کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
5590169 1570114 2017 5 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
prepare_taxa_charts.py: A Python program to automate generation of publication ready taxonomic pie chart images from QIIME
موضوعات مرتبط
علوم زیستی و بیوفناوری بیوشیمی، ژنتیک و زیست شناسی مولکولی ژنتیک
پیش نمایش صفحه اول مقاله
prepare_taxa_charts.py: A Python program to automate generation of publication ready taxonomic pie chart images from QIIME
چکیده انگلیسی
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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Genomics Data - Volume 12, June 2017, Pages 97-101
نویسندگان
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