Bioinformatics advances最新文献

{"title":"visPedigree: a comprehensive R package for tidying, analyzing, and visualizing breeding pedigrees.","authors":"Sheng Luan, Jie Kong, Zhenglong Xia, Ziyi Kang, Guangfeng Qiang, Kun Luo, Juan Sui","doi":"10.1093/bioadv/vbag210","DOIUrl":"https://doi.org/10.1093/bioadv/vbag210","url":null,"abstract":"<p><strong>Motivation: </strong>Pedigrees support relatedness control, mate allocation, inbreeding monitoring, and diversity assessment, but large breeding populations are often curated, analyzed, and visualized using separate tools and data structures. This fragmentation complicates routine analysis and reporting, particularly in high-fecundity systems with extensive full-sib structure, irregular pedigree depth, and repeated cohort-based evaluation.</p><p><strong>Results: </strong>We present visPedigree, a software package for large breeding pedigrees built around a new tidyped object model. It integrates pedigree standardization, structural validation, candidate-centered tracing, pedigree statistics, inbreeding and partial inbreeding analysis, founder- and ancestor-based diversity summaries, effective population size estimation, relationship matrices, and scalable visualization. The package also provides Shannon/Hill-based diversity measures, the pedhalflife() temporal diagnostic, and compact visualization of full-sib-dominated subsets. In a giant freshwater prawn pedigree, it produced an interpretable ancestry overview and quantified recent diversity erosion. Simulated benchmarks showed that representative workflows completed within seconds for pedigrees containing up to one million individuals on a standard laptop.</p><p><strong>Availability and implementation: </strong>visPedigree is implemented in the R language and is freely available at https://cran.r-project.org/package=visPedigree, with documentation at https://luansheng.github.io/visPedigree/.</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"6 1","pages":"vbag210"},"PeriodicalIF":2.6,"publicationDate":"2026-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13479741/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148802239","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":"A leakage-aware genomic prediction pipeline for meropenem resistance in <i>Klebsiella pneumoniae</i> using transformer-based resistome representation learning.","authors":"İlkay Sibel Kervancı","doi":"10.1093/bioadv/vbag193","DOIUrl":"10.1093/bioadv/vbag193","url":null,"abstract":"<p><strong>Motivation: </strong>Antimicrobial resistance (AMR) in <i>Klebsiella pneumoniae</i>, particularly to carbapenems such as meropenem, is a major global health problem. Machine learning is increasingly used to predict resistance from genomic markers; however, many models fail to capture high-level gene-gene interactions and may exhibit inflated performance due to lineage-biased prediction. Existing genomic prediction models largely rely on flat feature representations that fail to capture epistatic gene interactions, and commonly suffer from inflated performance estimates due to phylogenetic data leakage. To address these limitations simultaneously, a leakage-aware hybrid TabTransformer-CatBoost pipeline was developed, combining self-attention-based resistome representation learning with gradient boosting classification under clade-aware data partitioning. A self-attention encoder converts sparse gene presence-absence profiles into contextualized latent embeddings, which are subsequently classified using gradient boosting to capture lineage-aware AMR patterns.</p><p><strong>Results: </strong>The proposed architecture outperformed classical baselines including Logistic Regression, Random Forest, XGBoost, and optimized CatBoost models. Internal accuracy reached 92.59% for the Chained Hybrid configuration (area under the receiver operating characteristic curve, AUROC = 0.8670, F1 = 0.8537). Performance gains primarily originated from the embedding stage, as confirmed by ablation analysis. External validation across independent multinational cohorts (<i>n</i> = 305) demonstrated generalizability (AUROC = 0.8105; F1 = 0.7552). Permutation testing produced near-zero Matthews Correlation Coefficient (MCC) = 0.0091, indicating predictions reflect genuine biological signal rather than noise. These results establish attention-based genomic embedding with gradient boosting as a scalable, interpretable, and leakage-aware framework for clinical AMR prediction.