Projects per year
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 (JCC) score, which provides a way to evaluate the reliability of transcript-level abundance estimates as well as 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 while 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.
Original language | English |
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Article number | e201800175 |
Journal | Life Science Alliance |
Volume | 2 |
Issue number | 1 |
Early online date | 17 Jan 2019 |
DOIs | |
Publication status | Published - 1 Feb 2019 |
Bibliographical note
© 2019 Soneson et al.Fingerprint
Dive into the research topics of 'A junction coverage compatibility score to quantify the reliability of transcript abundance estimates and annotation catalogs'. Together they form a unique fingerprint.Projects
- 2 Finished
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Long-read Nanopore Sequencing to Identify Isoform-Specific Patterns of mRNA Methylation
Hussain, S. (PI)
Biotechnology and Biological Sciences Research Council
15/01/18 → 14/01/19
Project: Research council
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Characterising the Epitanscriptome Using Catalysis-Dependent RIPseq Approaches
Hussain, S. (PI)
Biotechnology and Biological Sciences Research Council
1/04/16 → 31/08/19
Project: Research council