Human

Recent emerging studies suggest that a substantial fraction of microRNA (miRNA) genes is likely to form clusters in terms of evolutionary conservation and biological implications, posing a significant challenge for the research community and shifting the bottleneck of scientific discovery from miRNA singletons to miRNA clusters. In addition, the advance in molecular sequencing technique such as next-generation sequencing (NGS) has facilitated researchers to comprehensively characterize miRNAs with low abundance on genome-wide scale in multiple species.

The complex traits of an organism are associated with a complex interplay between genetic factors (GFs) and environmental factors (EFs). However, compared with protein-coding genes and microRNAs, there is a paucity of computational methods and bioinformatic resource platform for understanding the associations between lncRNA and EF.

High-throughput screening (HTS) is a common technique for both drug discovery and basic research, but researchers often struggle with how best to derive hits from HTS data. While a wide range of hit identification techniques exist, little information is available about their sensitivity and specificity, especially in comparison to each other. To address this, we have developed the open-source NoiseMaker software tool for generation of realistically noisy virtual screens.

Characterizing the binding behaviors of RNA-binding proteins (RBPs) is important for understanding their functional roles in gene expression regulation. However, current high-throughput experimental methods for identifying RBP targets, such as CLIP-seq and RNAcompete, usually suffer from the false negative issue. Here, we develop a deep boosting based machine learning approach, called DeBooster, to accurately model the binding sequence preferences and identify the corresponding binding targets of RBPs from CLIP-seq data.