- Technical Note
- Open Access
VING: a software for visualization of deep sequencing signals
BMC Research Notes volume 8, Article number: 419 (2015)
Next generation sequencing (NGS) data treatment often requires mapping sequenced reads onto a reference genome for further analysis. Mapped data are commonly visualized using genome browsers. However, such software are not suited for a publication-ready and versatile representation of NGS data coverage, especially when multiple experiments are simultaneously treated.
We developed ‘VING’, a stand-alone R script that takes as input NGS mapping files and genome annotations to produce accurate snapshots of the NGS coverage signal for any specified genomic region. VING offers multiple viewing options, including strand-specific views and a special heatmap mode for representing multiple experiments in a single figure.
VING produces high-quality figures for NGS data representation in a genome region of interest. It is available at http://vm-gb.curie.fr/ving/. We also developed a Galaxy wrapper, available in the Galaxy tool shed with installation and usage instructions.
NGS is now widely used to study all aspects of gene expression from chromatin conformation (Hi-C) to protein-DNA binding (chromatin immunoprecipitation sequencing, ChIP-seq), transcription (native elongating transcript sequencing, NET-seq), RNA abundance (RNA-seq) and translation (ribosome profiling). A common step in most NGS approaches is the mapping of sequenced reads to a reference genome and analysis of the resulting signal. Multiple tools have been developed for quantitative analysis of NGS data. However, data visualization remains difficult because of the large quantity of information to display. Genome browsers such as Artemis , IGV  or Gbrowse  enable rapid navigation along the genome and coverage visualization, but are not fit for accurate, publication-quality image, neither for displaying multiple libraries. Alternatively, combinations of software such as BEDtools  and R or Matlab functions can produce customized plots, but require programming skills. Likewise, the Gviz R package , which enables customized display of a variety of genome annotation tracks, including NGS data, requires mastering the R environment and R objects. Here, we describe ‘VING’, an R package dedicated to the custom visualization of NGS data that can be easily launched using a single Unix command line, or within the Galaxy environment. VING introduces functionalities to handle data produced by the most recent NGS protocols, in a strand-specific manner. The code is optimized to enable a fast figure generation, even for the largest mapping files and genomes.
VING produces snapshots of genomic regions from any set of mapping and annotation files, using a single command line. VING combines: loading of bam mapping files with optional user-provided normalization factors, loading of gff annotation file(s), plotting of signal and annotated genomic features.
VING uses as input bam alignment files  and gff annotation files (description of the gff format can be found at http://www.sanger.ac.uk/resources/software/gff/). VING loads bam files using the Bioconductor package “Rsamtools”. Single-end or paired-end data are allowed and the library type can be specified as a parameter to assign reads to the proper strands. For paired-end data, each properly paired read is loaded as one single fragment. Users can also provide weights for normalization of each bam file. Annotation files are read by a custom function that only loads genomic features within coordinates defined by the users, enabling a faster operation. Users can also select the features to display.
The coverage signal (number of reads covering each nucleotide) is only computed for the requested genome area. Users may provide optional normalization factors for weighting each signal. These factors should be computed independently, either based on library sizes (RPM normalization) or using a dedicated package such as DESeq  or EdgeR . The signal is plotted in a strand-specific manner using any of the three visualization modes: “classic” coverage plots using solid areas (each library in a distinct panel, Fig. 1a); “line” plots using lines of different colors and/or styles (one panel for all libraries, limited to 16 libraries, Fig. 1b, c); “heatmap” views based on a color-code to reveal high/low-density coverage regions (one panel for each strand, libraries shown as lanes in each of the two panels, no limitation of samples, Fig. 1d, e). Output files can be produced in high-resolution (300 dpi) tiff, jpeg, png or pdf format.
Users can define a color and shape for each type of annotation feature (Fig. 1). Shapes include “box” (rectangle with an arrow at one side indicating the feature orientation), “rectangle” (plain rectangle), “arrow” (line with an arrow indicating the orientation) and “line” (straight line). VING automatically groups the different annotated features corresponding to the same ID such as untranslated regions (UTRs), exons and introns (or any other feature) from the same transcript, provided that these features are defined in the gff annotation file.
VING was tested on a variety of NGS data from different species, including yeast small RNA-seq (Fig. 1a), ChIP-seq (Fig. 1b), NET-seq (Fig. 1c), total RNA-seq (Fig. 1d), and human total RNA-seq data (Fig. 1e). Execution time depends on input files size. On an Intel Xeon 2,4 GHz processor with 32 Gb RAM, runtime ranged from 5 s and 2 min for the smaller (such as for Fig. 1a) and larger datasets (such as for Fig. 1d, e), respectively. Memory usage was under 500 Megabytes in all cases.
VING can be operated as a single command line. For graphical interface operation, we wrote a Galaxy wrapper enabling the users to input all parameters through the user-friendly Galaxy interface (available in the Galaxy Tool Shed: https://testtoolshed.g2.bx.psu.edu/view/rlegendre/ving).
