Bioinformatics最新文献_第4页

VarChat: the generative AI assistant for the interpretation of human genomic variations. VarChat：解读人类基因组变异的生成式人工智能助手。

IF 5.8 3区生物学

Bioinformatics Pub Date : 2024-04-05 DOI: 10.1093/bioinformatics/btae183

F. De Paoli, Silvia Berardelli, I. Limongelli, E. Rizzo, S. Zucca

{"title":"VarChat: the generative AI assistant for the interpretation of human genomic variations.","authors":"F. De Paoli, Silvia Berardelli, I. Limongelli, E. Rizzo, S. Zucca","doi":"10.1093/bioinformatics/btae183","DOIUrl":"https://doi.org/10.1093/bioinformatics/btae183","url":null,"abstract":"MOTIVATION\u0000In the modern era of genomic research, the scientific community is witnessing an explosive growth in the volume of published findings.While this abundance of data offers invaluable insights, it also places a pressing responsibility on genetic professionals and researchers to stay informed about the latest findings and their clinical significance. Genomic variant interpretation is currently facing a challenge in identifying the most up-to-date and relevant scientific papers, while also extracting meaningful information to accelerate the process from clinical assessment to reporting.Computer-aided literature search and summarization can play a pivotal role in this context. By synthesizing complex genomic findings into concise, interpretable summaries, this approach facilitates the translation of extensive genomic datasets into clinically relevant insights.\u0000\u0000\u0000RESULTS\u0000To bridge this gap, we present VarChat (varchat.engenome.com), an innovative tool based on generative AI, developed to find and summarize the fragmented scientific literature associated with genomic variants into brief yet informative texts.VarChat provides users with a concise description of specific genetic variants, detailing their impact on related proteins and possible effects on human health. Additionally, VarChat offers direct links to related scientific trustable sources, and encourages deeper research.\u0000\u0000\u0000AVAILABILITY\u0000varchat.engenome.com.","PeriodicalId":8903,"journal":{"name":"Bioinformatics","volume":null,"pages":null},"PeriodicalIF":5.8,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140740658","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}

引用次数: 0

phylaGAN: Data augmentation through conditional GANs and autoencoders for improving disease prediction accuracy using microbiome data. phylaGAN：通过条件 GAN 和自动编码器进行数据扩增，利用微生物组数据提高疾病预测的准确性。

IF 5.8 3区生物学

Bioinformatics Pub Date : 2024-04-03 DOI: 10.1093/bioinformatics/btae161

Divya Sharma, Wendy Lou, Wei Xu

{"title":"phylaGAN: Data augmentation through conditional GANs and autoencoders for improving disease prediction accuracy using microbiome data.","authors":"Divya Sharma, Wendy Lou, Wei Xu","doi":"10.1093/bioinformatics/btae161","DOIUrl":"https://doi.org/10.1093/bioinformatics/btae161","url":null,"abstract":"MOTIVATION\u0000Research is improving our understanding of how the microbiome interacts with the human body and its impact on human health. Existing machine learning methods have shown great potential in discriminating healthy from diseased microbiome states. However, Machine Learning based prediction using microbiome data has challenges such as, small sample size, imbalance between cases and controls and high cost of collecting large number of samples. To address these challenges, we propose a deep learning framework phylaGAN to augment the existing datasets with generated microbiome data using a combination of conditional generative adversarial network (C-GAN) and autoencoder. Conditional generative adversarial networks train two models against each other to compute larger simulated datasets that are representative of the original dataset. Autoencoder maps the original and the generated samples onto a common subspace to make the prediction more accurate.\u0000\u0000\u0000RESULTS\u0000Extensive evaluation and predictive analysis was conducted on two datasets, T2D study and Cirrhosis study showing an improvement in mean AUC using data augmentation by 11% and 5% respectively. External validation on a cohort classifying between obese and lean subjects, with a smaller sample size provided an improvement in mean AUC close to 32% when augmented through phylaGAN as compared to using the original cohort. Our findings not only indicate that the generative adversarial networks can create samples that mimic the original data across various diversity metrics, but also highlight the potential of enhancing disease prediction through machine learning models trained on synthetic data.\u0000\u0000\u0000AVAILABILITY AND IMPLEMENTATION\u0000https://github.com/divya031090/phylaGAN.","PeriodicalId":8903,"journal":{"name":"Bioinformatics","volume":null,"pages":null},"PeriodicalIF":5.8,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140746243","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}

