Bioinformatics (Oxford, England)最新文献

{"title":"ct-validation: an open framework for benchmarking target-indication evidence against clinical trial outcomes.","authors":"Klim Kostiuk, Daniel Igumnov, Peter Fedichev, Amir Feizi","doi":"10.1093/bioinformatics/btag788","DOIUrl":"https://doi.org/10.1093/bioinformatics/btag788","url":null,"abstract":"<p><strong>Summary: </strong>Human genetic evidence provides a strong foundation for selecting drug targets. However, consistent benchmarking of target-indication evidence against clinical trial outcomes has been hindered by proprietary data and customised pipelines. To address this, we present the Python package ct-validation, which evaluates whether target-indication pairs supported by specific evidence sources are enriched for clinical progression.This package computes relative success (RS) and odds ratios (OR) with confidence intervals across clinical phase transitions. It also supports ontology-based semantic disease matching and offers interfaces in Python, on the command line, and via the Model Context Protocol (MCP). By remapping the benchmark from Minikel et al. (2024) to the Experimental Factor Ontology (EFO), we reproduced their per-transition enrichment, with a maximum difference of only 1.4% across any transition.In a systematic 3x3 benchmark that independently varies the genetic evidence and clinical trial sources, we found that a single curated open clinical trial source reproduces the signal of the proprietary comparator (RS = 2.92 vs 2.65; 2.34 vs 2.55 without oncology). However, aggregating open clinical trial registries compresses this signal (RS = 1.52); this loss is primarily driven by the breadth of the indication data rather than the genetic evidence.</p><p><strong>Availability and implementation: </strong>ct-validation is implemented in Python (version 3.11 or higher), distributed via pip, and freely available at https://github.com/gero-science/ct-validation, with support for Python, command-line, and Model Context Protocol (MCP) interfaces. Archived code: https://doi.org/10.5281/zenodo.22901497. Benchmark data: https://doi.org/10.5281/zenodo.22901451.</p><p><strong>Supplementary information: </strong>Supplementary data are available at Bioinformatics online.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149228898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integration of polygenic risk with single cell methylation data using met-scDRS.","authors":"Xinzhe Li, Zhuozheng Shi, Kangcheng Hou, Katherine W Eyring, Cuining Liu, Matthew G Heffel, Chongyuan Luo, Daniel H Geschwind, Bogdan Pasaniuc","doi":"10.1093/bioinformatics/btag751","DOIUrl":"https://doi.org/10.1093/bioinformatics/btag751","url":null,"abstract":"<p><strong>Motivation: </strong>Although large scale genome-wide association studies (GWAS) have identified many risk variants for psychiatric traits their functional implication remain largely unknown. One potential mechanism for their risk is through cellular context-dependent epigenetics such as methylation. Previous methodologies have focused on integrating GWAS with single cell transcriptomics, which allowed identification of within-cell-type heterogeneity in disease risk association. We extended this framework to provide a method called met-scDRS that integrates GWAS with single cell epigenomics and investigates how psychiatric disease risk can manifest through methylomics at single cell resolution.</p><p><strong>Results: </strong>We propose an approach that integrates single cell methylomes with disease associated genes identified from GWAS at single-cell resolution. We found our method to be well calibrated and powered in different scenarios. We then analyzed a publicly available single cell methylome dataset of the human brain for 75 different traits and generated disease relevance scores for each cell for each trait. We find high brain specificity with major cell type enrichments congruent with known findings. We identify within-cell-type heterogeneity for psychiatric disease risk along the brain spatial axis as well as subpopulations that are differentially associated with diseases. Finally, we showed that genes with high correlation between methylation levels and disease relevance scores to be highly enriched in specific functional pathways such as synaptic formation and transmission.</p><p><strong>Availability: </strong>met-scDRS is freely available in GitHub as a python package under MIT license: https://github.com/bogdanlab/met-scDRS. Source code is also available from Zenodo with DOI: 10.5281/zenodo.21841506. All dataset used in this work are publicly available.</p><p><strong>Supplementary information: </strong>Supplementary data are available at Bioinformatics online.