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dc.contributor.advisorSanguinetti, Guidoen
dc.contributor.advisorBeggs, Jeanen
dc.contributor.advisorArmstrong, Douglasen
dc.contributor.authorHuang, Yuanhuaen
dc.date.accessioned2018-07-16T11:13:17Z
dc.date.available2018-07-16T11:13:17Z
dc.date.issued2018-07-02
dc.identifier.urihttp://hdl.handle.net/1842/31328
dc.description.abstractIn most eukaryotes, alternative splicing is an important regulatory mechanism of gene expression that results in a single gene coding for multiple protein isoforms, thus largely increases the diversity of the proteome. RNA-seq is widely used for genome-wide splicing isoform quantification, and several effective and powerful methods have been developed for splicing analysis with RNA-seq data. However, it remains problematic for genes with low coverages or large number of isoforms. These difficulties may in principle be ameliorated by exploiting correlations encoded in the structured data sources. This thesis contributes to developments of Bayesian methods for splicing analysis by leveraging additional information in multiple datasets with structured prior distributions. First, we developed DICEseq, the first isoform quantification method tailored to time-series RNA-seq experiments. DICEseq explicitly models the correlations between experiments at different time points to aid the quantification of isoforms across experiments. Numerical experiments on both simulated and real datasets show that DICEseq yields more accurate results than state-of-the-art methods, an advantage that can become considerable at low coverage levels. Furthermore, DICEseq permits to quantify the trade-off between temporal sampling of RNA and depth of sequencing, frequently an important choice when planning experiments. Second, we developed BRIE (Bayesian Regression for Isoform Estimation), a Bayesian hierarchical model which resolves the difficulties in splicing analysis in single-cell RNA-seq (scRNA-seq) data by learning an informative prior distribution from sequence features. This method combines the quantification and imputation for splicing analysis via a Bayesian way, which is particularly useful in scRNA-seq data due to its extreme low coverages and high technical noises. We validated BRIE on several scRNA-seq data sets, showing that BRIE yields reproducible estimates of exon inclusion ratios in single cells. Third, we provided an effective tool by using Bayes factor to sensitively detect differential splicing between different single cells. When applying BRIE to a few real datasets, we found interesting heterogeneity patterns in splicing events across cell population, for example alternative exons in DNMT3B. In summary, this thesis proposes structured Bayesian methods to integrate multiple datasets to improve splicing analysis and study its biological functions.en
dc.language.isoen
dc.publisherThe University of Edinburghen
dc.relation.hasversionAslanzadeh V., Huang Y., Sanguinetti G., and Beggs J. “Effects of transcription rate on the co-transcriptionality, efficiency and fidelity of splicing in budding yeast.” Genome Research, 2017, doi:10.1101/gr.225615.117.en
dc.relation.hasversionHuang Y., and Sanguinetti G. “BRIE: transcriptome-wide splicing quantification in single cells.” Genome Biology, 2017, 18(1): 123.en
dc.relation.hasversionHuang Y., and Sanguinetti G. “Statistical modeling of isoform dynamics from RNA-seq time series data.” Bioinformatics, 2016, 32(19): 2965-2972en
dc.relation.hasversionBarrass D., Reid J.. Huang Y., Hector R., Sanguinetti G., Granneman S., and Beggs J. “Transcriptome-wide RNA processing kinetics revealed using extremely short 4tU labeling.” Genome Biology, 2015, 16(1): 282en
dc.subjectalternative splicingen
dc.subjectBayesian methodsen
dc.subjectDICEseqen
dc.subjectisoform quantificationen
dc.subjectBayesian Regression for Isoform Estimationen
dc.subjectscRNA-seqen
dc.titleStructured Bayesian methods for splicing analysis in RNA-seq dataen
dc.typeThesis or Dissertationen
dc.type.qualificationlevelDoctoralen
dc.type.qualificationnamePhD Doctor of Philosophyen


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