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”.
This tool is developed with multithreading supported to afford high performance and a focus on easy installation. This combination makes it especially attractive to be integrated into production NGS pipelines.
You can run SMAca by typing at the terminal:
$ smaca sample1.bam sample2.bam sample3.bam
For a large number of samples, the ncpus option is recommended:
$ smaca --output results.batch1.csv --ncpus 24 $(cat samplelist.batch1.txt)
For additional options use:
$ smaca --help
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. |
| 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. |
SMA carriers with a single SMN1 copy are expected to have Pi_b values under 1/3. However, complex SMN reorganizations may leads to large differences between Pi_a, Pi_b and Pi_c. These cases should be analized carefully.
The scale_factor, that 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.
SMAca is available through PyP:
$ pip install smaca
If you are using the conda packaging manager (e.g. miniconda or anaconda), you can install SMAca from the bioconda channel:
$ conda config --add channels defaults $ conda config --add channels conda-forge $ conda config --add channels bioconda $ conda install smaca
Developers can clone the repository, create a conda/pip environment and install in editable mode:
$ 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=.
Daniel Lopez-Lopez, Rosario Carmona, Carlos Loucera, Virginia Aquino, Josefa Salgado, Angel Alonso, Joaquín Dopazo (2020). SMAca: SMN1 copy-number and sequence variant analysis from next generation sequencing data, XXX