The tool designed for getting consensus out of multiple SV callers' results. The method uses six independent, state-of-the-art structural variant callers for long-read sequencing along with a convolutional neural network for filtering high-quality variants. We provide a runtime environment in the form of a docker image, wrapping a nextflow pipeline for efficient processing using parallel computing.
Docker image: https://hub.docker.com/repository/docker/antonipietryga/consensussv-ont-pipeline/
docker pull antonipietryga/consensussv-ont-pipeline
If you use ConsensuSV-ONT, please cite: ConsensuSV-ONT - a modern method for accurate structural variant calling Antoni Pietryga, Mateusz Chiliński, Sachin Gadakh, Dariusz Plewczynski bioRxiv 2024.07.26.605267; doi: https://doi.org/10.1101/2024.07.26.605267
Since the algorithm is easy-to-run, the preparation of the sample is minimal. The algorithm works with fastq.gz files, and a csv file containing all the sample information is needed. There is only one column, that is the path to the fastq.gz file.
| Path |
|---|
| /HG00733.fastq.gz |
| /HG00514.fastq.gz |
Bear in mind that the column headers are provided only for the ease of the example, and should not be present in the csv file.
To get into the docker image for working with your data, it's best to mount local directories to the container:
docker run -v /mnt:/mnt -p 8082:8082 -it antonipietryga/consensussv-ont-pipelineOnce in the container, we can run the pipeline using the following command:
nextflow pipeline.nf --input files.csv Remember to put your samples in the file samples.csv according to the praparation of your samples.
All the parameters that can be used with the script are shown in the following table:
| Parameter | Description |
|---|---|
| --input | File location of the csv file that described all the samples according to the (#preparation-of-your-samples). |
| --threads | Max number of threads per task. |
| --mem | Max memory per thread. |
| --outdir | Output dir of ConsensuSV-ONT. |
| --ref | Reference genome |
The location of the output depends on your working directory, provided as the parameter. In that directory, two folders will be created:
- alignments - a folder where alignment files for each sample are stored
- vcfs - where you will find folders for each of the sample, containing all VCF files from the individual SV calling tools and consensuSV-ONT
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv3d (Conv3D) (None, 50, 50, 3, 4) 112
batch_normalization (BatchN (None, 50, 50, 3, 4) 16
ormalization)
max_pooling3d (MaxPooling3D (None, 25, 25, 3, 4) 0
)
conv3d_1 (Conv3D) (None, 25, 25, 3, 8) 872
batch_normalization_1 (Batc (None, 25, 25, 3, 8) 32
hNormalization)
max_pooling3d_1 (MaxPooling (None, 12, 12, 3, 8) 0
3D)
conv3d_2 (Conv3D) (None, 12, 12, 3, 16) 3472
batch_normalization_2 (Batc (None, 12, 12, 3, 16) 64
hNormalization)
max_pooling3d_2 (MaxPooling (None, 6, 6, 3, 16) 0
3D)
flatten (Flatten) (None, 1728) 0
dense (Dense) (None, 32) 55328
dense_1 (Dense) (None, 1) 33
=================================================================
Total params: 59,929
Trainable params: 59,873
Non-trainable params: 56```