引用次数: 0

Coding genomes with gapped pattern graph convolutional network 用间隙模式图卷积网络编码基因组

IF 5.8 3区生物学

Bioinformatics Pub Date : 2024-04-01 DOI: 10.1093/bioinformatics/btae188

Ruohan Wang, Yen Kaow Ng, Xiang-Li-Lan Zhang, Jianping Wang, S. Li

{"title":"Coding genomes with gapped pattern graph convolutional network","authors":"Ruohan Wang, Yen Kaow Ng, Xiang-Li-Lan Zhang, Jianping Wang, S. Li","doi":"10.1093/bioinformatics/btae188","DOIUrl":"https://doi.org/10.1093/bioinformatics/btae188","url":null,"abstract":"Abstract Motivation Genome sequencing technologies reveal a huge amount of genomic sequences. Neural network-based methods can be prime candidates for retrieving insights from these sequences because of their applicability to large and diverse datasets. However, the highly variable lengths of genome sequences severely impair the presentation of sequences as input to the neural network. Genetic variations further complicate tasks that involve sequence comparison or alignment. Results Inspired by the theory and applications of “spaced seeds,” we propose a graph representation of genome sequences called “gapped pattern graph.” These graphs can be transformed through a Graph Convolutional Network to form lower-dimensional embeddings for downstream tasks. On the basis of the gapped pattern graphs, we implemented a neural network model and demonstrated its performance on diverse tasks involving microbe and mammalian genome data. Our method consistently outperformed all the other state-of-the-art methods across various metrics on all tasks, especially for the sequences with limited homology to the training data. In addition, our model was able to identify distinct gapped pattern signatures from the sequences. Availability and implementation The framework is available at https://github.com/deepomicslab/GCNFrame.","PeriodicalId":8903,"journal":{"name":"Bioinformatics","volume":null,"pages":null},"PeriodicalIF":5.8,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140789647","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}

引用次数: 0

ViNe-Seg: deep-learning-assisted segmentation of visible neurons and subsequent analysis embedded in a graphical user interface ViNe-Seg：嵌入图形用户界面的深度学习辅助可见神经元分割及后续分析功能

IF 5.8 3区生物学

Bioinformatics Pub Date : 2024-04-01 DOI: 10.1093/bioinformatics/btae177

Nicolas Ruffini, Saleh Altahini, Stephan Weißbach, Nico Weber, Jonas Milkovits, Anna Wierczeiko, Hendrik Backhaus, Albrecht Stroh

