ArticleQuantitative biology (Beijing, China)2018
Modeling and analysis of RNA-seq data: a review from a statistical perspective.
Article in Quantitative biology (Beijing, China), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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Who cites it
40 citing papers in PubMed.
- Review
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- Computational methods and data resources for predicting tumor neoantigens.Briefings in bioinformatics · 2025Review
- Inference of gene coexpression networks from single-cell transcriptome data based on variance decomposition analysis.Briefings in bioinformatics · 2025Article
- Predicting and comparing transcription start sites in single cell populations.PLoS computational biology · 2025Article
- Leveraging State-of-the-Art AI Algorithms in Personalized Oncology: From Transcriptomics to Treatment.Diagnostics (Basel, Switzerland) · 2024Article
- Evaluation of network-guided random forest for disease gene discovery.BioData mining · 2024Article
- scFSNN: a feature selection method based on neural network for single-cell RNA-seq data.BMC genomics · 2024Article
- Coupling quantitative systems pharmacology modelling to machine learning and artificial intelligence for drug development: itsFrontiers in systems biology · 2024Article
- bakR: uncovering differential RNA synthesis and degradation kinetics transcriptome-wide with Bayesian hierarchical modeling.RNA (New York, N.Y.) · 2023Article
- Identifying Fungal Secondary Metabolites and Their Role in Plant Pathogenesis.Methods in molecular biology (Clifton, N.J.) · 2023Article
- RoseAP: an analytical platform for gene function ofFrontiers in plant science · 2023Article
- Inferring cancer common and specific gene networks via multi-layer joint graphical model.Computational and structural biotechnology journal · 2023Article
- Hypergraph geometry reflects higher-order dynamics in protein interaction networks.Scientific reports · 2022Article
- A simple guide to de novo transcriptome assembly and annotation.Briefings in bioinformatics · 2022Review
- RNA sequencing and its applications in cancer and rare diseases.Molecular biology reports · 2022Review
- Exploring the Common Mechanism of Fungal sRNA Transboundary Regulation of Plants Based on Ensemble Learning Methods.Frontiers in genetics · 2022Article
- Large-Scale Integrative Analysis of Soybean Transcriptome Using an Unsupervised Autoencoder Model.Frontiers in plant science · 2022Article
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2 authors.
Funding
Abstract
backgroundSince the invention of next-generation RNA sequencing (RNA-seq) technologies, they have become a powerful tool to study the presence and quantity of RNA molecules in biological samples and have revolutionized transcriptomic studies. The analysis of RNA-seq data at four different levels (samples, genes, transcripts, and exons) involve multiple statistical and computational questions, some of which remain challenging up to date.
resultsWe review RNA-seq analysis tools at the sample, gene, transcript, and exon levels from a statistical perspective. We also highlight the biological and statistical questions of most practical considerations.
conclusionsThe development of statistical and computational methods for analyzing RNA-seq data has made significant advances in the past decade. However, methods developed to answer the same biological question often rely on diverse statistical models and exhibit different performance under different scenarios. This review discusses and compares multiple commonly used statistical models regarding their assumptions, in the hope of helping users select appropriate methods as needed, as well as assisting developers for future method development.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.