi.org/10.1093/bioinformatics/btae265","url":null,"abstract":"MOTIVATION\u0000With the rapid advancement of single-cell sequencing technology, it becomes gradually possible to delve into the cellular responses to various external perturbations at the gene expression level. However, obtaining perturbed samples in certain scenarios may be considerably challenging, and the substantial costs associated with sequencing also curtail the feasibility of large-scale experimentation. A repertoire of methodologies has been employed for forecasting perturbative responses in single-cell gene expression. However, existing methods primarily focus on the average response of a specific cell type to perturbation, overlooking the single-cell specificity of perturbation responses and a more comprehensive prediction of the entire perturbation response distribution.\u0000\u0000\u0000RESULTS\u0000Here we present scPRAM, a method for predicting Perturbation Responses in single-cell gene expression based on Attention Mechanisms. Leveraging variational autoencoders and optimal transport, scPRAM aligns cell states before and after perturbation, followed by accurate prediction of gene expression responses to perturbations for unseen cell types through attention mechanisms. Experiments on multiple real perturbation datasets involving drug treatments and bacterial infections demonstrate that scPRAM attains heightened accuracy in perturbation prediction across cell types, species, and individuals, surpassing existing methodologies. Furthermore, scPRAM demonstrates outstanding capability in identifying differentially expressed genes under perturbation, capturing heterogeneity in perturbation responses across species, and maintaining stability in the presence of data noise and sample size variations.\u0000\u0000\u0000AVAILABILITY AND IMPLEMENTATION\u0000https://github.com/jiang-q19/scPRAM and https://doi.org/10.5281/zenodo.10935038.\u0000\u0000\u0000SUPPLEMENTARY INFORMATION\u0000Supplementary data are available at Bioinformatics online.","PeriodicalId":8903,"journal":{"name":"Bioinformatics","volume":null,"pages":null},"PeriodicalIF":5.8,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140701441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hi-GeoMVP: a hierarchical geometry-enhanced deep learning model for drug response prediction.","authors":"Yurui Chen, Louxin Zhang","doi":"10.1093/bioinformatics/btae204","DOIUrl":"https://doi.org/10.1093/bioinformatics/btae204","url":null,"abstract":"MOTIVATION\u0000Personalized cancer treatments require accurate drug response predictions. Existing deep learning methods show promise but higher accuracy is needed to serve the purpose of precision medicine. The prediction accuracy can be improved with not only topology but geometrical information of drugs.\u0000\u0000\u0000RESULTS\u0000A novel deep learning methodology for drug response prediction is presented, named Hi-GeoMVP. It synthesizes hierarchical drug representation with multi-omics data, leveraging graph neural networks and variational autoencoders for detailed drug and cell line representations. Multi-task learning is employed to make better prediction, while both 2D and 3D molecular representations capture comprehensive drug information. Testing on the GDSC dataset confirms Hi-GeoMVP's enhanced performance, surpassing prior state-of-the-art methods by improving the Pearson correlation coefficient from 0.934 to 0.941 and decreasing the root mean square error from 0.969 to 0.931. In the case of blind test, Hi-GeoMVP demonstrated robustness, outperforming the best previous models with a superior Pearson correlation coefficient in the drug-blind test. These results underscore Hi-GeoMVP's capabilities in drug response prediction, implying its potential for precision medicine.\u0000\u0000\u0000AVAILABILITY AND IMPLEMENTATION\u0000The source code is available at https://github.com/matcyr/Hi-GeoMVP.\u0000\u0000\u0000SUPPLEMENTARY INFORMATION\u0000Supplementary data is available at Bioinformatics online.","PeriodicalId":8903,"journal":{"name":"Bioinformatics","volume":null,"pages":null},"PeriodicalIF":5.8,"publicationDate":"2024-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140708510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"NeoAgDT: Optimization of personal neoantigen vaccine composition by digital twin simulation of a cancer cell population.","authors":"Anja Mösch, Filippo Grazioli, Pierre Machart, Brandon Malone","doi":"10.1093/bioinformatics/btae205","DOIUrl":"https://doi.org/10.1093/bioinformatics/btae205","url":null,"abstract":"MOTIVATION\u0000Neoantigen vaccines make use of tumor-specific mutations to enable the patient's immune system to recognize and eliminate cancer. Selecting vaccine elements, however, is a complex task which needs to take into account not only the underlying antigen presentation pathway but also tumor heterogeneity.\u0000\u0000\u0000RESULTS\u0000Here, we present NeoAgDT, a two-step approach consisting of: (1) simulating individual cancer cells to create a digital twin of the patient's tumor cell population and (2) optimizing the vaccine composition by integer linear programming based on this digital twin. NeoAgDT shows improved selection of experimentally-validated neoantigens over ranking-based approaches in a study of seven patients.