Bioinformatics (Oxford, England)最新文献

{"title":"PharaCon: a new framework for identifying bacteriophages via conditional representation learning.","authors":"Zeheng Bai, Yao-Zhong Zhang, Yuxuan Pang, Seiya Imoto","doi":"10.1093/bioinformatics/btaf085","DOIUrl":"10.1093/bioinformatics/btaf085","url":null,"abstract":"<p><strong>Motivation: </strong>Identifying bacteriophages (phages) within metagenomic sequences is essential for understanding microbial community dynamics. Transformer-based foundation models have been successfully employed to address various biological challenges. However, these models are typically pre-trained with self-supervised tasks that do not consider label variance in the pre-training data. This presents a challenge for phage identification as pre-training on mixed bacterial and phage data may lead to information bias due to the imbalance between bacterial and phage samples.</p><p><strong>Results: </strong>To overcome this limitation, we proposed a novel conditional BERT framework that incorporates label classes as special tokens during pre-training. Specifically, our conditional BERT model attaches labels directly during tokenization, introducing label constraints into the model's input. Additionally, we introduced a new fine-tuning scheme that enables the conditional BERT to be effectively utilized for classification tasks. This framework allows the BERT model to acquire label-specific contextual representations from mixed sequence data during pre-training and applies the conditional BERT as a classifier during fine-tuning, and we named the fine-tuned model as PharaCon. We evaluated PharaCon against several existing methods on both simulated sequence datasets and real metagenomic contig datasets. The results demonstrate PharaCon's effectiveness and efficiency in phage identification, highlighting the advantages of incorporating label information during both pre-training and fine-tuning.</p><p><strong>Availability and implementation: </strong>The source code and associated data can be accessed at https://github.com/Celestial-Bai/PharaCon.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11928753/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143485018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Relative quantification of proteins and post-translational modifications in proteomic experiments with shared peptides: a weight-based approach.","authors":"Mateusz Staniak, Ting Huang, Amanda M Figueroa-Navedo, Devon Kohler, Meena Choi, Trent Hinkle, Tracy Kleinheinz, Robert Blake, Christopher M Rose, Yingrong Xu, Pierre M Jean Beltran, Liang Xue, Małgorzata Bogdan, Olga Vitek","doi":"10.1093/bioinformatics/btaf046","DOIUrl":"10.1093/bioinformatics/btaf046","url":null,"abstract":"<p><strong>Motivation: </strong>Bottom-up mass spectrometry-based proteomics studies changes in protein abundance and structure across conditions. Since the currency of these experiments are peptides, i.e. subsets of protein sequences that carry the quantitative information, conclusions at a different level must be computationally inferred. The inference is particularly challenging in situations where the peptides are shared by multiple proteins or post-translational modifications. While many approaches infer the underlying abundances from unique peptides, there is a need to distinguish the quantitative patterns when peptides are shared.</p><p><strong>Results: </strong>We propose a statistical approach for estimating protein abundances, as well as site occupancies of post-translational modifications, based on quantitative information from shared peptides. The approach treats the quantitative patterns of shared peptides as convex combinations of abundances of individual proteins or modification sites, and estimates the abundance of each source in a sample together with the weights of the combination. In simulation-based evaluations, the proposed approach improved the precision of estimated fold changes between conditions. We further demonstrated the practical utility of the approach in experiments with diverse biological objectives, ranging from protein degradation and thermal proteome stability, to changes in protein post-translational modifications.</p><p><strong>Availability and implementation: </strong>The approach is implemented in an open-source R package MSstatsWeightedSummary. The package is currently available at https://github.com/Vitek-Lab/MSstatsWeightedSummary (doi: 10.5281/zenodo.14662989). Code required to reproduce the results presented in this article can be found in a repository https://github.com/mstaniak/MWS_reproduction (doi: 10.5281/zenodo.14656053).