Searching journal content for articles similar to Omasits et al. 23 (11): 1916.

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Method: Evaluation of strategies for evidence-driven genome annotation using long-read RNA-seq
- Alejandro Paniagua,
- Cristina Agustín-García,
- Francisco J. Pardo-Palacios,
- Thomas Brown,
- Maite De Maria,
- Nancy D. Denslow,
- Camila J. Mazzoni,
- and Ana Conesa
Genome Res. April 2025 35: 1053-1064; Published in Advance December 23, 2024, doi:10.1101/gr.279864.124
...and Ence 2012). Evidence-based annotation or evidence alignment methods use experimental data, such as RNA-seq, protein sequences, or expressed sequence tags (ESTs) from the organism of interest or related species, which are mapped to the to identify genes (Yandell and Ence 2012; Jung et al. 2020...
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Method: Tree-based differential testing using inferential uncertainty for RNA-seq
- Noor Pratap Singh,
- Euphy Y. Wu,
- Jason Fan,
- Michael I. Love,
- and Rob Patro
Genome Res. October 2025 35: 2326-2338; Published in Advance August 21, 2025, doi:10.1101/gr.279981.124
...future direction would also be to explore the usage of TreeTerminus trees and mehenDi for differential transcript usage (DTU) analysis.MethodsPreliminariesFor a set of samples in an RNA-seq experiment, TreeTerminus (Singh et al. 2023) outputs a forest of trees that summarizes the abundance uncertainty...
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Method: Aggregation of recount3 RNA-seq data improves inference of consensus and tissue-specific gene coexpression networks
- Prashanthi Ravichandran,
- Princy Parsana,
- Rebecca Keener,
- Kasper D. Hansen,
- and Alexis Battle
Genome Res. September 2025 35: 2087-2103; Published in Advance July 17, 2025, doi:10.1101/gr.280808.125
...identifies 27 unique tissue contextsWe downloaded uniformly processed RNA-seq samples from humans using the recount3 R package (Wilks et al. 2021) comprised of experiments from three data sources—The Sequence Read Archive (SRA) (Kodama et al. 2012; Katz et al. 2022), Genotype-tissue Expression (GTEx, version...
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Method: BRAKER3: Fully automated genome annotation using RNA-seq and protein evidence with GeneMark-ETP, AUGUSTUS, and TSEBRA
- Lars Gabriel,
- Tomáš Brůna,
- Katharina J. Hoff,
- Matthis Ebel,
- Alexandre Lomsadze,
- Mark Borodovsky,
- and Mario Stanke
Genome Res. May 2024 34: 769-777; Published in Advance June 12, 2024, doi:10.1101/gr.278090.123
...extrinsic and intrinsic evidence. Extrinsic evidence is extracted from transcripts and cross-species homologous proteins. RNA-seq reads offer direct evidence on introns and, if assembled, on a gene structure. Protein sequences from related s can be used to identify regions of a that encode proteins...
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Resource: Multitissue single-nucleus RNA-seq reveals cell type–specific regulatory patterns of alternative polyadenylation in pigs
- Qiuhan Wen,
- Zhen Wang,
- Qi Bao,
- Tianli Ding,
- Haihan Zhang,
- Jianbo Li,
- Zhuang Liu,
- Jieping Huang,
- and Guoqiang Yi
Genome Res. September 2025 35: 2116-2129; Published in Advance June 16, 2025, doi:10.1101/gr.280095.124
...To comprehensively profile alternative polyadenylation across pig tissues, we analyzed 10x Genomics single-nucleus RNA sequencing (10x snRNA-seq) data from our previous study as a preprint on bioRxiv (Chen et al. 2023), which identified 261 cell subtypes spanning 19 distinct pig tissues (Supplemental Table S1...
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Method: A gene regulatory network–aware graph learning method for cell identity annotation in single-cell RNA-seq data
- Mengyuan Zhao,
- Jiawei Li,
- Xiaoyi Liu,
- Ke Ma,
- Jijun Tang,
- and Fei Guo
Genome Res. July 2024 34: 1036-1051; Published in Advance August 12, 2024, doi:10.1101/gr.278439.123
...not only effectively identify cell types with expression characteristics but also distinguish similar cell types.scHGR identifies novel subtypes based on scRNA-seq of PBMCscHGR not only automatically assigns cell identities but also reveals novel subpopulations by taking abundantly annotated atlases...
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Method: TransMeta simultaneously assembles multisample RNA-seq reads
- Ting Yu,
- Xiaoyu Zhao,
- and Guojun Li
Genome Res. July 2022 32: 1398-1407; Published in Advance July 20, 2022, doi:10.1101/gr.276434.121
...the complexity of eukaryotic transcriptomes and identify the expressed transcripts and quantify their abundance precisely at a whole-transcriptome level (Wang et al. 2009; Wilhelm and Landry 2009; Marguerat and Bähler 2010; Ozsolak and Milos 2011). Assembling RNA-seq reads into full-length transcripts has become...
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Method: A graph neural network model to estimate cell-wise metabolic flux using single-cell RNA-seq data
- Norah Alghamdi,
- Wennan Chang,
- Pengtao Dang,
- Xiaoyu Lu,
- Changlin Wan,
- Silpa Gampala,
- Zhi Huang,
- Jiashi Wang,
- Qin Ma,
- Yong Zang,
- Melissa Fishel,
- Sha Cao,
- and Chi Zhang
Genome Res. October 2021 31: 1867-1884; Published in Advance July 22, 2021, doi:10.1101/gr.271205.120
...and the observed variation of metabolite abundance in the matched metabolomics data. We also applied scFEA on five publicly available scRNA-seq and spatial transcriptomics data sets and identified context- and cell group–specific metabolic variations. The cell-wise fluxome predicted by scFEA empowers a series...
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Method: Ultrafast functional profiling of RNA-seq data for nonmodel organisms
- Peng Liu,
- Jessica Ewald,
- Jose Hector Galvez,
- Jessica Head,
- Doug Crump,
- Guillaume Bourque,
- Niladri Basu,
- and Jianguo Xia
Genome Res. April 2021 31: 713-720; Published in Advance March 17, 2021, doi:10.1101/gr.269894.120
...and high-quality annotations, as well as the difficulties in obtaining large sample sizes especially from experimental settings. In general, RNA-seq studies in nonmodel organisms tend to have a relatively simple experimental design in which the main objective is to identify differentially expressed genes...
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Research: Differences in molecular sampling and data processing explain variation among single-cell and single-nucleus RNA-seq experiments
- John T. Chamberlin,
- Younghee Lee,
- Gabor T. Marth,
- and Aaron R. Quinlan
Genome Res. February 2024 34: 179-188; Published in Advance February 14, 2024, doi:10.1101/gr.278253.123
...and stability between the nucleus and cytoplasm (Fazal et al. 2019), the primary obstacle identified by initial studies was excessive data sparsity in snucRNA-seq. Nuclei contain a minority of total cellular mRNA but are inherently enriched for pre-mRNAs (Fig. 1A), yielding fewer total exonic read alignments...
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