\u0000\u0000\u0000AVAILABILITY\u0000The NeoAgDT code is published on Github: https://github.com/nec-research/neoagdt.\u0000\u0000\u0000SUPPLEMENTARY INFORMATION\u0000Supplementary data are available at Bioinformatics online.","PeriodicalId":8903,"journal":{"name":"Bioinformatics","volume":null,"pages":null},"PeriodicalIF":5.8,"publicationDate":"2024-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140707633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A data-adaptive methods in detecting exogenous methyltransferase accessible chromatin in human genome using nanopore sequencing.","authors":"Kailing Tu, Xuemei Li, Qilin Zhang, Wei Huang, Dan Xie","doi":"10.1093/bioinformatics/btae206","DOIUrl":"https://doi.org/10.1093/bioinformatics/btae206","url":null,"abstract":"MOTIVATION\u0000Identifying chromatin accessibility is one of the key steps in studying the regulation of eukaryotic genomes. The combination of exogenous methyltransferase and nanopore sequencing provides an strategy to identify open chromatin over long genomic ranges at the single-molecule scale. However, endogenous methylation, non-open-chromatin-specific exogenous methylation and base-calling errors limit the accuracy and hinders its application to complex genomes.\u0000\u0000\u0000RESULTS\u0000We systematically evaluated the impact of these three influence factors, and developed a model-based computational method, methyltransferase accessible genome region finder(MAGNIFIER), to address the issues. By incorporating control data, MAGNIFIER attenuates the three influence factors with data-adaptive comparison strategy. We demonstrate that MAGNIFIER is not only sensitive to identify the open chromatin with much improved accuracy, but also able to detect the chromatin accessibility of repetitive regions that are missed by NGS-based methods. By incorporating long-read RNA-seq data, we revealed the association between the accessible Alu elements and non-classic gene isoforms.\u0000\u0000\u0000AVAILABILITY\u0000Freely avaliable on web at https://github.com/Goatofmountain/MAGNIFIER.\u0000\u0000\u0000SUPPLEMENTARY INFORMATION\u0000Supplementary data are available at Bioinformatics online.","PeriodicalId":8903,"journal":{"name":"Bioinformatics","volume":null,"pages":null},"PeriodicalIF":5.8,"publicationDate":"2024-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140707991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Literature mining discerns latent disease-gene relationships.","authors":"Priyadarshini Rai, Atishay Jain, Shivani Kumar, Divya Sharma, Neha Jha, Smriti Chawla, Abhijith S. Raj, Apoorva Gupta, Sarita Poonia, A. Majumdar, Tanmoy Chakraborty, Gaurav Ahuja, Debarka Sengupta","doi":"10.1093/bioinformatics/btae185","DOIUrl":"https://doi.org/10.1093/bioinformatics/btae185","url":null,"abstract":"MOTIVATION\u0000Dysregulation of a gene's function, either due to mutations or impairments in regulatory networks, often triggers pathological states in the affected tissue. Comprehensive mapping of these apparent gene-pathology relationships is an ever-daunting task, primarily due to genetic pleiotropy and lack of suitable computational approaches. With the advent of high throughput genomics platforms and community scale initiatives such as the Human Cell Landscape (HCL) project (Han et al., 2020), researchers have been able to create gene expression portraits of healthy tissues resolved at the level of single cells. However, a similar wealth of knowledge is currently not at our finger-tip when it comes to diseases. This is because the genetic manifestation of a disease is often quite diverse and is confounded by several clinical and demographic covariates.\u0000\u0000\u0000RESULTS\u0000To circumvent this, we mined ∼18 million PubMed abstracts published till May 2019 and automatically selected ∼4.5 million of them that describe roles of particular genes in disease pathogenesis. Further, we fine-tuned the pretrained Bidirectional Encoder Representations from Transformers (BERT) for language modeling from the domain of Natural Language Processing (NLP) to learn vector representation of entities such as genes, diseases, tissues, cell-types etc., in a way such that their relationship is preserved in a vector space. The repurposed BERT predicted disease-gene associations that are not cited in the training data, thereby highlighting the feasibility of in-silico synthesis of hypotheses linking different biological entities such as genes and conditions.\u0000\u0000\u0000AVAILABILITY AND IMPLEMENTATION\u0000PathoBERT pretrained model: https://github.com/Priyadarshini-Rai/Pathomap-ModelBioSentVec based abstract classification model: https://github.com/Priyadarshini-Rai/Pathomap-ModelPathomap R package: https://github.com/Priyadarshini-Rai/Pathomap.