</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11879648/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143071284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SimMS: a GPU-accelerated cosine similarity implementation for tandem mass spectrometry.","authors":"Tornike Onoprishvili, Jui-Hung Yuan, Kamen Petrov, Vijay Ingalalli, Lila Khederlarian, Niklas Leuchtenmuller, Sona Chandra, Aurelien Duarte, Andreas Bender, Yoann Gloaguen","doi":"10.1093/bioinformatics/btaf081","DOIUrl":"10.1093/bioinformatics/btaf081","url":null,"abstract":"<p><strong>Motivation: </strong>Untargeted metabolomics involves a large-scale comparison of the fragmentation pattern of a mass spectrum against a database containing known spectra. Given the number of comparisons involved, this step can be time-consuming.</p><p><strong>Results: </strong>In this work, we present a GPU-accelerated cosine similarity implementation for Tandem Mass Spectrometry (MS), with an approximately 1000-fold speedup compared to the MatchMS reference implementation, without any loss of accuracy. This improvement enables repository-scale spectral library matching for compound identification without the need for large compute clusters. This impact extends to any spectral comparison-based methods such as molecular networking approaches and analogue search.</p><p><strong>Availability and implementation: </strong>All code, results, and notebooks supporting are freely available under the MIT license at https://github.com/pangeAI/simms/.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11886821/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143470193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Single-cell copy number calling and event history reconstruction.","authors":"Jack Kuipers, Mustafa Anıl Tuncel, Pedro F Ferreira, Katharina Jahn, Niko Beerenwinkel","doi":"10.1093/bioinformatics/btaf072","DOIUrl":"10.1093/bioinformatics/btaf072","url":null,"abstract":"<p><strong>Motivation: </strong>Copy number alterations are driving forces of tumour development and the emergence of intra-tumour heterogeneity. A comprehensive picture of these genomic aberrations is therefore essential for the development of personalised and precise cancer diagnostics and therapies. Single-cell sequencing offers the highest resolution for copy number profiling down to the level of individual cells. Recent high-throughput protocols allow for the processing of hundreds of cells through shallow whole-genome DNA sequencing. The resulting low read-depth data poses substantial statistical and computational challenges to the identification of copy number alterations.</p><p><strong>Results: </strong>We developed SCICoNE, a statistical model and MCMC algorithm tailored to single-cell copy number profiling from shallow whole-genome DNA sequencing data. SCICoNE reconstructs the history of copy number events in the tumour and uses these evolutionary relationships to identify the copy number profiles of the individual cells. We show the accuracy of this approach in evaluations on simulated data and demonstrate its practicability in applications to two breast cancer samples from different sequencing protocols.</p><p><strong>Availability and implementation: </strong>SCICoNE is available at https://github.com/cbg-ethz/SCICoNE.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11897432/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143411868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ORCO: Ollivier-Ricci Curvature-Omics-an unsupervised method for analyzing robustness in biological systems.","authors":"Anish K Simhal, Corey Weistuch, Kevin Murgas, Daniel Grange, Jiening Zhu, Jung Hun Oh, Rena Elkin, Joseph O Deasy","doi":"10.1093/bioinformatics/btaf093","DOIUrl":"10.1093/bioinformatics/btaf093","url":null,"abstract":"<p><strong>Motivation: </strong>Although recent advanced sequencing technologies have improved the resolution of genomic and proteomic data to better characterize molecular phenotypes, efficient computational tools to analyze and interpret large-scale omic data are still needed.</p><p><strong>Results: </strong>To address this, we have developed a network-based bioinformatic tool called Ollivier-Ricci curvature for omics (ORCO). ORCO incorporates omics data and a network describing biological relationships between the genes or proteins and computes Ollivier-Ricci curvature (ORC) values for individual interactions. ORC is an edge-based measure that assesses network robustness. It captures functional cooperation in gene signaling using a consistent information-passing measure, which can help investigators identify therapeutic targets and key regulatory modules in biological systems. ORC has identified novel insights in multiple cancer types using genomic data and in neurodevelopmental disorders using brain imaging data. This tool is applicable to any data that can be represented as a network.</p><p><strong>Availability and implementation: </strong>ORCO is an open-source Python package and is publicly available on GitHub at https://github.com/aksimhal/ORC-Omics.