{"title":"ViNe-Seg: deep-learning-assisted segmentation of visible neurons and subsequent analysis embedded in a graphical user interface","authors":"Nicolas Ruffini, Saleh Altahini, Stephan Weißbach, Nico Weber, Jonas Milkovits, Anna Wierczeiko, Hendrik Backhaus, Albrecht Stroh","doi":"10.1093/bioinformatics/btae177","DOIUrl":"https://doi.org/10.1093/bioinformatics/btae177","url":null,"abstract":"Abstract Summary Segmentation of neural somata is a crucial and usually the most time-consuming step in the analysis of optical functional imaging of neuronal microcircuits. In recent years, multiple auto-segmentation tools have been developed to improve the speed and consistency of the segmentation process, mostly, using deep learning approaches. Current segmentation tools, while advanced, still encounter challenges in producing accurate segmentation results, especially in datasets with a low signal-to-noise ratio. This has led to a reliance on manual segmentation techniques. However, manual methods, while customized to specific laboratory protocols, can introduce variability due to individual differences in interpretation, potentially affecting dataset consistency across studies. In response to this challenge, we present ViNe-Seg: a deep-learning-based semi-automatic segmentation tool that offers (i) detection of visible neurons, irrespective of their activity status; (ii) the ability to perform segmentation during an ongoing experiment; (iii) a user-friendly graphical interface that facilitates expert supervision, ensuring precise identification of Regions of Interest; (iv) an array of segmentation models with the option of training custom models and sharing them with the community; and (v) seamless integration of subsequent analysis steps. Availability and implementation ViNe-Seg code and documentation are publicly available at https://github.com/NiRuff/ViNe-Seg and can be installed from https://pypi.org/project/ViNeSeg/.","PeriodicalId":8903,"journal":{"name":"Bioinformatics","volume":null,"pages":null},"PeriodicalIF":5.8,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140772876","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}

引用次数: 0

Prioritization of oligogenic variant combinations in whole exomes 全外显子中寡变异组合的优先排序

IF 5.8 3区生物学

Bioinformatics Pub Date : 2024-04-01 DOI: 10.1093/bioinformatics/btae184

Barbara Gravel, Alexandre Renaux, Sofia Papadimitriou, Guillaume Smits, A. Nowé, Tom Lenaerts

{"title":"Prioritization of oligogenic variant combinations in whole exomes","authors":"Barbara Gravel, Alexandre Renaux, Sofia Papadimitriou, Guillaume Smits, A. Nowé, Tom Lenaerts","doi":"10.1093/bioinformatics/btae184","DOIUrl":"https://doi.org/10.1093/bioinformatics/btae184","url":null,"abstract":"Abstract Motivation Whole exome sequencing (WES) has emerged as a powerful tool for genetic research, enabling the collection of a tremendous amount of data about human genetic variation. However, properly identifying which variants are causative of a genetic disease remains an important challenge, often due to the number of variants that need to be screened. Expanding the screening to combinations of variants in two or more genes, as would be required under the oligogenic inheritance model, simply blows this problem out of proportion. Results We present here the High-throughput oligogenic prioritizer (Hop), a novel prioritization method that uses direct oligogenic information at the variant, gene and gene pair level to detect digenic variant combinations in WES data. This method leverages information from a knowledge graph, together with specialized pathogenicity predictions in order to effectively rank variant combinations based on how likely they are to explain the patient’s phenotype. The performance of Hop is evaluated in cross-validation on 36 120 synthetic exomes for training and 14 280 additional synthetic exomes for independent testing. Whereas the known pathogenic variant combinations are found in the top 20 in approximately 60% of the cross-validation exomes, 71% are found in the same ranking range when considering the independent set. These results provide a significant improvement over alternative approaches that depend simply on a monogenic assessment of pathogenicity, including early attempts for digenic ranking using monogenic pathogenicity scores. Availability and implementation Hop is available at https://github.com/oligogenic/HOP.","PeriodicalId":8903,"journal":{"name":"Bioinformatics","volume":null,"pages":null},"PeriodicalIF":5.8,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140791743","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}

引用次数: 0

A machine-readable specification for genomics assays 基因组学测定的机器可读规范

IF 5.8 3区生物学

Bioinformatics Pub Date : 2024-04-01 DOI: 10.1093/bioinformatics/btae168

A. S. Booeshaghi, Xi Chen, L. Pachter

引用次数: 0

EpiCarousel: memory- and time-efficient identification of metacells for atlas-level single-cell chromatin accessibility data EpiCarousel：为图集级单细胞染色质可及性数据识别元细胞提供记忆和时间效率

