pitagora-network.github.io

Quantitative Insights into Microbial Ecology (QIIME)

• Qiime ツールのインストール方法 は別のページに記載

Illumina Overview Tutorial

Getting started

Downloading the tutorial data from ftp://ftp.microbio.me/qiime/tutorial_files/moving_pictures_tutorial-1.9.0.tgz

For the purpose of this tutorial we will be using the dataset available here: https://github.com/galaxyproject/tools-iuc/tree/qiime/tools/qiime/test-data

Check our mapping file for errors

validate_mapping_file.py – Checks user’s metadata mapping file for required data, valid format

• Step 1: Go to the Galaxy server.
• Step 2: Upload mapping file
  • From the menu on the left-hand side, click on [Get Data] and then [Upload File] from your computer. Click [Choose local file] to select the QIIME mapping file “map.tsv” you downloaded to your computer.
  • Click [Execute] button to upload the file.
• Step 3: Validate mapping file
  • From the menu on the left-hand side, click on [Qiime] and then [Validate mapping file] to check for required data and format.
  • For [Metadata mapping filepath], select the tree you just uploaded “map.tsv”
  • Click on the [Execute] button

Demultiplexing and quality filtering sequences

split_libraries_fastq.py – This script performs demultiplexing of Fastq sequence data where barcodes and sequences are contained in two separate fastq files (common on Illumina runs).

!split_libraries_fastq.py -o slout/ -i forward_reads.fastq.gz -b barcodes.fastq.gz -m map.tsv

• Step 1: Go to the Galaxy server.
• Step 2: Upload files
  • From the menu on the left-hand side, click on [Get Data] and then [Upload File] from your computer. Click [Choose local file] to select files “forward_reads.fastq” and “barcodes.fastq”.
  • Click [Execute] button to upload the files.
• Step 3: Split fastq libraries
  • From the menu on the left-hand side, click on [Qiime] and then [Split fastq libraries] to performs demultiplexing of Fastq sequence data.
  • Select Options as follows:

Input fastq files  forward_reads.fastq.gz Metadata mapping files (optional)  map.tsv Barcode read files (optional)  barcodes.fastq.gz

• • Click on the [Execute] button

count_seqs.py –

!count_seqs.py -i slout/seqs.fna

• From the menu on the left-hand side, click on [Qiime] and then [Count sequences] Count the sequences in a fasta file
• Select Options as follows:

Input sequence file  52 Split fastq libraries on data 18, data 11, and data 19: sequences

• Click on the [Execute] button

53 Count sequences on data 52: sequence counts

[http://nbviewer.jupyter.org/github/biocore/qiime/blob/1.9.1/examples/ipynb/illumina_overview_tutorial.ipynb#OTU-picking:-using-an-open-reference-OTU-picking-protocol-by-searching-reads-against-the-Greengenes-database. OTU picking: using an open-reference OTU picking protocol by searching reads against the Greengenes database.]

pick_open_reference_otus.py – Perform open-reference OTU picking

!pick_open_reference_otus.py -o otus/ -i slout/seqs.fna -p ../uc_fast_params.txt

• Go to the Galaxy server.
• From the menu on the left-hand side, click on [Qiime] and then [Perform open-reference OTU picking]
• Select Options as follows:

Input sequence files  52 Split fastq libraries on data 18, data 11, and data 19: sequences

• Click on the [Execute] button

!biom summarize-table -i otus/otu_table_mc2_w_tax_no_pynast_failures.biom

Run diversity analyses

core_diversity_analyses.py – A workflow for running a core set of QIIME diversity analyses.

!core_diversity_analyses.py -o cdout/ -i otus/otu_table_mc2_w_tax_no_pynast_failures.biom -m map.tsv -t otus/rep_set.tre -e 1114

• Go to the Galaxy server.
• From the menu on the left-hand side, click on [Qiime] and then [Compute core set of diversity analyses]
• Select Options as follows:

OTU table  123 Perform open-reference OTU picking on data 52: OTU table with taxonomy  # OR  122 Perform open-reference OTU picking on data 52: OTU table Mapping file  11 map.tsv Sequencing depth to use for even sub-sampling and maximum rarefaction depth  1114 Tree file  126 Perform open-reference OTU picking on data 52: representative set tree

• Click on the [Execute] button

エラー

179 Compute core set of diversity analyses on data 11 and data 123: Core diversity report

tool error An error occurred with this dataset:

cannot find ‘tree_fp’ while searching for ‘phylogenetic.tree_fp’

Notes

• https://github.com/galaxyproject/tools-iuc/issues/1078 QIIME Contribution Fest - 9th & 10th January 2017 · Issue #1078 · galaxyproject/tools-iuc
• https://github.com/galaxyproject/tools-iuc/tree/qiime
  • https://github.com/galaxyproject/tools-iuc/tree/qiime/tools/qiime/test-data
• http://wiki.pitagora-galaxy.org/wiki/index.php/Workflows
  • http://wiki.pitagora-galaxy.org/wiki/index.php/Huttenhower_Lab_Workflows
• https://github.com/haruosuz/qiime
• QIIME Tutorials
• • Bioinformatics Analysis – MetaSUB
• Boston subway microbiome
  • “Operational taxonomic unit (OTU) tables were constructed with Quantitative Insights into Microbial Ecology (QIIME) software (51) version 1.8 (pick_closed_reference_otus.py from http://qiime.org/scripts/) with Greengenes reference version 13.5 at the 97% identity level.”

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後程削除します

たたき台としてどのツールを使うか検討：このページ の QIIME in Galaxy

Pitagora 0.3.2 に Tool Shed の package_python_2_7_qiime_1_9_1 のインストール

インストール前にこの作業が必要

$ sudo apt-get install pkgconfig

Pandas のインストールでエラーが出ている

'latin-1' codec can't encode character u'\u2018' in position 1794: ordinal not in range(256)