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Cited by 8,096Genomics and Phylogenetic StudiesGenomics and Chromatin DynamicsScientific Computing and Data Management

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

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Ramírez, Fidel, Ryan, Devon P, Grüning, Björn, Bhardwaj, Vivek, Kilpert, Fabian, Richter, Andreas S, Heyne, Steffen, Dündar, Friederike, Manke, Thomas·2016·Nucleic Acids Research
DOI 10.1093/nar/gkw257PMC PMC4987876
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Abstract

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.

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Figures

Figure 1. deepTools2 has enhanced features for quality control, data reduction, normalization and visualization of deeply sequenced data. (A) deepTools2 comes with two new quality control tools: plotCoverage, which displays sequencing depth distribution; and plotPCA, which plots the results of principal component analysis (PCA) on BAM or bigWig files. (B) Signal visualization by means of normalized bigWig tracks, produced by deepTools’ bamCompare (ChIP-seq samples) and bamCoverage, which can now handle MNase- and strand-specific RNA-seq samples. In the genes track, the gray color represents untranslated regions and the thin lines represent introns. (C) computeMatrix and plotHeatmap were used to summarize and cluster multiple bigWig scores over genomic intervals. The image shows the resulting k-means clustering of ChIP-seq signals of three histone marks around the transcription start site of genes (with k = 2). Subsequently, the clustered regions were used to plot MNase-seq and CAGE read coverages. In the image, each row corresponds to the same genomic region. (D) plotProfile now offers additional means for visualization. The top panel shows the average signal for different samples and different cluster of regions (left and right plot). In the middle, the same profiles are represented as heatmaps. The bottom panel shows summary profiles where colors correspond to the observed frequency of the signal within each cluster. All data are from the Drosophila melanogaster S2 cell line and are publically available (see Section Accession numbers).
Figure 1. deepTools2 has enhanced features for quality control, data reduction, normalization and visualization of deeply sequenced data. (A) deepTools2 comes with two new quality control tools: plotCoverage, which displays sequencing depth distribution; and plotPCA, which plots the results of principal component analysis (PCA) on BAM or bigWig files. (B) Signal visualization by means of normalized bigWig tracks, produced by deepTools’ bamCompare (ChIP-seq samples) and bamCoverage, which can now handle MNase- and strand-specific RNA-seq samples. In the genes track, the gray color represents untranslated regions and the thin lines represent introns. (C) computeMatrix and plotHeatmap were used to summarize and cluster multiple bigWig scores over genomic intervals. The image shows the resulting k-means clustering of ChIP-seq signals of three histone marks around the transcription start site of genes (with k = 2). Subsequently, the clustered regions were used to plot MNase-seq and CAGE read coverages. In the image, each row corresponds to the same genomic region. (D) plotProfile now offers additional means for visualization. The top panel shows the average signal for different samples and different cluster of regions (left and right plot). In the middle, the same profiles are represented as heatmaps. The bottom panel shows summary profiles where colors correspond to the observed frequency of the signal within each cluster. All data are from the Drosophila melanogaster S2 cell line and are publically available (see Section Accession numbers).CC BY-NC · PubMed Central

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Citations

Cites

Research works printed but not linked: 16

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Metadata

Authors
Ramírez, F., Ryan, D. P., Grüning, B., Bhardwaj, V., Kilpert, F., Richter, A. S., Heyne, S., Dündar, F., & Manke, T.
Affiliations
  • Max Planck Institute of Immunobiology and Epigenetics (DE)
  • University of Freiburg (DE)
  • Weill Cornell Medicine (US)
Corresponding author
Manke, Thomas
ORCID
Venue
Nucleic Acids Research
Publisher
Oxford University Press (OUP)
Published
2016-04-13
Volume
44
Issue
W1
Pages
W160-W165
ISSN
  • 0305-1048 (ISSN-L)
  • 1362-4962
DOI
10.1093/nar/gkw257
PMC
PMC4987876
OpenAlex
W2341539131
Cited by
8,096
References
2
Citations by year
2026: 1065 · 2025: 1325 · 2024: 1238 · 2023: 1188 · 2022: 1018 · 2021: 941 · 2020: 602 · 2019: 393 · 2018: 234 · 2017: 85 · 2016: 5
FWCI
124.25
Type
journal article
Language
en
Open access
Licence
Locations
  • Nucleic Acids Research (publishedVersion, cc-by-nc)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
  • FreiDok plus (Universitätsbibliothek Freiburg) (submittedVersion, other-oa)
  • HighWire Press Open Archive (submittedVersion)
  • MPG.PuRe (Max Planck Society) (submittedVersion)
Funders
Deutsche Forschungsgemeinschaft (SFB 992)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
  • Social Sciences › Decision Sciences › Information Systems and Management › Scientific Computing and Data Management
Keywords
read normalization, read quality control, read clustering, data visualization
MeSH terms
Animals, Base Sequence, Computer Graphics, Drosophila melanogaster, Humans, Software, Information Storage and Retrieval, Sequence Alignment, Sequence Analysis, DNA, Computational Biology, Internet, High-Throughput Nucleotide Sequencing

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