ENCODE Software
All software used or developed by the ENCODE Consortium
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- CellBender — sourceCellBender is a software package for eliminating technical artifacts from high-throughput single-cell RNA sequencing (scRNA-seq) data.Software type: other
- Distal Regulation E-G correlation — sourceCompute correlation metrics between DNase-seq signal at cCREs with DNase-seq signal at gene promoters or RNA expression levels of genes.
- Distal regulation ENCODE-rE2G — sourceTrain ENCODE-rE2G models on CRISPR enhancer screen data and apply to generate genome-wide predictions of enhancer-gene regulatory connections.
- mex_gene_archive — sourcemex_gene_archive is a minimal file format designed to meet the needs of archiving sparse gene matrices in a format compatible with the ENCODE 4 Data Coordination Center.Software type: other
- OpenMiChrom — sourceUsed to create an ensemble of 3D structures with chromatin dynamics simulation software with input data from the Sequence Annotations (bed file) from PyMEGABASE.
- PyMEGABASE — sourcePyMEGABASE is used to generate sequence annotations at the compartment and subcompartment level for physical modeling annotations.
- PROcapNet Model Zoo Pipeline — sourceSoftware for BPNet models using PRO-cap data.Software type: machine learning
- TF ChIP-seq BPNet Model Zoo Pipeline — sourcePlaceholder description.Software type: machine learning
- ATAC-seq DNase-seq ChromBPNet Model Zoo Pipeline — sourcePlaceholder description.Software type: machine learning
- ChromBPNet — sourceChromBPNet is a fully convolutional neural network that uses dilated convolutions with residual connections to enable large receptive fields with efficient parameterization.Software type: machine learning
- pyrangesGenomicRanges for Python.
- pandasPandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language.
- SwanSwan is a Python library designed for the analysis and visualization of transcriptomes.Software type: other
- ABC-Enhancer-Gene-Prediction — sourceCell type specific enhancer-gene predictions using ABC model (Fulco, Nasser et al, Nature Genetics 2019)
- EPIraction — sourceThe EPIraction algorithm uses Tikhonov-regularized least squares models to predict the interacting promoter-enhancer pairs.
- AnalyzeSpearATACSoftware used to analyze Greenleaf lab's SpearATAC (perturbation followed by snATAC-seq) data.