Development of SUPPA for alternative splicing analysis from RNA-seq in plants across multiple conditions

点击翻译按钮获取中文摘要

Genes are the repositories of hereditary information and proteins are the machines that carry out the functions of living cells. The term 'gene expression' usually refers to the process by which a gene gives rise to a protein. In eukaryotes, gene expression is complex and when protein-coding genes are expressed, the DNA sequence is first copied into a precursor messenger RNA (pre-mRNA) by transcription. The pre-mRNA undergoes several processing steps to form mature messenger RNAs (mRNAs) which direct synthesis of the corresponding protein (translation). An extremely important RNA processing step called alternative splicing (AS) generates different mRNA transcripts (i.e. isoforms) from the same gene and thereby modulates transcript and protein levels and functions. The majority of protein-coding genes undergo AS and the relative amounts of AS isoforms changes dynamically as cells and organisms develop and grow. High-throughput methods such as RNA sequencing (RNA-seq) are now capable of generating data on tens of thousands of transcripts from cells or particular stages of development or different conditions. To be able to analyse the dynamic changes in transcripts and AS, and to understand how this is regulated, we need computational tools that will allow the accurate measurement of these different mRNA transcript isoforms from these large datasets. The tool which we will develop here will enable the high resolution analysis of dynamic changes in gene expression at the individual transcript and AS event level. Being able to distinguish the abundance of different transcript isoforms is important because one of the main approaches scientists use to associate genes with functions is to monitor gene expression: i.e. where and when genes are switched on or off, and at what level. The RNA-seq technology and programmes to analyse the data are continually being improved. Significant recent advances are the release of computational programmes that can quantify transcript isoform abundance (e.g. Sailfish, Salmon, Kallisto) and can generate measures of AS (e.g. SUPPA) from large datasets very quickly. We have been using these programmes to analyse RNA-seq data from Arabidopsis. We also have an excellent experimental system which allows us to validate the RNA-seq results. Detailed comparisons have helped us to identify a number of discrepancies and issues with the SUPPA programme where it does not accurately report on, for example, complex AS events. We have taken initial steps in improving SUPPA with good success and here we aim to modify SUPPA in a number of ways so that it 1) accurately measures AS, and 2) can be applied to experiments with multiple conditions such as time-course series. The significance of this is that it will allow clustering of AS responses, correlation of AS indices with gene expression, and the building of splicing networks to understand the regulation of AS. Research groups in the UK and around the world use RNA-seq to analyse gene expression in plants and animals. Currently there are many limitations in quantifying transcripts and AS in a dynamic way - the improved version of SUPPA will deliver this function. It will be relevant not only to Arabidopsis but also to other plant and crop species, and to animal and human studies.