GigaScience最新文献

{"title":"New causal discovery algorithm over censored variables identifies subtype-specific drivers of breast cancer progression.","authors":"Tyler C Lovelace, Panayiotis V Benos","doi":"10.1093/gigascience/giag060","DOIUrl":"10.1093/gigascience/giag060","url":null,"abstract":"<p><strong>Background: </strong>Many research domains are producing large, multi-scale, multi-modal datasets at growing rates with mixed variable types (continuous, discrete, censored). Identifying possible cause-effect associations in such datasets is essential for predicting outcomes and proposing possible interventions. Probabilistic graphical models (PGMs) have emerged as a robust, interpretable way to analyze such datasets, but current graph learning algorithms cannot incorporate time-to-event (censored) variables, which are important in many systems (e.g., patient survival). Instead, regression models are typically used for survival analysis of single censored variables, but these cannot assess cause-effect interactions.</p><p><strong>Results: </strong>Here, we present a new mathematical framework to incorporate multiple censored variables into mixed graphical models. A novel efficient algorithm, CausalCoxMGM, is implemented, which is extensively evaluated on synthetic and real-life high-dimensional biomedical datasets (cardiovascular disease, breast cancer). CausalCoxMGM was able to recover effectors of censored variables, supported by literature, and provided new mechanistic insights on the differences between ER+ and ER- breast cancers.</p><p><strong>Conclusions: </strong>CausalCoxMGM is a flexible computational framework for learning potential cause-effect relations from observational data of mixed data types, including multiple censored variables. The resulting graphs are interpretable and can be used to generate testable hypotheses or build efficient predictors of any outcome.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":" ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13235964/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147987201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Chromosome-level genome assembly and transcriptomes of the leaf insect Cryptophyllium westwoodii provide insights into the evolution of leaf-like masquerade.","authors":"Chuyang Mao, Zhiwei Dong, Zihe Li, Botong Zhou, Yi Hu, Jun Li, Guichun Liu, Zheng Zhou, Jinwu He, Yuhan Wu, Wenting Wan, Haoran Gao, Ruoping Zhao, Wenhui Nie, Wen Wang, Xueyan Li","doi":"10.1093/gigascience/giag022","DOIUrl":"10.1093/gigascience/giag022","url":null,"abstract":"<p><strong>Background: </strong>Leaf insects in the family Phylliidae are regarded as nature's ultimate masqueraders, evolving the leaf-resembling morphology to avoid predation. However, the lack of a high-quality reference genome for the leaf insects has hindered the exploration of the genetic mechanisms of leaf-like masquerade in insects.</p><p><strong>Results: </strong>We generated a chromosome-level genome assembly of Cryptophyllium westwoodii using Nanopore and Hi-C sequencing. 98.3% of the 4.12 Gb assembly (scaffold N50 = 256.9 Mb, 98.6% BUSCO completeness) was anchored onto 15 pseudo-chromosomes, including 13 autosomes, an X chromosome, and a putative B chromosome. Genome annotation predicted a total of 2.29 Gb repetitive sequences and 19,131 protein-coding genes. The chromosomal collinearity analysis indicated that many homologous gene fragments were detected between B chromosome and the other 14 A chromosomes in C. westwoodii, suggesting the B chromosome could have a mosaic origin based on homologous gene fragments from A chromosomes. Comparative genomic and transcriptomic analyses indicated that resilin gene with 24 copies expanded in C. westwoodii, of which 10 copies showed significantly different expression in the laterally leaf-like abdominal expansions at five developmental stages. These findings suggest that Cuticle genes, particularly resilin, may contribute to the leaf masquerade morphology in C. westwoodii, implying their possible role in the evolution of this adaptive trait.</p><p><strong>Conclusions: </strong>This study not only provides the first chromosome-level reference genome of leaf insects in Phylliidae, but also offers new insights into the leaf-like masquerade in leaf insects.