Bioinformatics advances最新文献

{"title":"Benchmarking accelerated next-generation sequencing analysis pipelines.","authors":"Pubudu Saneth Samarakoon, Ghislain Fournous, Lars T Hansen, Ashen Wijesiri, Sen Zhao, Rodriguez Alex A, Tarak Nath Nandi, Ravi Madduri, Alexander D Rowe, Gard Thomassen, Eivind Hovig, Sabry Razick","doi":"10.1093/bioadv/vbaf085","DOIUrl":"10.1093/bioadv/vbaf085","url":null,"abstract":"<p><strong>Motivation: </strong>Industry-standard central processing unit (CPU)-based next-generation sequencing (NGS) analysis tools have led to longer runtimes, affecting their utility in time-sensitive clinical practices and population-scale research studies. To address this, researchers have developed accelerated NGS platforms like DRAGEN and Parabricks, which have significantly reduced runtimes-from days to hours. However, these studies have evaluated accelerated platforms independently without sufficiently assessing computational resource usage or thoroughly investigating speedup scalability, a gap our study is designed to address.</p><p><strong>Results: </strong>Corroborating previous studies, accelerated pipelines demonstrated shorter runtimes than CPU-only approaches, with Parabricks-H100 demonstrating the highest speedups, followed by DRAGEN. In mapping, DRAGEN outperformed Parabricks (L4 and A100) and matched H100 speedups. Parabricks (A100 and H100) variant calling demonstrated higher speedups than DRAGEN. Moreover, DRAGEN and Parabricks-H100 mapping showed positive trends in the coverage-based scalability analysis, while other configurations failed to scale effectively. Our profiler analysis provided new insights into the relationships between Parabricks' performances and resource usage patterns, revealing its potential for further improvements. Our findings and cost comparison help researchers select accelerated platforms based on coverage needs, timeframes, and budget, while suggesting optimization strategies.</p><p><strong>Availability and implementation: </strong>Datasets are described in the 'Data availability' section. Our NGS pipelines are available at https://github.com/NAICNO/accelerated_genomics.</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf085"},"PeriodicalIF":2.4,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12092081/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144112841","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":"HIV multidrug class resistance prediction with a time sliding anchor approach.","authors":"Nurhan Arslan, Ralf Eggeling, Bernhard Reuter, Kristel Van Leathem, Marta Pingarilho, Perpétua Gomes, Anders Sönnerborg, Rolf Kaiser, Maurizio Zazzi, Nico Pfeifer","doi":"10.1093/bioadv/vbaf099","DOIUrl":"10.1093/bioadv/vbaf099","url":null,"abstract":"<p><strong>Motivation: </strong>The emergence of multidrug class resistance (MDR) in Human Immunodeficiency Virus (HIV) is a rare but significant challenge in antiretroviral therapy (ART). MDR, which may arise from prolonged drug exposure, treatment failures, or transmission of resistant strains, accelerates disease progression and poses particular challenges in resource-limited settings with restricted access to resistance testing and advanced therapies. Early prediction of future MDR development is important to inform therapeutic decisions and mitigate its occurrence.</p><p><strong>Results: </strong>In this study, we employ various machine learning classifiers to predict future resistance to all four major antiretroviral drug classes using features extracted from clinical HIV sequence data. We systematically explore several variations of the problem that differ in the pre-existing resistance level and the temporal gap between sample collection and observed MDR occurrence. Our models show the ability to predict multidrug class resistance even in the most challenging variations, albeit at a reduced accuracy. Feature importance analysis reveals that our models primarily utilize known drug resistance mutations for easier classification tasks, but rely on new mutations for the difficult task of distinguishing four class drug resistance from three class drug resistance.</p><p><strong>Availability and implementation: </strong>All analysis was performed using the Euresist Integrated DataBase (EIDB). Researchers wishing to reproduce, validate or extend these findings can request access to the latest EIDB release via the Euresist Network.