Abstract
BackgroundSynthetic lethality experiments identify pairs of genes with complementary function. More direct functional associations (for example greater probability of membership in a single protein complex) may be inferred between genes that share synthetic lethal interaction partners than genes that are directly synthetic lethal. Probabilistic algorithms that identify gene modules based on motif discovery are highly appropriate for the analysis of synthetic lethal genetic interaction data and have great potential in integrative analysis of heterogeneous datasets.ResultsWe have developed Genetic Interaction Motif Finding (GIMF), an algorithm for unsupervised motif discovery from synthetic lethal interaction data. Interaction motifs are characterized by position weight matrices and optimized through expectation maximization. Given a seed gene, GIMF performs a nonlinear transform on the input genetic interaction data and automatically assigns genes to the motif or non-motif category. We demonstrate the capacity to extract known and novel pathways for Saccharomyces cerevisiae (budding yeast). Annotations suggested for several uncharacterized genes are supported by recent experimental evidence. GIMF is efficient in computation, requires no training and automatically down-weights promiscuous genes with high degrees.ConclusionGIMF effectively identifies pathways from synthetic lethality data with several unique features. It is mostly suitable for building gene modules around seed genes. Optimal choice of one single model parameter allows construction of gene networks with different levels of confidence. The impact of hub genes the generic probabilistic framework of GIMF may be used to group other types of biological entities such as proteins based on stochastic motifs. Analysis of the strongest motifs discovered by the algorithm indicates that synthetic lethal interactions are depleted between genes within a motif, suggesting that synthetic lethality occurs between-pathway rather than within-pathway.
Highlights
Synthetic lethality experiments identify pairs of genes with complementary function
We have developed an algorithm for finding genes in the same pathway, which we shall refer to as Genetic Interaction Motif Finding by expectation maximization (GIMF)
Given a seed gene, the remaining genes are naturally categorized as motif genes or non-motif genes
Summary
Synthetic lethality experiments identify pairs of genes with complementary function. More direct functional associations (for example greater probability of membership in a single protein complex) may be inferred between genes that share synthetic lethal interaction partners than genes that are directly synthetic lethal. Apart from the traditional hierarchical way of gene function annotation, functional genomics takes a bottom-up approach to assemble gene interaction networks based on all pair-wise gene interactions detected From such genetic interaction maps, Functional modules representing various biological pathways and processes can be extracted by computational approaches. Given the incomplete and error-prone synthetic lethal interaction map, it is highly desirable to investigate methods that extract biologically relevant information probabilistically, which accommodates network properties such as degree distribution and confidence of the links. Along this line, we have developed in this study a probabilistic model for characterizing synthetic lethal interaction motifs and an algorithm that automatically groups genes sharing similar motifs into pathways. When applied to the SGA dataset, our method automatically uncovers known and novel gene modules that correlate favourably with Gene Ontology (GO) annotations
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