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Life Sci Alliance. 2019 Jan 17;2(1). pii: e201800175. doi: 10.26508/lsa.201800175. Print 2019 Feb.

A junction coverage compatibility score to quantify the reliability of transcript abundance estimates and annotation catalogs.

Author information

1
Institute of Molecular Life Sciences, University of Zurich, Zurich, Switzerland charlotte.soneson@fmi.ch.
2
SIB Swiss Institute of Bioinformatics, University of Zurich, Zurich, Switzerland.
3
Department of Biostatistics, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA.
4
Department of Genetics, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA.
5
Department of Computer Science, Stony Brook University, NY, USA.
6
Department of Biology and Biochemistry, University of Bath, Bath, UK.
7
F. Hoffmann-La Roche Ltd, Pharma Research and Early Development, Neuroscience, Ophthalmology and Rare Diseases, Roche Innovation Center Basel, Basel, Switzerland.
8
Institute of Molecular Life Sciences, University of Zurich, Zurich, Switzerland mark.robinson@imls.uzh.ch.

Abstract

Most methods for statistical analysis of RNA-seq data take a matrix of abundance estimates for some type of genomic features as their input, and consequently the quality of any obtained results is directly dependent on the quality of these abundances. Here, we present the junction coverage compatibility score, which provides a way to evaluate the reliability of transcript-level abundance estimates and the accuracy of transcript annotation catalogs. It works by comparing the observed number of reads spanning each annotated splice junction in a genomic region to the predicted number of junction-spanning reads, inferred from the estimated transcript abundances and the genomic coordinates of the corresponding annotated transcripts. We show that although most genes show good agreement between the observed and predicted junction coverages, there is a small set of genes that do not. Genes with poor agreement are found regardless of the method used to estimate transcript abundances, and the corresponding transcript abundances should be treated with care in any downstream analyses.

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