Briefings in bioinformatics最新文献

MicroRNAs (miRNAs) are critical regulators in various biological processes to cleave or repress translation of messenger RNAs (mRNAs). Accurately predicting miRNA targets is essential for developing miRNA-based therapies for diseases such as cancer and cardiovascular disease. Traditional miRNA target prediction methods often struggle due to incomplete knowledge of miRNA-target interactions and lack interpretability. To address these limitations, we propose miCGR, an end-to-end deep learning framework for predicting functional miRNA targets. MiCGR employs 2D convolutional neural networks alongside an enhanced Chaos Game Representation (CGR) of both miRNA sequences and their candidate target site (CTS) on mRNA. This advanced CGR transforms genetic sequences into informative 2D graphical representations based on sequence composition and subsequence frequencies, and explicitly incorporates important prior knowledge of seed regions and subsequence positions. Unlike one-dimensional methods based solely on sequence characters, this approach identifies functional motifs within sequences, even if they are distant in the original sequences. Our model outperforms existing methods in predicting functional targets at both the site and gene levels. To enhance interpretability, we incorporate Shapley value analysis for each subsequence within both miRNA sequences and their target sites, allowing miCGR to achieve improved accuracy, particularly with more lenient CTS selection criteria. Finally, two case studies demonstrate the practical applicability of miCGR, highlighting its potential to provide insights for optimizing artificial miRNA analogs that surpass endogenous counterparts.

Recent studies suggest cGAS-STING pathway may play a crucial role in the genesis and development of hepatocellular carcinoma (HCC), closely associated with classical pathways and tumor immunity. We aimed to develop models predicting survival and anti-PD-1/PD-L1 outcomes centered on the cGAS-STING pathway in HCC. We identified classical pathways highly correlated with cGAS-STING pathway and constructed transformer survival model preserving raw structure of pathways. We also developed explainable XGBoost model for predicting anti-PD-1/PD-L1 outcomes using SHAP algorithm. We trained and validated transformer survival model on pan-cancer cohort and tested it on three independent HCC cohorts. Using 0.5 as threshold across cohorts, we divided each HCC cohort into two groups and calculated P values with log-rank test. TCGA-LIHC: C-index = 0.750, P = 1.52e-11; ICGC-LIRI-JP: C-index = 0.741, P = .00138; GSE144269: C-index = 0.647, P = .0233. We trained and validated [area under the receiver operating characteristic curve (AUC) = 0.777] XGBoost model on immunotherapy datasets and tested it on GSE78220 (AUC = 0.789); we also tested XGBoost model on HCC anti-PD-L1 cohort (AUC = 0.719). Our deep learning model and XGBoost model demonstrate potential in predicting survival risks and anti-PD-1/PD-L1 outcomes in HCC. We deployed these two prediction models to the GitHub repository and provided detailed instructions for their usage: deep learning survival model, https://github.com/mlwalker123/CSP_survival_model; XGBoost immunotherapy model, https://github.com/mlwalker123/CSP_immunotherapy_model.

Motivation: Accurately predicting the degradation capabilities of proteolysis-targeting chimeras (PROTACs) for given target proteins and E3 ligases is important for PROTAC design. The distinctive ternary structure of PROTACs presents a challenge to traditional drug-target interaction prediction methods, necessitating more innovative approaches. While current state-of-the-art (SOTA) methods using graph neural networks (GNNs) can discern the molecular structure of PROTACs and proteins, thus enabling the efficient prediction of PROTACs' degradation capabilities, they rely heavily on limited crystal structure data of the POI-PROTAC-E3 ternary complex. This reliance underutilizes rich PROTAC experimental data and neglects intricate interaction relationships within ternary complexes.

Results: In this study, we propose a model based on cross-modal strategy and ternary attention technology, ET-PROTACs, to predict the targeted degradation capabilities of PROTACs. Our model capitalizes on the strengths of cross-modal methods by using equivariant GNN graph neural networks to process the graph structure and spatial coordinates of PROTAC molecules concurrently while utilizing sequence-based methods to learn the protein sequence information. This integration of cross-modal information is cohesively harnessed and channeled into a ternary attention mechanism, specially tailored for the unique structure of PROTACs, enabling the congruent modeling of both PROTAC and protein modalities. Experimental results demonstrate that the ET-PROTACs model outperforms existing SOTA methods. Moreover, visualizing attention scores illuminates crucial residues and atoms pivotal in specific POI-PROTAC-E3 interactions, thus offering invaluable insights and guidance for future pharmaceutical research.

