Introduction

SPRING, meaning Snv PRioritization via the INtegration of Genomic data, is a bioinformatics method for the prediction of disease-causing nonsynonymous single nucleotide variants (SNVs) in exome sequencing studies.

Given a query disease and a set of candidate nonsynonymous SNVs, SPRING calculates a q-value to indicate the statistical significance that a variant is causative for a query disease and hence provides a means of prioritizing candidate variants. SPRING achieves this goal by integrating six deleterious scores calculated by existing methods (SIFT, PolyPhen2, LRT, MutationTaster, GERP and PhyloP) and five association scores derived from a variety of genomic data sources (gene ontology, protein-protein interactions, protein sequences, protein domain annotations and gene pathway annotations).

We have conducted a series of comprehensive validation experiments to assess the effectiveness of SPRING. Results show that SPRING is valid for diseases whose genetic bases are either partly known or completely unknown, effective for diseases with a variety of inheritance styles, and capable of identifying disease-causing nonsynonymous SNVs in exome sequencing studies. We have also applied SPRING to detect causative de novo mutations for autism, epileptic encephalopathies, and intellectual disability, showing the qualified potential of SPRING in identifying causative de novo mutations.

We provide in this website (1) an online service for prioritizing candidate nonsynonymous SNVs, (2) the standalone SPRING software for Windows, Mac and Linux, and (3) genome-wide predictions of disease-causing nonsynonymous SNVs for 5,080 diseases. If you have any comments, suggestions, and questions, please do not hesitate to send us an email.

 

Please cite: Jiaxin Wu, Yanda Li, Rui Jiang*, Integrating multiple genomic data to predict disease-causing nonsynonymous single nucleotide variants in exome sequencing studies, PLoS Genetics, 10(3): e1004237, 2014. [View online] [Download PDF] [Download Citation]