ArticleNucleic acids research2015
Count ratio model reveals bias affecting NGS fold changes.
Article in Nucleic acids research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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Who cites it
9 citing papers in PubMed.
- Identifying similar populations across independent single cell studies without data integration.NAR genomics and bioinformatics · 2025Article
- Targeted protein degradation reveals a direct role of SPT6 in RNAPII elongation and termination.Molecular cell · 2021Article
- Prenatal Androgenization Alters the Development of GnRH Neuron and Preoptic Area RNA Transcripts in Female Mice.Endocrinology · 2020Article
- Dissecting newly transcribed and old RNA using GRAND-SLAM.Bioinformatics (Oxford, England) · 2018Article
- Improved Ribo-seq enables identification of cryptic translation events.Nature methods · 2018Article
- Identification of Genes Enriched in GnRH Neurons by Translating Ribosome Affinity Purification and RNAseq in Mice.Endocrinology · 2018Article
- Integrated transcriptomic analysis of Trichosporon Asahii uncovers the core genes and pathways of fluconazole resistance.Scientific reports · 2017Article
- Detecting circular RNAs: bioinformatic and experimental challenges.Nature reviews. Genetics · 2016Review
- Molecular Pathology and Personalized Medicine: The Dawn of a New Era in Companion Diagnostics-Practical Considerations about Companion Diagnostics for Non-Small-Cell-Lung-Cancer.Journal of personalized medicine · 2016Review
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Various biases affect high-throughput sequencing read counts. Contrary to the general assumption, we show that bias does not always cancel out when fold changes are computed and that bias affects more than 20% of genes that are called differentially regulated in RNA-seq experiments with drastic effects on subsequent biological interpretation. Here, we propose a novel approach to estimate fold changes. Our method is based on a probabilistic model that directly incorporates count ratios instead of read counts. It provides a theoretical foundation for pseudo-counts and can be used to estimate fold change credible intervals as well as normalization factors that outperform currently used normalization methods. We show that fold change estimates are significantly improved by our method by comparing RNA-seq derived fold changes to qPCR data from the MAQC/SEQC project as a reference and analyzing random barcoded sequencing data. Our software implementation is freely available from the project website http://www.bio.ifi.lmu.de/software/lfc.
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Registered trials
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