Bioinformatics advances最新文献

Summary: The proliferation of biological sequence data, due to developments in molecular biology techniques, has led to the creation of numerous open access databases on gene and protein sequencing. However, the lack of direct equivalence between identifiers across these databases difficults data integration. To address this challenge, we introduce ginmappeR, an integrated R package facilitating the translation of gene and protein identifiers between databases. By providing a unified interface, ginmappeR streamlines the integration of diverse data sources into biological workflows, so it enhances efficiency and user experience.

Availability and implementation: from Bioconductor: https://bioconductor.org/packages/ginmappeR.

Motivation: With larger and more diverse studies becoming the standard in genome-wide association studies (GWAS), accurate estimation of ancestral proportions is increasingly important for summary-statistics-based methods such as those for imputing association summary statistics, adjusting allele frequencies (AFs) for ancestry, and prioritizing disease candidate variants or genes. Existing methods for estimating ancestral proportions in GWAS rely on the availability of study reference AFs, which are often inaccessible in current GWAS due to privacy concerns.

Results: In this study, we propose ZMIX (Z-score-based estimation of ethnic MIXing proportions), a novel method for estimating ethnic mixing proportions in GWAS using only association Z-scores, and we compare its performance to existing reference AF-based methods in both real-world and simulated GWAS settings. We found that ZMIX offered comparable results to the reference AF-based methods in simulation and real-world studies. When applied to summary-statistics imputation, all three methods produced high-quality imputations with almost identical results.

Availability and implementation: https://github.com/statsleelab/gauss.

Motivation: Benzo[a]pyrene, a notorious DNA-damaging carcinogen, belongs to the family of polycyclic aromatic hydrocarbons commonly found in tobacco smoke. Surprisingly, nucleotide excision repair (NER) machinery exhibits inefficiency in recognizing specific bulky DNA adducts including Benzo[a]pyrene Diol-Epoxide (BPDE), a Benzo[a]pyrene metabolite. While sequence context is emerging as the leading factor linking the inadequate NER response to BPDE adducts, the precise structural attributes governing these disparities remain inadequately understood. We therefore combined the domains of molecular dynamics and machine learning to conduct a comprehensive assessment of helical distortion caused by BPDE-Guanine adducts in multiple gene contexts. Specifically, we implemented a dual approach involving a random forest classification-based analysis and subsequent feature selection to identify precise topological features that may distinguish adduct sites of variable repair capacity. Our models were trained using helical data extracted from duplexes representing both BPDE hotspot and nonhotspot sites within the TP53 gene, then applied to sites within TP53, cII, and lacZ genes.

Results: We show our optimized model consistently achieved exceptional performance, with accuracy, precision, and f1 scores exceeding 91%. Our feature selection approach uncovered that discernible variance in regional base pair rotation played a pivotal role in informing the decisions of our model. Notably, these disparities were highly conserved among TP53 and lacZ duplexes and appeared to be influenced by the regional GC content. As such, our findings suggest that there are indeed conserved topological features distinguishing hotspots and nonhotpot sites, highlighting regional GC content as a potential biomarker for mutation.

Availability and implementation: Code for comparing machine learning classifiers and evaluating their performance is available at https://github.com/jdavies24/ML-Classifier-Comparison, and code for analysing DNA structure with Curves+ and Canal using Random Forest is available at https://github.com/jdavies24/ML-classification-of-DNA-trajectories.

Motivation: Recent RNA-centric experimental methods have significantly expanded our knowledge of proteins with known RNA-binding functions. However, the complete regulatory network and pathways for many of these RNA-binding proteins (RBPs) in different cellular contexts remain unknown. Although critical to understanding the role of RBPs in health and disease, experimentally mapping the RBP-RNA interactomes in every single context is an impossible task due the cost and manpower required. Additionally, identifying relevant RNAs bound by RBPs is challenging due to their diverse binding modes and function.

Results: To address these challenges, we developed RBP interaction mapper RBPInper an integrative framework that discovers global RBP interactome using statistical data fusion. Experiments on splicing factor proline and glutamine rich (SFPQ) datasets revealed cogent global SFPQ interactome. Several biological processes associated with this interactome were previously linked with SFPQ function. Furthermore, we conducted tests using independent dataset to assess the transferability of the SFPQ interactome to another context. The results demonstrated robust utility in generating interactomes that transfers to unseen cellular context. Overall, RBPInper is a fast and user-friendly method that enables a systems-level understanding of RBP functions by integrating multiple molecular datasets. The tool is designed with a focus on simplicity, minimal dependencies, and straightforward input requirements. This intentional design aims to empower everyday biologists, making it easy for them to incorporate the tool into their research.

Availability and implementation: The source code, documentation, and installation instructions as well as results for use case are freely available at https://github.com/AneneLab/RBPInper. A user can easily compile similar datasets for a target RBP.

Motivation: Latent unknown clustering integrating multi-omics data is a novel statistical model designed for multi-omics data analysis. It integrates omics data with exposures and an outcome through a latent cluster, elucidating how exposures influence processes reflected in multi-omics measurements, ultimately affecting an outcome. A significant challenge in multi-omics analysis is the issue of list-wise missingness. To address this, we extend the model to incorporate list-wise missingness within an integrated imputation framework, which can also handle sporadic missingness when necessary.