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":" ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13108252/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147325659","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Autoencoder/RandomForest-TabPFN for cross-cancer metabolomics: prostate and breast cancer diagnosis using paper spray and ion mobility-mass spectrometry techniques.","authors":"Sven Hauns, Frederico G Pinto, Costerwell Khyriem, Ankita Singh, Azzat Al-Sadi, Talal Al Yazeedi, Rasheed Mohammad, Babacar Cisse, Timothy J Garrett, Mohammed Uddin, Nelson C Soares, Rolf Backofen, Omer S Alkhnbashi","doi":"10.1093/gigascience/giag053","DOIUrl":"10.1093/gigascience/giag053","url":null,"abstract":"<p><p>Accurate and rapid disease diagnosis, particularly in prostate cancer (PC) and breast cancer (BC), is critical for early intervention and improved patient outcomes. Metabolomic signatures represent a robust molecular framework for elucidating cancer-associated biochemical reprogramming. The use of artificial intelligence (AI) in biology in recent years has become widespread and promising. This study introduces a novel predictive method that integrates an Autoencoder, random forest-based feature selection and Tabular Prior-data Fitted Network (TabPFN) to achieve high diagnostic accuracy from metabolomics data of prostate and BC patients. The datasets were acquired using paper spray ionization mass spectrometry and flow injection-traveling-wave ion mobility-mass spectrometry of individuals diagnosed with PC and BC. When leveraging metabolomic profiling data from two distinct sources, PC urine and serum samples, the proposed model achieved an accuracy up to 98.75% in distinguishing diseased from healthy conditions. Additionally, we employed a BC dataset containing metabolic and lipidomic signatures acquired from core needle biopsies using a miniature MS platform coupled with PSI to assess the fidelity of our implementation across distinct cancer types. Our results on a well-characterized targeted dataset show that we can effectively reduce high-dimensional data into latent feature representations. At the same time, TabPFN captures tumor progression-related changes and feature interaction, thereby enhancing the possibility that the model will be a highly potent and effective tool for stage-specific diagnostic precision. Most existing machine learning approaches for disease diagnosis primarily rely on imaging, genomics, or clinical parameters, often overlooking the critical role of metabolites in identifying disease-specific biochemical signatures. By integrating metabolite-specific data with a robust deep-learning approach, this study demonstrates the transformative potential of AI in metabolomics-based diagnostics. The proposed model offers scalability and versatility, with applications extending beyond oncology to a much broader disease profiling aspect. These findings emphasize the value of combining multi-source metabolomic data with deep learning to advance personalized medicine and enhance diagnostic efficiency in clinical practice.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":" ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13251742/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147836684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RNA-SeqEZPZ: a point-and-click pipeline for comprehensive transcriptomics analysis with interactive visualizations.","authors":"Cenny Taslim, Yuan Zhang, Galen Rask, Genevieve C Kendall, Emily R Theisen","doi":"10.1093/gigascience/giaf133","DOIUrl":"10.1093/gigascience/giaf133","url":null,"abstract":"<p><strong>Background: </strong>RNA sequencing (RNA-seq) analysis has become a routine task in numerous genomic research labs, driven by the reduced cost of bulk RNA sequencing experiments. These studies generate billions of reads that require easy-to-run, comprehensive, and reproducible analysis. However, many labs rely on in-house scripts, which can be challenging for bench scientists to use and hinder standardization and reproducibility. While existing RNA-seq pipelines attempt to address these challenges, they often lack a complete end-to-end user interface.</p><p><strong>Findings: </strong>To bridge this gap, we developed RNA-SeqEZPZ, an automated pipeline with a user-friendly point-and-click interface, enabling rigorous and reproducible RNA-seq analysis without requiring programming or bioinformatics expertise. For advanced users, the pipeline can also be executed from the command line, allowing customization of steps to suit specific applications. The innovation of this pipeline lies in the combination of 3 key features: (i) all software is packaged within a Singularity container, eliminating installation issues; (ii) it offers a graphical, point-and-click interface from raw FASTQ files through differential expression and pathway analysis; and (iii) it includes a Nextflow implementation, enabling scalability and portability for seamless execution across various platforms, including job submission in the cloud and cluster computing. Additionally, RNA-SeqEZPZ generates a comprehensive statistical report and offers an option for batch adjustment to minimize effects of noise due to technical variation across replicates. Reports can also be reviewed by a bioinformatician to ensure the overall quality of the analysis.