Bioinformatics advances最新文献_第2页

cfTools: an R/Bioconductor package for deconvolving cell-free DNA via methylation analysis. cfTools：一个R/Bioconductor包，用于通过甲基化分析反卷积无细胞DNA。

IF 2.4

Bioinformatics advances Pub Date : 2025-05-06 eCollection Date: 2025-01-01 DOI: 10.1093/bioadv/vbaf108

Ran Hu, Shuo Li, Mary L Stackpole, Qingjiao Li, Xianghong Jasmine Zhou, Wenyuan Li

{"title":"cfTools: an R/Bioconductor package for deconvolving cell-free DNA via methylation analysis.","authors":"Ran Hu, Shuo Li, Mary L Stackpole, Qingjiao Li, Xianghong Jasmine Zhou, Wenyuan Li","doi":"10.1093/bioadv/vbaf108","DOIUrl":"10.1093/bioadv/vbaf108","url":null,"abstract":"Motivation: Cell-free DNA (cfDNA) released by dying cells from damaged or diseased tissues can lead to elevated tissue-specific DNA, which is traceable and quantifiable through unique DNA methylation patterns. Therefore, tracing cfDNA origins by analyzing its methylation profiles holds great potential for detecting and monitoring a range of diseases, including cancers. However, deconvolving tissue-specific cfDNA remains challenging for broader applications and research due to the scarcity of specialized, user-friendly bioinformatics tools.Results: To address this, we developed cfTools, an R package that streamlines cfDNA tissue-of-origin analysis for disease detection and monitoring. Integrating advanced cfDNA tissue deconvolution algorithms with R/Bioconductor compatibility, cfTools offers data preparation and analysis functions with flexible parameters for user-friendliness. By identifying abnormal cfDNA compositions, cfTools can infer the presence of underlying pathological conditions, including but not limited to cancer. It simplifies bioinformatics tasks and enables users without advanced expertise to easily derive biologically interpretable insights from standard preprocessed sequencing data, thus increasing its accessibility and broadening its application in cfDNA-based disease studies.Availability and implementation: cfTools and its supplementary package cfToolsData are freely available at Bioconductor: https://bioconductor.org/packages/release/bioc/html/cfTools.html and https://bioconductor.org/packages/release/data/experiment/html/cfToolsData.html. The development version of cfTools is maintained on GitHub: https://github.com/jasminezhoulab/cfTools.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf108"},"PeriodicalIF":2.4,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12124914/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144200913","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}

引用次数: 0

AnnSQL: a Python SQL-based package for fast large-scale single-cell genomics analysis using minimal computational resources. AnnSQL：一个基于Python sql的包，用于使用最少的计算资源进行快速大规模单细胞基因组分析。

IF 2.4

Bioinformatics advances Pub Date : 2025-05-05 eCollection Date: 2025-01-01 DOI: 10.1093/bioadv/vbaf105

Kenny Pavan, Arpiar Saunders

{"title":"AnnSQL: a Python SQL-based package for fast large-scale single-cell genomics analysis using minimal computational resources.","authors":"Kenny Pavan, Arpiar Saunders","doi":"10.1093/bioadv/vbaf105","DOIUrl":"10.1093/bioadv/vbaf105","url":null,"abstract":"Summary: As single-cell genomics technologies continue to accelerate biological discovery, software tools that use elegant syntax and minimal computational resources to analyze atlas-scale datasets are increasingly needed. Here, we introduce AnnSQL, a Python package that constructs an AnnData-inspired database using the in-process DuckDb engine, enabling orders-of-magnitude performance enhancements for parsing single-cell genomics datasets with the ease of SQL. We highlight AnnSQL functionality and demonstrate transformative runtime improvements by comparing AnnData or AnnSQL operations on a 4.4 million cell single-nucleus RNA-seq dataset: AnnSQL-based operations were executed in minutes on a laptop for which equivalent operations in AnnData or Seurat largely failed (or were ∼700× slower) on a high-performance computing cluster. AnnSQL lowers computational barriers for large-scale single-cell/nucleus RNA-seq analysis on a personal computer, while demonstrating a promising computational infrastructure extendable for complete single-cell workflows across various genome-wide measurements.Availability and implementation: AnnSQL is a pip installable package that can be found at https://github.com/ArpiarSaundersLab/annsql along with documentation at https://docs.annsql.com.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf105"},"PeriodicalIF":2.4,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12098940/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144144639","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}

引用次数: 0

GenePioneer: a comprehensive Python package for identification of essential genes and modules in cancer. GenePioneer：一个全面的Python包，用于识别癌症中的基本基因和模块。

