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.
Oligonucleotide microarray for identification of Enterococcus species.
For detection of most members of the Enterococcaceae, the specificity of a novel oligonucleotide microarray (ECC-PhyloChip) consisting of 41 hierarchically nested 16S or 23S rRNA gene-targeted probes was evaluated with 23 pure cultures (including 19 Enterococcus species). Target nucleic acids were prepared by PCR amplification of a 4.5-kb DNA fragment containing large parts of the 16S and 23S rRNA genes and were subsequently labeled fluorescently by random priming. Each tested member of the Enterococcaceae was correctly identified on the basis of its unique microarray hybridization pattern. The evaluated ECC-PhyloChip was successfully applied for identification of Enterococcus faecium and Enterococcus faecalis in artificially contaminated milk samples demonstrating the utility of the ECC-PhyloChip for parallel identification and differentiation of Enterococcus species in food samples.