</p><p><strong>Conclusions: </strong>RNA-SeqEZPZ is a robust, accessible, and scalable solution for comprehensive RNA-seq analysis, enabling researchers to focus on biological insights rather than computational challenges.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":" ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12857227/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145495174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PIMENTO: a primer inference toolkit to facilitate large-scale calling of amplicon sequence variants.","authors":"Christian Atallah, Lorna Richardson, Martin Beracochea, Robert D Finn","doi":"10.1093/gigascience/giag083","DOIUrl":"10.1093/gigascience/giag083","url":null,"abstract":"<p><p>The identification of amplicon sequence variants from DNA metabarcoding data is a common method for revealing the taxonomic makeup of environmental samples, and for allowing comparative studies between similar datasets. A significant hurdle to the large-scale calling of amplicon sequence variants from publicly available nucleotide datasets is the heterogeneous presence of primer sequences in reads, the removal of which is a necessary pre-processing step for this form of analysis. Furthermore, as the details of the experimental primers are rarely captured in the metadata associated with the sequence records, there is a need for a method that can automatically infer the presence and identity of primers in sequencing data. In this work, we introduce the PrIMER infereNce TOolkit (PIMENTO), a Python package that uses a dual-strategy approach for identifying primers that are present in sequencing reads to enable their removal, and therefore facilitate amplicon sequence variant calling at scale.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":" ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13508698/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148630106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Haplotype-resolved chromosome-level genome assemblies of four Diamesa species reveal the genetic basis of cold tolerance and high-altitude adaptations in arctic chironomids.","authors":"Sarah L F Martin, Renato La Torre, Bram Danneels, Ave Tooming-Klunderud, Morten Skage, Spyridon Kollias, Ole Kristian Tørresen, Mohsen Falahati Anbaran, Elisabeth Stur, Kjetill S Jakobsen, Michael D Martin, Torbjørn Ekrem","doi":"10.1093/gigascience/giaf160","DOIUrl":"10.1093/gigascience/giaf160","url":null,"abstract":"<p><strong>Background: </strong>Arctic and alpine insects experience extreme environmental stressors, yet the genomic basis of their adaptation is poorly understood. Diamesa midges (Diptera: Chironomidae) are cold-adapted insects inhabiting glacial and high-altitude freshwater ecosystems, but no chromosome-level genomes have been available to date.</p><p><strong>Findings: </strong>We present the first haplotype-resolved, chromosome-level genomes for four Diamesa species (Diamesa hyperborea, Diamesa lindrothi, Diamesa serratosioi, and Diamesa tonsa), assembled using PacBio HiFi sequencing and Hi-C scaffolding. The assemblies show high completeness and k-mer representation. Phylogenomic analyses place Diamesinae as sister to other Chironomidae except Podonominae, and comparisons suggest introgression between the distinct species D. hyperborea and D. tonsa. Comparative genomic analyses across 20 Diptera species identified significant gene family contractions in Diamesa related to oxygen transport and metabolism, consistent with adaptation to high-altitude, low-oxygen environments. Expansions were observed in histone-related and Toll-like receptor gene families, suggesting roles in chromatin remodeling and immune regulation under cold stress. A glucose dehydrogenase gene family was significantly expanded across all cold-adapted species studied, implicating it in cryoprotectant synthesis and oxidative stress mitigation. Diamesa exhibited the largest gene family contraction at any phylogenetic node, with limited overlap in expansions with other cold-adapted Diptera, indicating lineage-specific adaptation.</p><p><strong>Conclusions: </strong>Our findings support the hypothesis that genome size condensation and selective gene family changes underpin survival in cold environments. These new genome assemblies provide a valuable resource for studying adaptation, speciation, and conservation in cold-specialist insects. Future integration of gene expression and population genomics will further clarify the evolutionary resilience of Diamesa in a warming world.