ArticleBioinformatics (Oxford, England)2015
WemIQ: an accurate and robust isoform quantification method for RNA-seq data.
Article in Bioinformatics (Oxford, England), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Anti-bias training for (sc)RNA-seq: experimental and computational approaches to improve precision.Briefings in bioinformatics · 2021Article
- Application of single-cell RNA sequencing technology in liver diseases: a narrative review.Annals of translational medicine · 2021Review
- Alternative splicing: Human disease and quantitative analysis from high-throughput sequencing.Computational and structural biotechnology journal · 2021Review
- Quantile regression for challenging cases of eQTL mapping.Briefings in bioinformatics · 2020Article
- Alternative splicing programming of axon formation.Wiley interdisciplinary reviews. RNA · 2020Review
- Technological advances and computational approaches for alternative splicing analysis in single cells.Computational and structural biotechnology journal · 2020Review
- Modeling and analysis of RNA-seq data: a review from a statistical perspective.Quantitative biology (Beijing, China) · 2018Article
- BCseq: accurate single cell RNA-seq quantification with bias correction.Nucleic acids research · 2018Article
- Single-cell RNAseq for the study of isoforms-how is that possible?Genome biology · 2018Review
- MSIQ: JOINT MODELING OF MULTIPLE RNA-SEQ SAMPLES FOR ACCURATE ISOFORM QUANTIFICATION.The annals of applied statistics · 2018Article
- Exploring the Complexity of Cortical Development Using Single-Cell Transcriptomics.Frontiers in neuroscience · 2018Review
- CORNAS: coverage-dependent RNA-Seq analysis of gene expression data without biological replicates.BMC bioinformatics · 2017Article
- Single-cell RNA-sequencing of the brain.Clinical and translational medicine · 2017Review
- Single-Cell Alternative Splicing Analysis with Expedition Reveals Splicing Dynamics during Neuron Differentiation.Molecular cell · 2017Article
- Design and computational analysis of single-cell RNA-sequencing experiments.Genome biology · 2016Review
- Dynamics in Transcriptomics: Advancements in RNA-seq Time Course and Downstream Analysis.Computational and structural biotechnology journal · 2015Review
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3 authors.
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Abstract
motivationThe deconvolution of isoform expression from RNA-seq remains challenging because of non-uniform read sampling and subtle differences among isoforms.
resultsWe present a weighted-log-likelihood expectation maximization method on isoform quantification (WemIQ). WemIQ integrates an effective bias removal with a weighted expectation maximization (EM) algorithm to distribute reads among isoforms efficiently. The weight represents the oversampling or undersampling of sequence reads and is estimated through a generalized Poisson model without any presumption on the bias sources and formats. WemIQ significantly improves the quantification of isoform and gene expression as well as the derived exon inclusion rates. It provides robust expression estimates across different laboratories and protocols, which is valuable for the integrative analysis of RNA-seq. For the recent single-cell RNA-seq data, WemIQ also provides the opportunity to distinguish bias heterogeneity from true biological heterogeneity and uncovers smaller cell-to-cell expression variability.
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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.