MIPTools is a suite of computational tools that are used for molecular inversion probe design, data processing, and analysis.
A working copy of Singularity is required: https://www.sylabs.io/docs/. Singularity is best installed with sudo. While it is said to be possible to install with unprivileged user with some features missing, MIPTools hasn't been tested on such an installation.
Singularity is available for most Linux systems. It is also possible to install and use on Mac OS using virtual machines with a little bit of extra work.
Note that the snap package is a rapid way to install the go language required
by Singularity (e.g. on Ubuntu/Debian: sudo snap install go --classic).
The MIPTools container, built and ready to use, can be downloaded here. You can download the development version or any previous release:
# Download the latest stable release
wget https://baileylab.brown.edu/MIPTools/download/miptools_v0.4.0.sif
# Download the development version
# The development version is updated every two weeks
wget https://baileylab.brown.edu/MIPTools/download/miptools_dev.sifNote that these prebuilt versions do not include the bcl2fastq software due
to its license. If you plan to use MIPTools to demultiplex bcl files, you must
build the container yourself.
MIPTools can also be built from source code using the definition file provided in this GitHub repository.
The process can take about 10-30 minutes to build, depending on the number of
CPU cores available. By default, the build process will use 6 CPU cores. This
should pose no problems with most modern computers, but if the computer used
for building the container has less then 6 cpu cores available, change the
"CPU_COUNT=6" value at the top of the MIPTools.def file to a suitable
number before running the following code. On the other hand, if you have access
to more CPU power, by all means, use them by setting the same parameter to a
higher value.
You must have sudo privelege to build the image. You do not need sudo to use the image. So if you want to run the container on an environment without sudo, either download a prebuilt image (see above) or build the container on your own machine where you do have sudo privilege and copy the image file to the computer without sudo. Note that the Singularity program itself must have been installed with sudo.
If you plan to use MIPTools to demultiplex bcl files, you should download
bcl2fastq separately. Currently, you can download it from
here,
but this may change in the future. You must download the file: bcl2fastq2 Conversion Software v2.20 Installer (Linux rpm) and place it in the
MIPTools/programs directory.
You can install the most recent release using the following:
# Install stable version v0.4.0
git clone --branch v0.4.0 https://github.com/bailey-lab/MIPTools.gitYou can alternatively install the development version:
# Install dev version
git clone https://github.com/bailey-lab/MIPTools.gitNext, simply build the container and you should be all set to get started using MIPTools!
cd MIPTools
sudo singularity build miptools.sif MIPTools.defmiptools.sif is a single portable file which has all the programs needed
for MIP design, data analysis, and a lot more. More information about the extra
programs and their uses will be added over time.
Although miptools.sif contains all programs needed, it does not include the
data to be analyzed or other resources to be used. Every time we run
Singularity we will bind needed directories to the container. There are
three resources directories which are required for most operations. In addition
to those, some apps need a data_dir and analysis_dir. The -B option is
used for each binding:
singularity some-command -B path_on_host:path_on_container path_to_containerThe path on the left side of the colon specifies where on your computer the
directory is and the right side is the location in the container where the
directory should be bound (mounted) to. You should only change the left side of
the column according to the location of the resource you are providing, and
should never change the path on the right side. Each binding is specified
with a separate -B option. See below for examples.
Three resource directories are required for most operations. These live outside
the container and must be bound to the container at run time with the -B
option. In addition, a data directory and an analysis directory will be used for
most
operations.
Expand for details on the directory structure and container binding.
-
base_resources: Provided in the GitHub repository. It contains common resources across projects. It should be bound to the container with
-B [path to base resources dir outside of the container]:/opt/resources. This makes the base_resources directory available to the container and it would be reached at/opt/resourcespath within the container. The/opt/resourcespart of this argument must not be altered. For example, if my base resources are located in my computer at/home/base, I would bind it to the container with-B /home/base:/opt/resources. -
species_resources: Contains resources shared by projects using the same target species (Pf, human, etc.). Bind this to
/opt/species_resourcesin the container. For example, if I am working with Plasmodium falciparum sequences and I have the necessary files in my computer at/home/pf3d/, then the binding parameter is-B /home/pf3d:/opt/species_resources.Contents of species_resources directory:
-
file_locations.tsv: This file is required for all operations. It is a tab separated text file showing where each required file will be located in the container. Each line corresponds to one file. First field states the species for the file, second field states what kind of file it is and the last field is the absolute path to the file.