</p><p><strong>Availability and implementation: </strong>The source code for the TabTransformer-CatBoost framework, including preprocessing pipelines and pre-trained embeddings, is available at https://github.com/SibelKervanci/kp-meropenem-tabtransformer.</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"6 1","pages":"vbag193"},"PeriodicalIF":2.6,"publicationDate":"2026-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13532757/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148876635","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":"CIViC MCP: integrating large language models with the Clinical Interpretations of Variants in Cancer.","authors":"Lars Schimmelpfennig, Quentin Cody, Joshua F McMichael, Adam C Coffman, Mariam Khanfar, Jinglun Li, Jennie X Yao, Jason Saliba, Arpad Danos, Susanna Kiwala, Alex Wagner, Javier Sanz-Cruzado, Jake Lever, Malachi Griffith, Obi L Griffith","doi":"10.1093/bioadv/vbag209","DOIUrl":"10.1093/bioadv/vbag209","url":null,"abstract":"<p><strong>Motivation: </strong>The Clinical Interpretations of Variants in Cancer (CIViC) knowledgebase provides a community-driven, open-source platform for discussing the biological and clinical significance of molecular variants in cancer. Large language models (LLMs) offer a natural-language interface to CIViC, enabling users to discover and summarize curated oncology knowledge, but LLMs cannot guarantee coverage of specialized, rapidly updated resources like CIViC from pretraining alone.</p><p><strong>Results: </strong>To enable users to make complex connections between CIViC information, we developed the CIViC Model Context Protocol (MCP) server. It allows users to interface with the CIViC API through natural language via LLMs. This facilitates the rapid summarization of expertly curated cancer variant interpretations. Our benchmark shows the server improves the accuracy of clinical significance retrieval while avoiding the far greater latency of GPT-5's Agent Mode, which navigates the web by simulating human inputs.</p><p><strong>Availability and implementation: </strong>The CIViC MCP server is detailed at https://github.com/griffithlab/civic-mcp-server. The repository includes instructions for accessing the server through the Claude desktop app (our recommended approach) and hosting it locally with GPT-5, as well as a Python script for directly querying the MCP server. We also provide an MCP-supported chatbot for CIViC users at https://civicdb.org/mcp-chat.</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"6 1","pages":"vbag209"},"PeriodicalIF":2.6,"publicationDate":"2026-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13466716/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148727459","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":"Pooling multimodal cancer data across unaligned embedding spaces maintains tumor of origin signal.","authors":"Raphael Kirchgaessner, Kaya Keutler, Shruthilayaa Sivakumar, Xubo Song, Kyle Ellrott","doi":"10.1093/bioadv/vbag159","DOIUrl":"10.1093/bioadv/vbag159","url":null,"abstract":"<p><strong>Summary: </strong>AI-based embeddings offer the possibilities of encoding complex biological data into low-dimensional spaces, called embedding spaces, that maintain the relationships between entities. Vector pooling is the process of aggregating an array of embedded vectors, either usually by summing or averaging, to summarize the total movement with the embedding space. Embedded vector pooling allows sampling of an arbitrary number of points to be summarized into a fixed sized vector, and is frequently used to sample networks of embedded values or to summarize protein language model vectors. There is an open question about the compatibility of embedding spaces that are created without any coordination. It has been assumed that signals in these unaligned embedding spaces would be destroyed if vectors were pooled into summed values. To challenge this idea, we created a number of benchmarks that utilized unaligned embedded values and pooled them into heterogeneous vectors to test information retrieval. To power this benchmark, we trained embedding models across different cancer data modalities and tested how well pooled heterogeneous vectors were able to retain biologically relevant information. Our research shows that signal from unaligned embedded values is conserved and able to still be used for learning tasks, such as data modality and tumor of origin recognition.