Periodic Reporting for period 1 - MoDATS (Model-based Data Analysis of Transcription and Splicing)
Reporting period: 2016-01-05 to 2018-01-04
This is important because understanding the basic workings of cells is curcial for treating disease and also for bio-engineering. It’s also important because we are developing computational approaches that help other biologists better understand their data.
Wallace EWJ, Beggs JD. 2017. Extremely fast and incredibly close: cotranscriptional splicing in budding yeast. RNA 23: 601–610.
The accompanying image, taken from the paper, compares the different splicing measurements (SMIT, nascent RNA-seq, 4tU-seq) and other relevant features of yeast mRNAs (intron length, mRNA abundance). It highlights the different features and splicing patterns of ribosomal vs non-ribosomal RNAs.
We also developed computational models and software to understand a later stage of gene expression, the production of protein from mRNA by ribosomes.
Carja O, Xing T, Wallace EWJ, Plotkin JB, Shah P. 2017. riboviz: analysis and visualization of ribosome profiling datasets. BMC Bioinformatics 18: 461.
In addition to the research outputs, we presented the work to colleagues in university seminars and conferences in Europe and the USA. Dr. Wallace shared his knowledge with colleagues at Edinburgh, and trained other researchers in fundamentals of data processing in Data Carpentry workshops and at the Natural History Museum in London.