Structure alignment-based classification of RNA-binding pockets reveals regional RNA recognition motifs on protein surfaces.

BACKGROUND: Many critical biological processes are strongly related to protein-RNA interactions. Revealing the protein structure motifs for RNA-binding will provide valuable information for deciphering protein-RNA recognition mechanisms and benefit complementary structural design in bioengineering. RNA-binding events often take place at pockets on protein surfaces. The structural classification of local binding pockets determines the major patterns of RNA recognition. RESULTS: In this work, we provide a novel framework for systematically identifying the structure motifs of protein-RNA binding sites in the form of pockets on regional protein surfaces via a structure alignment-based method. We first construct a similarity network of RNA-binding pockets based on a non-sequential-order structure alignment method for local structure alignment. By using network community decomposition, the RNA-binding pockets on protein surfaces are clustered into groups with structural similarity. With a multiple structure alignment strategy, the consensus RNA-binding pockets in each group are identified. The crucial recognition patterns, as well as the protein-RNA binding motifs, are then identified and analyzed. CONCLUSIONS: Large-scale RNA-binding pockets on protein surfaces are grouped by measuring their structural similarities. This similarity network-based framework provides a convenient method for modeling the structural relationships of functional pockets. The local structural patterns identified serve as structure motifs for the recognition with RNA on protein surfaces.

Reveel: large-scale population genotyping using low-coverage sequencing data.

MOTIVATION: Population low-coverage whole-genome sequencing is rapidly emerging as a prominent approach for discovering genomic variation and genotyping a cohort. This approach combines substantially lower cost than full-coverage sequencing with whole-genome discovery of low-allele frequency variants, to an extent that is not possible with array genotyping or exome sequencing. However, a challenging computational problem arises of jointly discovering variants and genotyping the entire cohort. Variant discovery and genotyping are relatively straightforward tasks on a single individual that has been sequenced at high coverage, because the inference decomposes into the independent genotyping of each genomic position for which a sufficient number of confidently mapped reads are available. However, in low-coverage population sequencing, the joint inference requires leveraging the complex linkage disequilibrium (LD) patterns in the cohort to compensate for sparse and missing data in each individual. The potentially massive computation time for such inference, as well as the missing data that confound low-frequency allele discovery, need to be overcome for this approach to become practical. RESULTS: Here, we present Reveel, a novel method for single nucleotide variant calling and genotyping of large cohorts that have been sequenced at low coverage. Reveel introduces a novel technique for leveraging LD that deviates from previous Markov-based models, and which is aimed at computational efficiency as well as accuracy in capturing LD patterns present in rare haplotypes. We evaluate Reveel's performance through extensive simulations as well as real data from the 1000 Genomes Project, and show that it achieves higher accuracy in low-frequency allele discovery and substantially lower computation cost than previous state-of-the-art methods. AVAILABILITY AND IMPLEMENTATION: http://reveel.stanford.edu/ CONTACT: : serafim@cs.stanford.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.