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11893153/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143560368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting circRNA-disease associations with shared units and multi-channel attention mechanisms.","authors":"Xue Zhang, Quan Zou, Mengting Niu, Chunyu Wang","doi":"10.1093/bioinformatics/btaf088","DOIUrl":"10.1093/bioinformatics/btaf088","url":null,"abstract":"<p><strong>Motivation: </strong>Circular RNAs (circRNAs) have been identified as key players in the progression of several diseases; however, their roles have not yet been determined because of the high financial burden of biological studies. This highlights the urgent need to develop efficient computational models that can predict circRNA-disease associations, offering an alternative approach to overcome the limitations of expensive experimental studies. Although multi-view learning methods have been widely adopted, most approaches fail to fully exploit the latent information across views, while simultaneously overlooking the fact that different views contribute to varying degrees of significance.</p><p><strong>Results: </strong>This study presents a method that combines multi-view shared units and multichannel attention mechanisms to predict circRNA-disease associations (MSMCDA). MSMCDA first constructs similarity and meta-path networks for circRNAs and diseases by introducing shared units to facilitate interactive learning across distinct network features. Subsequently, multichannel attention mechanisms were used to optimize the weights within similarity networks. Finally, contrastive learning strengthened the similarity features. Experiments on five public datasets demonstrated that MSMCDA significantly outperformed other baseline methods. Additionally, case studies on colorectal cancer, gastric cancer, and nonsmall cell lung cancer confirmed the effectiveness of MSMCDA in uncovering new associations.</p><p><strong>Availability and implementation: </strong>The source code and data are available at https://github.com/zhangxue2115/MSMCDA.git.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11919450/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143568377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AsaruSim: a single-cell and spatial RNA-Seq Nanopore long-reads simulation workflow.","authors":"Ali Hamraoui, Laurent Jourdren, Morgane Thomas-Chollier","doi":"10.1093/bioinformatics/btaf087","DOIUrl":"10.1093/bioinformatics/btaf087","url":null,"abstract":"<p><strong>Motivation: </strong>The combination of long-read sequencing technologies like Oxford Nanopore with single-cell RNA sequencing (scRNAseq) assays enables the detailed exploration of transcriptomic complexity, including isoform detection and quantification, by capturing full-length cDNAs. However, challenges remain, including the lack of advanced simulation tools that can effectively mimic the unique complexities of scRNAseq long-read datasets. Such tools are essential for the evaluation and optimization of isoform detection methods dedicated to single-cell long-read studies.</p><p><strong>Results: </strong>We developed AsaruSim, a workflow that simulates synthetic single-cell long-read Nanopore datasets, closely mimicking real experimental data. AsaruSim employs a multi-step process that includes the creation of a synthetic count matrix, generation of perfect reads, optional PCR amplification, introduction of sequencing errors, and comprehensive quality control reporting. Applied to a dataset of human peripheral blood mononuclear cells, AsaruSim accurately reproduced experimental read characteristics.</p><p><strong>Availability and implementation: </strong>The source code and full documentation are available at https://github.com/GenomiqueENS/AsaruSim.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11897429/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143476919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sul-BertGRU: an ensemble deep learning method integrating information entropy-enhanced BERT and directional multi-GRU for S-sulfhydration sites prediction.","authors":"Xirun Wei, Qiao Ning, Kuiyang Che, Zhaowei Liu, Hui Li, Shikai Guo","doi":"10.1093/bioinformatics/btaf078","DOIUrl":"10.1093/bioinformatics/btaf078","url":null,"abstract":"<p><strong>Motivation: </strong>S-sulfhydration, a crucial post-translational protein modification, is pivotal in cellular recognition, signaling processes, and the development and progression of cardiovascular and neurological disorders, so identifying S-sulfhydration sites is crucial for studies in cell biology. Deep learning shows high efficiency and accuracy in identifying protein sites compared to traditional methods that often lack sensitivity and specificity in accurately locating nonsulfhydration sites. Therefore, we employ deep learning methods to tackle the challenge of pinpointing S-sulfhydration sites.