prepare_taxa_charts.py: A Python program to automate generation of publication ready taxonomic pie chart images from QIIME
QIIME (Quantitative Insights Into Microbial Ecology) is one of the most popular open-source bioinformatics suite for performing metagenome, 16S rRNA amplicon and Internal Transcribed Spacer (ITS) data analysis. Although, it is very comprehensive and powerful tool, it lacks a method to provide publication ready taxonomic pie charts. The script plot_taxa_summary.py bundled with QIIME generate a html file and a folder containing taxonomic pie chart and legend as separate images. The images have randomly generated alphanumeric names. Therefore, it is difficult to associate the pie chart with the legend and the corresponding sample identifier. Even if the option to have the legend within the html file is selected while executing plot_taxa_summary.py, it is very tedious to crop a complete image (having both the pie chart and the legend) due to unequal image sizes. It requires a lot of time to manually prepare the pie charts for multiple samples for publication purpose. Moreover, there are chances of error while identifying the pie chart and legend pair due to random alphanumeric names of the images. To bypass all these bottlenecks and make this process efficient, we have developed a python based program, prepare_taxa_charts.py, to automate the renaming, cropping and merging of taxonomic pie chart and corresponding legend image into a single, good quality publication ready image. This program not only augments the functionality of plot_taxa_summary.py but is also very fast in terms of CPU time and user friendly.
Using QIIME to Analyze 16S rRNA Gene Sequences from Microbial Communities
QIIME (canonically pronounced “chime”) is a software application that performs microbial community analysis. It is an acronym for Quantitative Insights Into Microbial Ecology, and has been used to analyze and interpret nucleic acid sequence data from fungal, viral, bacterial, and archaeal communities. The following protocols describe how to install QIIME on a single computer and use it to analyze microbial 16S sequence data from nine distinct microbial communities. Curr. Protoc. Bioinform. 36:10.7.1‐10.7.20. © 2011 by John Wiley & Sons, Inc.
Far-field dispersal modeling for fuel-air-explosive devices
A computer model for simulating the explosive dispersal of a fuel agent in the far-field regime is described and is applied to a wide variety of initial conditions to judge their effect upon the resulting fuel/air cloud. This work was directed toward modeling the dispersal process associated with Fuel-Air-Explosives devices. The far-field dispersal regime is taken to be that time after the initial burster charge detonation in which the shock forces no longer dominate the flow field and initial canister and fuel mass breakup has occurred. The model was applied to a low vapor pressure fuel, a high vapor pressure fuel and a solid fuel. A strong dependence of the final cloud characteristics upon the initial droplet size distribution was demonstrated. The predicted fuel-air clouds were highly non-uniform in concentration. 18 refs., 86 figs., 4 tabs.