ArticleBioinformatics (Oxford, England)2013
Inference of alternative splicing from RNA-Seq data with probabilistic splice graphs.
Article in Bioinformatics (Oxford, England), 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 25 citations in OpenAlex.
- Bioinformatics perspectives on transcriptomics: A comprehensive review of bulk and single-cell RNA sequencing analyses.Quantitative biology (Beijing, China) · 2025Review
- Highly efficient clustering of long-read transcriptomic data with GeLuster.Bioinformatics (Oxford, England) · 2024Article
- NanoSplicer: accurate identification of splice junctions using Oxford Nanopore sequencing.Bioinformatics (Oxford, England) · 2022Article
- Deriving Ranges of Optimal Estimated Transcript Expression due to Nonidentifiability.Journal of computational biology : a journal of computational molecular cell biology · 2022Article
- A model for isoform-level differential expression analysis using RNA-seq data without pre-specifying isoform structure.PloS one · 2022Article
- McSplicer: a probabilistic model for estimating splice site usage from RNA-seq data.Bioinformatics (Oxford, England) · 2021Article
- Exact transcript quantification over splice graphs.Algorithms for molecular biology : AMB · 2021Article
- Identification of novel alternative splicing biomarkers for breast cancer with LC/MS/MS and RNA-Seq.BMC bioinformatics · 2020Article
- Detecting, Categorizing, and Correcting Coverage Anomalies of RNA-Seq Quantification.Cell systems · 2019Article
- Temporal dynamics in meta longitudinal RNA-Seq data.Scientific reports · 2019Article
- The determinants of alternative RNA splicing in human cells.Molecular genetics and genomics : MGG · 2017Review
- Article
- Comparisons of computational methods for differential alternative splicing detection using RNA-seq in plant systems.BMC bioinformatics · 2014Article
- IUTA: a tool for effectively detecting differential isoform usage from RNA-Seq data.BMC genomics · 2014Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
motivationAlternative splicing and other processes that allow for different transcripts to be derived from the same gene are significant forces in the eukaryotic cell. RNA-Seq is a promising technology for analyzing alternative transcripts, as it does not require prior knowledge of transcript structures or genome sequences. However, analysis of RNA-Seq data in the presence of genes with large numbers of alternative transcripts is currently challenging due to efficiency, identifiability and representation issues.
resultsWe present RNA-Seq models and associated inference algorithms based on the concept of probabilistic splice graphs, which alleviate these issues. We prove that our models are often identifiable and demonstrate that our inference methods for quantification and differential processing detection are efficient and accurate. AVAILABILITY: Software implementing our methods is available at http://deweylab.biostat.wisc.edu/psginfer. CONTACT: cdewey@biostat.wisc.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.