GigaScience最新文献

{"title":"GEfetch2R: fetching single-cell/bulk RNA-seq data from public repositories to R and benchmarking the subsequent format conversion tools.","authors":"Yabing Song, Jianbin Wang, Jiaxin Gao","doi":"10.1093/gigascience/giag039","DOIUrl":"10.1093/gigascience/giag039","url":null,"abstract":"<p><strong>Background: </strong>Downloading and reanalyzing the existing single-cell RNA sequencing (scRNA-seq) data provides an efficient choice to gain clues and new insights. However, no tool can fetch the diverse scRNA-seq data types (raw data, count matrix, and processed object) distributed in various repositories, process and load the downloaded data to R, convert formats between scRNA-seq objects, and benchmark the format conversion tools.</p><p><strong>Findings: </strong>Here, we present GEfetch2R, an R package with Docker image to (i) download diverse scRNA-seq data types, including raw data (SRA and ENA), count matrix (GEO, UCSC Cell Browser, and PanglaoDB), and processed objects (GEO, Zenodo, CELLxGENE, and HCA); (ii) process the downloaded data, load the count matrices/annotations/rds files to R (SeuratObject/DESeqDataSet), filter the SeuratObject based on cell metadata and genes, and dissect and extract the RData files; and (iii) convert formats between the widely used scRNA-seq objects, including SeuratObject, AnnData, SingleCellExperiment, CellDataSet/cell_data_set, and loom, and benchmark format conversion tools in terms of information kept, usability, running time, and scalability to guide the tool selection. Furthermore, GEfetch2R can also download, process, and load bulk RNA-seq raw data (SRA and ENA) and count matrices (GEO) to R (DESeqDataSet).</p><p><strong>Conclusions: </strong>GEfetch2R is an R package that facilitates researchers in accessing and exploring existing gene expression data from various public repositories. It can function as a data downloader (supports all 3 scRNA-seq and 2 bulk RNA-seq data types), a data processor (processes and loads the output/downloaded count matrices and annotations to R), and an object format converter (between the widely used scRNA-seq objects).</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/PMC13147242/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147662321","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":"ChronoRoot 2.0: an open AI-powered platform for 2D temporal plant phenotyping.","authors":"Nicolás Gaggion, Noelia A Boccardo, Rodrigo Bonazzola, María Florencia Legascue, María Florencia Mammarella, Florencia Sol Rodriguez, Federico Emanuel Aballay, Florencia Belén Catulo, Andana Barrios, Luciano J Santoro, Franco Accavallo, Santiago Nahuel Villarreal, Leonardo I Pereyra-Bistrain, Moussa Benhamed, Martin Crespi, Martiniano María Ricardi, Ezequiel Petrillo, Thomas Blein, Federico Ariel, Enzo Ferrante","doi":"10.1093/gigascience/giag018","DOIUrl":"10.1093/gigascience/giag018","url":null,"abstract":"<p><strong>Background: </strong>Plant developmental plasticity, particularly in root system architecture, is fundamental to understanding adaptability and agricultural sustainability. Existing automated phenotyping solutions face limitations, including binary segmentation approaches, restricted structural analysis capabilities, and text-based interfaces that limit accessibility, with most focusing solely on root structures while overlooking valuable information from simultaneous analysis of multiple plant organs.</p><p><strong>Findings: </strong>ChronoRoot 2.0 builds upon established low-cost hardware while significantly enhancing software capabilities and usability. The system employs nnUNet architecture for multi-class segmentation, demonstrating significant accuracy improvements while simultaneously tracking 6 distinct plant structures encompassing root, shoot, and seed components: main root, lateral roots, seed, hypocotyl, leaves, and petiole. This architecture enables easy retraining and incorporation of additional training data without requiring machine learning expertise. The platform introduces dual specialized graphical interfaces: a Standard Interface for detailed architectural analysis with novel gravitropic response parameters and a Screening Interface enabling high-throughput analysis of multiple plants through automated tracking. Functional principal component analysis integration enables discovery of novel phenotypic parameters through temporal pattern comparison. We demonstrate multi-species analysis, with Arabidopsis thaliana and Solanum lycopersicum, both morphologically distinct plant species. Three use cases in Arabidopsis thaliana and validation with tomato seedlings demonstrate enhanced capabilities: circadian growth pattern characterization, gravitropic response analysis in transgenic plants, and high-throughput etiolation screening across multiple genotypes.</p><p><strong>Conclusions: </strong>ChronoRoot 2.0 maintains the low-cost, modular hardware advantages of its predecessor while dramatically improving accessibility through intuitive graphical interfaces and expanded analytical capabilities. The open-source platform makes sophisticated temporal plant phenotyping more accessible to researchers without computational expertise.