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.
RAxML-III: a fast program for maximum likelihood-based inference of large phylogenetic trees
MOTIVATION
The computation of large phylogenetic trees with statistical models such as maximum likelihood or bayesian inference is computationally extremely intensive. It has repeatedly been demonstrated that these models are able to recover the true tree or a tree which is topologically closer to the true tree more frequently than less elaborate methods such as parsimony or neighbor joining. Due to the combinatorial and computational complexity the size of trees which can be computed on a Biologist's PC workstation within reasonable time is limited to trees containing approximately 100 taxa.


RESULTS
In this paper we present the latest release of our program RAxML-III for rapid maximum likelihood-based inference of large evolutionary trees which allows for computation of 1.000-taxon trees in less than 24 hours on a single PC processor. We compare RAxML-III to the currently fastest implementations for maximum likelihood and bayesian inference: PHYML and MrBayes. Whereas RAxML-III performs worse than PHYML and MrBayes on synthetic data it clearly outperforms both programs on all real data alignments used in terms of speed and final likelihood values. Availability


SUPPLEMENTARY INFORMATION
RAxML-III including all alignments and final trees mentioned in this paper is freely available as open source code at http://wwwbode.cs.tum/~stamatak


CONTACT
stamatak@cs.tum.edu.