A5076690302- Genomics and Chromatin Dynamics
- Gene expression and cancer classification
- Single-cell and spatial transcriptomics
- Cancer-related molecular mechanisms research
University of Virginia
2020-2021
Office of Public Health Genomics
2020-2021
Genomic region sets summarize functional genomics data and define locations of interest in the genome such as regulatory regions or transcription factor binding sites. The number publicly available has increased dramatically, leading to challenges analysis.
Functional genomics experiments, like ChIP-Seq or ATAC-Seq, produce results that are summarized as a region set. There is no way to objectively evaluate the effectiveness of set similarity metrics. We present Bedshift, tool for perturbing BED files by randomly shifting, adding, and dropping regions from reference file. The perturbed can be used benchmark metrics, well other applications. highlight differences in behavior between such Jaccard score most sensitive added dropped regions, while...
Functional genomics experiments, like ChIP-Seq or ATAC-Seq, produce results that are summarized as a region set. Many tools have been developed to analyze sets, including computing similarity metrics compare them. However, there is no way objectively evaluate the effectiveness of set metrics. In this paper we present Bedshift , command-line tool and Python API generate new BED files by making random perturbations an original file. Perturbed known file therefore useful benchmark To...
Motivation Genomic region sets summarize functional genomics data and define locations of interest in the genome such as regulatory regions or transcription factor binding sites. The number publicly available has increased dramatically, leading to challenges analysis. Results We propose a new method represent genomic vectors, embeddings, using an adapted word2vec approach. compared our approach two simpler methods based on interval unions term frequency-inverse document frequency evaluated...