Briefings in bioinformatics最新文献

Single-cell RNA sequencing (scRNA-seq) technology is one of the most cost-effective and efficacious methods for revealing cellular heterogeneity and diversity. Precise identification of cell types is essential for establishing a robust foundation for downstream analyses and is a prerequisite for understanding heterogeneous mechanisms. However, the accuracy of existing methods warrants improvement, and highly accurate methods often impose stringent equipment requirements. Moreover, most unsupervised learning-based approaches are constrained by the need to input the number of cell types a prior, which limits their widespread application. In this paper, we propose a novel algorithm framework named WLGG. Initially, to capture the underlying nonlinear information, we introduce a weighted distance penalty term utilizing the Gaussian kernel function, which maps data from a low-dimensional nonlinear space to a high-dimensional linear space. We subsequently impose a Lasso constraint on the regularized Gaussian graphical model to enhance its ability to capture linear data characteristics. Additionally, we utilize the Eigengap strategy to predict the number of cell types and obtain predicted labels via spectral clustering. The experimental results on 14 test datasets demonstrate the superior clustering accuracy of the WLGG algorithm over 16 alternative methods. Furthermore, downstream analysis, including marker gene identification, pseudotime inference, and functional enrichment analysis based on the similarity matrix and predicted labels from the WLGG algorithm, substantiates the reliability of WLGG and offers valuable insights into biological dynamic biological processes and regulatory mechanisms.

In this paper, we propose DGCL, a dual-graph neural networks (GNNs)-based contrastive learning (CL) integrated with mixed molecular fingerprints (MFPs) for molecular property prediction. The DGCL-MFP method contains two stages. In the first pretraining stage, we utilize two different GNNs as encoders to construct CL, rather than using the method of generating enhanced graphs as before. Precisely, DGCL aggregates and enhances features of the same molecule by the Graph Isomorphism Network and the Graph Attention Network, with representations extracted from the same molecule serving as positive samples, and others marked as negative ones. In the downstream tasks training stage, features extracted from the two above pretrained graph networks and the meticulously selected MFPs are concated together to predict molecular properties. Our experiments show that DGCL enhances the performance of existing GNNs by achieving or surpassing the state-of-the-art self-supervised learning models on multiple benchmark datasets. Specifically, DGCL increases the average performance of classification tasks by 3.73$%$ and improves the performance of regression task Lipo by 0.126. Through ablation studies, we validate the impact of network fusion strategies and MFPs on model performance. In addition, DGCL's predictive performance is further enhanced by weighting different molecular features based on the Extended Connectivity Fingerprint. The code and datasets of DGCL will be made publicly available.

Single-cell cross-modal joint clustering has been extensively utilized to investigate the tumor microenvironment. Although numerous approaches have been suggested, accurate clustering remains the main challenge. First, the gene expression matrix frequently contains numerous missing values due to measurement limitations. The majority of existing clustering methods treat it as a typical multi-modal dataset without further processing. Few methods conduct recovery before clustering and do not sufficiently engage with the underlying research, leading to suboptimal outcomes. Additionally, the existing cross-modal information fusion strategy does not ensure consistency of representations across different modes, potentially leading to the integration of conflicting information, which could degrade performance. To address these challenges, we propose the 'Recover then Aggregate' strategy and introduce the Unified Cross-Modal Deep Clustering model. Specifically, we have developed a data augmentation technique based on neighborhood similarity, iteratively imposing rank constraints on the Laplacian matrix, thus updating the similarity matrix and recovering dropout events. Concurrently, we integrate cross-modal features and employ contrastive learning to align modality-specific representations with consistent ones, enhancing the effective integration of diverse modal information. Comprehensive experiments on five real-world multi-modal datasets have demonstrated this method's superior effectiveness in single-cell clustering tasks.

Bioinformatics has become an interdisciplinary subject due to its universal role in molecular biology research. The current status of Russia's bioinformatics research in Russia is not known. Here, we review the history of bioinformatics in Russia, present the current landscape, and highlight future directions and challenges. Bioinformatics research in Russia is driven by four major industries: information technology, pharmaceuticals, biotechnology, and agriculture. Over the past three decades, despite a delayed start, the field has gained momentum, especially in protein and nucleic acid research. Dedicated and shared centers for genomics, proteomics, and bioinformatics are active in different regions of Russia. Present-day bioinformatics in Russia is characterized by research issues related to genetics, metagenomics, OMICs, medical informatics, computational biology, environmental informatics, and structural bioinformatics. Notable developments are in the fields of software (tools, algorithms, and pipelines), use of high computation power (e.g. by the Siberian Supercomputer Center), and large-scale sequencing projects (the sequencing of 100 000 human genomes). Government funding is increasing, policies are being changed, and a National Genomic Information Database is being established. An increased focus on eukaryotic genome sequencing, the development of a common place for developers and researchers to share tools and data, and the use of biological modeling, machine learning, and biostatistics are key areas for future focus. Universities and research institutes have started to implement bioinformatics modules. A critical mass of bioinformaticians is essential to catch up with the global pace in the discipline.