</p><p><strong>Availability and implementation: </strong>All code and computational experiments related to this publication can be found at https://github.com/EllrottLab/heterogeneous-embedding-vectors.</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"6 1","pages":"vbag159"},"PeriodicalIF":2.6,"publicationDate":"2026-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13420497/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148632890","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":"scGraphVerse: a modular workflow for single-cell gene network inference.","authors":"Francesco Cecere, Daniela De Canditiis, Annamaria Carissimo, Claudia Angelini","doi":"10.1093/bioadv/vbag208","DOIUrl":"10.1093/bioadv/vbag208","url":null,"abstract":"<p><strong>Motivation: </strong>Inferring gene networks from single-cell RNA sequencing data is challenging due to high sparsity, dimensionality, and technical noise. Current pipelines lack the multi-dataset integration and comprehensive post-processing analysis.</p><p><strong>Results: </strong>scGraphVerse is an R package that integrates multiple algorithms (GENIE3, GRNBoost2, ZILGM, PCzinb, and JRF) with extensive evaluation and visualization tools. Its modular workflow supports early, late, and joint integration strategies for multi-dataset analysis, providing standardized input/output interfaces and biological interpretation tools, including community detection, pathway enrichment, and literature mining. Benchmarking on simulated data showed model-based methods (PCzinb and ZILGM) perform well with limited sample sizes, while JRF performs best as the network size and dataset numbers increase. A PBMC case study demonstrates JRF's ability to identify literature-supported regulatory communities across donors.</p><p><strong>Availability and implementation: </strong>The package is available in Bioconductor 3.22 at https://bioconductor.org/packages/release/bioc/html/scGraphVerse.html. Code and examples: https://github.com/ngsFC/scGV_analysis.</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"6 1","pages":"vbag208"},"PeriodicalIF":2.6,"publicationDate":"2026-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13524424/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148851987","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":"jsPCA: fast, scalable, and interpretable identification of spatial domains and variable genes across multi-slice and multi-sample spatial transcriptomics data.","authors":"Ines Assali, Paul Escande, Franck Picard, Paul Villoutreix","doi":"10.1093/bioadv/vbag206","DOIUrl":"10.1093/bioadv/vbag206","url":null,"abstract":"<p><strong>Motivation: </strong>Spatial transcriptomics technologies record genome-wide measurements of gene expression with high spatial resolution. These technologies generate large and high-dimensional datasets requiring efficient automated methods for their analysis. We introduce joint spatial PCA (jsPCA), a novel, fast, scalable and interpretable method for the automatic identification of spatial domains and variable genes in multi-slice and multi-sample spatial transcriptomics data.</p><p><strong>Results: </strong>jsPCA relies on a simple mathematical formulation of a spatial covariance defined as the product of the gene expression covariance with the spatial autocorrelation. The principal components of this spatial covariance yield a biologically meaningful low-dimensional representation. From this representation, spatial domains are derived by simple clustering and spatially variable genes are identified directly from the principal component coefficients. A joint representation of multiple slices and samples without spatial alignment is obtained by computing common principal components via joint diagonalization. By leveraging data sparsity and non-convex manifold optimization, jsPCA leads to computing time in the order of seconds to minutes, substantially outperforming state-of-the-art approaches. We benchmarked jsPCA against 10 state-of-the-art methods on two reference databases. Our approach demonstrated excellent performance, comparable or better than state-of-the-art methods, while being much faster, interpretable, and scalable to very large datasets.