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149229188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Distilling Direct Effects via Conditional Differential Gene Expression Analysis.","authors":"Jiaqi Gu, Andrew Skelton, James Staley, Pierce Popson, Lei Peng, Xiaoyu Song, Juliet Knowles, Zihuai He","doi":"10.1093/bioinformatics/btag784","DOIUrl":"https://doi.org/10.1093/bioinformatics/btag784","url":null,"abstract":"<p><strong>Motivation: </strong>Differential gene expression (DGE) analysis is foundational for interpreting RNA sequencing data, but it conflates direct biological effects with correlations propagated through gene co-expression. Across three RNA sequencing datasets (including a genome-scale perturb-seq experiment), we find that only a small fraction of differentially expressed genes have direct effects on the trait of interest, while the majority are undirected or passengers whose associations are mediated through other genes.</p><p><strong>Results: </strong>To distinguish direct effect genes, we introduce conditional differential gene expression (CDGE) analysis, a framework that tests for conditional rather than marginal association between each gene and the trait of interest. Implemented via the GhostKnockoff procedure with lasso regression, CDGE delivers false discovery rate control, operates on summary statistics from existing DGE pipelines, and accommodates batch effects. The genes identified by CDGE mediate the effects of most other differentially expressed genes and show stronger enrichment for known protein-protein interactions and biological pathways than DGE-identified genes. These results suggest that the field has been systematically over-interpreting DGE outputs, and that distinguishing direct from mediated effects is essential for prioritizing genes for functional follow-up and therapeutic development.</p><p><strong>Availability: </strong>To facilitate public access to knockoff-based CDGE analysis, we have developed the R package \"KnockoffCDGE\" as an integrated implementation, which is freely available at https://github.com/GuJQ5/KnockoffCDGE (DOI: 10.5281/zenodo.23241517).</p><p><strong>Supplementary information: </strong>Supplementary data are available at Bioinformatics online.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149229239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MARDy 2.0: a platform for exploring and analysing antifungal resistance mutations.","authors":"Joe Webber, Hugh Gifford, Johanna Rhodes, Rhys A Farrer","doi":"10.1093/bioinformatics/btag786","DOIUrl":"https://doi.org/10.1093/bioinformatics/btag786","url":null,"abstract":"<p><strong>Motivation: </strong>The rising incidence of antifungal drug resistance represents a major and growing threat to global public health and food security. Therefore, understanding the genetic and molecular mechanisms that underpin antifungal resistance is essential for both clinical management and agricultural disease control.</p><p><strong>Results: </strong>We present MARDy 2.0, an updated platform replacing the original Mycology Antifungal Resistance Database. MARDy 2.0 provides an interactive platform for exploring antifungal resistance data from the community resource FungAMR, and protein structures from UniProtKB or predicted structures using AlphaFold. The platform provides enhanced tools for data exploration and analysis and is freely accessible via an interactive web interface that supports search filtering and downloads, BLAST-based sequence similarity searches, an experimental natural-language query interface for exploratory analysis, a mutation plot for visualising mutations in a protein context, and structural predictions showing mutation locations. MARDy 2.0 provides a platform to facilitate research into antifungal resistance evolution, surveillance, and mitigation strategies. MARDy should be used and cited alongside the underlying resources it integrates, FungAMR.</p><p><strong>Availability: </strong>Accessible at https://mardy.net.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149225590","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FMCerNet: Frequency-Aware Multi-Label Learning for Fine-Grained Cervical Cytology Screening.","authors":"Linyun Zhou, Qiuyu Cai, Xiaoling Wang, Weihan Li, Jian Yang, Mingli Song, Jing Zhang, Feng Zhou, Qihan You, Yudong Wang, Zunlei Feng, Xiuming Zhang","doi":"10.1093/bioinformatics/btag759","DOIUrl":"https://doi.org/10.1093/bioinformatics/btag759","url":null,"abstract":"<p><strong>Motivation: </strong>Automated cytology screening is essential for early cervical cancer prevention, where gigapixel whole-slide images (WSIs) are typically partitioned into tiles for tile-level feature extraction and subsequent WSI-level diagnosis aggregation. However, existing tile-level methods remain constrained by limited input sizes, which frequently obscures sparse and morphologically subtle abnormal cells. Furthermore, these methods commonly formulate tile-level representation learning as a coarse single-label classification task, thereby failing to capture the complex co-occurrence patterns and morphological heterogeneity of cervical cytological abnormalities.