sequintools

sequintools is a suite of tools for manipulating and reporting on Next Generation Sequencing data that has sequins added to the sample.

Installation

If you're a Rust programmer, sequintools can be installed with cargo.

$ cargo install sequintools

Alternatively, one can use cargo binstall to install a sequintools binary directly from GitHub:

$ cargo binstall sequintools

Note that sequintools has a dependency on cmake and clang, and these will need to be installed on your device before you can install sequintools. On a linux device you would usually install these using apt, e.g.

sudo apt update && sudo apt install -y cmake clang

If you're not using a linux device then you'll need to research how to install the dependencies on your device. Alteratively you can use the docker build which will already have the dependencies required installed.

Docker

We also provide sequintools as a Docker container.

This command line below is used to fetch its latest version.

docker pull ghcr.io/sequinsbio/sequintools:latest

As an example, you could run the calibration step as below, make sure the example folder is under you current path.

docker run --rm -v $PWD:/data ghcr.io/sequinsbio/sequintools \
    sequintools calibrate \
    -b /data/example/resources/sequin_regions.chrQ_mirror.bed \
    -f 40 \
    -o /data/calibrated.bam \
    --write-index \
    /data/example/example.bam

Building

sequintools is written in Rust, so you'll need to grab a Rust installation in order to compile it.

To build sequintools:

$ git clone https://github.com/sequinsbio/sequintools
$ cd sequintools
$ cargo build --release
$ sudo cp target/release/sequintools /usr/local/bin

Usage

To get going, we provide some example data in the example directory. This is simulated data that will allow you to quickly run commands from sequintools and familarise yourself with the input file requirements.

calibrate

calibrate downsamples sequin reads so that they more closely match the sample data. The more closely the sequin data resembles the sample data it controls for, the better the control will be.

The simplest form of calibration is to adjust the mean coverage for all sequin regions to a standard coverage. In this example, the mean coverage for each sequin in the data set is set to 40X.

sequintools calibrate \
    -b example/resources/sequin_regions.chrQ_mirror.bed \
    -f 40 \
    -o calibrated.bam \
    --write-index \
    example/example.bam

Alternatively, you can use the sample data in the same BAM file to adjust the sequin coverage to more cloesly represent the coverage of the controlled region. To do this, you need to provide two BED files one containing the sequin regions on the decoy chromosome and the other containing the reference genome locations they control for. The name column (4th column) must match between the two BED files.

sequintools calibrate \
    -b example/resources/sequin_regions.chrQ_mirror.bed \
    -H example/resources/sequin_regions.hg38.bed \
    -o calibrated.bam \
    --write-index \
    example/example.bam

Make the same adjustments as the previous command, but exclude the sample data so that the output BAM only has calibrated sequin data (this is much faster than the previous command).

sequintools calibrate \
    -b example/resources/sequin_regions.chrQ_mirror.bed \
    -H example/resources/sequin_regions.hg38.bed \
    -o calibrated.bam \
    --write-index \
    --exclude-uncalibrated-reads \
    example/example.bam

bedcov

The bedcov command collects statistics from a BAM file for regions in a BED file and writes them as a CSV. Statistics include the minimum and maximum coverage per base, mean coverage, standard deviation of coverage and the coefficient of variance.

sequintools bedcov \
    example/resources/sequin_regions.hg38.bed \
    example/example.bam