Briefings in bioinformatics最新文献

Deciphering the specificity of T-cell receptor (TCR) repertoires is crucial for monitoring adaptive immune responses and developing targeted immunotherapies and vaccines. To elucidate the specificity of previously unseen TCRs, many methods employ the BLOSUM62 matrix to find TCRs with similar amino acid (AA) sequences. However, while BLOSUM62 reflects the AA substitutions within conserved regions of proteins with similar functions, the remarkable diversity of TCRs means that both TCRs with similar and dissimilar sequences can bind the same epitope. Therefore, reliance on BLOSUM62 may bias detection towards epitope-specific TCRs with similar biochemical properties, overlooking those with more diverse AA compositions. In this study, we introduce tcrBLOSUMa and tcrBLOSUMb, specialized AA substitution matrices for CDR3 alpha and CDR3 beta TCR chains, respectively. The matrices reflect AA frequencies and variations occurring within TCRs that bind the same epitope, revealing that both CDR3 alpha and CDR3 beta display tolerance to a wide range of AA substitutions and differ noticeably from the standard BLOSUM62. By accurately aligning distant TCRs employing tcrBLOSUMb, we were able to improve clustering performance and capture a large number of epitope-specific TCRs with diverse AA compositions and physicochemical profiles overlooked by BLOSUM62. Utilizing both the general BLOSUM62 and specialized tcrBLOSUM matrices in existing computational tools will broaden the range of TCRs that can be associated with their cognate epitopes, thereby enhancing TCR repertoire analysis.

Contamination with exogenous DNA presents a significant challenge in ancient DNA (aDNA) studies of single organisms. Failure to address contamination from microbes, reagents, and present-day sources can impact the interpretation of results. Although field and laboratory protocols exist to limit contamination, there is still a need to accurately distinguish between endogenous and exogenous data computationally. Here, we propose a workflow to reduce exogenous contamination based on a metagenomic classifier. Unlike previous methods that relied exclusively on DNA sequencing reads mapping specificity to a single reference genome to remove contaminating reads, our approach uses Kraken2-based filtering before mapping to the reference genome. Using both simulated and empirical shotgun aDNA data, we show that this workflow presents a simple and efficient method that can be used in a wide range of computational environments-including personal machines. We propose strategies to build specific databases used to profile sequencing data that take into consideration available computational resources and prior knowledge about the target taxa and likely contaminants. Our workflow significantly reduces the overall computational resources required during the mapping process and reduces the total runtime by up to ~94%. The most significant impacts are observed in low endogenous samples. Importantly, contaminants that would map to the reference are filtered out using our strategy, reducing false positive alignments. We also show that our method results in a negligible loss of endogenous data with no measurable impact on downstream population genetics analyses.

Spatial transcriptomics (ST technology allows for the detection of cellular transcriptome information while preserving the spatial location of cells. This capability enables researchers to better understand the cellular heterogeneity, spatial organization, and functional interactions in complex biological systems. However, current technological methods are limited by low resolution, which reduces the accuracy of gene expression levels. Here, we propose scstGCN, a multimodal information fusion method based on Vision Transformer and Graph Convolutional Network that integrates histological images, spot-based ST data and spatial location information to infer super-resolution gene expression profiles at single-cell level. We evaluated the accuracy of the super-resolution gene expression profiles generated on diverse tissue ST datasets with disease and healthy by scstGCN along with their performance in identifying spatial patterns, conducting functional enrichment analysis, and tissue annotation. The results show that scstGCN can predict super-resolution gene expression accurately and aid researchers in discovering biologically meaningful differentially expressed genes and pathways. Additionally, scstGCN can segment and annotate tissues at a finer granularity, with results demonstrating strong consistency with coarse manual annotations. Our source code and all used datasets are available at https://github.com/wenwenmin/scstGCN and https://zenodo.org/records/12800375.

Background: This study aims to explore the link between depression and dysmenorrhea by using an integrated and innovative approach that combines genomic, transcriptomic, and protein interaction data/information from various resources.