IF 2.4

Bioinformatics advances Pub Date : 2025-04-29 eCollection Date: 2025-01-01 DOI: 10.1093/bioadv/vbaf094

Amirhossein Haerianardakani, Golnaz Taheri

引用次数: 0

Assessing accuracy and specificity of faecal source library for microbial source-tracking, using SourceTracker as case study. 以SourceTracker为例，评估粪便源库用于微生物源追踪的准确性和特异性。

IF 2.4

Bioinformatics advances Pub Date : 2025-04-29 eCollection Date: 2025-01-01 DOI: 10.1093/bioadv/vbaf103

Timothy J Y Lim, Yussi M Palacios Delgado, Anna Lintern, David T McCarthy, Rebekah Henry

{"title":"Assessing accuracy and specificity of faecal source library for microbial source-tracking, using SourceTracker as case study.","authors":"Timothy J Y Lim, Yussi M Palacios Delgado, Anna Lintern, David T McCarthy, Rebekah Henry","doi":"10.1093/bioadv/vbaf103","DOIUrl":"10.1093/bioadv/vbaf103","url":null,"abstract":"Motivation: Understanding the quality of the source library prior to undertaking library-dependent microbial source-tracking (MST) is an essential, but often overlooked, primary analysis step.Results: We propose an assessment approach to validate the quality of amplicon-derived faecal source libraries. This approach was demonstrated on a faecal source library consisting of 16S rRNA paired-end amplicon sequences, obtained from various animal types in Victoria, Australia. First, a leave-one-out (LOO) analysis was performed to assess the accuracy of source category groupings by identifying the number of samples incorrectly assigned to a different source category (i.e. animal type). Following a quality control procedure to decide retaining/removing/grouping incorrectly assigned samples, we then assessed if the sample sizes for each source type were sufficient to properly characterize the source fingerprints. Results from LOO demonstrated 15.5% of samples were incorrectly assigned, with high error rates in birds and wallabies within our source library. Increasing the sample size improved source identification accuracy. However, accuracy eventually plateaued in a source-specific manner. Importantly, this highlights the importance of conducting thorough assessments to understand the quality and limitations of the source library prior to library-dependent MST applications.Availability and implementation: QIIME2 is available via https://qiime2.org/; SourceTracker v2.0.1 is available via https://github.com/caporaso-lab/sourcetracker2; Pipeline for LOO is available via https://github.com/MonashOWL/Bioinformatics-IlluminaMGI/tree/main/16S/LOO; Pipeline for sample size assessment is available via https://github.com/MonashOWL/Bioinformatics-IlluminaMGI/tree/main/16S/Source%20variability.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf103"},"PeriodicalIF":2.4,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12092083/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144112782","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}

引用次数: 0

bamSliceR: a Bioconductor package for rapid, cross-cohort variant and allelic bias analysis. bamSliceR：用于快速、跨队列变异和等位基因偏倚分析的Bioconductor软件包。

IF 2.4

Bioinformatics advances Pub Date : 2025-04-28 eCollection Date: 2025-01-01 DOI: 10.1093/bioadv/vbaf098

Yizhou Peter Huang, Lauren Harmon, Eve Deering-Gardner, Xiaotu Ma, Josiah Harsh, Zhaoyu Xue, Hong Wen, Marcel Ramos, Sean Davis, Timothy J Triche

{"title":"bamSliceR: a Bioconductor package for rapid, cross-cohort variant and allelic bias analysis.","authors":"Yizhou Peter Huang, Lauren Harmon, Eve Deering-Gardner, Xiaotu Ma, Josiah Harsh, Zhaoyu Xue, Hong Wen, Marcel Ramos, Sean Davis, Timothy J Triche","doi":"10.1093/bioadv/vbaf098","DOIUrl":"10.1093/bioadv/vbaf098","url":null,"abstract":"Motivation: The National Cancer Institute Genomic Data Commons (GDC) provides controlled access to sequencing data from thousands of subjects, enabling large-scale study of impactful genetic alterations such as simple and complex germline and structural variants. However, efficient analysis requires significant computational resources and expertise, especially when calling variants from raw sequence reads. To solve these problems, we developed bamSliceR, a R/bioconductor package that builds upon the GenomicDataCommons package to extract aligned sequence reads from cross-GDC meta-cohorts, followed by targeted analysis of variants and effects (including transcript-aware variant annotation from transcriptome-aligned GDC RNA data).Results: Here, we demonstrate population-scale genomic and transcriptomic analyses with minimal compute burden using bamSliceR, identifying recurrent, clinically relevant sequence, and structural variants in the TARGET acute myeloid leukemia (AML) and BEAT-AML cohorts. We then validate results in the (non-GDC) Leucegene cohort, demonstrating how the bamSliceR pipeline can be seamlessly applied to replicate findings in non-GDC cohorts. These variants directly yield clinically impactful and biologically testable hypotheses for mechanistic investigation.Availability and implementation: bamSliceR has been submitted to the Bioconductor project, where it is presently under review, and is available on GitHub at https://github.com/trichelab/bamSliceR.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf098"},"PeriodicalIF":2.4,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12089696/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144112774","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}

