Package: recombulator-x
RECOMBULATOR-Xrecombulator-x is a Python module and command line tool for computing the recombination rates between short tandem repeats (STRs) markers and other polymorphisms (SNPs and INDELs) along the X chromosome starting from pedigree data in forensic genetics.:open_book:Documentation website:page_facing_up: Please citePaperrecombulator-x is written in Python (3.7) and can be used either as a module or as a command-line tool.
It is the first open source implementation of the estimation method introduced inNothnagel et al., 2012, which is the gold-standard for the estimation of recombination rates for X-chromosomal markers. We designed recombulator-x to solve some practical issues with the original R implementation. Its main advantages are:performance: much faster than the original implementation, thanks to algorithmic improvements (dynamic programming)open source: full source code and documentation available from githubinput parsing: reads pedigree data in standard (PED) formatuser friendly: easy installation (via pip) and usage with a simple command-line toolcomprehensive toolkit: it can deal with short tandem repeats, SNPs and INDELs.We thank Prof. Michael Nothnagel for kindly sharing the original R implementation with us, which was an important reference for the development.:open_book: DocumentationFull documentation is available online at the :open_book:dedicated website, or in this repository underdocs.:wrench: InstallationYou can install recombulator-x via thepipcommand from the standard PyPI repository:pipinstallrecombulator-x:mortar_board: OverviewSTRs located on the X chromosome are a valuable resource for solving complex kinship cases in forensic genetics thanks to their peculiar inheritance mode. At the same time, the usage of multiple markers linked along the same chromosome, while increasing the evidential weight, also requires proper consideration of the recombination rates between markers in the biostatistical evaluation of kinship.For more details on X-STR kinship analyses in forensic seeGomes et al., 2020andTillmar et al., 2017.In the case of forensic X-STRs, recombination rates have been either inferred from population samples through high-density multi-point single nucleotide polymorphism (SNP) data or directly estimated in large pedigree-based studies.The main statistical approach for the estimation of recombination rates from pedigrees computes the likelihood of kinship by taking into account all possible recombinations within the maternal haplotype, thus resorting to the exponential complexity of the underlying algorithm (seeNothnagel et al., 2012for a thorough description of the likelihood computation). Despite a computational update in C++ allowing multi-core parallelization, this approach is expected to be unsuitable when panels of more than 15 X-STRs are considered (Diegoli et al., 2016).We developed recombulator-x to overcome this issue. Built upon the same statistical framework of the previous work (Nothnagel et al., 2012), recombulator-x uses a new computational strategy to infer recombination rates for X-STRs, while taking also the probability of mutation into account.:boom: Additional featuresRecombulator-X can analyse also SNPs and INDELs.Data consistency checksAutomatic family preprocessing and informative family extractionMultiple likelihood implementations includedAccelerated likelihood computation with the JIT Python compiler NumbaMutation rates can be estimated for each marker separately or as a unique parameterSimulation of pedigrees typed with STRs:rocket: Benchmarkrecombulator-x far exceeds the computational speed of the previous approach and it is scalable to many more markers.Indeed, performance has been the main focus of recombulator-x: in a test with simulated data of the same size as the two previous works, the time necessary for the likelihood computation of a single family drops from "several months" on 32 cores of a HPC node for the previous approach to 20 minutes on a single core with recombulator-x. This is due to algorithmic improvement time complexity going from exponential to linear with our approach. Conversely, even though the algorithm time complexity is still exponential for type II families, the speed improvement is substantial with respect to the the previous implementation. Moreover, its capacity of dealing with SNPs and INDELs makes it a comprehensive toolkit for addressing linked markers in forensics.:computer: Usagerecombulator-x uses the PED files based onPLINKpedigree files as input. The PED file format stores sample pedigree information (i.e., the familial relationships between samples) and the genotypes. More information on the input file can be found in this repository underdocs/3_usage.md.The program can be used both as a Python module or a command-line tool. A detailed notebook for the Python module can be foundhere.