Single-cell multi-omics analysis identifies context-specific gene regulatory gates and mechanisms
In this study, we introduce scGATE (single-cell gene regulatory gate) as a novel computational tool for inferring TF-gene interaction networks and reconstructing Boolean logic gates involving regulatory TFs using scRNA-seq data. In contrast to current Boolean models, scGATE eliminates the need for individual formulations and likelihood calculations for each Boolean rule (e.g. AND, OR, XOR). By employing a Bayesian framework, scGATE infers the Boolean rule after fitting the model to the data, resulting in significant reductions in time-complexities for logic-based studies. We have applied assay for transposase-accessible ch...
Source: Briefings in Bioinformatics - April 23, 2024 Category: Bioinformatics Authors: Seyed Amir Malekpour Laleh Haghverdi Mehdi Sadeghi Source Type: research
BayesKAT: bayesian optimal kernel-based test for genetic association studies reveals joint genetic effects in complex diseases
Brief Bioinform. 2024 Mar 27;25(3):bbae182. doi: 10.1093/bib/bbae182.ABSTRACTGenome-wide Association Studies (GWAS) methods have identified individual single-nucleotide polymorphisms (SNPs) significantly associated with specific phenotypes. Nonetheless, many complex diseases are polygenic and are controlled by multiple genetic variants that are usually non-linearly dependent. These genetic variants are marginally less effective and remain undetected in GWAS analysis. Kernel-based tests (KBT), which evaluate the joint effect of a group of genetic variants, are therefore critical for complex disease analysis. However, choosi...
Source: Briefings in Bioinformatics - April 23, 2024 Category: Bioinformatics Authors: Sikta Das Adhikari Yuehua Cui Jianrong Wang Source Type: research
CovEpiAb: a comprehensive database and analysis resource for immune epitopes and antibodies of human coronaviruses
Brief Bioinform. 2024 Mar 27;25(3):bbae183. doi: 10.1093/bib/bbae183.ABSTRACTCoronaviruses have threatened humans repeatedly, especially COVID-19 caused by SARS-CoV-2, which has posed a substantial threat to global public health. SARS-CoV-2 continuously evolves through random mutation, resulting in a significant decrease in the efficacy of existing vaccines and neutralizing antibody drugs. It is critical to assess immune escape caused by viral mutations and develop broad-spectrum vaccines and neutralizing antibodies targeting conserved epitopes. Thus, we constructed CovEpiAb, a comprehensive database and analysis resource ...
Source: Briefings in Bioinformatics - April 23, 2024 Category: Bioinformatics Authors: Xue Zhang JingCheng Wu Yuanyuan Luo Yilin Wang Yujie Wu Xiaobin Xu Yufang Zhang Ruiying Kong Ying Chi Yisheng Sun Shuqing Chen Qiaojun He Feng Zhu Zhan Zhou Source Type: research
Eravacycline, an antibacterial drug, repurposed for pancreatic cancer therapy: insights from a molecular-based deep learning model
CONCLUSION: Our study highlights the potential of drug repurposing for cancer treatment using ML. Eravacycline showed promising results in inhibiting cancer cell proliferation, migration and inducing apoptosis in PDAC. These findings demonstrate that our developed ML drug repurposing models can be applied to a wide range of new oncology therapeutics, to identify potential anti-cancer agents. This highlights the potential and presents a promising approach for identifying new therapeutic options.PMID:38647152 | PMC:PMC11033730 | DOI:10.1093/bib/bbae108 (Source: Briefings in Bioinformatics)
Source: Briefings in Bioinformatics - April 22, 2024 Category: Bioinformatics Authors: Adi Jabarin Guy Shtar Valeria Feinshtein Eyal Mazuz Bracha Shapira Shimon Ben-Shabat Lior Rokach Source Type: research
A comparative benchmarking and evaluation framework for heterogeneous network-based drug repositioning methods
Brief Bioinform. 2024 Mar 27;25(3):bbae172. doi: 10.1093/bib/bbae172.ABSTRACTComputational drug repositioning, which involves identifying new indications for existing drugs, is an increasingly attractive research area due to its advantages in reducing both overall cost and development time. As a result, a growing number of computational drug repositioning methods have emerged. Heterogeneous network-based drug repositioning methods have been shown to outperform other approaches. However, there is a dearth of systematic evaluation studies of these methods, encompassing performance, scalability and usability, as well as a sta...