</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf099"},"PeriodicalIF":2.4,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12104520/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144152955","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":"DRMAAtic: dramatically improve your cluster potential.","authors":"Alessio Del Conte, Hamidreza Ghafouri, Damiano Clementel, Ivan Mičetić, Damiano Piovesan, Silvio C E Tosatto, Alexander Miguel Monzon","doi":"10.1093/bioadv/vbaf112","DOIUrl":"10.1093/bioadv/vbaf112","url":null,"abstract":"<p><strong>Motivation: </strong>The accessibility and usability of high-performance computing (HPC) resources remain significant challenges in bioinformatics, particularly for researchers lacking extensive technical expertise. While Distributed Resource Managers (DRMs) optimize resource utilization, the complexities of interfacing with these systems often hinder broader adoption. DRMAAtic addresses these challenges by integrating the Distributed Resource Management Application API (DRMAA) with a user-friendly RESTful interface, simplifying job management across diverse HPC environments. This framework empowers researchers to submit, monitor, and retrieve computational jobs securely and efficiently, without requiring deep knowledge of underlying cluster configurations.</p><p><strong>Results: </strong>We present DRMAAtic, a flexible and scalable tool that bridges the gap between web interfaces and HPC infrastructures. Built on the Django REST Framework, DRMAAtic supports seamless job submission and management via HTTP calls. Its modular architecture enables integration with any DRM supporting DRMAA APIs and offers robust features such as role-based access control, throttling mechanisms, and dependency management. Successful applications of DRMAAtic include the RING web server for protein structure analysis, the CAID Prediction Portal for disorder and binding predictions, and the Protein Ensemble Database deposition server. These deployments demonstrate DRMAAtic's potential to enhance computational workflows, improve resource efficiency, and facilitate open science in life sciences.</p><p><strong>Availability and implementation: </strong>https://github.com/BioComputingUP/DRMAAtic, https://drmaatic.biocomputingup.it/.</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf112"},"PeriodicalIF":2.4,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12133261/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144217680","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":"GAIN-BRCA: a graph-based AI-net framework for breast cancer subtype classification using multiomics data.","authors":"Jai Chand Patel, Sushil Kumar Shakyawar, Sahil Sethi, Chittibabu Guda","doi":"10.1093/bioadv/vbaf116","DOIUrl":"10.1093/bioadv/vbaf116","url":null,"abstract":"<p><strong>Motivation: </strong>Contextual integration of multiomic datasets from the same patient could improve the accuracy of subtype prediction algorithms to help with better prognosis and management of breast cancer. Previous machine learning models have underexplored the graph-based integration, hence unable to leverage the biological associations among different omics modalities. Here, we developed a graph-based method, GAIN-BRCA, using the native features from mRNA, DNA methylation (CpG), and miRNA data as well as the synthesized features from their interactions. GAIN-BRCA computes weightage from miRNA-mRNA and CpG-mRNA interactions to derive a new transformed feature vector that captures the essential biological context.</p><p><strong>Results: </strong>GAIN-BRCA demonstrates superior performance with an AUROC of 0.98. GAIN-BRCA, with an accuracy of 0.92 also outperformed the existing methods like MOGONET and moBRCA-net with accuracies of 0.72 and 0.86, respectively. Kaplan-Meier survival analysis revealed subtype-specific prognostic genes, including KRAS in Luminal A (<i>P</i> value = 0.041), TOX in Luminal B (<i>P</i> value = 0.008), and MITF and TOB1 in HER2+ (<i>P</i> values = 0.029 and 0.025, respectively). However, no single gene demonstrated a significant survival correlation unique to the Basal subtype. GAIN-BRCA framework, in combination with SHAP, has identified several subtype-specific biomarkers to aid in the development of precision therapeutics for breast cancer subtypes.</p><p><strong>Availability and implementation: </strong>GAIN-BRCA code is publicly accessible on https://github.com/GudaLab/GAIN-BRCA.