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HOMER / Hypergeometric Optimization of Motif EnRichment
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Performs peak finding and downstream data analysis for next-generation sequencing analysis. HOMER affords several tools and methods to make use of ChIP-Seq, GRO-Seq, RNA-Seq, DNase-Seq, Hi-C and other types of functional genomics sequencing data sets. This software offers support to UCSC visualization, peaks annotation, quantification of transcripts and repeats or differential features, enrichment and expression.
dREG / discriminative Regulatory-Element detection from GRO-seq
Detects regulatory elements using maps of RNA polymerase derived from run-on and sequencing assays, including global run-on sequencing (GRO-seq), precision nuclear run-on sequencing (PRO-seq) and chromatin run-on and sequencing (ChRO-seq). dREG is a machine learning tool that can identify regulatory elements genome-wide in a number of applications. The web application allows visualization of input PRO-seq data, dREG signal, and dREG peak calls as a private track hub on the WashU Epigenome Browser.
Examines epigenomic and transcriptomic next generation sequencing (NGS) data. Octopus-toolkit can be used for antibody- or enzyme-mediated experiments and studies for the quantification of gene expression. It can accelerate the data mining of public epigenomic and transcriptomic NGS data for basic biomedical research. This tool provides a private and a public mode: one to process the user’s own data, and the other to analyze public NGS data by retrieving raw files from the GEO database.
Defines the boundaries of transcription units de novo using a two state hidden-Markov model (HMM). groHMM is a complete pipeline for the accurate identification of the boundaries of transcriptional activity across the genome using GRO-seq data and classification of these transcription units using a database of available annotations. It automates many common next-generation sequencing data analysis tasks. Advanced features include a general HMM implementation useful for data segmentation (e.g. transcription unit identification, and Pol II wave calling). groHMM can reveal interesting insights into cell type-specific transcription by identifying novel transcription units, and serve as a complete and useful tool for evaluating functional genomic elements in cells.
Automates the processing and analysis of several commonly used Next Generation Sequencing (NGS) datasets including: ChIP-seq, RNA-seq, Global Run On sequencing (GRO-seq), micrococcal nuclease footprint sequencing (MNase-seq), DNase hypersensitivity sequencing (DNase-seq), and transposase-accessible chromatin using sequencing ATAC-seq datasets. CIPHER provides an analysis mode that accomplishes complex bioinformatics tasks such as enhancer prediction. It supplies functions to integrate various NGS datasets together.
FStitch / Fast Read Stitcher
A fast and simple algorithm to detect nascent RNA transcription in global nuclear run-on sequencing (GRO-seq). FStitch takes advantage of two popular machine-learning techniques, a hidden Markov model (HMM) and logistic regression to robustly classify which regions of the genome are transcribed. It builds on the strengths of previous approaches but is accurate, dependent on very little training data, robust to varying read depth, annotation agnostic, and fast.
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