引用次数: 0

Grape-Pi: graph-based neural networks for enhanced protein identification in proteomics pipelines. 在蛋白质组学管道中增强蛋白质鉴定的基于图的神经网络。

IF 2.4

Bioinformatics advances Pub Date : 2025-04-26 eCollection Date: 2025-01-01 DOI: 10.1093/bioadv/vbaf095

Chunhui Gu, Seyyed Mahmood Ghasemi, Yining Cai, Johannes F Fahrmann, James P Long, Hiroyuki Katayama, Chong Wu, Jody Vykoukal, Jennifer B Dennison, Samir Hanash, Kim-Anh Do, Ehsan Irajizad

{"title":"Grape-Pi: graph-based neural networks for enhanced protein identification in proteomics pipelines.","authors":"Chunhui Gu, Seyyed Mahmood Ghasemi, Yining Cai, Johannes F Fahrmann, James P Long, Hiroyuki Katayama, Chong Wu, Jody Vykoukal, Jennifer B Dennison, Samir Hanash, Kim-Anh Do, Ehsan Irajizad","doi":"10.1093/bioadv/vbaf095","DOIUrl":"10.1093/bioadv/vbaf095","url":null,"abstract":"Motivation: Protein identification via mass spectrometry (MS) is the primary method for untargeted protein detection. However, the identification process is challenging due to data complexity and the need to control false discovery rates (FDR) of protein identification. To address these challenges, we developed a graph neural network (GNN)-based model, Graph Neural Network using Protein-Protein Interaction for Enhancing Protein Identification (Grape-Pi), which is applicable to all proteomics pipelines. This model leverages protein-protein interaction (PPI) data and employs two types of message-passing layers to integrate evidence from both the target protein and its interactors, thereby improving identification accuracy.Results: Grape-Pi achieved significant improvements in area under receiver-operating characteristic curve (AUC) in differentiating present and absent proteins: 18% and 7% in two yeast samples and 9% in gastric samples over traditional methods in the test dataset. Additionally, proteins identified via Grape-Pi in gastric samples demonstrated a high correlation with mRNA data and identified gastric cancer proteins, like MAP4K4, missed by conventional methods.Availability and implementation: Grape-Pi is freely available at https://zenodo.org/records/11310518 and https://github.com/FDUguchunhui/GrapePi.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf095"},"PeriodicalIF":2.4,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12096076/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144129661","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}

引用次数: 0

PPIFold: a tool for analysis of protein-protein interaction from AlphaPullDown. PPIFold：来自AlphaPullDown的蛋白质相互作用分析工具。

IF 2.4

Bioinformatics advances Pub Date : 2025-04-24 eCollection Date: 2025-01-01 DOI: 10.1093/bioadv/vbaf090

Quentin Rouger, Emmanuel Giudice, Damien F Meyer, Kévin Macé

引用次数: 0

AlphaMissenseR: an integrated framework for investigating missense mutations in human protein-coding genes. AlphaMissenseR：一个研究人类蛋白质编码基因错义突变的综合框架。

IF 2.4

Bioinformatics advances Pub Date : 2025-04-23 eCollection Date: 2025-01-01 DOI: 10.1093/bioadv/vbaf093

Tram N Nguyen, Tyrone Lee, Nitesh Turaga, Robert Gentleman, Ludwig Geistlinger, Martin Morgan

{"title":"AlphaMissenseR: an integrated framework for investigating missense mutations in human protein-coding genes.","authors":"Tram N Nguyen, Tyrone Lee, Nitesh Turaga, Robert Gentleman, Ludwig Geistlinger, Martin Morgan","doi":"10.1093/bioadv/vbaf093","DOIUrl":"https://doi.org/10.1093/bioadv/vbaf093","url":null,"abstract":"Summary: AlphaMissense is an AI model from Google DeepMind that predicts the pathogenicity of every possible missense mutation in the human proteome. We present AlphaMissenseR, an R/Bioconductor package that facilitates performant and reproducible access to these predictions and that provides functionality for analysis, visualization, validation, and benchmarking. AlphaMissenseR integrates with Bioconductor facilities for genomic region analysis, and provides multi-level visualization and interactive exploration of variant pathogenicity in a genome browser and on 3D protein structures. In addition, AlphaMissenseR integrates with major clinical and experimental variant databases for contrasting predicted and clinically derived pathogenicity scores, and for systematic benchmarking of existing and new variant effect prediction methods across a large collection of deep mutational scanning assays.Availability and implementation: AlphaMissense data resources are distributed under the CC-BY 4.0 license and the AlphaMissenseR package is available from Bioconductor (https://bioconductor.org/packages/AlphaMissenseR) under the Artistic 2.0 license.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf093"},"PeriodicalIF":2.4,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12040380/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144043436","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}