</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"6 1","pages":"vbag206"},"PeriodicalIF":2.6,"publicationDate":"2026-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13532759/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148876633","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":"shinyDeepGxP: a user-friendly R shiny app for predicting surface protein abundance from scRNA-seq expression using deep learning in blood cells.","authors":"Hui-Mei Tsai, Tzu-Hung Hsiao, Yu-Ching Hsu, Li-Ju Wang, Yu-Chiao Chiu, Eric Y Chuang, Yidong Chen","doi":"10.1093/bioadv/vbag203","DOIUrl":"10.1093/bioadv/vbag203","url":null,"abstract":"<p><strong>Motivation: </strong>Understanding accurate immune cell heterogeneity and function in single-cell datasets requires access to protein-level information, which is often unavailable due to experimental limitations.</p><p><strong>Results: </strong>We present shinyDeepGxP, an interactive web application featuring our deep learning model, DeepGxP, for predicting surface protein abundance from single-cell RNA-sequencing (scRNA-seq) data. This platform makes DeepGxP accessible to researchers without programming skills. Users can upload scRNA-seq count matrices and use \"Predict Protein\" to predict the abundance of 224 biologically relevant surface proteins. shinyDeepGxP provides visualizations to help identify distinct cell populations based on predicted protein profiles. Moreover, users can choose \"Explore Model\" to reveal key RNA predictors and their associated biological pathways for each protein. Overall, shinyDeepGxP is a user-friendly, freely available web tool that provides protein-level detail for RNA-only single-cell datasets, enabling multimodal discovery without additional experiments.</p><p><strong>Availability and implementation: </strong>shinyDeepGxP can be launched on https://shiny.crc.pitt.edu/deepgxp/.</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"6 1","pages":"vbag203"},"PeriodicalIF":2.6,"publicationDate":"2026-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13532762/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148876647","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":"Calibrating tetranucleotide-frequency distances for metagenomic binning with right-skewed distribution models.","authors":"Omar Hajjaji, Al-Sayed Al-Soudy, Rachid Daoud, Rachid Benhida, Morad M Mokhtar","doi":"10.1093/bioadv/vbag207","DOIUrl":"10.1093/bioadv/vbag207","url":null,"abstract":"<p><strong>Summary: </strong>Metagenomic binning is a pivotal step in reconstructing metagenome-assembled genomes (MAGs) from complex microbial communities, and it critically depends on reliable measures of similarity between contigs. In many workflows, tetranucleotide-frequency (TNF) distances are translated into probabilistic evidence of a shared genome of origin. Despite their central role, these distances are often modeled with convenient but poorly matched assumptions, even though they are intrinsically non-negative and frequently exhibit pronounced right-skewness-features that can distort tail behavior and weaken downstream thresholding decisions. In this work, we introduce a likelihood-based framework for characterizing intra- and inter-genomic TNF distance distributions with flexible right-skewed parametric models and for converting fitted distributions into calibrated distance-to-probability scores within a MaxBin-style scheme. Our approach provides a principled statistical basis for distributional assessment, probability calibration, and transparent operating-point selection, with the goal of improving robustness and interpretability in TNF-driven binning.</p><p><strong>Availability and implementation: </strong>All codes related to the article are available through a public GitHub repository at https://github.com/omar-hajjaji/Calibrating-TNF-Distances-for-Metagenomic-Binning-with-Right-Skewed-Distribution-Models.</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"6 1","pages":"vbag207"},"PeriodicalIF":2.6,"publicationDate":"2026-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13441183/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148681396","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":"CNV-Finder: streamlining copy number variation discovery.","authors":"Nicole Kuznetsov, Kensuke Daida, Mary B Makarious, Bashayer Al-Mubarak, Kajsa Atterling Brolin, Laksh Malik, Cedric Kouam, Breeana Baker, Raquel Real, Kathryn Step, Lara M Lange, Lesley Wu, Miriam Ostrozovicova, Katherine M Andersh, Pin-Jui Kung, Yasser Mecheri, Yi-Wen Tay, Behloul Soundous Malek, Nada Al Tassan, Maria Teresa Periñan, Samantha Hong, Mathew J Koretsky, Lana Sargeant, Kristin