gbtools: Interactive tools for metagenome visualization and binning in R

Refer to the gbtools manual for detailed instructions.

Read the paper published in Frontiers in Microbiology

News

• Validate input files within R environment with gbt_checkinput() (v2.6.0), validator no longer depends on non-core Perl module
• You can now specify custom colors for taxonomic markers in plots with the markCustomPalette= parameter in the plot function (v2.5.8)
• Bug in Fastg-fishing script caused by inconsistent SPAdes header names has been fixed (v2.5.7)

Quick start

Here are the bare basics that you can do in gbtools, using the Olavius example data (look in the example_data/Olavius_metagenome folder in this package).

The following commands are all in the R environment.

1. Install gbtools

From source package

The tar.gz archives can be found in the R_source_package folder.

install.packages("sp") # Dependency
install.packages("plyr") # Dependency
install.packages("gbtools_2.6.0.tar.gz",repos=NULL,type="source")

Directly from GitHub with devtools

The devtools package allows you to install R packages directly from GitHub, similar to installing packages from the CRAN repository.

library(devtools)
install_github("kbseah/genome-bin-tools/gbtools") # Install latest version of the R package

2. Import data and see summary statistics

First check that the input data are correctly formatted

gbt_checkinput (covstats=c("SampleG1.covstats","SampleA2.covstats"), # Coverage data
                ssu="olavius_metagenome.ssu.tab", # SSU gene annotations
                mark=c("amphora2_results.tab","blobology_results.tab")) # Marker genes
# Should give a message that no errors are found

Now import the data into the R environment

d <- gbt (covstats=c("SampleG1.covstats","SampleA2.covstats"), # Coverage data
          ssu="olavius_metagenome.ssu.tab", # SSU gene annotations
          mark=c("amphora2_results.tab","blobology_results.tab"), # Marker genes
          marksource=c("amphora2","blob")) # Names for the marker gene sets

See summary stats by typing name of the gbt object

d
summary (d) # same thing

3. Plot coverage vs. GC

plot (d, # Plots the first set of coverage data by default
      ssu=TRUE, # Annotate SSU genes with crosshairs
      textlabels=TRUE, # Add labels for SSU genes
      legend=TRUE) # Add legend for marker genes

4. Plot differential coverage

plot (d, slice=c(1,2)) # Plot one set of coverage data vs. another

5. Interactively select bins from plot

plot (d,slice=1,marker=FALSE) # Turn off color overlays
d.bin1 <- choosebin (d, slice=1) # Click on the plot to define the region you want
summary(d.bin1) # Summarize the newly-created bin
points(d.bin1, slice=1) # Overlay the new bin on your plot

6. Import bins from 3rd party tools and plot them

d.metabat_bins <- importBins (d, file="metabat_bins")
multiBinPlot (d, bins=d.metabat_bins)

Each bin gets plotted in a different color

Getting help

Documentation for each function can be accessed in R by typing ? followed by function name at the command line (the gbtools plot function is filed under plot.gbt to distinguish it from the generic plot function).

Problems with using gbtools? Create a new issue using the GitHub issue-tracker on the right. Or send me an email, with "gbtools help" in the subject line.

Problems with input file formats? Read the wiki and use the input_validator.pl script to check your input files.

Paper(s) using gbtools

Petersen et al. 2016. Nature Microbiology 2: 16195.

Rubin-Blum et al. 2017. Nature Microbiology 2: 17093.

Drop me a message if I've overlooked your publication(s)!

Citations

Citation: Seah BK and Gruber-Vodicka HR (2015). gbtools: Interactive visualization of metagenome bins in R. Front. Microbiol. 6:1451. doi: 10.3389/fmicb.2015.01451

Cite dependencies if you use them:

• R - R Core Team. 2014. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. (http://www.R-project.org/)
• BBMap - Bushnell B. 2015. BBMap (http://sourceforge.net/projects/bbmap/)
• AMPHORA2 - Wu M, Scott AJ. 2012. Bioinformatics 28 (7) : 1033-1034.
• barrnap - Seemann T. 2014. barrnap (http://www.vicbioinformatics.com/software.barrnap.shtml)
• Usearch - Edgar RC 2010. Bioinformatics 26 (19) : 2460-2461.
• Vsearch - https://github.com/torognes/vsearch
• ARB-SILVA - Quast C et al. 2013. Nucleic Acids Research 41 (D1) : D590-D596.
• tRNAscan-SE - Lowe T, Eddy S. 1997. Nucleic Acids Research 25 : 955-964.
• Blobology - Kumar S et al. 2013. Frontiers in Genetics 4 : 237

Contact: Brandon Seah ([email protected]) Department of Symbiosis, Max Planck Institute for Marine Microbiology