For example, the line "pf fasta_genome /opt/species_resources/genomes/genome.fa" would mean that the fasta genome file for the species 'pf' will be found at '/opt/species_resources/genomes/genome.fa' within the container. This also means that there is a file at /home/pf3d/genomes/genome.fa in my computer, assuming I bound /home/pf3d to /opt/species_resources in the container.
-
fasta file: This file is required for all operations. Genome reference sequence in fasta format.
-
bowtie2_genome: This file is required for probe design operations only. It is the reference genome indexed using bowtie2. If this is not available, it can be generated using MIPTools.
-
bwa_genome: This file is required for data analysis operations only. It is the reference genome indexed using bwa. If this is not available, it can be generated using MIPTools.
-
snps: This is an optional file. However, it is extremely useful in probe designs to avoid probe arms landing on variant regions, etc. So it should always be used except in rare cases where such a file is not available for the target species. The format of the file is vcf. Individual genotypes are not necessary (a.k.a. sites only vcf). The only requirement is that the INFO field for each variant has a field showing the population allele frequency of alternate alleles. By default, AF field is used. The AF field lists the allele frequencies of each alternate allele, and does not list the frequency of the reference allele. Vcf files may have other INFO fields that include allele frequency information. If such a field is to be used, there are two settings in the design settings file (.rinfo file) that must be modified. allele_frequency_name field must be set to the INFO field name to be used; af_start_index may have to be set to a 1 (instead of default 0) depending on whether the reference allele frequency is provided in the new field. For example, if we want to use the 1000 genomes vcf file, the allele frequencies are provided in the CAF field and they include the reference allele. We would have to change the allele_frequency_name field to CAF from the default AF; and set af_start_index to 1 because the first alternate allele's frequency is provided in the second place (following the reference allele).
-
refgene: RefGen style gene/gene prediction table in GenePred format. These are available at http://genome.ucsc.edu under Tools/Table Browser for most species. The fields in the file are "bin, name, chrom, strand, txStart, txEnd, cdsStart, cdsEnd, exonCount, exonStarts, exonEnds, score, name2, cdsStartStat, cdsEndStat, exonFrames". This file is required for probe design operations if genic information is to be used. For example, if probes need to be designed for exons of a gene, or a gene name is given as design target. If a gene name will be provided, it must match the name2 column of the RefGen file. If you are creating this file manually, the only fields necessary are: chrom, strand, exonStarts, exonEnds and name2. All other fields can be set to an arbitrary value (none, for example) but not left empty. The order of columns must not be changed.
Note: If you have gff3/gtf formatted files, they can be converted to GenePred format using Jim Kent's programs gff3ToGenePred and gtfToGenePred.
-
refgene_tabix: RefGen file, sorted and indexed using tabix. File requirement is the same as the refgene file. tabix is available within the MIPTools container, so you don't have to install it yourself.
-
-
project_resources: Contains project specific files (probe sequences, sample information, etc.). Bind this to
/opt/project_resources -
data_dir: Contains data to be analyzed. Typically, nothing will be written to this directory. Bind this directory to
/opt/data. -
analysis_dir: Where analysis will be carried out and all output files will be saved. Bind it to
/opt/analysisThis is the only directory that needs write permission as the output will be saved here.
data_dir and analysis_dir will have different content for different app
operations. Also, one app's analysis directory may be the next app's data
directory in the pipeline.
Resources required vary widely depending on the project. Both designs and data analysis can be parallelized, so the more CPUs you have the better. Plenty of storage is also recommended. For designs on large target regions (>5kb), files can take up 10 GB or more per region. Consider allocating > 5 GB RAM for a large design region (multiply the RAM requirement by CPU number if parallelizing). For a typical MIP data analysis involving ~1000 MIPs and ~1000 samples, consider using at least 20 CPUs and 20 GB RAM to get the analysis done within 10-12 h. You should expect ~200 GB disk space used for such an analysis as well, although most files can be removed after processing steps to reduce long term disk usage.
Further documentation for MIPTools is available online and in a public Google Drive folder here for various use cases (MIP design, data analysis, etc.).
Please send any questions/comments to: [email protected].
Join our Google Group: https://groups.google.com/g/miptools