Understanding the functional impact of genetic mutations on protein structures is essential for advancing cancer research and developing targeted therapies. The main challenge lies in accurately mapping these mutations to protein structures and analysing their effects on protein function. To address this, Mut-Map (https://genemutation.org/) is a comprehensive computational pipeline designed to integrate mutation data from the Catalogue Of Somatic Mutations In Cancer database with protein structural data from the Protein Data Bank and AlphaFold models. The pipeline begins by taking a UniProt ID and proceeds through mapping corresponding Protein Data Bank structures, renumbering residues, and assessing disorder percentages. It then overlays mutation data, categorizes mutations based on structural context, and visualizes them using advanced tools like MolStar. This approach allows for a detailed analysis of how mutations may disrupt protein function by affecting key regions such as DNA interfaces, ligand-binding sites, and dimer interactions. To validate the pipeline, a case study on the TP53 gene, a critical tumour suppressor often mutated in cancers, was conducted. The analysis highlighted the most frequent mutations occurring at the DNA-binding interface, providing insights into their potential role in cancer progression. Mut-Map offers a powerful resource for elucidating the structural implications of cancer-associated mutations, paving the way for more targeted therapeutic strategies and advancing our understanding of protein structure-function relationships.

Sequences derived from organisms sharing common evolutionary origins exhibit similarity, while unique sequences, absent in related organisms, act as good diagnostic marker candidates. However, the approach focused on identifying dissimilar regions among closely-related organisms poses challenges as it requires complex multiple sequence alignments, making computation and parsing difficult. To address this, we have developed a biologically inspired universal NAUniSeq algorithm to find the unique sequences for microorganism diagnosis by traveling through the phylogeny of life. Mapping through a phylogenetic tree ensures a low number of cross-contamination and false positives. We have downloaded complete taxonomy data from Taxadb database and sequence data from National Center for Biotechnology Information Reference Sequence Database (NCBI-Refseq) and, with the help of NetworkX, created a phylogenetic tree. Sequences were assigned over the graph nodes, k-mers were created for target and non-target nodes and search was performed over the graph using the depth first search algorithm. In a memory efficient alternative NoSQL approach, we created a collection of Refseq sequences in MongoDB database using tax-id and path of FASTA files. We queried the MongoDB collection for the target and non-target sequences. In both the approaches, we used an alignment free sliding window k-mer-based procedure that quickly compares k-mers of target and non-target sequences and returns unique sequences that are not present in the non-target. We have validated our algorithm with target nodes Mycobacterium tuberculosis, Neisseria gonorrhoeae, and Monkeypox and generated unique sequences. This universal algorithm is a powerful tool for generating diagnostic sequences, enabling the accurate identification of microbial strains with high phylogenetic precision.

Enhancing patient response to immune checkpoint inhibitors (ICIs) is crucial in cancer immunotherapy. We aim to create a data-driven mathematical model of the tumor immune microenvironment (TIME) and utilize deep reinforcement learning (DRL) to optimize patient-specific ICI therapy combined with chemotherapy (ICC). Using patients' genomic and transcriptomic data, we develop an ordinary differential equations (ODEs)-based TIME dynamic evolutionary model to characterize interactions among chemotherapy, ICIs, immune cells, and tumor cells. A DRL agent is trained to determine the personalized optimal ICC therapy. Numerical experiments with real-world data demonstrate that the proposed TIME model can predict ICI therapy response. The DRL-derived personalized ICC therapy outperforms predefined fixed schedules. For tumors with extremely low CD8 + T cell infiltration ('extremely cold tumors'), the DRL agent recommends high-dosage chemotherapy alone. For tumors with higher CD8 + T cell infiltration ('cold' and 'hot tumors'), an appropriate chemotherapy dosage induces CD8 + T cell proliferation, enhancing ICI therapy outcomes. Specifically, for 'hot tumors', chemotherapy and ICI are administered simultaneously, while for 'cold tumors', a mid-dosage of chemotherapy makes the TIME 'hotter' before ICI administration. However, in several 'cold tumors' with rapid resistant tumor cell growth, ICC eventually fails. This study highlights the potential of utilizing real-world clinical data and DRL algorithm to develop personalized optimal ICC by understanding the complex biological dynamics of a patient's TIME. Our ODE-based TIME dynamic evolutionary model offers a theoretical framework for determining the best use of ICI, and the proposed DRL agent may guide personalized ICC schedules.

