ArticleProceedings of the National Academy of Sciences of the United States of America2011
Sparse linear modeling of next-generation mRNA sequencing (RNA-Seq) data for isoform discovery and abundance estimation.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 69 papers.
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Who cites it
69 citing papers in PubMed.
- Integrated Metabolome and Transcriptome Analysis Reveals the Effect of Anthocyanins on Flower Color Variation inBiology · 2026Article
- Enhancing RNA-seq bias mitigation with the Gaussian self-benchmarking framework: towards unbiased sequencing data.BMC genomics · 2024Article
- Enhancing RNA-seq analysis by addressing all co-existing biases using a self-benchmarking approach with 2D structural insights.Briefings in bioinformatics · 2024Article
- Transcriptomic and metabolic analysis unveils the mechanism behind leaf color development inFrontiers in molecular biosciences · 2024Article
- A safety framework for flow decomposition problems via integer linear programming.Bioinformatics (Oxford, England) · 2023Article
- Efficient Minimum Flow Decomposition via Integer Linear Programming.Journal of computational biology : a journal of computational molecular cell biology · 2022Article
- Techniques for Profiling the Cellular Immune Response and Their Implications for Interventional Oncology.Cancers · 2022Review
- IntAPT: integrated assembly of phenotype-specific transcripts from multiple RNA-seq profiles.Bioinformatics (Oxford, England) · 2021Article
- Modern Approaches for Transcriptome Analyses in Plants.Advances in experimental medicine and biology · 2021Article
- Alternative splicing: Human disease and quantitative analysis from high-throughput sequencing.Computational and structural biotechnology journal · 2021Review
- Integrated transcriptomic and metabolomic data reveal the flavonoid biosynthesis metabolic pathway in Perilla frutescens (L.) leaves.Scientific reports · 2020Article
- Integrated metabolomic and transcriptomic analysis of the anthocyanin regulatory networks in Salvia miltiorrhiza Bge. flowers.BMC plant biology · 2020Article
- AIDE: annotation-assisted isoform discovery with high precision.Genome research · 2019Article
- Reconstruction of full-length circular RNAs enables isoform-level quantification.Genome medicine · 2019Article
- IKZF2 Drives Leukemia Stem Cell Self-Renewal and Inhibits Myeloid Differentiation.Cell stem cell · 2019Article
- Modeling and analysis of RNA-seq data: a review from a statistical perspective.Quantitative biology (Beijing, China) · 2018Article
- Splice Expression Variation Analysis (SEVA) for inter-tumor heterogeneity of gene isoform usage in cancer.Bioinformatics (Oxford, England) · 2018Article
- Evaluating approaches to find exon chains based on long reads.Briefings in bioinformatics · 2018Article
- Bayesian nonparametric discovery of isoforms and individual specific quantification.Nature communications · 2018Article
- MSIQ: JOINT MODELING OF MULTIPLE RNA-SEQ SAMPLES FOR ACCURATE ISOFORM QUANTIFICATION.The annals of applied statistics · 2018Article
9 more citing papers are in PubMed but not listed here.
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5 authors.
Funding
Abstract
Since the inception of next-generation mRNA sequencing (RNA-Seq) technology, various attempts have been made to utilize RNA-Seq data in assembling full-length mRNA isoforms de novo and estimating abundance of isoforms. However, for genes with more than a few exons, the problem tends to be challenging and often involves identifiability issues in statistical modeling. We have developed a statistical method called "sparse linear modeling of RNA-Seq data for isoform discovery and abundance estimation" (SLIDE) that takes exon boundaries and RNA-Seq data as input to discern the set of mRNA isoforms that are most likely to present in an RNA-Seq sample. SLIDE is based on a linear model with a design matrix that models the sampling probability of RNA-Seq reads from different mRNA isoforms. To tackle the model unidentifiability issue, SLIDE uses a modified Lasso procedure for parameter estimation. Compared with deterministic isoform assembly algorithms (e.g., Cufflinks), SLIDE considers the stochastic aspects of RNA-Seq reads in exons from different isoforms and thus has increased power in detecting more novel isoforms. Another advantage of SLIDE is its flexibility of incorporating other transcriptomic data such as RACE, CAGE, and EST into its model to further increase isoform discovery accuracy. SLIDE can also work downstream of other RNA-Seq assembly algorithms to integrate newly discovered genes and exons. Besides isoform discovery, SLIDE sequentially uses the same linear model to estimate the abundance of discovered isoforms. Simulation and real data studies show that SLIDE performs as well as or better than major competitors in both isoform discovery and abundance estimation. The SLIDE software package is available at https://sites.google.com/site/jingyijli/SLIDE.zip.
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