Methods: A two-sample, bidirectional, and multivariate Mendelian randomization (MR) approach was applied to determine causality between dysmenorrhea and depression. Genome-wide association study (GWAS) data were used to identify genetic variants associated with both dysmenorrhea and depression, followed by colocalization analysis of shared genetic influences. Expression quantitative trait locus (eQTL) data were analyzed from public databases to pinpoint target genes in relevant tissues. Additionally, a protein-protein interaction (PPI) network was constructed using the STRING database to analyze interactions among identified proteins.

Results: MR analysis confirmed a significant causal effect of depression on dysmenorrhea ['odds ratio' (95% confidence interval) = 1.51 (1.19, 1.91), P = 7.26 × 10-4]. Conversely, no evidence was found to support a causal effect of dysmenorrhea on depression (P = .74). Genetic analysis, using GWAS and eQTL data, identified single-nucleotide polymorphisms in several genes, including GRK4, TRAIP, and RNF123, indicating that depression may impact reproductive function through these genetic pathways, with a detailed picture presented by way of analysis in the PPI network. Colocalization analysis highlighted rs34341246(RBMS3) as a potential shared causal variant.

Conclusions: This study suggests that depression significantly affects dysmenorrhea and identifies key genes and proteins involved in this interaction. The findings underline the need for integrated clinical and public health approaches that screen for depression among women presenting with dysmenorrhea and suggest new targeted preventive strategies.

microRNAs (miRNAs) are central post-transcriptional gene expression regulators in healthy and diseased states. Despite decades of effort, deciphering miRNA targets remains challenging, leading to an incomplete miRNA interactome and partially elucidated miRNA functions. Here, we introduce microT-CNN, an avant-garde deep convolutional neural network model that moves the needle by integrating hundreds of tissue-matched (in-)direct experiments from 26 distinct cell types, corresponding to a unique training and evaluation set of >60 000 miRNA binding events and ~30 000 unique miRNA-gene target pairs. The multilayer sequence-based design enables the prediction of both host and virus-encoded miRNA interactions, providing for the first time up to 67% of direct genuine Epstein-Barr virus- and Kaposi's sarcoma-associated herpesvirus-derived miRNA-target pairs corresponding to one out of four binding events of virus-encoded miRNAs. microT-CNN fills the existing gap of the miRNA-target prediction by providing functional targets beyond the canonical sites, including 3' compensatory miRNA pairings, prompting 1.4-fold more validated miRNA binding events compared to other implementations and shedding light on previously unexplored facets of the miRNA interactome.

Reusing massive collections of publicly available biomedical data can significantly impact knowledge discovery. However, these public samples and studies are typically described using unstructured plain text, hindering the findability and further reuse of the data. To combat this problem, we propose txt2onto 2.0, a general-purpose method based on natural language processing and machine learning for annotating biomedical unstructured metadata to controlled vocabularies of diseases and tissues. Compared to the previous version (txt2onto 1.0), which uses numerical embeddings as features, this new version uses words as features, resulting in improved interpretability and performance, especially when few positive training instances are available. Txt2onto 2.0 uses embeddings from a large language model during prediction to deal with unseen-yet-relevant words related to each disease and tissue term being predicted from the input text, thereby explaining the basis of every annotation. We demonstrate the generalizability of txt2onto 2.0 by accurately predicting disease annotations for studies from independent datasets, using proteomics and clinical trials as examples. Overall, our approach can annotate biomedical text regardless of experimental types or sources. Code, data, and trained models are available at https://github.com/krishnanlab/txt2onto2.0.