</p><p><strong>Software availability: </strong>https://chronoroot.github.io.</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/PMC13042295/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147316867","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":"FEDRANN: effective long-read overlap detection based on dimensionality reduction and approximate nearest neighbors.","authors":"Jia-Yuan Zhang, Changjiu Miao, Teng Qiu, Junyi He, Wenqi Cao, Wei Lin, Xiaoshuang Xia, Lei He, Chunlei Yang, Yuhui Sun, Tao Zeng, Yuxiang Li, Xun Xu, Yijun Ruan, Yuliang Dong","doi":"10.1093/gigascience/giag048","DOIUrl":"10.1093/gigascience/giag048","url":null,"abstract":"<p><p>Overlap detection is a key step in de novo genome assembly pipelines based on the overlap-layout-consensus paradigm. Existing methods for overlap detection either rely on heuristic seed-and-extension strategies or locality-sensitive hashing (LSH), both of which struggle to handle repetitive genomic regions and the computational burden of large-scale datasets. Here, we present FEDRANN, a novel strategy for overlap graph construction that integrates feature extraction, dimensionality reduction (DR), and approximate nearest neighbor (ANN) search. We find the pipeline combining inverse document frequency (IDF) transformation, sparse random projection (SRP), and NNDescent enables accurate detection of overlaps across diverse datasets. We developed an efficient open-source implementation of this pipeline named Fedrann (https://github.com/jzhang-dev/fedrann). Through systematic benchmarking on real long-read sequencing data, we demonstrate that Fedrann produces overlap graphs comparable to or better than those generated by existing state-of-the-art tools, including MECAT2, minimap2, and wtdbg2, while maintaining competitive runtime. By integrating Fedrann into the Shasta assembler, we successfully reconstructed human whole genomes, achieving high assembly contiguity and quality. Despite being implemented primarily in Python, Fedrann achieves performance parity with tools written in compiled languages by leveraging C-accelerated numerical libraries and optimized batch-based matrix operations. Our results suggest that the combination of DR and ANN techniques offers a robust, scalable framework for accurate overlap detection in long-read assembly and broader sequence similarity search tasks.</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/PMC13201080/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147856134","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":"Characterizing a species-rich and understudied tropical insect fauna using DNA barcoding.","authors":"David R Hemprich-Bennett, Ezekiel Donkor, Bernard Adams, Naana Afua Acquaah, Eva D Ofori, Samuel Anie-Amoah, Abigail Bailey, Hugh Charles J Godfray, Owen T Lewis, Fred Aboagye-Antwi, Talya D Hackett","doi":"10.1093/gigascience/giag028","DOIUrl":"10.1093/gigascience/giag028","url":null,"abstract":"<p><strong>Background: </strong>West Africa has high biodiversity that is relatively understudied, especially for insects. Studies of West African arthropod diversity can therefore help address important questions regarding conservation, ecosystem services, insecticide use, and other species-control interventions in agriculture and disease management. We intensively sampled arthropods in Ghana using complementary trapping methods, generated DNA barcodes, and classified sequences by Barcode Index Numbers (BINs), a species proxy. Using this dataset, we investigate assemblage composition, temporal activity patterns, and the state of regional biodiversity sampling.</p><p><strong>Results: </strong>Sequencing DNA from 95,996 individuals captured using Malaise, yellow pan, pitfall, Heath and Centre for Disease Control (CDC) traps, we identified 10,120 unique BINs. The rate of species accumulation did not approach an asymptote for any taxonomic group or trap type, indicating high biodiversity. The different trap types sampled different subsets of the local community, with the greatest similarity between yellow pan and pitfall traps. More insects and species (BINs) were trapped during the day than at night. Our dataset shared more BINs in the Barcode of Life Database with South Africa than with any other country, although this predominantly reflects the limited sampling and DNA sequencing campaigns in Africa.</p><p><strong>Conclusion: </strong>This study more than doubles the published BINs for West Africa, offering insights into the biodiversity of an ecologically important but understudied taxon and region. Using multiple trap types allowed a more complete assessment of the local arthropod assemblage. The public release of these data will support and stimulate further taxonomic and ecological work in the region.