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Abstract Motivation Identification and interpretation of non-coding variations that affect disease risk remain a paramount challenge in genome-wide association studies (GWAS) of complex diseases. Experimental efforts have provided comprehensive annotations of functional elements in the human genome. On the other hand, advances in computational biology, especially machine learning approaches, have facilitated accurate predictions of cell-type-specific functional annotations. Integrating functional annotations with GWAS signals has advanced the understanding of disease mechanisms. In previous studies, functional annotations were treated as static of a genomic region, ignoring potential functional differences imposed by different genotypes across individuals. Results We develop a computational approach, Openness Weighted Association Studies (OWAS), to leverage and aggregate predictions of chromosome accessibility in personal genomes for prioritizing GWAS signals. The approach relies on an analytical expression we derived for identifying disease associated genomic segments whose effects in the etiology of complex diseases are evaluated. In extensive simulations and real data analysis, OWAS identifies genes/segments that explain more heritability than existing methods, and has a better replication rate in independent cohorts than GWAS. Moreover, the identified genes/segments show tissue-specific patterns and are enriched in disease relevant pathways. We use rheumatic arthritis and asthma as examples to demonstrate how OWAS can be exploited to provide novel insights on complex diseases. Availability and implementation The R package OWAS that implements our method is available at https://github.com/shuangsong0110/OWAS. Supplementary information Supplementary data are available at Bioinformatics online. 

Abstract Transfer RNA-derived fragments (tRFs) are a new class of small non-coding RNAs and play important roles in biological and physiological processes. Prediction of tRF target genes and binding sites is crucial in understanding the biological functions of tRFs in the molecular mechanisms of human diseases. We developed a publicly accessible web-based database, tRFtarget (http://trftarget.net), for tRF target prediction. It contains the computationally predicted interactions between tRFs and mRNA transcripts using the two state-of-the-art prediction tools RNAhybrid and IntaRNA, including location of the binding sites on the target, the binding region, and free energy of the binding stability with graphic illustration. tRFtarget covers 936 tRFs and 135 thousand predicted targets in eight species. It allows researchers to search either target genes by tRF IDs or tRFs by gene symbols/transcript names. We also integrated the manually curated experimental evidence of the predicted interactions into the database. Furthermore, we provided a convenient link to the DAVID® web server to perform downstream functional pathway analysis and gene ontology annotation on the predicted target genes. This database provides useful information for the scientific community to experimentally validate tRF target genes and facilitate the investigation of the molecular functions and mechanisms of tRFs.