deepTools2: a next generation web server for deep-sequencing data analysis

By Fidel Ramírez, D. Ryan, Björn A. Grüning, Vivek Bhardwaj, Fabian Kilpert, Andreas S. Richter, Steffen Heyne, F. Dündar, Thomas Manke. 2016

6,826 citations recorded by the source corpus.

We present an update to our Galaxy-based web server for processing and visualizing deeply sequenced data. Its core tool set, deepTools, allows users to perform complete bioinformatic workflows ranging from quality controls and normalizations of aligned reads to integrative analyses, including clustering and visualization approaches. Since we first described our deepTools Galaxy server in 2014, we have implemented new solutions for many requests from the community and our users. Here, we introduce significant enhancements and new tools to further improve data visualization and interpretation. deepTools continue to be open to all users and freely available as a web service at deeptools.ie-freiburg.mpg.de. The new deepTools2 suite can be easily deployed within any Galaxy framework via the toolshed repository, and we also provide source code for command line usage under Linux and Mac OS X. A public and documented API for access to deepTools functionality is also available.

Subfields: Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

Citations in the corpus

A Unified Model for the Function of YTHDF Proteins in Regulating m6A-Modified mRNA (2020, methodology): “To compare the two calculated coverage, deepTools bamCompare was used and visualized using deepTools plotHeatmap (Ramírez et al., 2016).”
A Single-Cell Atlas of In Vivo Mammalian Chromatin Accessibility. (2018, positive): “…quantitative comparisons across all of these tissues, we merged all peaks identified in our sci-ATAC-seq samples (across each entire tissue) and the ENCODE samples with BEDTools and then used the deepTools program (Ramı́rez et al., 2016) to count reads overlapping each peak for each tissue.”
Comprehensive integration of single-cell data (2018, methodology): “We created normalized read coverage tracks (bigwig format) for each BAM file using the program bamCoverage in the deepTools package [Ramı́rez et al., 2016] with the binSize parameter set to 1 and using the reads per kilobase per million mapped reads (RPKM) normalization option.”
UALCAN: An update to the integrated cancer data analysis platform (2022, methodology): “Downloaded data were cleaned using Trim galore file was generated using the bamCoverage module of deepTools [23] .”
Exploring Epigenomic Datasets by ChIPseeker (2022, background): “…Schroeder, Fargo, & Li, 2013), which only provide annotation with a single dataset; ngs.plot (Shen, Shao, Liu, & Nestler, 2014) and deeptools2 (Ramírez et al., 2016), which only present the visualization of metaplots and heatmaps; and regioneR (Gel et al., 2016), which supplies the comparison…”
The transcriptional landscape of polyploid wheat (2018, methodology): “We calculatedmeta-gene profiles for each category by computing the read density of each histone mark over different triads categories usingDeeptools (61) computeMatrix scale-regions and plotted it with plotProfile.”
FactorNet: a deep learning framework for predicting cell type specific transcription factor binding from nucleotide-resolution sequential data (2017, methodology): “We developed the data generators using a combination of keras [38], pyfasta[39], pybedtools [40], and pyBigWig [41], FactorNet can accept an arbitrary number of bigWig files as input features, and we found the following signals to be highly informative for prediction:”
Zc3h13/Flacc is required for adenosine methylation by bridging the mRNA-binding factor Rbm15/Spenito to the m6A machinery component Wtap/Fl(2)d (2018, methodology): “The deepTools suite was used for metagene analysis (Ramirez et al. 2016).”