RGT: a toolbox for the integrative analysis of high throughput regulatory genomics data

Abstract

Background Massive amounts of data are produced by combining next-generation sequencing with complex biochemistry techniques to characterize regulatory genomics profiles, such as protein–DNA interaction and chromatin accessibility. Interpretation of such high-throughput data typically requires different computation methods. However, existing tools are usually developed for a specific task, which makes it challenging to analyze the data in an integrative manner. Results We here describe the Regulatory Genomics Toolbox (RGT), a computational library for the integrative analysis of regulatory genomics data. RGT provides different functionalities to handle genomic signals and regions. Based on that, we developed several tools to perform distinct downstream analyses, including the prediction of transcription factor binding sites using ATAC-seq data, identification of differential peaks from ChIP-seq data, and detection of triple helix mediated RNA and DNA interactions, visualization, and finding an association between distinct regulatory factors. Conclusion We present here RGT; a framework to facilitate the customization of computational methods to analyze genomic data for specific regulatory genomics problems. RGT is a comprehensive and flexible Python package for analyzing high throughput regulatory genomics data and is available at: https://github.com/CostaLab/reg-gen. The documentation is available at: https://reg-gen.readthedocs.io

Annotation and assessment of functional variants in regulatory regions using epigenomic data in farm animals

“…In addition, potential functional variants (Categories 1-5) located within footprint regions were analyzed using the motifbreakR ( 66 ) package in R v4.0. The motifDB database, specifically JASPAR 2018 ( 67 ), was selected as the data source for predicting the transcription factors to which the SNPs may bind.…”

“…The footprint analysis was primarily performed as the following steps: (i) the board peaks were called from the merged ATAC-seq or Dnase-seq data using the MACS2 v2.1.0 broad module ( 62, 64 ); (ii) the broad peaks meeting the criteria of P < 10 -10 and 10 -10 < P < 10 -5 overlapping OCR were merged with BEDTools v2.26.0 ( 63 ) as significant broad peaks; (iii) the Hmm-based IdeNtification of Transcription factor footprints (HINT) framework of Regulatory Genomics Toolbox (RGT) v0.13.2 ( 65 ) was employed to analysis footprints using the significant broad peaks. The HINT framework was utilized with specific parameters depending on whether ATAC-seq or Dnase-seq data was used (--atac-seq or --dnase-seq) and considering paired-end sequencing data (--paired-end).…”

“…The HINT framework was utilized with specific parameters depending on whether ATAC-seq or Dnase-seq data was used (--atac-seq or --dnase-seq) and considering paired-end sequencing data (--paired-end). The organism information (--organism=) was also specified; and (iv) the cutoff value for footprint score was determined as more than the 20% quantile of all footprint score generated by the HINT framework of GRT v0.13.2 ( 65 ).…”

Decreased voluntary alcohol intake and ventral striatal epigenetic and transcriptional remodeling in male Acss2 KO mice

“…Aligned, deduplicated tags were sorted by chromosome position and indexed using samtools sort and index, respectively. RGT-THOR 46 v0.13.2 was used to detect differentially enriched peaks using input samples to control for sonication efficiency artifacts at regions of open chromatin. RPM-adjusted counts over each differential peak were then assessed using a python v3 script.…”

A graphSAGE discovers synergistic combinations of Gefitinib, paclitaxel, and Icotinib for Lung adenocarcinoma management by targeting human genes and proteins: the RAIN protocol

“…The review further explores the most suitable treatment protocols to effectively extend the survival duration of patients with non-small cell lung cancer (NSCLC) possessing this co-mutation. [52][53][54][55] According to yan's published work in Frontiers in Oncology, BRAF mutations commonly occur in nonsmokers, women, and aggressive histological types of Non-small cell lung cancer (NSCLC) constitutes 1%-2% of adenocarcinomas. Traditional chemotherapy presents limited efficacy in patients with non-small cell lung cancer (NSCLC) harboring mutations in the BRAF gene.…”