\u0000\u0000\u0000SUPPLEMENTARY INFORMATION\u0000Supplementary data are available at Bioinformatics online.","PeriodicalId":8903,"journal":{"name":"Bioinformatics","volume":null,"pages":null},"PeriodicalIF":5.8,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140711872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"scDAC: deep adaptive clustering of single-cell transcriptomic data with coupled autoencoder and dirichlet process mixture model.","authors":"Sijing An, Jinhui Shi, Runyan Liu, Yaowen Chen, Jing Wang, Shuofeng Hu, Xinyu Xia, Guohua Dong, Xiaochen Bo, Zhen He, Xiaomin Ying","doi":"10.1093/bioinformatics/btae198","DOIUrl":"https://doi.org/10.1093/bioinformatics/btae198","url":null,"abstract":"MOTIVATION\u0000Clustering analysis for single-cell RNA sequencing (scRNA-seq) data is an important step in revealing cellular heterogeneity. Many clustering methods have been proposed to discover heterogenous cell types from scRNA-seq data. However, adaptive clustering with accurate cluster number reflecting intrinsic biology nature from large-scale scRNA-seq data remains quite challenging.\u0000\u0000\u0000RESULTS\u0000Here we propose a single-cell Deep Adaptive Clustering (scDAC) model by coupling the Autoencoder (AE) and the Dirichlet Process Mixture Model (DPMM). By jointly optimizing the model parameters of AE and DPMM, scDAC achieves adaptive clustering with accurate cluster numbers on scRNA-seq data. We verify the performance of scDAC on five subsampled datasets with different numbers of cell types and compare it with fifteen widely-used clustering methods across nine scRNA-seq datasets. Our results demonstrate that scDAC can adaptively find accurate numbers of cell types or subtypes and outperforms other methods. Moreover, the performance of scDAC is robust to hyperparameter changes.\u0000\u0000\u0000AVAILABILITY\u0000The scDAC is implemented in Python. The source code is available at https://github.com/labomics/scDAC.\u0000\u0000\u0000SUPPLEMENTARY INFORMATION\u0000Supplementary data are available at Bioinformatics online.","PeriodicalId":8903,"journal":{"name":"Bioinformatics","volume":null,"pages":null},"PeriodicalIF":5.8,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140714203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"VirusPredictor: XGBoost-based software to predict virus-related sequences in human data.","authors":"Guangchen Liu, Xun Chen, Yihui Luan, Dawei Li","doi":"10.1093/bioinformatics/btae192","DOIUrl":"https://doi.org/10.1093/bioinformatics/btae192","url":null,"abstract":"MOTIVATION\u0000Discovering disease causative pathogens, particularly viruses without reference genomes, poses a technical challenge as they are often unidentifiable through sequence alignment. Machine learning prediction of patient high-throughput sequences unmappable to human and pathogen genomes may reveal sequences originating from uncharacterized viruses. Currently, there is a lack of software specifically designed for accurately predicting such viral sequences in human data.\u0000\u0000\u0000RESULTS\u0000We developed a fast XGBoost method and software VirusPredictor leveraging an in-house viral genome database. Our two-step XGBoost models first classify each query sequence into one of three groups: infectious virus, endogenous retrovirus (ERV) or non-ERV human. The prediction accuracies increased as the sequences became longer, ie, 0.76, 0.93, and 0.98 for 150-350 (Illumina short reads), 850-950 (Sanger sequencing data), and 2,000-5,000 bp sequences, respectively. Then, sequences predicted to be from infectious viruses are further classified into one of six virus taxonomic subgroups, and the accuracies increased from 0.92 to > 0.98 when query sequences increased from 150-350 to > 850 bp. The results suggest that Illumina short reads should be de novo assembled into contigs (e.g., ∼1,000 bp or longer) before prediction whenever possible. We applied VirusPredictor to multiple real genomic and metagenomic datasets and obtained high accuracies. VirusPredictor, a user-friendly open-source Python software, is useful for predicting the origins of patients' unmappable sequences. This study is the first to classify ERVs in infectious viral sequence prediction. This is also the first study combining virus sub-group predictions.\u0000\u0000\u0000AVAILABILITY\u0000www.dllab.org/software/VirusPredictor.html.\u0000\u0000\u0000SUPPLEMENTARY INFORMATION\u0000Supplementary data are available at Bioinformatics online.","PeriodicalId":8903,"journal":{"name":"Bioinformatics","volume":null,"pages":null},"PeriodicalIF":5.8,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140719747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"rnaCrosslinkOO: An Object-Oriented R Package for the Analysis of RNA Structural Data Generated by RNA Crosslinking Experiments.","authors":"Jonathan L. Price, Omer Ziv, M. Pinckert, Andrew Lim, Eric A. Miska","doi":"10.1093/bioinformatics/btae193","DOIUrl":"https://doi.org/10.1093/bioinformatics/btae193","url":null,"abstract":"SUMMARY\u0000RNA (Ribonucleic Acid) molecules have secondary and tertiary structures in vivo which play a crucial role in cellular processes such as the regulation of gene expression, RNA processing and localisation. The