ArticleBMC genomics2014
TIGAR2: sensitive and accurate estimation of transcript isoform expression with longer RNA-Seq reads.
Article in BMC genomics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Analysis of gene expression of Babesia gibsoni cultured with diminazene aceturate using RNA sequencing.The Journal of veterinary medical science · 2025Article
- Clock Gene Expression in Eel Retina and Hypothalamus: Response to Photoperiod and Moonlight.Journal of experimental zoology. Part A, Ecological and integrative physiology · 2025Article
- Oarfish: Enhanced probabilistic modeling leads to improved accuracy in long read transcriptome quantification.bioRxiv : the preprint server for biology · 2024Article
- Alternative RNA Splicing in the Retina: Insights and Perspectives.Cold Spring Harbor perspectives in medicine · 2023Review
- Antiemetic effects of baclofen in a shrew model of postoperative nausea and vomiting: Whole-transcriptome analysis in the nucleus of the solitary tract.CNS neuroscience & therapeutics · 2022Article
- Perplexity: evaluating transcript abundance estimation in the absence of ground truth.Algorithms for molecular biology : AMB · 2022Article
- LIQA: long-read isoform quantification and analysis.Genome biology · 2021Article
- A Bayesian framework for inter-cellular information sharing improves dscRNA-seq quantification.Bioinformatics (Oxford, England) · 2020Article
- Factorial study of the RNA-seq computational workflow identifies biases as technical gene signatures.NAR genomics and bioinformatics · 2020Article
- Article
- Event Analysis: Using Transcript Events To Improve Estimates of Abundance in RNA-seq Data.G3 (Bethesda, Md.) · 2018Article
- Article
- Evaluation and comparison of computational tools for RNA-seq isoform quantification.BMC genomics · 2017Article
- Improved data-driven likelihood factorizations for transcript abundance estimation.Bioinformatics (Oxford, England) · 2017Article
- Salmon provides fast and bias-aware quantification of transcript expression.Nature methods · 2017Article
- The exon quantification pipeline (EQP): a comprehensive approach to the quantification of gene, exon and junction expression from RNA-seq data.Nucleic acids research · 2016Article
- Bayesian identification of bacterial strains from sequencing data.Microbial genomics · 2016Article
- RapMap: a rapid, sensitive and accurate tool for mapping RNA-seq reads to transcriptomes.Bioinformatics (Oxford, England) · 2016Article
- A Bayesian approach for estimating allele-specific expression from RNA-Seq data with diploid genomes.BMC genomics · 2016Article
- Identification of Immunoglobulin Gene Sequences from a Small Read Number of mRNA-Seq Using Hybridomas.PloS one · 2016Article
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7 authors.
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Abstract
backgroundHigh-throughput RNA sequencing (RNA-Seq) enables quantification and identification of transcripts at single-base resolution. Recently, longer sequence reads become available thanks to the development of new types of sequencing technologies as well as improvements in chemical reagents for the Next Generation Sequencers. Although several computational methods have been proposed for quantifying gene expression levels from RNA-Seq data, they are not sufficiently optimized for longer reads (e.g. >250 bp).
resultsWe propose TIGAR2, a statistical method for quantifying transcript isoforms from fixed and variable length RNA-Seq data. Our method models substitution, deletion, and insertion errors of sequencers based on gapped-alignments of reads to the reference cDNA sequences so that sensitive read-aligners such as Bowtie2 and BWA-MEM are effectively incorporated in our pipeline. Also, a heuristic algorithm is implemented in variational Bayesian inference for faster computation. We apply TIGAR2 to both simulation data and real data of human samples and evaluate performance of transcript quantification with TIGAR2 in comparison to existing methods.
conclusionsTIGAR2 is a sensitive and accurate tool for quantifying transcript isoform abundances from RNA-Seq data. Our method performs better than existing methods for the fixed-length reads (100 bp, 250 bp, 500 bp, and 1000 bp of both single-end and paired-end) and variable-length reads, especially for reads longer than 250 bp.
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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.