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Flynn, E. D.

Publications and source records attributed to Flynn, E. D..

2 recordsLinked to original sources

Modeling RNA-binding protein specificity in vivo by precisely registering protein-RNA crosslink sites

RNA-binding proteins (RBPs) regulate post-transcriptional gene expression by recognizing short and degenerate sequence elements in their target transcripts. Despite the expanding list of RBPs with in vivo binding sites mapped genomewide using crosslinking and immunoprecipitation (CLIP), defining precise RBP binding specificity remains challenging. We previously demonstrated that the exact protein-RNA crosslink sites can be mapped using CLIP data at single-nucleotide resolution and observed that crosslinking frequently occurs at specific positions in RBP motifs. Here we have developed a computational method, named mCross, to jointly model RBP binding specificity while precisely registering the crosslinking position in motif sites. We applied mCross to 112 RBPs using ENCODE eCLIP data and validated the reliability of the resulting motifs by genome-wide analysis of allelic binding sites also detected by CLIP. We found that the prototypical SR protein SRSF1 recognizes GGA clusters to regulate splicing in a much larger repertoire of transcripts than previously appreciated.

molecular biology

Distinct Epigenomic Patterns Are Associated with Haploinsufficiency and Predict Risk Genes of Developmental Disorders

Haploinsufficiency is a major mechanism of genetic risk in developmental disorders. Accurate prediction of haploinsufficient genes is essential for prioritizing and interpreting deleterious variants in genetic studies. Current methods based on mutation intolerance in population data suffer from inadequate power for genes with short transcripts. Here we showed haploinsufficiency is strongly associated with epigenomic patterns, and then developed a new computational method (Episcore) to predict haploinsufficiency from epigenomic data from a broad range of tissue and cell types using machine learning methods. Based on data from recent exome sequencing studies of developmental disorders, Episcore achieved better performance in prioritizing loss of function de novo variants than current methods. We further showed that Episcore was less biased with gene size, and was complementary to mutation intolerance metrics for prioritizing loss of function variants. Our approach enables new applications of epigenomic data and facilitates discovery and interpretation of novel risk variants in studies of developmental disorders.

genetics