</p><p><strong>Results: </strong>We propose the Frequency-Aware Multi-Label Learning Network (FMCerNet), a tile-level feature extractor equipped with a Large-Field Frequency-Enhanced ViT (LFreq-ViT) backbone and a Coarse-to-Fine multi-label classification (C2F) head. The former incorporates a Dual-Tree Complex Wavelet Transform module to efficiently extract frequency-domain features from large-scale tiles, whereas the latter enhances fine-grained tile representation decoding through hierarchical supervision. Experiments on both public and private datasets demonstrate that FMCerNet improves the accuracy of positive screening and fine-grained subtype classification while maintaining high inference speed.</p><p><strong>Availability and implementation: </strong>Code at https://github.com/imAzhou/FMCerNet and archived on Zenodo (doi:10.5281/zenodo.22886105).</p><p><strong>Supplementary information: </strong>Supplementary files are available with the manuscript.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149221071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ARCHER-LD: Rapid Long-Range Linkage Disequilibrium Calculations at Biobank Scale using GPU Acceleration.","authors":"Rachit Kumar, Pankhuri Singhal, David Zhang, Christopher Carson, Mitchell Conery, Alex Rodriguez, Tarak Nath Nandi, Marylyn D Ritchie, Mathialakan Thavappiragasam, Benjamin F Voight, K Madduri Ravi, Anurag Verma","doi":"10.1093/bioinformatics/btag783","DOIUrl":"https://doi.org/10.1093/bioinformatics/btag783","url":null,"abstract":"<p><strong>Summary: </strong>Linkage disequilibrium (LD) information from one's own dataset is considered optimal for downstream analyses such as statistical fine-mapping. However, the computational complexity (≈N22 computations for N variants) lead most studies to use external reference panels, such as from 1000 Genomes. To capture LD across all variant pairs in biobank-scale whole-genome sequencing (WGS) datasets with hundreds of millions of variants, new computational strategies are essential. We present a novel approach that uses multi-GPU distributed computing to compute R2 for every variant pair in a dataset. On chromosome 22 (1.8 million variants) of the 30x WGS 1000 Genomes dataset, our method using eight consumer-level 12GB GPUs (NVIDIA RTX 2080TIs) takes <10 minutes, while the same calculation with PLINK using a high-end 64-threaded CPU (Intel Xeon Gold 6338) takes > 80 minutes, corresponding to a ≈8x speedup. We further demonstrate true biobank-scale performance in the Penn Medicine Biobank (PMBB; 57,170 samples), computing chromosome 1 LD (≈1.38 million variants) in 49 minutes versus 22.7 hours for PLINK, a ≈28x speedup. With this method, we successfully computed on the aforementioned 30x WGS 1000 Genomes dataset (≈120 million variants and ≈2500 samples) the entire LD matrix ($>$1e16, or 10 quadrillion elements) in under 6 hours using 512 NVIDIA 40GB A100 GPUs on the Department of Energy Argonne Leadership Computing Facility Polaris Supercomputer. We make this tool, coded in Python using CuPy, publicly available. Using this tool, researchers can leverage the full extent of their genomic data without relying on external LD reference panels and acquire more accurate, population-specific findings, particularly for groups underrepresented in existing databases.</p><p><strong>Availability and implementation: </strong>ARCHER-LD is an open-source tool available on GitHub under the Apache License 2.0 at https://github.com/rachitk/ARCHER-LD. The version of record used in this manuscript is archived via Zenodo at https://doi.org/10.5281/zenodo.23399887.</p><p><strong>Supplementary information: </strong>Supplementary material is available at Bioinformatics online.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149225595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DNA-BFN: Bayesian Flow Networks for Generative Modeling of Regulatory DNA Sequences.","authors":"Han Yu, Qixiu Du, Lei Wei, Xiaowo Wang","doi":"10.1093/bioinformatics/btag785","DOIUrl":"https://doi.org/10.1093/bioinformatics/btag785","url":null,"abstract":"<p><strong>Motivation: </strong>Designing regulatory DNA sequences is challenging due to their discrete nucleotide alphabet and complex higher-order dependencies underlying regulatory function. Many existing generative models, particularly diffusion-based approaches, learn by injecting stochastic noise into sequences and training denoising processes, which can distort discrete sequence structure and weaken biological constraints.