</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf116"},"PeriodicalIF":2.4,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12151285/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144267982","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":"BonoboFlow: viral genome assembly and haplotype reconstruction from nanopore reads.","authors":"Christian Ndekezi, Drake Byamukama, Frank Kato, Denis Omara, Angella Nakyanzi, Fortunate Natwijuka, Susan Mugaba, Alfred Ssekagiri, Nicholas Bbosa, Obondo James Sande, Magambo Phillip Kimuda, Denis K Byarugaba, Anne Kapaata, Jyoti Sutar, Jayanta Bhattacharya, Pontiano Kaleebu, Sheila N Balinda","doi":"10.1093/bioadv/vbaf115","DOIUrl":"10.1093/bioadv/vbaf115","url":null,"abstract":"<p><strong>Summary: </strong>Viral genome sequencing and analysis are crucial for understanding the diversity and evolution of viruses. Traditional Sanger sequencing is limited by low sequence depth and is labor intensive. Next-Generation Sequencing (NGS) methods, such as Illumina, offer improved sequencing depth and throughput but face challenges with accurate reconstruction of viral genomes due to genome fragmentation. Third-generation sequencing platforms, such as PacBio and Oxford Nanopore Technologies (ONT), generate long reads with high throughput. However, PacBio is constrained by substantial resource requirements, while ONT suffers from inherently high error rates. Moreover, standardized pipelines for ONT sequencing encompassing basecalling to genome assembly remain limited.</p><p><strong>Results: </strong>Here, we introduce BonoboFlow, a standardized Nextflow pipeline designed to streamline ONT-based viral genome assembly/haplotype reconstruction. BonoboFlow integrates key processing steps, including basecalling, read filtering, chimeric read removal, error correction, draft genome assembly/haplotype reconstruction, and genome polishing. The pipeline accepts raw POD5 or basecalled FASTQ files as input, produces FASTA consensus files as output, and uses a reference genome (in FASTA format) for contaminant read filtering. BonoboFlow's containerized implementation via Docker and Singularity ensures seamless deployment across diverse computing environments. While BonoboFlow excels in assembling small and medium viral genomes, it showed challenges when reconstructing large viral genomes.</p><p><strong>Availability and implementation: </strong>BonoboFlow and corresponding containerized images are publicly available at https://github.com/nchis09/BonoboFlow and https://hub.docker.com/r/nchis09/bonobo_image. The test dataset is available at SRA repository Accession number: PRJNA1137155, http://www.ncbi.nlm.nih.gov/bioproject/1137155.</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf115"},"PeriodicalIF":2.4,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12141814/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144251088","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":"RNA knowledge-graph analysis through homogeneous embedding methods.","authors":"Francesco Torgano, Mauricio Soto Gomez, Matteo Zignani, Jessica Gliozzo, Emanuele Cavalleri, Marco Mesiti, Elena Casiraghi, Giorgio Valentini","doi":"10.1093/bioadv/vbaf109","DOIUrl":"10.1093/bioadv/vbaf109","url":null,"abstract":"<p><strong>Motivation: </strong>We recently introduced RNA-knowledge graph (KG), an ontology-based KG that integrates biological data on RNAs from over 60 public databases. RNA-KG captures functional relationships and interactions between RNA molecules and other biomolecules, chemicals, and biomedical concepts such as diseases and phenotypes, all represented within graph-structured bio-ontologies. We present the first comprehensive computational analysis of RNA-KG, evaluating the potential of graph representation learning and machine learning models to predict node types and edges within the graph.</p><p><strong>Results: </strong>We performed node classification experiments to predict up to 81 distinct node types, and performed both generic- and specific-edge prediction tasks. Generic-edge prediction focused on identifying the presence of an edge irrespective of its type, while specific-edge prediction targeted specific interactions between ncRNAs, e.g. between microRNAs (miRNA-miRNA) or between small interfering RNA-messenger and RNA-messenger molecules (siRNA-mRNA), or relationships between ncRNA and biomedical concepts, e.g. miRNA-disease or lncRNA-Gene Ontology term relationships. Using embedding methods for homogeneous graphs, such as Large-scale Information Network Embedding (LINE) and node2vec, in combination with machine learning models like decision trees and random forests, we achieved balanced accuracy exceeding 90% for the 20 most common node types and over 80% for most specific-edge prediction tasks. These results show that simple embedding methods for homogeneous graphs can successfully predict nodes and edges of the RNA-KG, paving the way to discover novel ncRNA interactions and laying the foundation for further exploration, and utilization of this rich information source to enhance prediction accuracy and support further research into the \"RNA world.