IF 5.8 3区生物学

Bioinformatics Pub Date : 2024-04-01 DOI: 10.1093/bioinformatics/btae191

Sijie Li, Yuxi Li, Yu Sun, Yaru Li, Xiaoyang Chen, Songming Tang, Shengquan Chen

{"title":"EpiCarousel: memory- and time-efficient identification of metacells for atlas-level single-cell chromatin accessibility data","authors":"Sijie Li, Yuxi Li, Yu Sun, Yaru Li, Xiaoyang Chen, Songming Tang, Shengquan Chen","doi":"10.1093/bioinformatics/btae191","DOIUrl":"https://doi.org/10.1093/bioinformatics/btae191","url":null,"abstract":"Abstract Summary Recent technical advancements in single-cell chromatin accessibility sequencing (scCAS) have brought new insights to the characterization of epigenetic heterogeneity. As single-cell genomics experiments scale up to hundreds of thousands of cells, the demand for computational resources for downstream analysis grows intractably large and exceeds the capabilities of most researchers. Here, we propose EpiCarousel, a tailored Python package based on lazy loading, parallel processing, and community detection for memory- and time-efficient identification of metacells, i.e. the emergence of homogenous cells, in large-scale scCAS data. Through comprehensive experiments on five datasets of various protocols, sample sizes, dimensions, number of cell types, and degrees of cell-type imbalance, EpiCarousel outperformed baseline methods in systematic evaluation of memory usage, computational time, and multiple downstream analyses including cell type identification. Moreover, EpiCarousel executes preprocessing and downstream cell clustering on the atlas-level dataset with 707 043 cells and 1 154 611 peaks within 2 h consuming <75 GB of RAM and provides superior performance for characterizing cell heterogeneity than state-of-the-art methods. Availability and implementation The EpiCarousel software is well-documented and freely available at https://github.com/biox-nku/epicarousel. It can be seamlessly interoperated with extensive scCAS analysis toolkits.","PeriodicalId":8903,"journal":{"name":"Bioinformatics","volume":null,"pages":null},"PeriodicalIF":5.8,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140776675","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}

引用次数: 0

Causal-ARG: a causality-guided framework for annotating properties of antibiotic resistance genes 因果-ARG：注释抗生素耐药基因特性的因果指导框架

IF 5.8 3区生物学

Bioinformatics Pub Date : 2024-04-01 DOI: 10.1093/bioinformatics/btae180

Weizhong Zhao, Junze Wu, Xingpeng Jiang, Tingting He, Xiaohua Hu

{"title":"Causal-ARG: a causality-guided framework for annotating properties of antibiotic resistance genes","authors":"Weizhong Zhao, Junze Wu, Xingpeng Jiang, Tingting He, Xiaohua Hu","doi":"10.1093/bioinformatics/btae180","DOIUrl":"https://doi.org/10.1093/bioinformatics/btae180","url":null,"abstract":"Abstract Motivation The crisis of antibiotic resistance, which causes antibiotics used to treat bacterial infections to become less effective, has emerged as one of the foremost challenges to public health. Identifying the properties of antibiotic resistance genes (ARGs) is an essential way to mitigate this issue. Although numerous methods have been proposed for this task, most of these approaches concentrate solely on predicting antibiotic class, disregarding other important properties of ARGs. In addition, existing methods for simultaneously predicting multiple properties of ARGs fail to account for the causal relationships among these properties, limiting the predictive performance. Results In this study, we propose a causality-guided framework for annotating properties of ARGs, in which causal inference is utilized for representation learning. More specifically, the hidden biological patterns determining the properties of ARGs are described by a Gaussian Mixture Model, and procedure of causal representation learning is used to derive the hidden features. In addition, a causal graph among different properties is constructed to capture the causal relationships among properties of ARGs, which is integrated into the task of annotating properties of ARGs. The experimental results on a real-world dataset demonstrate the effectiveness of the proposed framework on the task of annotating properties of ARGs. Availability and implementation The data and source codes are available in GitHub at https://github.com/David-WZhao/CausalARG.","PeriodicalId":8903,"journal":{"name":"Bioinformatics","volume":null,"pages":null},"PeriodicalIF":5.8,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140760936","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}