ability to investigate these structures will enhance our understanding of their function and contribute to the diagnosis and treatment of diseases caused by RNA dysregulation. However, there are no mature pipelines or packages for processing and analysing complex in vivo RNA structural data. Here, we present rnaCrosslinkOO (RNA Crosslink Object-Oriented), a novel software package for the comprehensive analysis of data derived from the COMRADES (Crosslinking of Matched RNA and Deep Sequencing) method. rnaCrosslinkOO offers a comprehensive pipeline from raw sequencing reads to the identification and comparison of RNA structural features. It includes read processing and alignment, clustering of duplexes, data exploration, folding and comparisons of RNA structures. rnaCrosslinkOO also enables comparisons between conditions, the identification of inter-RNA interactions, and the incorporation of reactivity data to improve structure prediction.\u0000\u0000\u0000AVAILABILITY AND IMPLEMENTATION\u0000rnaCrosslinkOO is freely available to non-commercial users and implemented in R, with the source code and documentation accessible at [https://CRAN.R-project.org/package=rnaCrosslinkOO]. The software is supported on Linux, macOS, and Windows platforms.","PeriodicalId":8903,"journal":{"name":"Bioinformatics","volume":null,"pages":null},"PeriodicalIF":5.8,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140718326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"shinyseg: a web application for flexible cosegregation and sensitivity analysis.","authors":"Christian Carrizosa, Dag E Undlien, Magnus D Vigeland","doi":"10.1093/bioinformatics/btae201","DOIUrl":"https://doi.org/10.1093/bioinformatics/btae201","url":null,"abstract":"MOTIVATION\u0000Cosegregation analysis is a powerful tool for identifying pathogenic genetic variants, but its implementation remains challenging. Existing software is either limited in scope or too demanding for many end users. Moreover, current solutions lack methods for assessing the robustness of cosegregation evidence, which is important due to its reliance on uncertain estimates.\u0000\u0000\u0000RESULTS\u0000We present shinyseg, a comprehensive web application for clinical cosegregation analysis. Our app streamlines penetrance specification based on either liability classes or epidemiological data such as risks, hazard ratios, and age of onset distribution. In addition, it incorporates sensitivity analyses to assess the robustness of cosegregation evidence, and offers support in clinical interpretation.\u0000\u0000\u0000AVAILABILITY AND IMPLEMENTATION\u0000The shinyseg app is freely available at https://chrcarrizosa.shinyapps.io/shinyseg, with documentation and complete R source code on https://chrcarrizosa.github.io/shinyseg and https://github.com/chrcarrizosa/shinyseg.\u0000\u0000\u0000SUPPLEMENTARY INFORMATION\u0000Supplementary data are available at Bioinformatics online.","PeriodicalId":8903,"journal":{"name":"Bioinformatics","volume":null,"pages":null},"PeriodicalIF":5.8,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140716879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A web-based platform for extracting and modeling knowledge from biomedical literature as a labeled graph.","authors":"Antonio Di Maria, Lorenzo Bellomo, Fabrizio Billeci, Alfio Cardillo, S. Alaimo, Paolo Ferragina, Alfredo Ferro, A. Pulvirenti","doi":"10.1093/bioinformatics/btae194","DOIUrl":"https://doi.org/10.1093/bioinformatics/btae194","url":null,"abstract":"MOTIVATION\u0000The rapid increase of bio-medical literature makes it harder and harder for scientists to keep pace with the discoveries on which they build their studies. Therefore, computational tools have become more widespread, among which network analysis plays a crucial role in several life-science contexts. Nevertheless, building correct and complete networks about some user-defined biomedical topics on top of the available literature is still challenging.\u0000\u0000\u0000RESULTS\u0000We introduce NetMe 2.0, a web-based platform that automatically extracts relevant biomedical entities and their relations from a set of input texts-i.e., in the form of full-text or abstract of PubMed Central's papers, free texts, or PDFs uploaded by users-and models them as a BioMedical Knowledge Graph (BKG). NetMe 2.0 also implements an innovative Retrieval Augmented Generation module (Graph-RAG) that works on top of the relationships modeled by the BKG and allows the distilling of well-formed sentences that explain their content. The experimental results show that NetMe 2.0 can infer comprehensive and reliable biological networks with significant Precision-Recall metrics when compared to state-of-the-art approaches.\u0000\u0000\u0000AVAILABILITY\u0000https://netme.click/.\u0000\u0000\u0000SUPPLEMENTARY INFORMATION\u0000Supplementary data are available at Bioinformatics.","PeriodicalId":8903,"journal":{"name":"Bioinformatics","volume":null,"pages":null},"PeriodicalIF":5.8,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140718539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}