</p><p><strong>Results: </strong>In this work, we introduce a deep learning approach called Sul-BertGRU, designed specifically for predicting S-sulfhydration sites in proteins, which integrates multi-directional gated recurrent unit (GRU) and BERT. First, Sul-BertGRU proposes an information entropy-enhanced BERT (IE-BERT) to preprocess protein sequences and extract initial features. Subsequently, confidence learning is employed to eliminate potential S-sulfhydration samples from the nonsulfhydration samples and select reliable negative samples. Then, considering the directional nature of the modification process, protein sequences are categorized into left, right, and full sequences centered on cysteines. We build a multi-directional GRU to enhance the extraction of directional sequence features and model the details of the enzymatic reaction involved in S-sulfhydration. Ultimately, we apply a parallel multi-head self-attention mechanism alongside a convolutional neural network to deeply analyze sequence features that might be missed at a local level. Sul-BertGRU achieves sensitivity, specificity, precision, accuracy, Matthews correlation coefficient, and area under the curve scores of 85.82%, 68.24%, 74.80%, 77.44%, 55.13%, and 77.03%, respectively. Sul-BertGRU demonstrates exceptional performance and proves to be a reliable method for predicting protein S-sulfhydration sites.</p><p><strong>Availability and implementation: </strong>The source code and data are available at https://github.com/Severus0902/Sul-BertGRU/.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11908646/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143470197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DeepES: deep learning-based enzyme screening to identify orphan enzyme genes.","authors":"Keisuke Hirota, Felix Salim, Takuji Yamada","doi":"10.1093/bioinformatics/btaf053","DOIUrl":"10.1093/bioinformatics/btaf053","url":null,"abstract":"<p><strong>Motivation: </strong>Progress in sequencing technology has led to determination of large numbers of protein sequences, and large enzyme databases are now available. Although many computational tools for enzyme annotation were developed, sequence information is unavailable for many enzymes, known as orphan enzymes. These orphan enzymes hinder sequence similarity-based functional annotation, leading gaps in understanding the association between sequences and enzymatic reactions.</p><p><strong>Results: </strong>Therefore, we developed DeepES, a deep learning-based tool for enzyme screening to identify orphan enzyme genes, focusing on biosynthetic gene clusters and reaction class. DeepES uses protein sequences as inputs and evaluates whether the input genes contain biosynthetic gene clusters of interest by integrating the outputs of the binary classifier for each reaction class. The validation results suggested that DeepES can capture functional similarity between protein sequences, and it can be implemented to explore orphan enzyme genes. By applying DeepES to 4744 metagenome-assembled genomes, we identified candidate genes for 236 orphan enzymes, including those involved in short-chain fatty acid production as a characteristic pathway in human gut bacteria.</p><p><strong>Availability and implementation: </strong>DeepES is available at https://github.com/yamada-lab/DeepES. Model weights and the candidate genes are available at Zenodo (https://doi.org/10.5281/zenodo.11123900).</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11881691/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143256487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AcImpute: a constraint-enhancing smooth-based approach for imputing single-cell RNA sequencing data.","authors":"Wei Zhang, Tiantian Liu, Han Zhang, Yuanyuan Li","doi":"10.1093/bioinformatics/btae711","DOIUrl":"10.1093/bioinformatics/btae711","url":null,"abstract":"<p><strong>Motivation: </strong>Single-cell RNA sequencing (scRNA-seq) provides a powerful tool for studying cellular heterogeneity and complexity. However, dropout events in single-cell RNA-seq data severely hinder the effectiveness and accuracy of downstream analysis. Therefore, data preprocessing with imputation methods is crucial to scRNA-seq analysis.</p><p><strong>Results: </strong>To address the issue of oversmoothing in smoothing-based imputation methods, the presented AcImpute, an unsupervised method that enhances imputation accuracy by constraining the smoothing weights among cells for genes with different expression levels. Compared with nine other imputation methods in cluster analysis and trajectory inference, the experimental results can demonstrate that AcImpute effectively restores gene expression, preserves inter-cell variability, preventing oversmoothing and improving clustering and trajectory inference performance.</p><p><strong>Availability and implementation: </strong>The code is available at https://github.com/Liutto/AcImpute.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11890269/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143560314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}