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":" ","pages":""},"PeriodicalIF":11.8,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12908713/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145804052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The genomes of 5 mantises provide insights into sex chromosome evolution and Mantodea phylogeny clarification.","authors":"Hangwei Liu, Lihong Lei, Fan Jiang, Bo Zhang, Hengchao Wang, Yutong Zhang, Hanbo Zhao, Guirong Wang, Wei Fan","doi":"10.1093/gigascience/giaf158","DOIUrl":"10.1093/gigascience/giaf158","url":null,"abstract":"<p><strong>Background: </strong>Praying mantises, members of the order Mantodea, play important roles in agriculture, medicine, bionics, and entertainment. However, the scarcity of genomic resources has hindered extensive studies on mantis evolution and behavior.</p><p><strong>Results: </strong>Here, we present the chromosome-scale reference genomes of 5 mantis species: the European mantis (Mantis religiosa), Chinese mantis (Tenodera sinensis), triangle dead leaf mantis (Deroplatys truncata), orchid mantis (Hymenopus coronatus), and metallic mantis (Metallyticus violacea). The assembled genome sizes range from ∼2.3 to 4.2 Gb, with contig N50 size 1-109 Mb and 85%-99% of sequence anchored to chromosomes. The annotated protein-coding gene number ranges from 17,804 to 19,017, with a BUSCO complete rate of 96.7%-98.4%. We found that transposable element expansion is the major force governing genome size in Mantodea and suggest that translocations between the X chromosome and an autosome have occurred in the lineage of the family Mantidae. In addition, we found that the lineage of M. violacea has accumulated fewer substitutions than the lineages of other mantises. Furthermore, our genome-wide analyses showed that D. truncata is sister to H. coronatus compared with M. religiosa and T. sinensis, which helps resolve the phylogenic controversies of the Deroplatys genus.</p><p><strong>Conclusions: </strong>The high-quality genome assemblies of the 5 mantises provide a valuable resource for evolution studies of Mantodea and genetic improvement and breeding of beneficial biological control agents.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":" ","pages":""},"PeriodicalIF":11.8,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12908712/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145774156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From the brain cell atlas to precision neurology: a review of the application of AI-driven multi-omics in brain science.","authors":"Youzhe He, Yanrong Wei, Jingxi Zhi, Chunyu Huang, Lei Han, Shiping Liu, Lifang Wang","doi":"10.1093/gigascience/giag075","DOIUrl":"10.1093/gigascience/giag075","url":null,"abstract":"<p><p>Recent advances in multi-omics technologies have catalyzed the construction of comprehensive brain cell atlases, providing essential data foundations for artificial intelligence (AI)-driven analyses in precision neurology. This review systematically examines how the integration of AI with single-cell multi-omics and spatial multi-omics advances the resolution in deciphering brain cellular architecture across health and disease states. Through systematic evaluation of multi-omics datasets from neurodegenerative, psychiatric, and neurodevelopmental disorders, we demonstrate how AI facilitates disease subtype stratification, biomarker discovery, and therapeutic target identification. We critically address translational challenges, including data standardization, model interpretability, and regulatory frameworks for clinical implementation. Notably, the establishment of the International Consortium for Primate Brain Mapping in 2025 exemplifies ongoing global collaborative efforts toward systematic multi-omics atlas construction across species and disease states. This synthesis underscores a paradigm shift toward AI-enabled, mechanism-driven analyses, ultimately positioning precision neurology as a realizable framework for individualized diagnosis and targeted interventions in complex brain disorders. .