The qBED track: a novel genome browser visualization for point processes

Moudgil, Arnav; Li, Daofeng; Hsu, Silas; Purushotham, Deepak; Wang, Ting; Mitra, Robi D.

doi:10.1093/bioinformatics/btaa771

Cited by 6 publications

(8 citation statements)

References 31 publications

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“…The development of the qBed and dynseq formats provides such examples. qBed is a new track type to display dense and quantitative data from a high-resolution point process ( 18 ), and dynseq displays scores over individual nucleotides in the genome, which can be derived from a variety of scoring methods using machine learning models (Nair et al. , in preparation).…”

Section: New Featuresmentioning

confidence: 99%

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WashU Epigenome Browser update 2022

Purushotham

Harrison

et al. 2022

Nucleic Acids Research

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WashU Epigenome Browser (https://epigenomegateway.wustl.edu/browser/) is a web-based genomic data exploration tool that provides visualization, integration, and analysis of epigenomic datasets. The newly renovated user interface and functions have enabled researchers to engage with the browser and genomic data more efficiently and effectively since 2018. Here, we introduce a new integrated panel design in the browser that allows users to interact with 1D (genomic features), 2D (such as Hi-C), 3D (genome structure), and 4D (time series) data in a single web page. The browser can display three-dimensional chromatin structures with the 3D viewer module. The 4D tracks, called ‘Dynamic’ tracks, animatedly display time-series data, allowing for a more striking visual impact to identify the gene or genomic region candidates as a function of time. Genomic data, such as annotation features, numerical values, and chromatin interaction data can all be viewed in the dynamic track mode. Imaging data from microscopy experiments can also be displayed in the browser. In addition to software development, we continue to service and expand the data hubs we host for large consortia including 4DN, Roadmap Epigenomics, TaRGET and ENCODE, among others. Our growing user/developer community developed additional track types as plugins, such as qBed and dynseq tracks, which extend the utility of the browser. The browser serves as a foundation for additional genomics platforms including the WashU Virus Genome Browser (for COVID-19 research) and the Comparative Genome Browser. The WashU Epigenome Browser can also be accessed freely through Amazon Web Services at https://epigenomegateway.org/.

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Section: New Featuresmentioning

confidence: 99%

“…As a result, more genomes and data are added to the Browser upon community requests, and new functions, including those developed by the community (e.g. qBed ( 18 ), dynseq (Nair et al. , in preparation)), continue to enrich the Browser.…”

Section: Introductionmentioning

confidence: 99%

WashU Epigenome Browser update 2022

Purushotham

Harrison

et al. 2022

Nucleic Acids Research

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“…Aligned reads are validated as insertions if adjacent to a TTAA site in the genome. Bona fide insertions are then converted to qBED format (née .ccf) 57 . SRT barcodes were incorporated into the barcode column of the qBED.…”

Section: Methodsmentioning

confidence: 99%

“…Specifically, peaks were called using the call_peaks_macs python script, which follows the algorithm used by MACS to call ChIP-Seq peaks 58 modified for the analysis of calling card data. The main peak calling function is passed an experiment frame, a background frame, and an TTAA_frame, all in qBED/ccf format 57 . It then builds interval trees containing all of the background and experiment hops (insertion events) and all of the TTAAs.…”

Section: Methodsmentioning

confidence: 99%

Measuring Transcription Factor Binding and Gene Expression using Barcoded Self-Reporting Transposon Calling Cards and Transcriptomes

Lalli

Dong

Chen

et al. 2021

Preprint

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Calling cards technology using self-reporting transposons enables the identification of DNA-protein interactions through RNA sequencing. By introducing a DNA barcode into the calling card itself, we have drastically reduced the cost and labor requirements of calling card experiments in bulk populations of cells. An additional barcode incorporation during reverse transcription enables simultaneous transcriptome measurement in a facile and affordable protocol. We demonstrate that barcoded self-reporting transposons recover in vitro binding sites for four basic helix-loop-helix transcription factors with important roles in cell fate specification: ASCL1, MYOD1, NEUROD2, and NGN1. Further, simultaneous calling cards and transcriptional profiling during transcription factor overexpression identified both binding sites and gene expression changes for two of these factors. RNA-based identification of transcription factor binding sites and gene expression through barcoded self-reporting transposon calling cards and transcriptomes is a novel and powerful method to infer gene regulatory networks in a population of cells.

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“…The regions covered by any reads are extracted and a read coverage file is created with BED format. For each cell cluster, the read coverage files of individual cells are combined into a single track with qBED format for convenient visualization [33], with the cells sorted by the number of covered regions in each cell. All the resulting track files could be directly uploaded and visualized using browser apps such as the WashU Epigenome Browser [34].…”

Section: Generation Of Genome Browser Tracksmentioning

confidence: 99%

CUT&RUNTools 2.0: A pipeline for single-cell and bulk-level CUT&RUN and CUT&Tag data analysis

Sankaran

Yuan

2021

Preprint

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Genome-wide profiling of transcription factor binding and chromatin states is a widely-used approach for mechanistic understanding of gene regulation. Recent technology development has enabled such profiling at single-cell resolution. However, an end-to-end computational pipeline for analyzing such data is still lacking. To fill this gap, we have developed a flexible pipeline for analysis and visualization of single-cell CUT&RUN and CUT&Tag data, which provides functions for sequence alignment, quality control, dimensionality reduction, cell clustering, data aggregation, and visualization. Furthermore, it is also seamlessly integrated with the functions in original CUT&RUNTools for population-level analyses. As such, this provides a valuable toolbox for the community.

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The qBED track: a novel genome browser visualization for point processes

Cited by 6 publications

References 31 publications

WashU Epigenome Browser update 2022

WashU Epigenome Browser update 2022

Measuring Transcription Factor Binding and Gene Expression using Barcoded Self-Reporting Transposon Calling Cards and Transcriptomes

CUT&RUNTools 2.0: A pipeline for single-cell and bulk-level CUT&RUN and CUT&Tag data analysis

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