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description: Analysis of the Streptococcus pneumoniae PBPs and inferred MICs.
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# SPN-PBP-AMR

## PBP Analysis and Predicted MICs

This _Streptococcus pneumoniae_ task carries out an analysis of the _pbp1A_, _pbp2B_ and _pbp2X_ genes, firstly assigning an allele code to each one and then using a machine learning approach to estimate the MICs (minimum inhibitory concentration) for a set of antimicrobials. The  resistance phenotype is then interpreted using CLSI guidelines, including where there are different thresholds for the meningital and non-meningital forms. In testing, the MICs were predicted to >97% correct (to one dilution) and resistance categorisation was >93%.

The initial development of the machine learning model is described in [Li _et al_ (2016)](https://www.ncbi.nlm.nih.gov/pubmed/27302760) while the method is benchmarked in [Li _et al_ (2017)](https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-017-4017-7). The software and databases for this task have been provided by Ben Metcalf at the Centre for Disease Control (CDC) and is available from [https://github.com/BenJamesMetcalf/Spn\_Scripts\_Reference](https://github.com/BenJamesMetcalf/Spn_Scripts_Reference)
