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VING: a software for visualization of deep sequencing signals.

Descrimes M, Ben Zouari Y, Wery M, Legendre R, Gautheret D, Morillon A - BMC Res Notes (2015)

Bottom Line: 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 produces high-quality figures for NGS data representation in a genome region of interest.

View Article: PubMed Central - PubMed

Affiliation: ncRNA, Epigenetics and Genome Fluidity, Institut Curie, PSL Research University, CNRS UMR3244, Université Pierre et Marie Curie, 26 rue d'Ulm, 75248, Paris Cedex 05, France. marc.descrimes@curie.fr.

ABSTRACT

Background: 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.

Results: 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.

Conclusions: 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.

No MeSH data available.


Related in: MedlinePlus

Examples of NGS signal visualization using VING. a Strand-specific “classic” visualization of 21–25 nucleotides small RNA densities along the SPAC167.03c locus in rdp1Δ Schizosaccharomyces pombe control cells (vector) or cells overexpressing Dcr1. Signal from each library is shown in a separate panel. Reads mapped on the + and − strands are shown on the top and bottom sides of the 0 horizontal line, respectively (additional representation in different colors optional). Annotated genomic features are represented as “box” (ORF) and “line” (mRNA). Original data described in [9]. The Y axis (log2 tag densities) shows the log2 of the number of reads (or pairs of reads in case of paired-end sequencing) at each position. b Unstranded “line” visualization of RNA Polymerase II ChIP-seq profile along the YDL140C (RPO21) locus in a wild-type strain of Saccharomyces cerevisiae. Signal intensity for each library is represented by a different colored line (IP, black; input, green). Strands are as in the “classic” view. Annotated ORF are represented as “box”. Original data described in [10]. Y axis see above. c Strand-specific “line” visualization of the NET-seq profile along the same region as B in wild-type (black) and dst1Δ (red) cells of S. cerevisiae. Original data described in [11]. Y axis see above. d Strand-specific “heatmap” visualization of the paired-end total RNA-seq signal along the YBR019C-YBR020W (GAL10-GAL1) locus in two biological replicates of S. cerevisiae wild-type cells grown in glucose- or shifted for 1 h in galactose-containing medium. Distinct panels are used for each strand. In each panel, each lane corresponds to one library. Signal intensities range from white (low) to dark blue (high). Annotated ORF are represented as “box”. Original data described in [12]. e Strand-specific “heatmap” visualization of the paired-end total RNA-seq signal along the HOTAIR locus in MCF-7, HeLa-S3 and NHLF cell lines. Annotated transcripts and exons are represented as “arrow” and “rectangle”. Original data from the ENCODE project described in [13]
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Fig1: Examples of NGS signal visualization using VING. a Strand-specific “classic” visualization of 21–25 nucleotides small RNA densities along the SPAC167.03c locus in rdp1Δ Schizosaccharomyces pombe control cells (vector) or cells overexpressing Dcr1. Signal from each library is shown in a separate panel. Reads mapped on the + and − strands are shown on the top and bottom sides of the 0 horizontal line, respectively (additional representation in different colors optional). Annotated genomic features are represented as “box” (ORF) and “line” (mRNA). Original data described in [9]. The Y axis (log2 tag densities) shows the log2 of the number of reads (or pairs of reads in case of paired-end sequencing) at each position. b Unstranded “line” visualization of RNA Polymerase II ChIP-seq profile along the YDL140C (RPO21) locus in a wild-type strain of Saccharomyces cerevisiae. Signal intensity for each library is represented by a different colored line (IP, black; input, green). Strands are as in the “classic” view. Annotated ORF are represented as “box”. Original data described in [10]. Y axis see above. c Strand-specific “line” visualization of the NET-seq profile along the same region as B in wild-type (black) and dst1Δ (red) cells of S. cerevisiae. Original data described in [11]. Y axis see above. d Strand-specific “heatmap” visualization of the paired-end total RNA-seq signal along the YBR019C-YBR020W (GAL10-GAL1) locus in two biological replicates of S. cerevisiae wild-type cells grown in glucose- or shifted for 1 h in galactose-containing medium. Distinct panels are used for each strand. In each panel, each lane corresponds to one library. Signal intensities range from white (low) to dark blue (high). Annotated ORF are represented as “box”. Original data described in [12]. e Strand-specific “heatmap” visualization of the paired-end total RNA-seq signal along the HOTAIR locus in MCF-7, HeLa-S3 and NHLF cell lines. Annotated transcripts and exons are represented as “arrow” and “rectangle”. Original data from the ENCODE project described in [13]

Mentions: 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 [7] or EdgeR [8]. 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.Fig. 1


VING: a software for visualization of deep sequencing signals.