</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":" ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13354932/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148307888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"NEXT-scASV: a Nextflow pipeline for allele-specific variant calling from single-cell RNA-seq data.","authors":"Andrey Shevtsov, Andrey Buyan, Vladimir Nozdrin, Pavel Akhtyamov, Alexey Stupnikov, Georgy Meshcheryakov, Ivan V Kulakovskiy, Yulia A Medvedeva","doi":"10.1093/gigascience/giag042","DOIUrl":"10.1093/gigascience/giag042","url":null,"abstract":"<p><p>The rapid accumulation of single-cell sequencing data presents major computational challenges in reproducibility, scaling, and handling data characteristics such as sparsity and technical variations, which complicate even basic analyses. The next level of complexity is the allele-specific analysis, focused on identifying differential gene expression or regulation between homologous chromosomes by estimating the allelic imbalance of read counts at individual single-nucleotide variants. Here, we present NEXT-scASV, a scalable Nextflow pipeline for calling allele-specific variants (ASVs) from 5' single-cell RNA sequencing data. NEXT-scASV automates the entire process-from read alignment and quality control to variant calling and statistical evaluation of the allelic imbalance-within a containerized environment, ensuring reproducibility and ease of deployment across platforms. NEXT-scASV is able to perform de novo ASV detection from single-cell sequencing data without prior genotyping. Its modular design allows for massive parallelization, efficiently handling the scale of modern atlas-level studies. We validate NEXT-scASV on a dataset of 135,000 peripheral blood mononuclear cells from 57 donors, demonstrating that it processes large-scale data efficiently, completing analysis in a week on a cluster with one node and 100 threads. Crucially, the pipeline reliably identifies ASVs even in rare cell populations (e.g., gdT GZMBhi and memory B IGHMhi cells), which remains elusive for bulk analyses. We also successfully detect allele-specific regulation of long non-coding RNAs and other lowly expressed, cell type-specific genes. Genes linked to detected ASVs show a high concordance (80%) with previously reported eQTLs. This strong validation confirms that NEXT-scASV produces biologically relevant results, making it a powerful tool for uncovering allele-specific regulation in large-scale, complex single-cell studies.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":" ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13148397/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147633224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CPSM: an R package for cancer patient survival risk model using transcriptomics and clinical data.","authors":"Harpreet Kaur, Pijush Das, Kevin Camphausen, Uma Shankavaram","doi":"10.1093/gigascience/giag067","DOIUrl":"10.1093/gigascience/giag067","url":null,"abstract":"<p><p>Traditional Kaplan-Meier curves capture aggregate survival trends within broad patient subgroups but overlook the heterogeneity of individual patients. In contrast, single-patient survival risk models bridge this gap by incorporating each patient's unique clinical, genomic, and demographic characteristics, generating personalized survival curves. These individualized visualizations enhance patient-clinician communication by translating complex statistics into intuitive, time-based visuals that are easier to interpret. However, the complexity, high dimensionality, and heterogeneity of multiomics data present significant challenges for analysis, interpretation, and model development. To address these challenges, we introduce the Cancer Patient Survival Model (CPSM), an R package designed to deliver individualized survival and risk predictions through a fully integrated, reproducible computational pipeline. CPSM includes 10 core functions organized into 4 key steps: (i) data preprocessing and normalization, (ii) feature selection, (iii) survival risk group prediction modeling, and (iv) visualization and nomogram construction. We demonstrate the utility of CPSM using publicly available datasets from The Cancer Genome Atlas for 4 cancer types: glioblastoma multiforme (GBM), acute myeloid leukemia (LAML), pancreatic adenocarcinoma (PAAD), and breast invasive cancer (BRCA). CPSM efficiently handles high-dimensional datasets with over 60,000 RNA transcripts and diverse clinical variables, enabling robust and interpretable individualized survival predictions under varying data conditions. Model performance was evaluated using repeated cross-validation with uncertainty quantification, ensuring robust and reliable estimates in high-dimensional, small-sample settings. In summary, CPSM provides an efficient, user-friendly, end-to-end solution for integrating patient data and generating personalized survival and risk predictions. Its integrated visual tools enhance interpretability and support more informed clinical decision-making. The package is freely available on Bioconductor (https://bioconductor.org/packages/devel/bioc/html/CPSM.html) and GitHub (https://github.com/hks5august/CPSM).</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":" ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13289734/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148149392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}