Results: Simulation studies demonstrate that our integrated imputation approach produces consistent and less biased estimates, closely reflecting true underlying values. We applied this model to data from the ISGlobal/ATHLETE "Exposome Data Challenge Event" to explore the association between maternal exposure to hexachlorobenzene and childhood body mass index by integrating incomplete proteomics data from 1301 children. The model successfully estimated proteomics profiles for two clusters representing higher and lower body mass index, characterizing the potential profiles linking prenatal hexachlorobenzene levels and childhood body mass index.

Availability and implementation: The proposed methods have been implemented in the R package LUCIDus. The source code is available at https://github.com/USCbiostats/LUCIDus.

Motivation: Unraveling the connection between genes and traits is crucial for solving many biological puzzles. Ribonucleic acid molecules and proteins, derived from these genetic instructions, play crucial roles in shaping cell structures, influencing reactions, and guiding behavior. This fundamental biological principle links genetic makeup to observable traits, but integrating and extracting meaningful relationships from this complex, multimodal data present a significant challenge.

Results: We introduce evolSOM, a novel R package that allows exploring and visualizing the conservation or displacement of biological variables, easing the integration of phenotypic and genotypic attributes. It enables the projection of multi-dimensional expression profiles onto interpretable two-dimensional grids, aiding in the identification of conserved or displaced genes/phenotypes across multiple conditions. Variables displaced together suggest membership to the same regulatory network, where the nature of the displacement may hold biological significance. The conservation or displacement of variables is automatically calculated and graphically presented by evolSOM. Its user-friendly interface and visualization capabilities enhance the accessibility of complex network analyses.

Availability and implementation: The package is open-source under the GPL ( $\ge$ 3) and is available at https://github.com/sanprochetto/evolSOM, along with a step-by-step vignette and a full example dataset that can be accessed at https://github.com/sanprochetto/evolSOM/tree/main/inst/extdata.

Summary: We introduce refseqR, an R package that offers a user-friendly solution, enabling common computational operations on RefSeq entries (GenBank, NCBI). The package is specifically designed to interact with records curated from the RefSeq database. Most importantly, the interoperability and integration with several Bioconductor objects allow connections to be applied to other projects.

Availability and implementation: The package refseqR is implemented in R and published under the MIT open-source license. The source code, documentation, and usage instructions are available on CRAN (https://CRAN.R-project.org/package=refseqR).

Motivation: Despite significant progress in biomedical information extraction, there is a lack of resources for Named Entity Recognition (NER) and Named Entity Normalization (NEN) of protein-containing complexes. Current resources inadequately address the recognition of protein-containing complex names across different organisms, underscoring the crucial need for a dedicated corpus.

Results: We introduce the Complex Named Entity Corpus (CoNECo), an annotated corpus for NER and NEN of complexes. CoNECo comprises 1621 documents with 2052 entities, 1976 of which are normalized to Gene Ontology. We divided the corpus into training, development, and test sets and trained both a transformer-based and dictionary-based tagger on them. Evaluation on the test set demonstrated robust performance, with F-scores of 73.7% and 61.2%, respectively. Subsequently, we applied the best taggers for comprehensive tagging of the entire openly accessible biomedical literature.

Availability and implementation: All resources, including the annotated corpus, training data, and code, are available to the community through Zenodo https://zenodo.org/records/11263147 and GitHub https://zenodo.org/records/10693653.

Motivation: Enhancers play critical roles in cell-type-specific transcriptional control. Despite the identification of thousands of candidate enhancers, unravelling their regulatory relationships with their target genes remains challenging. Therefore, computational approaches are needed to accurately infer enhancer-gene regulatory relationships.

Results: In this study, we propose a new method, IVEA, that predicts enhancer-gene regulatory interactions by estimating promoter and enhancer activities. Its statistical model is based on the gene regulatory mechanism of transcriptional bursting, which is characterized by burst size and frequency controlled by promoters and enhancers, respectively. Using transcriptional readouts, chromatin accessibility, and chromatin contact data as inputs, promoter and enhancer activities were estimated using variational Bayesian inference, and the contribution of each enhancer-promoter pair to target gene transcription was calculated. Our analysis demonstrates that the proposed method can achieve high prediction accuracy and provide biologically relevant enhancer-gene regulatory interactions.

Availability and implementation: The IVEA code is available on GitHub at https://github.com/yasumasak/ivea. The publicly available datasets used in this study are described in Supplementary Table S4.

Motivation: As fewer than 1% of proteins have protein function information determined experimentally, computationally predicting the function of proteins is critical for obtaining functional information for most proteins and has been a major challenge in protein bioinformatics. Despite the significant progress made in protein function prediction by the community in the last decade, the general accuracy of protein function prediction is still not high, particularly for rare function terms associated with few proteins in the protein function annotation database such as the UniProt.

Results: We introduce TransFew, a new transformer model, to learn the representations of both protein sequences and function labels [Gene Ontology (GO) terms] to predict the function of proteins. TransFew leverages a large pre-trained protein language model (ESM2-t48) to learn function-relevant representations of proteins from raw protein sequences and uses a biological natural language model (BioBert) and a graph convolutional neural network-based autoencoder to generate semantic representations of GO terms from their textual definition and hierarchical relationships, which are combined together to predict protein function via the cross-attention. Integrating the protein sequence and label representations not only enhances overall function prediction accuracy, but delivers a robust performance of predicting rare function terms with limited annotations by facilitating annotation transfer between GO terms.

Availability and implementation: https://github.com/BioinfoMachineLearning/TransFew.