Descrimes M, Ben Zouari Y, Wery M, Legendre R, Gautheret D, Morillon A - BMC Res Notes (2015)

Examples of NGS signal visualization using VING. a Strand-specific “classic” visualization of 21–25 nucleotides small RNA densities along the SPAC167.03c locus in rdp1Δ Schizosaccharomyces pombe control cells (vector) or cells overexpressing Dcr1. Signal from each library is shown in a separate panel. Reads mapped on the + and − strands are shown on the top and bottom sides of the 0 horizontal line, respectively (additional representation in different colors optional). Annotated genomic features are represented as “box” (ORF) and “line” (mRNA). Original data described in [9]. The Y axis (log2 tag densities) shows the log2 of the number of reads (or pairs of reads in case of paired-end sequencing) at each position. b Unstranded “line” visualization of RNA Polymerase II ChIP-seq profile along the YDL140C (RPO21) locus in a wild-type strain of Saccharomyces cerevisiae. Signal intensity for each library is represented by a different colored line (IP, black; input, green). Strands are as in the “classic” view. Annotated ORF are represented as “box”. Original data described in [10]. Y axis see above. c Strand-specific “line” visualization of the NET-seq profile along the same region as B in wild-type (black) and dst1Δ (red) cells of S. cerevisiae. Original data described in [11]. Y axis see above. d Strand-specific “heatmap” visualization of the paired-end total RNA-seq signal along the YBR019C-YBR020W (GAL10-GAL1) locus in two biological replicates of S. cerevisiae wild-type cells grown in glucose- or shifted for 1 h in galactose-containing medium. Distinct panels are used for each strand. In each panel, each lane corresponds to one library. Signal intensities range from white (low) to dark blue (high). Annotated ORF are represented as “box”. Original data described in [12]. e Strand-specific “heatmap” visualization of the paired-end total RNA-seq signal along the HOTAIR locus in MCF-7, HeLa-S3 and NHLF cell lines. Annotated transcripts and exons are represented as “arrow” and “rectangle”. Original data from the ENCODE project described in [13]
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Related In: Results  -  Collection

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Fig1: Examples of NGS signal visualization using VING. a Strand-specific “classic” visualization of 21–25 nucleotides small RNA densities along the SPAC167.03c locus in rdp1Δ Schizosaccharomyces pombe control cells (vector) or cells overexpressing Dcr1. Signal from each library is shown in a separate panel. Reads mapped on the + and − strands are shown on the top and bottom sides of the 0 horizontal line, respectively (additional representation in different colors optional). Annotated genomic features are represented as “box” (ORF) and “line” (mRNA). Original data described in [9]. The Y axis (log2 tag densities) shows the log2 of the number of reads (or pairs of reads in case of paired-end sequencing) at each position. b Unstranded “line” visualization of RNA Polymerase II ChIP-seq profile along the YDL140C (RPO21) locus in a wild-type strain of Saccharomyces cerevisiae. Signal intensity for each library is represented by a different colored line (IP, black; input, green). Strands are as in the “classic” view. Annotated ORF are represented as “box”. Original data described in [10]. Y axis see above. c Strand-specific “line” visualization of the NET-seq profile along the same region as B in wild-type (black) and dst1Δ (red) cells of S. cerevisiae. Original data described in [11]. Y axis see above. d Strand-specific “heatmap” visualization of the paired-end total RNA-seq signal along the YBR019C-YBR020W (GAL10-GAL1) locus in two biological replicates of S. cerevisiae wild-type cells grown in glucose- or shifted for 1 h in galactose-containing medium. Distinct panels are used for each strand. In each panel, each lane corresponds to one library. Signal intensities range from white (low) to dark blue (high). Annotated ORF are represented as “box”. Original data described in [12]. e Strand-specific “heatmap” visualization of the paired-end total RNA-seq signal along the HOTAIR locus in MCF-7, HeLa-S3 and NHLF cell lines. Annotated transcripts and exons are represented as “arrow” and “rectangle”. Original data from the ENCODE project described in [13]
Mentions: 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 [7] or EdgeR [8]. 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.Fig. 1

Bottom Line: 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 produces high-quality figures for NGS data representation in a genome region of interest.

View Article: PubMed Central - PubMed

Affiliation: ncRNA, Epigenetics and Genome Fluidity, Institut Curie, PSL Research University, CNRS UMR3244, Université Pierre et Marie Curie, 26 rue d'Ulm, 75248, Paris Cedex 05, France. marc.descrimes@curie.fr.

ABSTRACT

Background: 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.

Results: 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.

Conclusions: 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.

No MeSH data available.


Related in: MedlinePlus