An expectation-maximization algorithm for probabilistic reconstructions of full-length isoforms from splice graphs.
Reconstructing full-length transcript isoforms from sequence fragments (such as ESTs) is a major interest and challenge for bioinformatic analysis of pre-mRNA alternative splicing. This problem has been formulated as finding traversals across the splice graph, which is a directed acyclic graph (DAG) representation of gene structure and alternative splicing. In this manuscript we introduce a probabilistic formulation of the isoform reconstruction problem, and provide an expectation-maximization (EM) algorithm for its maximum likelihood solution. Using a series of simulated data and expressed sequences from real human genes, we demonstrate that our EM algorithm can correctly handle various situations of fragmentation and coupling in the input data. Our work establishes a general probabilistic framework for splice graph-based reconstructions of full-length isoforms.
Xing
Yi
Y
Molecular Biology Institute, Center for Computational Biology, Department of Chemistry and Biochemistry, University of California Los Angeles, USA. yxing@ucla.edu
Yu
Tianwei
T
Wu
Ying Nian
YN
Roy
Meenakshi
M
Kim
Joseph
J
Lee
Christopher
C
D000465:Algorithms; D017398:Alternative Splicing; D002892:Chromosomes, Human, Pair 22; D003198:Computer Simulation; D020224:Expressed Sequence Tags; D006681:HLA-D Antigens / Q000235:genetics / Q000378:metabolism; D006801:Humans; D016013:Likelihood Functions; D020033:Protein Isoforms / Q000235:genetics* / Q000378:metabolism; D014335:Tropomyosin / Q000235:genetics / Q000378:metabolism
D006681:HLA-D Antigens; C084245:HLA-DM antigens; D020033:Protein Isoforms; C490775:TPM1 protein, human; D014335:Tropomyosin