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.
- v1.2.3 add bioconda recipe
- v1.2.1 add HG38/GRCh38 support
You can run SMAca by typing at the terminal:
$ smaca --reference hg38 sample1.bam sample2.bam sample3.bam
SMAca works fine with WGS, WES and panels as long as SMN locus and control genes are covered. The recommendation is to use the same capture kit for the whole set of samples, and analyze a large cohort at the same time. In general, the larger the set of BAMs to analyze at the same time, the easiest to interpret the results. The ncpus option is recommended:
$ smaca --output results.batch1.csv --reference hg19 --ncpus 12 $(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 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. |
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.
If you are using the conda packaging manager (recommended):
$ conda install -c bioconda 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=.
Please, cite as:
Lopez‐Lopez, D, Loucera, C, Carmona, R, et al. SMN1 copy‐number and sequence variant analysis from next‐generation sequencing data. Human Mutation. 2020; 1– 5. 10.1002/humu.24120