Weitz Group @ Georgia Tech

Theoretical Ecology and Quantitative Biology



Fiber Walks

BiMAT: a MATLAB package to facilitate the analysis and visualization of bipartite networks

  • BiMat is a package designed for the analysis and visualization of bipartite ecological networks, thought it may be used
    for any type of bipartite networks. The package aims to consolidate under the same MATLAB environment, some of
    the most popular algorithms and metrics for the analysis of bipartite ecological networks. More speci cally, the package focus on bipartite modularity and nestedness values. Further, BiMat include the necessary tools for analyzing the statistical signi ficance of these values, together with tools for visualizing bipartite networks in such way that any of these patterns becomes more apparent to the user. See the documentation for more details.

Dynamic models of giant viruses, virophage, and eukaryotic hosts

  • A series of scripts to analyze the ecological dynamics of giant viruses, virophage and eukaryotic hosts. Preprint available on the arxiv

    Taylor, Cortez and Weitz (in press) The virus of my virus is my friend: ecological effects of virophage with alternative modes of coinfection. Journal of Theoretical Biology.

Simulating Assortative Ancestries (SimAA)

  • Matlab scripts to simulate assortative mating within genealogical dynamics. The scripts were used for all figures in the article Let my people go (home) to Spain: a genealogical model of Jewish identities since 1492, available here:

Scripts to simulate neutral evolution with fixed genome sizes (Haegeman & Weitz, BMC Genomics, 2012)

  • The code can be used to simulate the gene frequency distribution arising from alternative models of genome evolution with fixed genome sizes. The code can also be used to infer the best fit parameters for alternative models given observed gene frequency data. The code is written in MATLAB. Note that the differential equation solver used in this version is ode45 which has greater numerical robustness, particularly for large datasets, when compared to ode15s (which is faster but may, in some instances, lead to numerical instabilities). The single .txt file attached should be separated into individual

    Bart Haegeman and Joshua S Weitz (2012) A neutral theory of genome evolution and the frequency distribution of genes BMC Genomics 13:196.

Host-phage infection network datasets

  • A data set of 38 host-phage infection matrices that were collected and presented in the article Statistical structure of host–phage interactions. Each excel datasheet represents a different study. Rows represent hosts and columns phages. One cells represents infection and zero cells no-infection.

Genomic fluidity scripts

  • A collection of Perl scripts to calculate gene-level similarity among annotated genomes as used in Kislyuk et al. BMC Genomics 12: 32 (2011). The scripts can be executed from the command line and the only dependencies are BioPerl and NCBI BLAST. Separately, we include a MATLAB script to calculate fluidity and its variance directly from matrices of shared and total gene counts.

Imaging and Analysis Platform for Automatic Phenotyping and Trait Ranking of Plant Root Systems

  • The article "Imaging and Analysis Platform for Automatic Phenotyping and Trait Ranking of Plant Root Systems" was published in Plant Physiology on January 27, 2010.
    Click here to read the full version of the article.
    To learn more about the project goals and our collaborators go to the RootNet website.
    Here you can download the images used for root trait analysis:

Fluctuation Domains in Adaptive Evolution

  • An R package accompanies our 2010 paper on fluctuation domains. The package includes the source code for simulations and R scripts that create each of the figures in the paper. This can be used to re-run simulations shown in the paper with different parameter values to explore how each influences the onset of fluctuations. Currently only tested on a Linux machine with GNU Standard Library (GSL) installed. The source code is now released on DRYAD using the Creative Commons public domain license.

LikelyBin

  • LikelyBin is an unsupervised metagenomic binner. The binner application is written in Perl and C and is tested to run on Linux distributions. The releases are available below.

    If you use LikelyBin, please cite:

    • Andrey Kislyuk, Srijak Bhatnagar, Jonathan Dushoff and Joshua S. Weitz. Unsupervised Statistical Clustering of Environmental Shotgun Sequences. BMC Bioinformatics 2009, 10:316.