引用次数: 0

Regional analysis to delineate intrasample heterogeneity with RegionalST 利用 RegionalST 进行区域分析以划分样本内异质性

IF 5.8 3区生物学

Bioinformatics Pub Date : 2024-04-01 DOI: 10.1093/bioinformatics/btae186

Yue Lyu, Chong Wu, Wei Sun, Ziyi Li

{"title":"Regional analysis to delineate intrasample heterogeneity with RegionalST","authors":"Yue Lyu, Chong Wu, Wei Sun, Ziyi Li","doi":"10.1093/bioinformatics/btae186","DOIUrl":"https://doi.org/10.1093/bioinformatics/btae186","url":null,"abstract":"Abstract Motivation Spatial transcriptomics has greatly contributed to our understanding of spatial and intra-sample heterogeneity, which could be crucial for deciphering the molecular basis of human diseases. Intra-tumor heterogeneity, e.g. may be associated with cancer treatment responses. However, the lack of computational tools for exploiting cross-regional information and the limited spatial resolution of current technologies present major obstacles to elucidating tissue heterogeneity. Results To address these challenges, we introduce RegionalST, an efficient computational method that enables users to quantify cell type mixture and interactions, identify sub-regions of interest, and perform cross-region cell type-specific differential analysis for the first time. Our simulations and real data applications demonstrate that RegionalST is an efficient tool for visualizing and analyzing diverse spatial transcriptomics data, thereby enabling accurate and flexible exploration of tissue heterogeneity. Overall, RegionalST provides a one-stop destination for researchers seeking to delve deeper into the intricacies of spatial transcriptomics data. Availability and implementation The implementation of our method is available as an open-source R/Bioconductor package with a user-friendly manual available at https://bioconductor.org/packages/release/bioc/html/RegionalST.html.","PeriodicalId":8903,"journal":{"name":"Bioinformatics","volume":null,"pages":null},"PeriodicalIF":5.8,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140773347","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}

引用次数: 0

Integrative annotation scores of variants for impact on RNA binding protein activities 变体对 RNA 结合蛋白活性影响的整合注释得分

IF 5.8 3区生物学

Bioinformatics Pub Date : 2024-04-01 DOI: 10.1093/bioinformatics/btae181

Jingqi Duan, A. Gasch, S. Keleş

{"title":"Integrative annotation scores of variants for impact on RNA binding protein activities","authors":"Jingqi Duan, A. Gasch, S. Keleş","doi":"10.1093/bioinformatics/btae181","DOIUrl":"https://doi.org/10.1093/bioinformatics/btae181","url":null,"abstract":"Abstract Motivation The ENCODE project generated a large collection of eCLIP-seq RNA binding protein (RBP) profiling data with accompanying RNA-seq transcriptomes of shRNA knockdown of RBPs. These data could have utility in understanding the functional impact of genetic variants, however their potential has not been fully exploited. We implement INCA (Integrative annotation scores of variants for impact on RBP activities) as a multi-step genetic variant scoring approach that leverages the ENCODE RBP data together with ClinVar and integrates multiple computational approaches to aggregate evidence. Results INCA evaluates variant impacts on RBP activities by leveraging genotypic differences in cell lines used for eCLIP-seq. We show that INCA provides critical specificity, beyond generic scoring for RBP binding disruption, for candidate variants and their linkage-disequilibrium partners. As a result, it can, on average, augment scoring of 46.2% of the candidate variants beyond generic scoring for RBP binding disruption and aid in variant prioritization for follow-up analysis. Availability and implementation INCA is implemented in R and is available at https://github.com/keleslab/INCA.","PeriodicalId":8903,"journal":{"name":"Bioinformatics","volume":null,"pages":null},"PeriodicalIF":5.8,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140780814","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}

引用次数: 0