</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/PMC13108253/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147473325","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":"Context-aware simulation enables systematic optimization of long-read mapping parameters.","authors":"Jiang Hu, Dongming Fang, Xin Jin, Chentao Yang","doi":"10.1093/gigascience/giag079","DOIUrl":"10.1093/gigascience/giag079","url":null,"abstract":"<p><p>Long-read mapping performance is critical for downstream genomic analyses but remains sensitive to parameter selection. We present CycSim, a context-aware long-read simulator that learns sequence-context-dependent error profiles from empirical data and generates realistic simulated reads. CycSim more faithfully recapitulated real long-read characteristics than existing simulators, providing a high-fidelity simulation framework with known ground truth. Using this framework, we identified a Cyclone-specific parameter set that achieved 2.78-fold faster mapping than an ONT-oriented baseline while maintaining comparable variant-calling performance. For SV-oriented optimization, CycSim-guided refinement improved mapping efficiency by 8.14-34.16% across ONT, HiFi, and Cyclone HG002 datasets; increased SV F1 scores by 0.57-1.75 percentage points; and showed consistent improvements across independent benchmark datasets and different SV callers. Together, these results demonstrate the utility of CycSim for platform- and analysis-goal-specific algorithm development, benchmarking, and parameter optimization.</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/PMC13401045/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148411352","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":"Genome Assembly of Three Shrub Mangroves in the Genus Acanthus Reveals Two Polyploidy Events and Expansion of Genes Linked to Root Adaptation in Coastal Habitats.","authors":"Wanapinun Nawae, Chaiwat Naktang, Peeraphat Paenpong, Duangjai Sangsrakru, Thippawan Yoocha, Sonicha U-Thoomporn, Wasitthee Kongkachana, Poonsri Wanthongchai, Suchart Yamprasai, Chonlawit Samart, Sithichoke Tangphatsornruang, Wirulda Pootakham","doi":"10.1093/gigascience/giaf162","DOIUrl":"10.1093/gigascience/giaf162","url":null,"abstract":"<p><strong>Background: </strong>The genomes of mangrove Acanthus species have not been reported, despite their ecological and medicinal importance. Here, we generated reference genomes for three shrub mangroves in the genus Acanthus to clarify their whole-genome duplication and hybridization events and identify genomic features underlying their evolution.</p><p><strong>Results: </strong>Using PacBio and Hi-C data, we generated a chromosome-scale genome assembly of the recently identified allotetraploid species Acanthus tetraploideus (2n = 96). The genomes of diploid progenitors, Acanthus ilicifolius and Acanthus ebracteatus (2n = 48), were assembled from single-tube long fragment read data. We identified an Acanthus-specific whole-genome duplication (WGD) event that occurred ∼43 million years ago (Mya). Ancestral karyotype reconstruction revealed a shift in haploid chromosome number from 11 to 24 in the progenitors, following the WGD and subsequent chromosomal fission events. The hybridization that formed A. tetraploideus was estimated to have occurred 0.7-1.8 Mya. Phylogenomic and synteny analyses clearly showed that A. tetraploideus inherited subgenomes SG1 and SG2 from A. ilicifolius and A. ebracteatus, respectively. Gene structure and retention analyses revealed a smaller and more structurally flexible genome in A. ebracteatus and SG2 compared with A. ilicifolius and SG1. Gene family and machine learning analyses identified expansions in protein families related to Casparian strip formation, root development, and salt stress response. Several of these families were expanded in A. ilicifolius and SG1 but contracted in A. ebracteatus and SG2. These genomic patterns might have contributed to the establishment of A. tetraploideus within the habitat of A. ebracteatus. For all three species, population analysis revealed clear genetic divergence between samples from the eastern and western coasts of Thailand.</p><p><strong>Conclusions: </strong>These genome assemblies clarify the polyploidy and hybridization history of Acanthus and highlight gene family changes potentially associated with coastal root adaptation and habitat establishment in intertidal environments. This study provides valuable genomic resources and insights into the evolutionary adaptation of plants to intertidal environments.</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/PMC12903786/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145888972","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":"pyRootHair: Machine learning accelerated software for high-throughput phenotyping of plant root hair traits.","authors":"Ian Tsang, Lawrence Percival-Alwyn, Stephen Rawsthorne, James Cockram, Fiona Leigh, Jonathan A Atkinson","doi":"10.1093/gigascience/giaf141","DOIUrl":"10.1093/gigascience/giaf141","url":null,"abstract":"<p><strong>Background: </strong>Root hairs play a key role in plant nutrient and water uptake. Historically, root hair traits have largely been quantified manually. As such, this process has been laborious and low-throughput. However, given their importance for plant health and development, high-throughput quantification of root hair morphology could help underpin rapid advances in the genetic understanding of these traits. With recent increases in the accessibility and availability of artificial intelligence (AI) and machine learning techniques, the development of tools to automate plant phenotyping processes has been greatly accelerated.