</p><p><strong>Results: </strong>Here, we introduce DNA Bayesian Flow Networks (DNA-BFN), a generative framework that models regulatory DNA sequences through iterative Bayesian updates of per-position nucleotide distributions. By directly modeling per-position categorical beliefs, DNA-BFN realizes a diffusion-like generative process without explicitly perturbing discrete nucleotide sequences, enabling fully differentiable training while preserving discrete sequence structure. Across eight regulatory sequence datasets spanning diverse species, scales, and sequence lengths, DNA-BFN consistently outperforms state-of-the-art baselines, faithfully preserves sequence feature statistics and regulatory patterns, enables few-shot sequence design, and achieves faster sampling efficiency. These results show that framing sequence generation as progressive uncertainty reduction in probability space provides an effective and generalizable approach for nucleotide sequence design.</p><p><strong>Availability: </strong>The software and its source code are publicly available at https://github.com/WangLabTHU/DNA-BFN. The dataset, source code and model weights are available in the Zenodo repository: https://doi.org/10.5281/zenodo.20177972.</p><p><strong>Supplementary information: </strong>Supplementary data are available at Bioinformatics online.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149225599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Seq2Pocket: Augmenting protein language models for spatially consistent binding site prediction.","authors":"Vít Škrhák, Lukáš Polák, Marian Novotný, David Hoksza","doi":"10.1093/bioinformatics/btag778","DOIUrl":"https://doi.org/10.1093/bioinformatics/btag778","url":null,"abstract":"<p><strong>Motivation: </strong>Protein-ligand binding site prediction (LBS) is important for many areas of structural biology and molecular modeling, where, as in other tasks, protein language models (pLMs) have shown great promise. In their application to LBS, the pLM classifies each amino acid as binding or not, but translating these predictions into three-dimensional binding pockets remains challenging; in particular, residue-centric predictions tend to produce spatially fragmented pockets.</p><p><strong>Results: </strong>We present Seq2Pocket, a methodology for pocket-level LBS prediction that combines pLM finetuning, data enhancement, and structure-aware post-processing. First, we introduce sc-PDBenhanced, an extended training dataset that augments an existing sc-PDB dataset with additional small-molecule and ion-binding sites, improving coverage of non-obvious and small pockets. Second, we employ an embedding-supported smoothing classifier to refine residue-level predictions. Third, we define the Pocket Fragmentation Index and use it to select a clustering approach that preserves a consistent mapping between predictions and ground-truth pockets. We evaluate Seq2Pocket on two tasks: general binding site prediction using the LIGYSIS benchmark and cryptic binding site prediction using CryptoBench benchmark. Across both benchmarks, Seq2Pocket achieves state-of-the-art DCCtop-N and DCCtop-(N+2) performance. In particular, for general binding site prediction on the LIGYSIS benchmark, it improves distance-center-to-center recall by up to 13%, outperforming existing predictors. This improvement originates from training data quality rather than model architecture, as demonstrated by an ablation study. We believe that these findings contribute to more reliable evaluation practices in ligand binding site prediction, highlight the importance of training data curation, and provide pocket-level prediction tools that are better suited for downstream applications such as drug discovery.</p><p><strong>Availability: </strong>The Seq2Pocket web interface is available at https://seq2pocket.projekty.ms.mff.cuni.cz, running the ESM-2 650M variant. The sc-PDBenhanced is published using Zenodo platform at https://zenodo.org/records/18271517 [Škrhák and Hoksza 2026]. The models are published at HuggingFace: https://huggingface.co/skrhakv/seq2pocket.</p><p><strong>Supplementary information: </strong>Supplementary data are available at Bioinformatics online.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149208256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deepweed: Automated Individual Segmentation and Tracking for Duckweed Lineage Analysis.","authors":"Tolulope Oyeniran, Bukola Akindipe, Tasmia Islam, Joshua D Scheel, Eric A Josephs, Samuel M D Oliveira","doi":"10.1093/bioinformatics/btag779","DOIUrl":"https://doi.org/10.1093/bioinformatics/btag779","url":null,"abstract":"<p><strong>Motivation: </strong>Aquatic duckweeds, including Wolffia australiana, are the smallest and fastest growing flowering plants on the planet, with numerous emerging biotechnological applications. Duckweed populations grow through rapid clonal budding, but existing phenotypic characterization and image analysis tools typically operate only at the population level, tracking neither individual plants nor their lineage structure. Deepweed addresses this gap, combining automated individual segmentation, temporal tracking, and clonal lineage reconstruction from time-lapse imaging of duckweed.