MotifBinner processes high-throughput sequencing data of an RNA virus population that was sequenced using the Primer ID approach as described in Jabara et al, 2011. A random sequence tag is included in the initial primer during the conversion from RNA to cDNA so that each input template is tagged with a unique primer ID. After amplification with PCR and sequencing, each sequence with the same PID should be from the same input template.
This data is cleaned by scanning each sequence for a primer ID. A prefix and a suffix that surrounds the primer ID must be supplied. If the prefix and the suffix is found and the distance between them matches the primer ID length (supplied by user), then the letters between the prefix and suffix is taken as the primer ID for that sequence. Fuzzy matching is supported. All sequences with the same primer ID are then grouped together in bins so that each bin consists of sequences which had the same primer ID.
Bins that are made up of sequences whose primer IDs probably have sequencing errors in them are then discarded using the consensus cutoff approach (see the vignette on consensus cutoffs and Zhou et al, 2015 for more details). Each bin is then inspected and the most outlying sequences are removed so that the bins can be aligned without difficulty. The alignments are used to generate consensus sequences using a majority rule. The consensus sequences together with a report on the binning of the dataset is reported.
Make sure you have a recent version of R: (Not required for Ubuntu 20.04) http://stackoverflow.com/questions/10476713/how-to-upgrade-r-in-ubuntu. Follow these instructions to set up the correct repositiory for apt.
Make sure that both r-base and r-base-dev is installed
sudo apt-get install r-base r-base-dev
Next, install devtools' depedancies and muscle using apt-get:
sudo apt-get install libssl-dev libxml2-dev libcurl4-gnutls-dev muscle
MotifBinner2 requires pandoc version > 1.15. apt will work for Ubuntu 20.04, but you might have to manually download an appropriate version for older versions.
Using apt for pandoc:
sudo apt install pandoc
pandoc -v
Manually installing pandoc:
Download a binary package from https://github.com/jgm/pandoc/releases/1.15 and then install it with dpkg: (Download the latest available release and update version numbers as needed)
wget https://github.com/jgm/pandoc/releases/download/1.15/pandoc-1.15-1-amd64.deb
sudo dpkg -i pandoc-1.15-1-amd64.deb
Next install all the dependencies from Bioconductor. This is done from within a root user R session and requires devtools:
install.packages('devtools', repo = 'http://cran.rstudio.com/')
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("Biostrings")
BiocManager::install("ShortRead")
At last install MotifBinner: From a local file: (Still as root)
library(devtools)
install_local('/path/to/file/MotifBinner_x.y.z.tar.gz')
Please note that you must use install_local from devtools - install.packages will not work. Change /path/to/file to the path to the installation file on your computer and x.y.z to match the installation file you have.
Or using the github repo:
library(devtools)
install_github('philliplab/MotifBinner2')
Lastly, MotifBinner includes a script that can be run from the commandline. You need to put this script somewhere convenient ('/usr/bin' for example) (Must be done from a root R session)
file.symlink(from = file.path(find.package('MotifBinner2'), 'MotifBinner2.R'),
to = '/usr/bin')
From the command line
MotifBinner2 -h
or (depending on your installation):
MotifBinner2.R -h
This will display help for all the options and an example call to MotifBinner.
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Cassandra B Jabara, Corbin D Jones, Jeffrey Roach, Jeffrey A Anderson, and Ronald Swanstrom. Accurate sampling and deep sequencing of the HIV- 1 protease gene using a Primer ID. Proceedings of the National Academy of Sciences of the United States of America, 108(50):20166–71, December 2011.
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Shuntai Zhou, Corbin Jones, Piotr Mieczkowski and Ronald Swanstrom. Primer ID validates template sampling depth and greatly reduced the error rate of next-generation sequencing of HIV1 genomic RNA populations. J Virol. 2015 Aug 15;89(16):8540-55. doi: 10.1128/JVI.00522-15. Epub 2015 Jun 3.