Package: smaca
summaryusageoutputinterpretationinstallationcitationsummarySpinal 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 historyv1.2.1 add HG38/GRCh38 supportusageYou can run SMAca by typing at the terminal:$ smaca --reference hg38 sample1.bam sample2.bam sample3.bamFor a large number of samples, thencpusoption is recommended:$ smaca --output results.batch1.csv --ncpus 12 $(cat samplelist.batch1.txt)For additional options use:$ smaca --helpoutputSMAca outputs a number of statistics for each sample:Pi_p:scaled proportion of SMN1 reads for positionsp.cov_x_p:raw coverage of genexat positionp.avg_cov_x:average coverage for the whole genex.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.interpretationSMA carriers with a single SMN1 copy are expected to havePi_bvalues under 1/3. However, complex SMN reorganizations may lead to large differences betweenPi_a,Pi_bandPi_c. These cases should be analyzed carefully.Thescale_factor, which is proportional to the absolute number of SMN1 and SMN2 copies, andcov_x_pcan be used to estimate the absolute SMN1:SMN2 copy-number as follows:genotypescale_factorcov_SMN1_p/cov_SMN2_p1:311/31:20.751/21:10.51In order to detect the so-calledsilent 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 expectedscale_factorshould be close to 0.75 (2:1) or 0.5 (2:0) and, in both cases, the scaled proportion of SMN1 readsPi_pshould be close to 1/2 in each position.installationIf 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 smacaIt also works with a barebone environment:$ conda create -n <env_name> python=<py_version>
$ pip install smacaSMAca is available through PyP. Follow the steps to properly installPySam:$ pip install smacaDevelopers 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=.citationPlease, 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.TODOCreate a conda package (bioconda)Refactor the code to follow the python good practice guidelines as much as possible