\"</p><p><strong>Availability and implementation: </strong>Python code to reproduce the experiments is available at https://github.com/AnacletoLAB/RNA-KG_homogeneous_emb_analysis.</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf109"},"PeriodicalIF":2.4,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12150776/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144267983","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":"MDTAP: a tool to analyze permeation events across membrane proteins.","authors":"Sruthi Sundaresan, Palur Venkata Raghuvamsi, Thenmalarchelvi Rathinavelan","doi":"10.1093/bioadv/vbaf102","DOIUrl":"10.1093/bioadv/vbaf102","url":null,"abstract":"<p><strong>Motivation: </strong>Molecular dynamics (MD) simulations provide critical insights into the transport of solutes, solvents, and drug molecules across protein channels embedded in a membrane bilayer. However, identifying and analyzing the permeation events from complex simulation data remains as a challenging and laborious task. Thus, an automated tool that facilitates the capture of permeation events of any molecular type across any channel is essential to streamline MD trajectory analysis and enhance the understanding of biological processes in a timely manner.</p><p><strong>Results: </strong>Molecular Dynamics Trajectory Analysis of Permeation (MDTAP) is a Linux/Mac-based software that automatically detects permeation events across membrane-embedded protein and nucleic acid channels. The tool accepts trajectories in DCD (CHARMM/NAMD) and PDB format (obtained from any MD simulation package) and employs bash scripts to analyze the input trajectories to characterize the molecular permeation. The efficiency of MDTAP is demonstrated using MD trajectories of <i>Escherichia coli</i> outer membrane protein Wzi and <i>E. coli</i> Aquaporin Z. MDTAP can also analyze permeation across heterogeneous lipid membranes and artificial nucleic acid channels, addressing their growing importance. Thus, MDTAP simplifies trajectory analysis and also reduces the need for manual inspection.</p><p><strong>Availability and implementation: </strong>MDTAP is open-source and is freely available on GitHub (https://github.com/MBL-lab/MDTAP), including source code, installation instructions, and usage documentation.</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf102"},"PeriodicalIF":2.4,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12104522/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144152998","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":"Quantifying the nuclear localization of fluorescently tagged proteins.","authors":"Julien Hurbain, Pieter Rein Ten Wolde, Peter S Swain","doi":"10.1093/bioadv/vbaf114","DOIUrl":"10.1093/bioadv/vbaf114","url":null,"abstract":"<p><strong>Motivation: </strong>Cells are dynamic, continually responding to intra- and extracellular signals. Measuring the response to these signals in individual cells is challenging. Signal transduction is fast, but reporters for downstream gene expression are slow: fluorescent proteins must be expressed and mature. An alternative is to fluorescently tag and monitor the intracellular locations of transcription factors and other effectors. These proteins enter or exit the nucleus in minutes, after upstream signalling modifies their phosphorylation state. Although such approaches are increasingly popular, there is no consensus on how to quantify nuclear localization.</p><p><strong>Results: </strong>Using budding yeast, we developed a convolutional neural network that determines nuclear localization from fluorescence and, optionally, bright-field images. Focusing on changing extracellular glucose, we generated ground-truth data using strains with a transcription factor and a nuclear protein tagged with fluorescent markers. We showed that the neural network-based approach outperformed seven published methods, particularly when predicting single-cell time series, which are key to determining how cells respond. Collectively, our results are conclusive-using machine learning to automatically determine the appropriate image processing consistently outperforms ad hoc approaches. Adopting such methods promises to both improve the accuracy and, with transfer learning, the consistency of single-cell analyses.