Using multi-omics data for clustering (cancer subtyping) is crucial for precision medicine research. Despite numerous methods having been proposed, current approaches either do not perform satisfactorily or lack biological interpretability, limiting the practical application of these methods. Based on the biological hypothesis that patients with the same subtype may exhibit similar dysregulated pathways, we developed an Iterative Pathway Fusion approach for enhanced Multi-omics Clustering (IPFMC), a novel multi-omics clustering method involving two data fusion stages. In the first stage, omics data are partitioned at each layer using pathway information, with crucial pathways iteratively selected to represent samples. Ultimately, the representation information from multiple pathways is integrated. In the second stage, similarity network fusion was applied to integrate the representation information from multiple omics. Comparative experiments with nine cancer datasets from The Cancer Genome Atlas (TCGA), involving systematic comparisons with 10 representative methods, reveal that IPFMC outperforms these methods. Additionally, the biological pathways and genes identified by our approach hold biological significance, affirming not only its excellent clustering performance but also its biological interpretability.

In recent years, the advent of spatial transcriptomics (ST) technology has unlocked unprecedented opportunities for delving into the complexities of gene expression patterns within intricate biological systems. Despite its transformative potential, the prohibitive cost of ST technology remains a significant barrier to its widespread adoption in large-scale studies. An alternative, more cost-effective strategy involves employing artificial intelligence to predict gene expression levels using readily accessible whole-slide images stained with Hematoxylin and Eosin (H&E). However, existing methods have yet to fully capitalize on multimodal information provided by H&E images and ST data with spatial location. In this paper, we propose mclSTExp, a multimodal contrastive learning with Transformer and Densenet-121 encoder for Spatial Transcriptomics Expression prediction. We conceptualize each spot as a "word", integrating its intrinsic features with spatial context through the self-attention mechanism of a Transformer encoder. This integration is further enriched by incorporating image features via contrastive learning, thereby enhancing the predictive capability of our model. We conducted an extensive evaluation of highly variable genes in two breast cancer datasets and a skin squamous cell carcinoma dataset, and the results demonstrate that mclSTExp exhibits superior performance in predicting spatial gene expression. Moreover, mclSTExp has shown promise in interpreting cancer-specific overexpressed genes, elucidating immune-related genes, and identifying specialized spatial domains annotated by pathologists. Our source code is available at https://github.com/shizhiceng/mclSTExp.

Cisplatin is one of the most commonly used chemotherapy drugs for treating solid tumors. As a genotoxic agent, cisplatin binds to DNA and forms platinum-DNA adducts that cause DNA damage and activate a series of signaling pathways mediated by various DNA-binding proteins (DBPs), ultimately leading to cell death. Therefore, DBPs play crucial roles in the cellular response to cisplatin and in determining cell fate. However, systematic studies of DBPs responding to cisplatin damage and their temporal dynamics are still lacking. To address this, we developed a novel and user-friendly stand-alone software, DEWNA, designed for dynamic entropy weight network analysis to reveal the dynamic changes of DBPs and their functions. DEWNA utilizes the entropy weight method, multiscale embedded gene co-expression network analysis and generalized reporter score-based analysis to process time-course proteome expression data, helping scientists identify protein hubs and pathway entropy profiles during disease progression. We applied DEWNA to a dataset of DBPs from A549 cells responding to cisplatin-induced damage across 8 time points, with data generated by data-independent acquisition mass spectrometry (DIA-MS). The results demonstrate that DEWNA can effectively identify protein hubs and associated pathways that are significantly altered in response to cisplatin-induced DNA damage, and offer a comprehensive view of how different pathways interact and respond dynamically over time to cisplatin treatment. Notably, we observed the dynamic activation of distinct DNA repair pathways and cell death mechanisms during the drug treatment time course, providing new insights into the molecular mechanisms underlying the cellular response to DNA damage.