</p><p><strong>Results: </strong>We present pyRootHair, a high-throughput, AI-powered software application to automate root hair trait extraction from microscope images of plant roots grown on agar plates. pyRootHair is capable of batch processing over 600 images per hour without manual input from the end user. In this study, we deploy pyRootHair on a panel of 24 diverse wheat (Triticum aestivum and Triticum turgidum ssp. durum) cultivars and uncover a large, previously unresolved amount of variation in many root hair traits. We show that the overall root hair profile falls under 2 distinct shape categories and that different root hair traits often correlate with each other. We also demonstrate that pyRootHair can be deployed on a range of plant species, including oat (Avena sativa), rice (Oryza sativa), teff (Eragrostis tef), and tomato (Solanum lycopersicum).</p><p><strong>Conclusions: </strong>The application of pyRootHair enables users to rapidly screen a large number of plant germplasm resources for variation in root hair morphology, supporting high-resolution measurements and high-throughput data analysis. This facilitates downstream investigation of the impacts of root hair genetic control and morphological variation on plant performance. pyRootHair is installable via PyPI (https://pypi.org/project/pyRootHair/) and can be accessed on GitHub at https://github.com/iantsang779/pyRootHair.</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/PMC12824728/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145512386","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":"Cell type transcriptomic modules reveal shared molecular mechanisms in Alzheimer's and Parkinson's disease.","authors":"Anwesha Bhattacharya, Edward A Fon, Alain Dagher, Yasser Iturria-Medina, Jo Anne Stratton, Chloe Savignac, Jack Stanley, Liam Hodgson, Badr Ait Hammou, David A Bennett, Danilo Bzdok","doi":"10.1093/gigascience/giag059","DOIUrl":"10.1093/gigascience/giag059","url":null,"abstract":"<p><strong>Background: </strong>Historically, Alzheimer's disease (AD) and Parkinson's disease (PD) have been investigated as 2 distinct disorders of the brain. However, a few similarities in neuropathology and clinical symptoms have been documented over the years. Traditional single-gene centric studies, such as differential gene expression analyses, have struggled to unravel the molecular basis for the observed pathological links between AD and PD.</p><p><strong>Results: </strong>We tailor a latent factor framework to analyze synchronous gene co-expression changes in AD or PD at sub-cell-type resolution. Utilizing large, single-nucleus transcriptomics datasets in AD (70,634 nuclei) and PD (340,902 nuclei) from postmortem human brains, we systematically extract and juxtapose disease-critical molecular signatures in the brain. Our transcriptomic analysis reveals shared molecular programs between AD and PD that localize to specific glial and neuronal cell types. In neurons, convergent gene groups in AD and PD relate to cytoskeletal dynamics and mitochondrial stress mechanisms. In microglia, overlapping gene modules implicate T cell activation mechanisms and synapse pruning pathways. In parallel, AD- and PD-associated gene groups in astrocytes are involved in heavy metal processing; oligodendrocytes highlight convergent dysregulation in myelin synthesis. Additionally, our analysis reveals apolipoprotein E gene (an AD risk gene), and the synuclein alpha gene (a PD risk gene) to have disease predictive roles in both AD- and PD-associated gene modules.</p><p><strong>Conclusion: </strong>Our multi-module sub-cell-type approach offers novel insights into the molecular basis of shared neuropathology in AD and PD.</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/PMC13289754/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147981278","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":"Cord blood DNA methylation and cell-type composition are not significantly associated with severe preeclampsia after cell-type and clinical covariate adjustment.","authors":"Xiaotong Yang, Wenting Liu, Zhixin Mao, Yuheng Du, Cameron Lassiter, Fadhl M Alakwaa, Paula A Benny, Lana X Garmire","doi":"10.1093/gigascience/giag002","DOIUrl":"10.1093/gigascience/giag002","url":null,"abstract":"<p><strong>Background: </strong>Preeclampsia is a severe pregnancy complication that threatens maternal and neonatal health and well-being. Previous studies on epigenome-wide association studies (EWAS) of preeclampsia produced inconsistent results in cord blood tissues, and one possible explanation is their failure to rigorously adjust for cell proportions, gestational age, or other necessary variables.