convert_unifrac_sample_mapping_to_otu_table.py

Quantitative Insights Into Microbial Ecology

Install

All systems
curl cmd.cat/convert_unifrac_sample_mapping_to_otu_table.py.sh
Debian Debian
apt-get install qiime
Ubuntu
apt-get install qiime
Windows (WSL2)
sudo apt-get update sudo apt-get install qiime
Raspbian
apt-get install qiime
Dockerfile
dockerfile.run/convert_unifrac_sample_mapping_to_otu_table.py

qiime

Quantitative Insights Into Microbial Ecology

QIIME (canonically pronounced ??Chime??) is a pipeline for performing microbial community analysis that integrates many third party tools which have become standard in the field. A standard QIIME analysis begins with sequence data from one or more sequencing platforms, including * Sanger, * Roche/454, and * Illumina GAIIx. With all the underlying tools installed, of which not all are yet available in Debian (or any other Linux distribution), QIIME can perform * library de-multiplexing and quality filtering; * denoising with PyroNoise; * OTU and representative set picking with uclust, cdhit, mothur, BLAST, or other tools; * taxonomy assignment with BLAST or the RDP classifier; * sequence alignment with PyNAST, muscle, infernal, or other tools; * phylogeny reconstruction with FastTree, raxml, clearcut, or other tools; * alpha diversity and rarefaction, including visualization of results, using over 20 metrics including Phylogenetic Diversity, chao1, and observed species; * beta diversity and rarefaction, including visualization of results, using over 25 metrics including weighted and unweighted UniFrac, Euclidean distance, and Bray-Curtis; * summarization and visualization of taxonomic composition of samples using pie charts and histograms and many other features. QIIME includes parallelization capabilities for many of the computationally intensive steps. By default, these are configured to utilize a mutli-core environment, and are easily configured to run in a cluster environment. QIIME is built in Python using the open-source PyCogent toolkit. It makes extensive use of unit tests, and is highly modular to facilitate custom analyses.