scDART: integrating unmatched scRNA-seq and scATAC-seq data and learning cross-modality relationship simultaneously | Genome Biology
It is a challenging task to integrate scRNA-seq and scATAC-seq data obtained from different batches. Researchers propose scDART, a deep learning framework that integrates scRNA-seq and scATAC-seq data and learns cross-modalities relationships simultaneously. Specifically, the design of scDART allows it to preserve cell trajectories in continuous cell populations and can be applied to trajectory inference on integrated data.
Exploring automatic inconsistency detection for literature-based gene ontology annotation | Bioinformatics
Literature-based gene ontology annotations (GOA) are biological database records that use controlled vocabulary to uniformly represent gene function information that is described in the primary literature. Assurance of the quality of GOA is crucial for supporting biological research. Researchers have created a reliable synthetic dataset to simulate four realistic types of GOA inconsistency in biological databases. Three automatic approaches are proposed. They provide reasonable performance on the task of distinguishing the four types of inconsistency and are directly applicable to detect inconsistencies in real-world GOA database records.
Accurate somatic variant detection using weakly supervised deep learning | Nature Communications
Identification of somatic mutations in tumor samples is commonly based on statistical methods in combination with heuristic filters. Here researchers develop VarNet, an end-to-end deep learning approach for identification of somatic variants from aligned tumor and matched normal DNA reads. VarNet is trained using image representations of 4.6 million high-confidence somatic variants annotated in 356 tumor whole genomes.