</p><p><strong>Methods: </strong>Here, we calculated the DNA methylation changes in cord blood from newborns affected by preeclampsia, using a multi-ethnic cohort from the Hawaii population (24 cases, 38 controls). We comprehensively adjusted for variables, such as maternal age, body mass index, and parity, and estimated the cell proportions. We also re-analyzed 2 previous datasets with adjustments to estimated cell proportions and conducted a pooled analysis by merging all 3 datasets together to increase the statistical power (58 cases, 71 controls). Lastly, we included idiopathic preterm (preterm delivery with no known reasons) cord blood samples (n = 11) to disentangle the effect of severe preeclampsia and small gestational age.</p><p><strong>Results: </strong>We showed that after adjusting cell-type proportions and patient clinical characteristics, most of the so-called statistically significant CpG methylation changes associated with severe preeclampsia disappeared in our own data, 2 public datasets, and the pooled analysis combining all 3 datasets. This result still holds after including idiopathic preterm samples in the control group. Rather, we found that gestation progression is accompanied by statistically significant proportion changes in several cell types, such as granulocytes, nucleated red blood cells (nRBCs), CD8T cells, and B cells, which contribute to most DNA methylation differences between case and control groups. Preeclampsia has interactions on cell proportion changes in granulocytes, monocytes, and nRBCs.</p><p><strong>Conclusions: </strong>In summary, our study shows that the previously reported differentially methylated patterns in cord blood are actually artifacts due to not properly adjusting for cell-type heterogeneity, gestational age, and clinical covariates. Severe preeclampsia is not associated with statistically significant DNA methylation changes but changes in cell proportion. This finding alerts to the scientific rigor needed in EWAS.</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/PMC13014471/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145989009","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":"Comparative evaluation of gene selection approaches in transcriptomics: bias correction and visualization with TransPro.","authors":"Dongyue Yu, Chen Li, Shuo Yan, Lujiale Guo, Jingyu Liang, Shengquan Chen, Wenjun Bu","doi":"10.1093/gigascience/giag057","DOIUrl":"10.1093/gigascience/giag057","url":null,"abstract":"<p><strong>Background: </strong>Differential gene selection is fundamental to transcriptomics; however, mainstream methods typically fit each gene independently via marginal analyses. Although multifactor designs can be accommodated, gene-gene interactions are not explicitly modeled, rendering results susceptible to systematic bias driven by coexpression patterns. Additionally, heterogeneous tools, inconsistent metrics, and fragmented workflows compromise the reproducibility and interpretability of downstream enrichment and visualization analyses.</p><p><strong>Findings: </strong>We developed TransPro, an open-source integrated framework comprising 2 complementary packages, TransProPy and TransProR, for systematic benchmarking, bias correction, and reproducible visualization in differential gene selection. TransProPy (Python) combines multivariate AUC-based complementarity quantification with ensemble learning for interaction-aware gene selection, whereas TransProR (R) provides standardized differential analysis, pathway enrichment, and 7 visualization workflows (circular dendrograms, chord diagrams, spiral plots, and interaction networks). Cross-dataset generalizability was assessed using 12 independent datasets spanning multiple cancer types and normal tissues with broad heterogeneity in data origin, platform, batch structure, and sample size. Under stringent thresholds, positively and negatively correlated genes maintained a near-equal proportion at the gene level, and activated and suppressed pathway proportions exhibited good concordance with gene-level correlation patterns; critically, TransProPy produced meaningful enrichment results under conditions where conventional methods failed. Quantitative comparisons of core-enriched gene proportions further supported these differences (Kruskal-Wallis and pairwise Wilcoxon tests, P < 0.001).</p><p><strong>Conclusions: </strong>TransPro establishes a unified, reproducible framework that corrects method-specific biases while bridging computational discovery and biological interpretation. All code, workflows, documentation, and example data are openly accessible to support reproducibility and community reuse.</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/PMC13215095/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147963232","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}