</p><p><strong>Availability and implementation: </strong>We performed our analysis in Python; code is available at https://git.ecdf.ed.ac.uk/v1jhurba/neunet-nucloc.git.</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf114"},"PeriodicalIF":2.4,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12133273/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144217683","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":"iDDN: determining trans-omics network structure and rewiring with integrative differential dependency networks.","authors":"Yizhi Wang, Yi Fu, Yingzhou Lu, Zhen Zhang, Robert Clarke, Sarah J Parker, David M Herrington, Guoqiang Yu, Yue Wang","doi":"10.1093/bioadv/vbaf086","DOIUrl":"10.1093/bioadv/vbaf086","url":null,"abstract":"<p><strong>Motivation: </strong>Mapping the gene networks that drive disease progression allows identifying molecules that rectify the network by normalizing pivotal regulatory elements. Upon mechanistic validation, these upstream normalizers represent attractive targets for developing therapeutic interventions to prevent the initiation or interrupt the pathways of disease progression. Differential network analysis aims to detect significant rewiring of regulatory network structures under different conditions. With few exceptions, most existing tools are limited to inferring differential networks from single-omics data that could be incomplete and prone to collapse when trans-omics multifactorial regulatory mechanisms are involved.</p><p><strong>Results: </strong>We previously developed an efficient differential network analysis method-Differential Dependency Networks (DDN), that enables joint learning of common network structure and rewiring under different conditions. We now introduce the integrative DDN (iDDN) tool that extends this framework with biologically principled designs to make robust multi-omics differential network inferences. The comparative experimental evaluations on both realistic simulations and case studies show that iDDN can help biologists more accurately identify, in a study-specific and often unknown trans-omics regulatory circuitry, a network of differentially wired molecules potentially responsible for phenotypic transitions.</p><p><strong>Availability and implementation: </strong>The Python package of iDDN is available at https://github.com/cbil-vt/iDDN. A user's guide is provided at https://iddn.readthedocs.io/.</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf086"},"PeriodicalIF":2.4,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12070392/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144065418","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":"Poplar: a phylogenomics pipeline.","authors":"Elizabeth Koning, Arjun Subedi, Raga Krishnakumar","doi":"10.1093/bioadv/vbaf104","DOIUrl":"10.1093/bioadv/vbaf104","url":null,"abstract":"<p><strong>Motivation: </strong>Generating phylogenomic trees from the genomic data is essential in understanding biological systems. Each step of this complex process has received extensive attention and has been significantly streamlined over the years. Given the public availability of data, obtaining genomes for a wide selection of species is straightforward. However, analyzing that data to generate a phylogenomic tree is a multistep process with legitimate scientific and technical challenges, often requiring a significant input from a domain-area scientist.</p><p><strong>Results: </strong>We present Poplar, a new, streamlined computational pipeline, to address the computational logistical issues that arise when constructing the phylogenomic trees. It provides a framework that runs state-of-the-art software for essential steps in the phylogenomic pipeline, beginning from a genome with or without an annotation, and resulting in a species tree. Running Poplar requires no external databases. In the execution, it enables parallelism for execution for clusters and cloud computing. The trees generated by Poplar match closely with state-of-the-art published trees. The usage and performance of Poplar is far simpler and quicker than manually running a phylogenomic pipeline.</p><p><strong>Availability and implementation: </strong>Freely available on GitHub at https://github.com/sandialabs/poplar. Implemented using Python and supported on Linux.</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf104"},"PeriodicalIF":2.4,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12159734/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144287400","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}