Source: Briefings in Bioinformatics - April 22, 2024 Category: Bioinformatics Authors: Yinghong Li Yinqi Yang Zhuohao Tong Yu Wang Qin Mi Mingze Bai Guizhao Liang Bo Li Kunxian Shu Source Type: research
Guided diffusion for molecular generation with interaction prompt
Brief Bioinform. 2024 Mar 27;25(3):bbae174. doi: 10.1093/bib/bbae174.ABSTRACTMolecular generative models have exhibited promising capabilities in designing molecules from scratch with high binding affinities in a predetermined protein pocket, offering potential synergies with traditional structural-based drug design strategy. However, the generative processes of such models are random and the atomic interaction information between ligand and protein are ignored. On the other hand, the ligand has high propensity to bind with residues called hotspots. Hotspot residues contribute to the majority of the binding free energies a...
Source: Briefings in Bioinformatics - April 22, 2024 Category: Bioinformatics Authors: Peng Wu Huabin Du Yingchao Yan Tzong-Yi Lee Chen Bai Song Wu Source Type: research
IGCNSDA: unraveling disease-associated snoRNAs with an interpretable graph convolutional network
In this study, we introduce IGCNSDA, an innovative and interpretable graph convolutional network (GCN) approach tailored for the efficient inference of snoRNA-disease associations. IGCNSDA leverages the GCN framework to extract node feature representations of snoRNAs and diseases from the bipartite snoRNA-disease graph. SnoRNAs with high similarity are more likely to be linked to analogous diseases, and vice versa. To facilitate this process, we introduce a subgraph generation algorithm that effectively groups similar snoRNAs and their associated diseases into cohesive subgraphs. Subsequently, we aggregate information from...
Source: Briefings in Bioinformatics - April 22, 2024 Category: Bioinformatics Authors: Xiaowen Hu Pan Zhang Dayun Liu Jiaxuan Zhang Yuanpeng Zhang Yihan Dong Yanhao Fan Lei Deng Source Type: research
TMBstable: a variant caller controls performance variation across heterogeneous sequencing samples
In this study, we introduce TMBstable, an innovative method that dynamically selects optimal variant calling strategies for specific genomic regions using a meta-learning framework, distinguishing it from traditional callers with uniform sample-wide strategies. The process begins with segmenting the sample into windows and extracting meta-features for clustering, followed by using a pre-trained meta-model to select suitable algorithms for each cluster, thereby addressing strategy-sample mismatches, reducing performance fluctuations and ensuring consistent performance across various samples. We evaluated TMBstable using bot...
Source: Briefings in Bioinformatics - April 18, 2024 Category: Bioinformatics Authors: Shenjie Wang Xiaoyan Zhu Xuwen Wang Yuqian Liu Minchao Zhao Zhili Chang Xiaonan Wang Yang Shao Jiayin Wang Source Type: research
Topological and geometric analysis of cell states in single-cell transcriptomic data
Brief Bioinform. 2024 Mar 27;25(3):bbae176. doi: 10.1093/bib/bbae176.ABSTRACTSingle-cell RNA sequencing (scRNA-seq) enables dissecting cellular heterogeneity in tissues, resulting in numerous biological discoveries. Various computational methods have been devised to delineate cell types by clustering scRNA-seq data, where clusters are often annotated using prior knowledge of marker genes. In addition to identifying pure cell types, several methods have been developed to identify cells undergoing state transitions, which often rely on prior clustering results. The present computational approaches predominantly investigate t...
Source: Briefings in Bioinformatics - April 18, 2024 Category: Bioinformatics Authors: Tram Huynh Zixuan Cang Source Type: research
TMBstable: a variant caller controls performance variation across heterogeneous sequencing samples
In this study, we introduce TMBstable, an innovative method that dynamically selects optimal variant calling strategies for specific genomic regions using a meta-learning framework, distinguishing it from traditional callers with uniform sample-wide strategies. The process begins with segmenting the sample into windows and extracting meta-features for clustering, followed by using a pre-trained meta-model to select suitable algorithms for each cluster, thereby addressing strategy-sample mismatches, reducing performance fluctuations and ensuring consistent performance across various samples. We evaluated TMBstable using bot...