</p><p><strong>Results: </strong>Deepweed processes time-lapse duckweed imaging datasets using deep-learning-based instance segmentation and multi-object tracking to assign persistent identities to individual fronds and infer mother-daughter relationships. The workflow outputs lineage graphs, individual-level morphological metrics, and population-level growth summaries, supported by lineage-annotated time-lapse visualizations. Applied to dense Wolffia populations, in both freely growing or controlled microfluidic environments, our framework can track hundreds of Wolffia plants simultaneously, resolves overlapping individuals, and reconstructs multi-generation lineage structure, enabling analyses that are inaccessible with population-level phenotyping methods.</p><p><strong>Availability: </strong>Deepweed is implemented in Python using PyTorch, scikit-image, OpenCV, and btrack. Source code, trained models, example datasets, and documentation are freely available at https://github.com/SamOliveiraLab/Deepweed. The Deepweed code is archived at https://doi.org/10.5281/zenodo.18844282 and the datasets are archived at https://doi.org/10.5281/zenodo.18843917.</p><p><strong>Supplementary information: </strong>Supplementary data are available at Bioinformatics online.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149208252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DNAharvester: A Nextflow Pipeline for Analysing Highly Degraded DNA from Ancient and Historical Specimens.","authors":"Bilal Sharif, Verena E Kutschera, Nikolay Oskolkov, Benjamin Guinet, Edana Lord, J Camilo Chacón-Duque, Jonas Oppenheimer, Tom van der Valk, David Díez- Del-Molino, Peter D Heintzman, Love Dalén","doi":"10.1093/bioinformatics/btag782","DOIUrl":"https://doi.org/10.1093/bioinformatics/btag782","url":null,"abstract":"<p><strong>Motivation: </strong>Ancient DNA (aDNA) research has advanced rapidly with the development of high-throughput sequencing, enabling genome-wide analyses of large collections of prehistoric specimens. However, analysing palaeontological and archaeological material with highly degraded DNA constitutes a major bioinformatic challenge. DNA from such samples is characterized by short fragment lengths, low endogenous content, post-mortem damage, and cross-species contamination, which can increase spurious mapping and reference bias, affecting downstream population genetic inferences.</p><p><strong>Results: </strong>We present DNAharvester, a modular and reproducible pipeline designed specifically for processing highly degraded DNA from ancient and historical specimens. DNAharvester integrates metagenomic filtering, competitive mapping, adaptive aligner selection (incorporating BWA-aln, BWA-mem, and Bowtie2), and systematic evaluation of reference bias and spurious mapping. By incorporating flexible mapping and filtering strategies, the pipeline can be adapted to varying sample preservation, focusing on maximizing authentic data recovery. DNAharvester features subworkflows for iterative assembly of mitogenomes, identification of genomic repeats and CpG sites, taxonomic classification, microbial/pathogen screening, genetic sex determination, and variant calling. To accommodate varying sequencing depths, the pipeline supports diploid variant calling, genotype likelihood estimation, and pseudo-haploid random allele calling. We validated the pipeline using simulated and empirical datasets, demonstrating its ability to systematically mitigate complex background contamination while preserving authentic genomic signals. By streamlining complex bioinformatic tasks through simple configuration files, DNAharvester establishes a standardized approach for analysing aDNA datasets and makes genomic analyses of ancient remains accessible to the broader research community.</p><p><strong>Availability and implementation: </strong>Implemented in Nextflow, DNAharvester provides a highly scalable, containerized framework that enhances reproducibility, portability, and robustness in aDNA analyses. DNAharvester is open source under the GNU General Public License v3.0 and freely available at https://github.com/NBISweden/DNAharvester.</p><p><strong>Supplementary information: </strong>Supplementary data are available at Bioinformatics online and Zenodo repository: https://doi.org/10.5281/zenodo.22725211.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149208226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}