Our Tools

An overview of all tools currently produced and supported by the Anderson Lab

Over the last few years the lab has slowly but surely began to develop a series of different computational, data analysis and image analysis tools. This is largely driven by our own needs, but in the spirit of collaboration, sharing and open science we are now making these tools available to everyone. Below is a brief description of each tool with a link to a where the tool can be downloaded and any associated publication. If you use these tools in your own research or improve them, please let us know and acknowledge our original source.


HAL - Hybrid Automata Library

HAL is a Java library that facilitates the development and implementation of hybrid matheamtical models. Hybrid Modeling is the integration of Agent-Based modeling and Partial Differential Equation (PDE) modeling. It is commonly used in mathematical oncology to mechanistically model tumor-tissue interactions. Tissue/Tumor is represented using the agent-based modeling, where each agent acts as a single cell. Diffusibles that interact with the tissue are represented using the PDEs. HALís components can be broadly classified into: on and off-lattice agent containers, finite difference diffusion fields, a GUI building system, and additional tools and utilities for computation and data collection. These components have a standardized interface that expedites the construction of complex models. This library has been used in several PSOC modeling efforts and has facilitated designing and sharing models at Moffitt IMO. For more information on how to install and use HAL head over to our dedicated website halloworld.org. Or if you prefer to download it directly from the IMO GitHub pages please head over to MathOnco/HAL. A preprint that explains how to use HAL in more detail, along with a specific example, is available on the BioRxiv (Bravo et al, 2018).


EvoFreq - Visualization and Animation of Evolutionary Dynamics

Meuller plot EvoFreq is a package for the R programming language that can be used to visualize evolutionary dynamics using simulated or experimental data. EvoFreq is designed to be easy to use, flexible, and create high quality plots and animations using the popular ggplot package. EvoFreq visualizes evolutionary dynamics using Muller plots and dendrograms. In Muller plots, each polygon represents the frequency of a mutation in the population, and nestedness shows phylogenetic relationships, while the dendrograms simply show the phylogenetic relationship between gentoypes. EvoFreq also provides tools to parse clonal architecture data from CALDER, PhyloWGS, and ClonEvol, making it easier view experimental data. Simulation results from HAL can be read directly by EvoFreq, again making it easy to view simulated data. While some of these features are specific to data/simulations from cancer research, EvoFreq can be used with a wide variety of datasets, and should be of use to the wider field of biology. To download it directly from the IMO GitHub pages please head over to MathOnco/EvoFreq. A preprint that explains how to use EvoFreq in application to both real and simulated data is available on the BioRxiv (Gatenbee et al, 2019). You may have noted that the HAL simulation above and the EvoFreq plot are directly related? In fact, HAL can output clonal dynamics that can easily be visualised with EvoFreq, take a look at this great blog post by Jeff West explaining exactly how this works and how easy it is to implement.


NeoPredPipe - Neoantigen Prediction Pipeline

Tumor phenotype space This pipeline was developed as a contiguous means of predicting putative neoantigens and their corresponding recognition potentials for both single and multi-region tumor samples. NeoPredPipe is able to quickly provide summary information for researchers, and clinicians alike, on neoantigen burdens while providing high-level insights into tumor heterogeneity given somatic mutation calls and, optionally, patient HLA haplotypes. Given an example dataset we show how NeoPredPipe is able to rapidly provide insights into neoantigen heterogeneity, burden, and immune stimulation potential. Through the integration of widely adopted tools for neoantigen discovery NeoPredPipe offers a contiguous means of processing single and multi-region sequence data. NeoPredPipe is user-friendly and adaptable for high-throughput performance. NeoPredPipe is freely available from the IMO GitHub pages MathOnco/NeoPredPipe. This pipeline was developed in collaboration with Trevor Grahams lab and was published in BMC Bioninformatics (Schenck et al, 2019).