期刊
NUCLEIC ACIDS RESEARCH
卷 46, 期 2, 页码 -出版社
OXFORD UNIV PRESS
DOI: 10.1093/nar/gkx1059
关键词
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资金
- US National Science Foundation [DBI-1262107, IIS-1646333]
- National Natural Science Foundation of China [61472205]
- Youth 1000-Talent Program of China
- Ningbo Science and Technology Discovery Grant [2014B82014]
- US National Science Foundation Grant [IIS-1646333]
Alternative splicing plays an important role in many cellular processes of eukaryotic organisms. The exon-inclusion ratio, also known as percent spliced in, is often regarded as one of the most effective measures of alternative splicing events. The existing methods for estimating exon-inclusion ratios at the genome scale all require the existence of a reference transcriptome. In this paper, we propose an alignment-free method, FreePSI, to perform genome-wide estimation of exon-inclusion ratios from RNASeq data without relying on the guidance of a reference transcriptome. It uses a novel probabilistic generative model based on k-mer profiles to quantify the exon-inclusion ratios at the genome scale and an efficient expectation-maximization algorithm based on a divide-and-conquer strategy and ultra-fast conjugate gradient projection descent method to solve the model. We compare FreePSI with the existing methods on simulated and real RNA-seq data in terms of both accuracy and efficiency and show that it is able to achieve very good performance even though a reference transcriptome is not provided. Our results suggest that FreePSI may have important applications in performing alternative splicing analysis for organisms that do not have quality reference transcriptomes. FreePSI is implemented in C++ and freely available to the public on GitHub.
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