Welcome

The slapnap container is a tool for using the Compile, Analyze and Tally NAb Panels (CATNAP; Yoon et al. 2015) database to develop predictive models of HIV-1 neutralization sensitivity to one or several broadly neutralizing antibodies (bnAbs).

Crystal structure of HIV-1 gp120 glycoprotein. Highlighted residues indicating sites most-predictive of VRC01 neutralization resistance. (Magaret et al. 2019)

The tool can be used for a wide variety of tasks. In its simplest form, slapnap can be used simply to access and format data from CATNAP in a way that is usable for machine learning analysis. The tool also offers fully automated and customizable machine learning analyses based on up to five different neutralization endpoints, complete with automated report generation to summarize results and identify the most predictive features.

This document serves as the user manual for the slapnap container. Here, we describe everything needed to utilize slapnap and understand its output. The documentation is organized into the following sections:

  • Section 1 provides a brief overview of Docker, including information on installing Docker and downloading the slapnap container.
  • Section 2 provides a brief overview of the CATNAP database and the details of how and when these data were accessed to build the slapnap container.
  • Section 3 provides a detailed description of how to make calls to slapnap and all options that are available at run time to customize its behavior.
  • Section 4 includes example calls to slapnap for accomplishing different tasks.
  • Section 5 describes the methodology used by slapnap to generate and analyze data.
  • Section 6 describes the contents of the automated report generated by slapnap.
  • Section 7 provides a description of the analysis data set created by slapnap.

If you have any issues or questions about using slapnap, please file an issue on GitHub.

References

Magaret, Craig A, David C Benkeser, Brian D Williamson, Bhavesh R Borate, Lindsay N Carpp, Ivelin S Georgiev, Ian Setliff, et al. 2019. “Prediction of VRC01 Neutralization Sensitivity by HIV-1 gp160 Sequence Features.” PLoS Computational Biology 15 (4): e1006952. https://doi.org/10.1371/journal.pcbi.1006952.

Yoon, Hyejin, Jennifer Macke, Anthony P West Jr, Brian Foley, Pamela J Bjorkman, Bette Korber, and Karina Yusim. 2015. “CATNAP: A Tool to Compile, Analyze and Tally Neutralizing Antibody Panels.” Nucleic Acids Research 43 (W1): W213–W219. https://doi.org/10.1093/nar/gkv404.