The VING program produces high-quality figures for NGS data representation in a genome region of interest. VING input and outputs have been rendered Galaxy-compatible so that automated coverage plots can be easily incorporated in Galaxy pipelines. The resulting, integrated view of a genome region is immediately suitable for figure production.
Availability and requirements
Project name: VING.
Project home page: http://vm-gb.curie.fr/ving/.
Operating system(s): Linux. VING has also been successfully tested on MacOSX and Windows 7.
Programming language: R.
Other requirements: Bioconductor packages GenomicRanges and Rsamtools.
License: GNU GPL (version 3, 29 June 2007).
Any restrictions to use by non-academics: none.
Availability of supporting data
Original raw data used in Fig. 1a, c–e were retrieved from the NCBI Gene Expression Omnibus, accession numbers GSE52535, GSE25107, GSE63444 and GSE26284, respectively. Original raw data used in Fig. 1b were retrieved from the NCBI Sequence Read Archive, accession number SRA030505. Truncated bam and gff files used for figure generation are provided on the VING website.
chromatin immunoprecipitation sequencing
general feature format
native elongating transcript sequencing
next generation sequencing
open reading frame
Rutherford K, Parkhill J, Crook J, Horsnell T, Rice P, Rajandream MA, Barrell B. Artemis: sequence visualization and annotation. Bioinformatics. 2000;16(10):944–5.
Robinson JT, Thorvaldsdottir H, Winckler W, Guttman M, Lander ES, Getz G, Mesirov JP. Integrative genomics viewer. Nat Biotechnol. 2011;29(1):24–6.
Stein LD, Mungall C, Shu S, Caudy M, Mangone M, Day A, Nickerson E, Stajich JE, Harris TW, Arva A, et al. The generic genome browser: a building block for a model organism system database. Genome Res. 2002;12(10):1599–610.
Quinlan AR, Hall IM. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics. 2010;26(6):841–2.
Hahne F, Durinck S, Ivanek R, Mueller A, Lianoglou S, Tan G, Parsons L. Gviz: plotting data and annotation information along genomic coordinates. R package (version 1.12.1). http://bioconductor.org/packages/release/bioc/html/Gviz.html.
Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R. The sequence alignment/map format and SAMtools. Bioinformatics. 2009;25(16):2078–9.
Anders S, Huber W. Differential expression analysis for sequence count data. Genome Biol. 2010;11(10):R106.
Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression. Bioinformatics. 2010;26(1):139–40.
Yu R, Jih G, Iglesias N, Moazed D. Determinants of heterochromatic siRNA biogenesis and function. Mol Cell. 2014;53(2):262–76.
Van Dijk EL, Chen CL, d’Aubenton-Carafa Y, Gourvennec S, Kwapisz M, Roche V, Bertrand C, Silvain M, Legoix-Né P, Loeillet S, et al. XUTs are a class of Xrn1-sensitive antisense regulatory non coding RNA in yeast. Nature. 2011;475(7354):114–7.
Churchman LS, Weissman JS. Nascent transcript sequencing visualizes transcription at nucleotide resolution. Nature. 2011;469(7330):368–73.
Manfrini N, Trovesi C, Wery M, Martina M, Cesena D, Descrimes M, Morillon A, d’Adda di Fagagna F, Longhese MP. RNA-processing proteins regulate Mec1/ATR activation by promoting generation of RPA-coated ssDNA. EMBO Rep. 2015;16(2):221–31.
Djebali S, Davis CA, Merkel A, Dobin A, Lassmann T, Mortazavi A, Tanzer A, Lagarde J, Lin W, Schlesinger F, et al. Landscape of transcription in human cells. Nature. 2012;489(7414):101–8.
MD and YBZ performed the programming and prepared the online documentation. RL developed the Galaxy wrapper. MD, MW, DG and AM conceived the project. MW, MD, DG and AM prepared the manuscript. All authors read and approved the final manuscript.
We thank N. Servant and A. Lermine from the bioinformatics platform of Institut Curie, and the eBio bioinformatics platform (Orsay) for their encouragement and advices. We are grateful to all the members of our labs for helpful discussions. D. Gautheret’s lab is supported by ANR 12-ADAP-0019 RNAdapt grant. A. Morillon’s lab is supported by the ANR “REGULncRNA”, ERC “EpincRNA” starting and ERC “DARK” consolidator grants.
Compliance with ethical guidelines
Competing interests The authors declare that they have no competing interests.
Marc Descrimes and Yousra Ben Zouari contributed equally to this work
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Descrimes, M., Zouari, Y.B., Wery, M. et al. VING: a software for visualization of deep sequencing signals. BMC Res Notes 8, 419 (2015). https://doi.org/10.1186/s13104-015-1404-5
- NGS signal visualization
- High-quality figure