Availability and implementation: The codes of our model are available at https://github.com/GuanyuYue/ET-PROTACs.

The burgeoning accumulation of large-scale biomedical data in oncology, alongside significant strides in deep learning (DL) technologies, has established multimodal DL (MDL) as a cornerstone of precision oncology. This review provides an overview of MDL applications in this field, based on an extensive literature survey. In total, 651 articles published before September 2024 are included. We first outline publicly available multimodal datasets that support cancer research. Then, we discuss key DL training methods, data representation techniques, and fusion strategies for integrating multimodal data. The review also examines MDL applications in tumor segmentation, detection, diagnosis, prognosis, treatment selection, and therapy response monitoring. Finally, we critically assess the limitations of current approaches and propose directions for future research. By synthesizing current progress and identifying challenges, this review aims to guide future efforts in leveraging MDL to advance precision oncology.

Given the adverse effects faced by rice due to abiotic stresses, the precise and rapid identification of single nucleotide polymorphisms (SNPs) associated with abiotic stress traits (ABST-SNPs) in rice is crucial for developing resistant rice varieties. The scarcity of high-quality data related to abiotic stress in rice has hindered the development of computational models and constrained research efforts aimed at rice improvement and breeding. Genome-wide association studies provide a better statistical power to consider ABST-SNPs in rice. Meanwhile, deep learning methods have shown their capability in predicting disease- or phenotype-associated loci, but have primarily focused on human species. Therefore, developing predictive models for identifying ABST-SNPs in rice is both urgent and valuable. In this paper, a model called RiceSNP-ABST is proposed for predicting ABST-SNPs in rice. Firstly, six training datasets were generated using a novel strategy for negative sample construction. Secondly, four feature encoding methods were proposed based on DNA sequence fragments, followed by feature selection. Finally, convolutional neural networks with residual connections were used to determine whether the sequences contained rice ABST-SNPs. RiceSNP-ABST outperformed traditional machine learning and state-of-the-art methods on the benchmark dataset and demonstrated consistent generalization on an independent dataset and cross-species datasets. Notably, multi-granularity causal structure learning was employed to elucidate the relationships among DNA structural features, aiming to identify key genetic variants more effectively. The web-based tool for the RiceSNP-ABST can be accessed at http://rice-snp-abst.aielab.cc.

Structural network control principles provided novel and efficient clues for the optimization of personalized drug targets (PDTs) related to state transitions of individual patients. However, most existing methods focus on one subnetwork or module as drug targets through the identification of the minimal set of driver nodes and ignore the state transition capabilities of other modules with different configurations of drug targets [i.e. multimodal drug targets (MDTs)] embedding the knowledge of previous drug targets (i.e. multiobjective optimization). Therefore, a novel multimodal multiobjective evolutionary optimization framework (called MMONCP) is proposed to optimize PDTs with network control principles. The key points of MMONCP are that a constrained multimodal multiobjective optimization problem is formed with discrete constraints on the decision space and multimodality characteristics, and a novel evolutionary algorithm denoted as CMMOEA-GLS-WSCD is designed by combining a global and local search strategy and a weighting-based special crowding distance strategy to balance the diversity of both objective and decision space. The experimental results on three cancer genomics data from The Cancer Genome Atlas indicate that MMONCP achieves a higher performance including algorithm convergence and diversity, the fraction of identified MDTs, and the area under the curve score than advanced algorithms. Additionally, MMONCP can detect the early state from the difference between the target activity and toxicity of MDTs and provide early treatment options for cancer treatment in precision medicine.

Human leukocyte antigen class I (HLA-I) and class II (HLA-II) proteins play an essential role in epitope binding and presentation to initiate an immune response. Accurate prediction of peptide-HLA (pHLA) binding and presentation is critical for developing effective immunotherapies. However, current tools can predict antigens exclusively for pHLA-I or pHLA-II, but not both; have constraints on peptide length; and commonly show unsatisfactory predictive accuracy. Here, we developed a convolution and attention-based model, CapHLA, trained with eluted ligand and binding affinity mass spectrometry data, to predict peptide presentation probability (PB) and binding affinities (BA) for HLA-I and HLA-II. In comparison with 11 other methods, CapHLA consistently showed improved performance in predicting pHLA BA and PB, particularly in HLA-II and non-classical peptide length datasets. Using CapHLA PB and BA predictions in combination with antigen expression level (EP) from transcriptomic data, we developed a neoantigen quality model for predicting immunotherapy response. In analyses of clinical response among 276 cancer patients given immunotherapy and overall survival in 7228 cancer patients, our neoantigen quality model outperformed other genetics-based models in predicting response to checkpoint inhibitors and patient prognosis. This study provides a versatile neoantigen screening tool, illustrating the prognostic value of neoantigen quality.