Source: Briefings in Bioinformatics - April 18, 2024 Category: Bioinformatics Authors: Shenjie Wang Xiaoyan Zhu Xuwen Wang Yuqian Liu Minchao Zhao Zhili Chang Xiaonan Wang Yang Shao Jiayin Wang Source Type: research
Topological and geometric analysis of cell states in single-cell transcriptomic data
Brief Bioinform. 2024 Mar 27;25(3):bbae176. doi: 10.1093/bib/bbae176.ABSTRACTSingle-cell RNA sequencing (scRNA-seq) enables dissecting cellular heterogeneity in tissues, resulting in numerous biological discoveries. Various computational methods have been devised to delineate cell types by clustering scRNA-seq data, where clusters are often annotated using prior knowledge of marker genes. In addition to identifying pure cell types, several methods have been developed to identify cells undergoing state transitions, which often rely on prior clustering results. The present computational approaches predominantly investigate t...
Source: Briefings in Bioinformatics - April 18, 2024 Category: Bioinformatics Authors: Tram Huynh Zixuan Cang Source Type: research
Attention-guided variational graph autoencoders reveal heterogeneity in spatial transcriptomics
In this study, we introduce AttentionVGAE (AVGN), which integrates slice images, spatial information and raw gene expression while calibrating low-quality gene expression. By combining the variational graph autoencoder with multi-head attention blocks (MHA blocks), AVGN captures spatial relationships in tissue gene expression, adaptively focusing on key features and alleviating the need for prior knowledge of cluster numbers, thereby achieving superior clustering performance. Particularly, AVGN attempts to balance the model's attention focus on local and global structures by utilizing MHA blocks, an aspect that current gra...
Source: Briefings in Bioinformatics - April 17, 2024 Category: Bioinformatics Authors: Lixin Lei Kaitai Han Zijun Wang Chaojing Shi Zhenghui Wang Ruoyan Dai Zhiwei Zhang Mengqiu Wang Qianjin Guo Source Type: research
stDiff: a diffusion model for imputing spatial transcriptomics through single-cell transcriptomics
Brief Bioinform. 2024 Mar 27;25(3):bbae171. doi: 10.1093/bib/bbae171.ABSTRACTSpatial transcriptomics (ST) has become a powerful tool for exploring the spatial organization of gene expression in tissues. Imaging-based methods, though offering superior spatial resolutions at the single-cell level, are limited in either the number of imaged genes or the sensitivity of gene detection. Existing approaches for enhancing ST rely on the similarity between ST cells and reference single-cell RNA sequencing (scRNA-seq) cells. In contrast, we introduce stDiff, which leverages relationships between gene expression abundance in scRNA-se...
Source: Briefings in Bioinformatics - April 17, 2024 Category: Bioinformatics Authors: Kongming Li Jiahao Li Yuhao Tao Fei Wang Source Type: research
Attention-guided variational graph autoencoders reveal heterogeneity in spatial transcriptomics
In this study, we introduce AttentionVGAE (AVGN), which integrates slice images, spatial information and raw gene expression while calibrating low-quality gene expression. By combining the variational graph autoencoder with multi-head attention blocks (MHA blocks), AVGN captures spatial relationships in tissue gene expression, adaptively focusing on key features and alleviating the need for prior knowledge of cluster numbers, thereby achieving superior clustering performance. Particularly, AVGN attempts to balance the model's attention focus on local and global structures by utilizing MHA blocks, an aspect that current gra...
Source: Briefings in Bioinformatics - April 17, 2024 Category: Bioinformatics Authors: Lixin Lei Kaitai Han Zijun Wang Chaojing Shi Zhenghui Wang Ruoyan Dai Zhiwei Zhang Mengqiu Wang Qianjin Guo Source Type: research
stDiff: a diffusion model for imputing spatial transcriptomics through single-cell transcriptomics
Brief Bioinform. 2024 Mar 27;25(3):bbae171. doi: 10.1093/bib/bbae171.ABSTRACTSpatial transcriptomics (ST) has become a powerful tool for exploring the spatial organization of gene expression in tissues. Imaging-based methods, though offering superior spatial resolutions at the single-cell level, are limited in either the number of imaged genes or the sensitivity of gene detection. Existing approaches for enhancing ST rely on the similarity between ST cells and reference single-cell RNA sequencing (scRNA-seq) cells. In contrast, we introduce stDiff, which leverages relationships between gene expression abundance in scRNA-se...
Source: Briefings in Bioinformatics - April 17, 2024 Category: Bioinformatics Authors: Kongming Li Jiahao Li Yuhao Tao Fei Wang Source Type: research
