ArticlePloS one2015
How Many Genes Are Expressed in a Transcriptome? Estimation and Results for RNA-Seq.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Bioinformatics perspectives on transcriptomics: A comprehensive review of bulk and single-cell RNA sequencing analyses.Quantitative biology (Beijing, China) · 2025Review
- Pulmonary Myeloid Cells in Mild Cases of COVID-19 Upregulate the Intracellular Fc ReceptorInternational journal of molecular sciences · 2025Article
- Article
- Review
- Transcriptome Analysis Unveils Gln3 Role in Amino Acids Assimilation and Fluconazole Resistance inJournal of microbiology and biotechnology · 2021Article
- Modern Approaches for Transcriptome Analyses in Plants.Advances in experimental medicine and biology · 2021Article
- A priori estimation of sequencing effort in complex microbial metatranscriptomes.Ecology and evolution · 2020Article
- Autotrophic carbon fixation pathways along the redox gradient in oxygen-depleted oceanic waters.Environmental microbiology reports · 2020Article
- Prospects and challenges of multi-omics data integration in toxicology.Archives of toxicology · 2020Review
- Metabolomic and Lipidomic Biomarkers for Premalignant Liver Disease Diagnosis and Therapy.Metabolites · 2020Review
- Article
- Microfluidic SlipChip device for multistep multiplexed biochemistry on a nanoliter scale.Lab on a chip · 2019Article
- RNA sequencing, selection of reference genes and demonstration of feeding RNAi in Thrips tabaci (Lind.) (Thysanoptera: Thripidae).BMC molecular biology · 2019Article
- The role of beta cell heterogeneity in islet function and insulin release.Journal of molecular endocrinology · 2018Review
- Enrichment of genomic resources and identification of simple sequence repeats from medicinally important3 Biotech · 2018Article
- VaDiR: an integrated approach to Variant Detection in RNA.GigaScience · 2018Article
- Comparative transcriptomic analysis of the evolution and development of flower size in Saltugilia (Polemoniaceae).BMC genomics · 2017Article
- Integration of quantitated expression estimates from polyA-selected and rRNA-depleted RNA-seq libraries.BMC bioinformatics · 2017Article
- Long non-coding RNAs are major contributors to transcriptome changes in sunflower meiocytes with different recombination rates.BMC genomics · 2016Article
Corrections and comments
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Authors and funding
2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
RNA-seq experiments estimate the number of genes expressed in a transcriptome as well as their relative frequencies. However, an undetermined number of genes can remain undetected due to their low expression relative to the sample size (sequence depth). Estimation of the true number of genes expressed in a transcriptome is essential in order to determine which genes are exclusively expressed in specific tissues or under particular conditions. A reliable estimate of the true number of expressed genes is also required to accurately measure transcriptome changes and to predict the sequencing depth needed to increase the proportion of detected genes. This problem is analogous to ecological sampling problems such as estimating the number of species at a given site. Here we present a non-parametric estimator for the number of undetected genes as well as for the extra sample size needed to detect a given proportion of the undetected genes. Our estimators are superior to ones already published by having smaller standard errors and biases. We applied our method to a set of 32 publicly available RNA-seq experiments, including the evaluation of 311 individually sequenced libraries. We found that in the majority of the cases more than one thousand genes are undetected, and that on average approximately 6% of the expressed genes per accession remain undetected. This figure increases to approximately 10% if individual sequencing libraries are analyzed. Our method is also applicable to metagenomic experiments. Using our method, the number of undetected genes as well as the sample size needed to detect them can be calculated, leading to more accurate and complete gene expression studies.
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