引用次数: 0

HORNET: tools to find genes with causal evidence and their regulatory networks using eQTLs. HORNET：利用eqtl寻找具有因果证据的基因及其调控网络的工具。

IF 2.4

Bioinformatics advances Pub Date : 2025-04-18 eCollection Date: 2025-01-01 DOI: 10.1093/bioadv/vbaf068

Noah Lorincz-Comi, Yihe Yang, Jayakrishnan Ajayakumar, Makaela Mews, Valentina Bermudez, William Bush, Xiaofeng Zhu

{"title":"HORNET: tools to find genes with causal evidence and their regulatory networks using eQTLs.","authors":"Noah Lorincz-Comi, Yihe Yang, Jayakrishnan Ajayakumar, Makaela Mews, Valentina Bermudez, William Bush, Xiaofeng Zhu","doi":"10.1093/bioadv/vbaf068","DOIUrl":"https://doi.org/10.1093/bioadv/vbaf068","url":null,"abstract":"Motivation: Nearly two decades of genome-wide association studies (GWAS) have identify thousands of disease-associated genetic variants, but very few genes with evidence of causality. Recent methodological advances demonstrate that Mendelian randomization (MR) using expression quantitative loci (eQTLs) as instrumental variables can detect potential causal genes. However, existing MR approaches are not well suited to handle the complexity of eQTL GWAS data structure and so they are subject to bias, inflation, and incorrect inference.Results: We present a whole-genome regulatory network analysis tool (HORNET), which is a comprehensive set of statistical and computational tools to perform genome-wide searches for causal genes using summary level GWAS data, i.e. robust to biases from multiple sources. Applying HORNET to schizophrenia, eQTL effects in the cerebellum were spread throughout the genome, and in the cortex were more localized to select loci.Availability and implementation: Freely available at https://github.com/noahlorinczcomi/HORNET or Mac, Windows, and Linux users.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf068"},"PeriodicalIF":2.4,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12014422/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144012382","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}

引用次数: 0

GeneSetPheno: a web application for the integration, summary, and visualization of gene and variant-phenotype associations across gene sets. GeneSetPheno：一个用于整合、总结和可视化基因和变异表型关联的web应用程序。

IF 2.4

Bioinformatics advances Pub Date : 2025-04-17 eCollection Date: 2025-01-01 DOI: 10.1093/bioadv/vbaf078

Jiru Han, Zachary F Gerring, Longfei Wang, Melanie Bahlo

{"title":"GeneSetPheno: a web application for the integration, summary, and visualization of gene and variant-phenotype associations across gene sets.","authors":"Jiru Han, Zachary F Gerring, Longfei Wang, Melanie Bahlo","doi":"10.1093/bioadv/vbaf078","DOIUrl":"https://doi.org/10.1093/bioadv/vbaf078","url":null,"abstract":"Motivation: The comprehensive study of genotype-phenotype relationships requires the integration of multiple data types to \"triangulate\" signals and derive meaningful biological conclusions. Large-scale biobanks and public resources generate a wealth of comprehensive results, facilitating the discovery of associations between genes or genetic variants and multiple phenotypes. However, analyzing these data across resources presents several challenges, including limited flexibility in gene set analysis, the integration of multipe databases, and the need for effective data visualization to aid interpretation.Results: GeneSetPheno is a user-friendly graphical interface that integrates, summarizes, and visualizes gene and variant-phenotype associations across genomic resources. It allows users to explore interrelationships between genetic variants and phenotypes, offering insights into the genetic factors driving phenotypic variation within user-defined gene sets. GeneSetPheno also supports comparisons across gene sets to identify shared or unique genetic variants, phenotypic associations, biological pathways, and potential gene-gene interactions. GeneSetPheno is a free and highly configurable tool for exploring the complex relationships between gene sets, genetic variants, and phenotypes. Target users include molecular biologists and clinicians who wish to explore a gene or gene set of particular interest.Availability and implementation: GeneSetPheno is freely accessible at: https://shiny.wehi.edu.au/han.ji/GeneSetPheno/. The source code is available on GitHub at: https://github.com/bahlolab/GeneSetPheno.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf078"},"PeriodicalIF":2.4,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12011357/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144012367","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}

引用次数: 0