ArticleNucleic acids research2011
Accurate quantification of transcriptome from RNA-Seq data by effective length normalization.
Article in Nucleic acids research, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 papers.
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Who cites it
63 citing papers in PubMed.
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- Expression based biomarkers and models to classify early and late-stage samples of Papillary Thyroid Carcinoma.PloS one · 2020Article
- DEBrowser: interactive differential expression analysis and visualization tool for count data.BMC genomics · 2019Article
- Re-programming of Pseudomonas syringae pv. actinidiae gene expression during early stages of infection of kiwifruit.BMC genomics · 2018Article
- Integrated transcriptomic analysis of Trichosporon Asahii uncovers the core genes and pathways of fluconazole resistance.Scientific reports · 2017Article
- Identification of long non-coding RNAs in two anthozoan species and their possible implications for coral bleaching.Scientific reports · 2017Article
- Positive selection rather than relaxation of functional constraint drives the evolution of vision during chicken domestication.Cell research · 2016Article
- hTERT promotes cell adhesion and migration independent of telomerase activity.Scientific reports · 2016Article
- A comparative analytical assay of gene regulatory networks inferred using microarray and RNA-seq datasets.Bioinformation · 2016Article
- The Mutational Landscape of the Oncogenic MZF1 SCAN Domain in Cancer.Frontiers in molecular biosciences · 2016Article
- Analysis of Whole Transcriptome Sequencing Data: Workflow and Software.Genomics & informatics · 2015Review
- Comparing the normalization methods for the differential analysis of Illumina high-throughput RNA-Seq data.BMC bioinformatics · 2015Article
- EMSAR: estimation of transcript abundance from RNA-seq data by mappability-based segmentation and reclustering.BMC bioinformatics · 2015Article
3 more citing papers are in PubMed but not listed here.
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10 authors.
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No grant is acknowledged in the PubMed record.
Abstract
We propose a novel, efficient and intuitive approach of estimating mRNA abundances from the whole transcriptome shotgun sequencing (RNA-Seq) data. Our method, NEUMA (Normalization by Expected Uniquely Mappable Area), is based on effective length normalization using uniquely mappable areas of gene and mRNA isoform models. Using the known transcriptome sequence model such as RefSeq, NEUMA pre-computes the numbers of all possible gene-wise and isoform-wise informative reads: the former being sequences mapped to all mRNA isoforms of a single gene exclusively and the latter uniquely mapped to a single mRNA isoform. The results are used to estimate the effective length of genes and transcripts, taking experimental distributions of fragment size into consideration. Quantitative RT-PCR based on 27 randomly selected genes in two human cell lines and computer simulation experiments demonstrated superior accuracy of NEUMA over other recently developed methods. NEUMA covers a large proportion of genes and mRNA isoforms and offers a measure of consistency ('consistency coefficient') for each gene between an independently measured gene-wise level and the sum of the isoform levels. NEUMA is applicable to both paired-end and single-end RNA-Seq data. We propose that NEUMA could make a standard method in quantifying gene transcript levels from RNA-Seq data.
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