Short tandem repeats (STRs) represent one of the most polymorphic variations in the human genome, finding extensive applications in forensics, population genetics and medical genetics. In contrast to the traditional capillary electrophoresis (CE) method, genotyping STRs using massive parallel sequencing technology offers enhanced sensitivity and accuracy. However, current methods are mainly designed for target sequencing with higher coverage for a specific STR locus, thereby constraining the utility of STRs in low- and medium-coverage whole genome sequencing (WGS) data. Here, we introduce STRsensor, a method designed to type STR alleles in low-coverage WGS data and target sequencing data, achieving a significant high detection ratio and accuracy. STRsensor employs two methods for STR allele-typing: the Kmers-based method and the CIGAR-based method. Furthermore, by incorporating a model for PCR stutters, STRsensor greatly enhances the accuracy of STR allele typing. With simulation data, we demonstrate that STRsensor achieves a detection ratio of 100$%$ and an accuracy of 99.37$%$ for a 30$times $ WGS data, outperforming the existing methods, such as STRait Razor, STRinNGS, and HipSTR. When applied to real target sequencing data from 687 individuals, STRsensor achieves a detection ratio of 99.64$%$ and an accuracy of 99.99$%$. Moreover, STRsensor is a computationally efficient method that runs 79 times faster than HipSTR and 10 000 times faster than STRinNGS. STRsensor is freely available on GitHub: https://github.com/ChenHuaLab/STRsensor.

The volume of microbiome data is growing at an exponential rate, and the current methodologies for big data mining are encountering substantial obstacles. Effectively managing and extracting valuable insights from these vast microbiome datasets has emerged as a significant challenge in the field of contemporary microbiome research. This comprehensive review delves into the utilization of foundation models and transfer learning techniques within the context of microbiome-based classification and prediction tasks, advocating for a transition away from traditional task-specific or scenario-specific models towards more adaptable, continuous learning models. The article underscores the practicality and benefits of initially constructing a robust foundation model, which can then be fine-tuned using transfer learning to tackle specific context tasks. In real-world scenarios, the application of transfer learning empowers models to leverage disease-related data from one geographical area and enhance diagnostic precision in different regions. This transition from relying on "good models" to embracing "adaptive models" resonates with the philosophy of "teaching a man to fish" thereby paving the way for advancements in personalized medicine and accurate diagnosis. Empirical research suggests that the integration of foundation models with transfer learning methodologies substantially boosts the performance of models when dealing with large-scale and diverse microbiome datasets, effectively mitigating the challenges posed by data heterogeneity.

Somatic variants play a crucial role in the occurrence and progression of cancer. However, in the absence of matched normal controls, distinguishing between germline and somatic variants becomes challenging in tumor samples. The existing tumor-only genomic analysis methods either suffer from limited performance or insufficient interpretability due to an excess of features. Therefore, there is an urgent need for an alternative approach that can address these issues and have practical implications. Here, we presented OncoTOP, a computational method for genomic analysis without matched normal samples, which can accurately distinguish somatic mutations from germline variants. Reference sample analysis revealed a 0% false positive rate and 99.7% reproducibility for variant calling. Assessing 2864 tumor samples across 18 cancer types yielded a 99.8% overall positive percent agreement and a 99.9% positive predictive value. OncoTOP can also accurately detect clinically actionable variants and subclonal mutations associated with drug resistance. For the prediction of mutation origins, the positive percent agreement stood at 97.4% for predicting somatic mutations and 95.7% for germline mutations. High consistency of tumor mutational burden (TMB) was observed between the results generated by OncoTOP and tumor-normal paired analysis. In a cohort of 97 lung cancer patients treated with immunotherapy, TMB-high patients had prolonged PFS (P = .02), proving the reliability of our approach in estimating TMB to predict therapy response. Furthermore, microsatellite instability status showed a strong concordance (97%) with polymerase chain reaction results, and leukocyte antigens class I subtypes and homozygosity achieved an impressive concordance rate of 99.3% and 99.9% respectively, compared to its tumor-normal paired analysis. Thus, OncoTOP exhibited high reliability in variant calling, mutation origin prediction, and biomarker estimation. Its application will promise substantial advantages for clinical genomic testing.