smaca 1.2.3

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• summary

• usage

• output

• interpretation

• installation

• citation

summary

Spinal Muscular Atrophy (SMA) is a severe neuromuscular autosomal recessive disorder affecting 1/10,000 live births. Most SMA patients present homozygous deletion of SMN1, while most SMA carriers present only a single SMN1 copy. The sequence similarity between SMN1 and SMN2, and the complexity of the SMN locus, make the estimation of the SMN1 copy-number difficult by next generation sequencing (NGS).

SMAca is a python tool to detect putative SMA carriers and estimate the absolute SMN1 copy-number in a population. Moreover, SMAca takes advantage of the knowledge of certain variants specific to SMN1 duplication to also identify the so-called “silent carriers” (i.e. individuals with two copies of SMN1 on one chromosome, but none on the other).

This tool is developed with multithreading support to afford high performance and a focus on easy installation. This combination makes it especially attractive to be integrated into production NGS pipelines.

release history

• v1.2.1 add HG38/GRCh38 support

usage

You can run SMAca by typing at the terminal:

$ smaca --reference hg38 sample1.bam sample2.bam sample3.bam

For a large number of samples, the ncpus option is recommended:

$ smaca --output results.batch1.csv --ncpus 12 $(cat samplelist.batch1.txt)

For additional options use:

$ smaca --help

output

SMAca outputs a number of statistics for each sample:

Pi_p:

scaled proportion of SMN1 reads for positions p.

cov_x_p:

raw coverage of gene x at position p.

avg_cov_x:

average coverage for the whole gene x.

std_control:

standard deviation for the average coverage of the 20 control genes.

g.27134T>G:

consensus sequence at position 27134, as well as counts for “A”, “C”, “G” and “T”.

g.27706_27707delAT:

consensus sequence at positions 27706-27707, as well as counts for “A”, “C”, “G” and “T”.

scale_factor:

scale factor proportional to the total SMN1 and SMN2 copy-number.

interpretation

SMA carriers with a single SMN1 copy are expected to have Pi_b values under 1/3. However, complex SMN reorganizations may lead to large differences between Pi_a, Pi_b and Pi_c. These cases should be analyzed carefully.

The scale_factor, which is proportional to the absolute number of SMN1 and SMN2 copies, and cov_x_p can be used to estimate the absolute SMN1:SMN2 copy-number as follows:

genotype	scale_factor	cov_SMN1_p/cov_SMN2_p
1:3	1	1/3
1:2	0.75	1/2
1:1	0.5	1

In order to detect the so-called silent carriers (i.e. individuals with two copies of SMN1 on one chromosome, but none on the other), the consensus sequence at the two locations should also be taken into account. Depending on the number of SMN2 copies, the expected scale_factor should be close to 0.75 (2:1) or 0.5 (2:0) and, in both cases, the scaled proportion of SMN1 reads Pi_p should be close to 1/2 in each position.

installation

If you are using the conda packaging manager (recommended), note that it has been tested on python 3.6 and 3.7, this is our recommended (it follows the guidelines of the PySam team) path for installing SMAca:

$ conda create -n <env_name> -c bioconda -c defaults python=<py_version> cython joblib numpy pysam
$ conda activate <env_name>
$ pip install smaca

It also works with a barebone environment:

$ conda create -n <env_name> python=<py_version>
$ pip install smaca

SMAca is available through PyP. Follow the steps to properly install PySam :

$ pip install smaca

Developers can clone the repository, create a conda/pip environment and install in editable mode. Be sure to attend the previous recommendations:

$ git clone git+https://www.github.com/babelomics/SMAca.git
$ cd SMAca
$ conda create -n <env_name> -c bioconda -c defaults python=<py_version> cython joblib numpy pysam
$ conda activate <env_name>
$ pip install --editable=.

Or, using standard python (follow the pysam recommendations):

$ git clone git+https://www.github.com/babelomics/SMAca.git
$ cd SMAca
$ python -m venv smaca_venv
$ source smaca_venv/bin/activate
$ pip install --editable=.

citation

Please, cite as:

Daniel Lopez-Lopez, Carlos Loucera, Rosario Carmona, Virginia Aquino, Josefa Salgado, Angel Alonso, Joaquín Dopazo (2020). SMAca: SMN1 copy-number and sequence variant analysis from next generation sequencing data.

TODO

• Create a conda package (bioconda)

• Refactor the code to follow the python good practice guidelines as much as possible