Alternatively spliced genes produce multiple spliced isoforms, called transcript variants. In differential alternative splicing, transcript variant abundance differs across sample types. Differential alternative splicing is common in animal systems and influences cellular development in many processes, but its extent and significance is not as well known in plants. To investigate differential alternative splicing in plants, we examined RNA‐Seq data from rice seedlings. The data included three biological replicates per sample type, approximately 30 million sequence alignments per replicate, and four sample types: roots and shoots treated with exogenous cytokinin delivered hydroponically or a mock treatment. Cytokinin treatment triggered expression changes in thousands of genes but had negligible effect on splicing patterns. However, many genes were differentially spliced between mock‐treated roots and shoots, indicating that our methods were sufficiently sensitive to detect differential splicing between data sets. Quantitative fragment analysis of reverse transcriptase‐PCR products made from newly prepared rice samples confirmed 9 of 10 differential splicing events between rice roots and shoots. Differential alternative splicing typically changed the relative abundance of splice variants that co‐occurred in a data set. Analysis of a similar (but less deeply sequenced) RNA‐Seq data set from
High-throughput mRNA sequencing (RNA-Seq) is a powerful tool for quantifying gene expression. Identification of transcript isoforms that are differentially expressed in different conditions, such as in patients and healthy subjects, can provide insights into the molecular basis of diseases. Current transcript quantification approaches, however, do not take advantage of the shared information in the biological replicates, potentially decreasing sensitivity and accuracy.
We present a novel hierarchical Bayesian model called Differentially Expressed Isoform detection from Multiple biological replicates (DEIsoM) for identifying differentially expressed (DE) isoforms from multiple biological replicates representing two conditions, e.g. multiple samples from healthy and diseased subjects. DEIsoM first estimates isoform expression within each condition by (1) capturing common patterns from sample replicates while allowing individual differences, and (2) modeling the uncertainty introduced by ambiguous read mapping in each replicate. Specifically, we introduce a Dirichlet prior distribution to capture the common expression pattern of replicates from the same condition, and treat the isoform expression of individual replicates as samples from this distribution. Ambiguous read mapping is modeled as a multinomial distribution, and ambiguous reads are assigned to the most probable isoform in each replicate. Additionally, DEIsoM couples an efficient variational inference and a post-analysis method to improve the accuracy and speed of identification of DE isoforms over alternative methods. Application of DEIsoM to an hepatocellular carcinoma (HCC) dataset identifies biologically relevant DE isoforms. The relevance of these genes/isoforms to HCC are supported by principal component analysis (PCA), read coverage visualization, and the biological literature.
The software is available at https://github.com/hao-peng/DEIsoM
Supplementary data are available at Bioinformatics online.
- NSF-PAR ID:
- 10394873
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- Bioinformatics
- Volume:
- 33
- Issue:
- 19
- ISSN:
- 1367-4803
- Page Range / eLocation ID:
- p. 3018-3027
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Availability and implementation Source code is available at: https://github.com/CompbioLabUcf/IntMTQ.
Supplementary information Supplementary data are available at Bioinformatics online.
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