The rational design of Ribonucleic acid (RNA) molecules is crucial for advancing therapeutic applications, synthetic biology, and understanding the fundamental principles of life. Traditional RNA design methods have predominantly focused on secondary structure-based sequence design, often neglecting the intricate and essential tertiary interactions. We introduce R3Design, a tertiary structure-based RNA sequence design method that shifts the paradigm to prioritize tertiary structure in the RNA sequence design. R3Design significantly enhances sequence design on native RNA backbones, achieving high sequence recovery and Macro-F1 score, and outperforming traditional secondary structure-based approaches by substantial margins. We demonstrate that R3Design can design RNA sequences that fold into the desired tertiary structures by validating these predictions using advanced structure prediction models. This method, which is available through standalone software, provides a comprehensive toolkit for designing, folding, and evaluating RNA at the tertiary level. Our findings demonstrate R3Design's superior capability in designing RNA sequences, which achieves around $44%$ in terms of both recovery score and Macro-F1 score in multiple datasets. This not only denotes the accuracy and fairness of the model but also underscores its potential to drive forward the development of innovative RNA-based therapeutics and to deepen our understanding of RNA biology.

Single-cell multi-omics techniques, which enable the simultaneous measurement of multiple modalities such as RNA gene expression and Assay for Transposase-Accessible Chromatin (ATAC) within individual cells, have become a powerful tool for deciphering the intricate complexity of cellular systems. Most current methods rely on motif databases to establish cross-modality relationships between genes from RNA-seq data and peaks from ATAC-seq data. However, these approaches are constrained by incomplete database coverage, particularly for novel or poorly characterized relationships. To address these limitations, we introduce single-cell Multi-omics Integration (scMI), a heterogeneous graph embedding method that encodes both cells and modality features from single-cell RNA-seq and ATAC-seq data into a shared latent space by learning cross-modality relationships. By modeling cells and modality features as distinct node types, we design an inter-type attention mechanism to effectively capture long-range cross-modality interactions between genes and peaks. Benchmark results demonstrate that embeddings learned by scMI preserve more biological information and achieve comparable or superior performance in downstream tasks including modality prediction, cell clustering, and gene regulatory network inference compared to methods that rely on databases. Furthermore, scMI significantly improves the alignment and integration of unmatched multi-omics data, enabling more accurate embedding and improved outcomes in downstream tasks.

Accurate identification of causal genes for cancer prognosis is critical for estimating disease progression and guiding treatment interventions. In this study, we propose CPCG (Cancer Prognosis's Causal Gene), a two-stage framework identifying gene sets causally associated with patient prognosis across diverse cancer types using transcriptomic data. Initially, an ensemble approach models gene expression's impact on survival with parametric and semiparametric hazard models. Subsequently, an iterative conditional independence test combined with graph pruning is utilized to infer the causal skeleton, thereby pinpointing prognosis-related genes. Experiments on transcriptomic data from 18 cancer types sourced from The Cancer Genome Atlas Project demonstrate CPCG's effectiveness in predicting prognosis under four evaluation metrics. Validations on 24 additional datasets covering 12 cancer types from the Gene Expression Omnibus and the Chinese Glioma Genome Atlas Project further demonstrate CPCG's robustness and generalizability. CPCG identifies a concise but reliable set of genes, obviating the need for gene combination enumeration for survival time estimation. These genes are also proved closely linked to crucial biological processes in cancer. Moreover, CPCG constructs a stable causal skeleton and exhibits insensitivity to the order of data shuffling. Overall, CPCG is a powerful tool for extracting cancer prognostic biomarkers, offering interpretability, generalizability, and robustness. CPCG holds promise for facilitating targeted interventions in clinical treatment strategies.

Complex direct and indirect relationships between multiple variables, termed higher order interactions (HOIs), are characteristics of all natural systems. Traditional differential and network analyses fail to account for the omic datasets richness and miss HOIs. We investigated peripheral blood DNA methylation data from Kabuki syndrome type 1 (KS1) and control individuals, identified 2,002 differentially methylated points (DMPs), and inferred 17 differentially methylated regions, which represent only 189 DMPs. We applied hypergraph models to measure HOIs on all the CpGs and revealed differences in the coordination of DMPs with lower entropy and higher coordination of the peripheral epigenome in KS1 implying reduced network complexity. Hypergraphs also capture epigenomic trans-relationships, and identify biologically relevant pathways that escape the standard analyses. These findings construct the basis of a suitable model for the analysis of organization in the epigenome in rare diseases, which can be applied to investigate mechanism in big data.