Levine, Cornelis Blauwendraat, Kimberley J Billingsley, Sara Bandres-Ciga, Hampton L Leonard, Soraya Bardien, Huw R Morris, Andrew B Singleton, Mike A Nalls, Dan Vitale","doi":"10.1093/bioadv/vbag205","DOIUrl":"10.1093/bioadv/vbag205","url":null,"abstract":"<p><strong>Motivation: </strong>Copy Number Variations (CNVs) play pivotal roles in complex disease etiology, often requiring large sample sizes to analyze disease associations. While genotyping arrays offer a cost-effective approach for CNV detection using Log R Ratio (LRR) and B Allele Frequency (BAF) signals, existing independent array-based callers suffer from high false positive rates and noise susceptibility, burdening manual validation.</p><p><strong>Results: </strong>We present CNV-Finder, a deep learning pipeline employing Long Short-Term Memory (LSTM) networks for large-scale CNV identification within user-defined genomic regions. Trained on expert-annotated samples from the Global Parkinson's Genetics Program across four neurodegenerative disease-associated genes (<i>PRKN, LINGO2, MAPT, SNCA</i>), CNV-Finder integrates human feedback to iteratively improve performance. In benchmarking across 105 936 samples spanning 11 ancestries and nearly 150 cohorts, the model achieved 91% and 89% visual confirmation rates for <i>PRKN</i> deletions and duplications at high-confidence thresholds. In two validation cohorts, CNV-Finder nominated 83% fewer candidates than a popular Hidden Markov Model-based caller while maintaining higher confirmation rates. Validation through MLPA, short-read, and long-read sequencing demonstrated robust performance, generalizing to diverse signatures including homozygous deletions and <i>SNCA</i> triplications absent from training. Our findings highlight human expertise's value in complex loci like 17q21.31.</p><p><strong>Availability and implementation: </strong>CNV-Finder is freely available at https://github.com/nvk23/CNV-Finder.</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"6 1","pages":"vbag205"},"PeriodicalIF":2.6,"publicationDate":"2026-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13503034/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148815119","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":"lncAPNet enables the deciphering of lncRNA-mRNA connections in patient transcriptomic data.","authors":"Vasileios Vasileiou, George I Gavriilidis, Pedro Faria Zeni, Marek Mraz, Evangelos Karatzas, Antonis Giakountis, Georgios A Pavlopoulos, Antonis Giannakakis, Fotis Psomopoulos","doi":"10.1093/bioadv/vbag204","DOIUrl":"https://doi.org/10.1093/bioadv/vbag204","url":null,"abstract":"<p><strong>Motivation: </strong>Long non-coding RNAs regulate gene expression through chromatin remodeling, transcriptional control, and post-transcriptional modulation, influencing physiological cell homeostasis but also disease onset. Yet most transcriptomic and network-based studies rely on descriptive linear co-expression analyses, missing nonlinear and mechanistic insights. Emerging ML/DL methods offer promise but remain limited by data sparsity, noise, insufficient biological priors, and poor interpretability, constraining systems-level lncRNA-mRNA motif discovery.</p><p><strong>Results: </strong>In this manuscript, we introduce lncAPNet, an extended version of the APNet workflow, which integrates graph-based nonlinear inference of lncRNA-mRNA interactions using NetBID2's and scMINERs activity logic within a lncRNA-focused SJARACNe co-expression network, coupled with PASNet, a biologically informed sparse deep learning model. This framework enables explainable identification of lncRNA drivers in three different cancer type case studies, two with bulk RNA-seq datasets [Chronic Lymphocytic Leukemia and Prostate Adenocarcinoma] and one by combining bulk RNA-seq and scRNA-seq omics datasets [Breast Invasive Carcinoma], uncovering lncRNA drivers that illuminate lncRNA-mediated programs in cancer progression.</p><p><strong>Availability and implementation: </strong>lncAPNet's R scripts, Python scripts, and Nextflow pipeline are available at the GitHub repository: https://github.com/BiodataAnalysisGroup/lncAPNet.</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"6 1","pages":"vbag204"},"PeriodicalIF":2.6,"publicationDate":"2026-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13485353/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148802298","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}