Michelle Nguyen
4/11/19
Lab Notebook #12: Moving Pictures Tutorial
Objective/Purpose: The objective of this lab was to learn how to utilize data to create a figure/image that reflects the biodiversity of the samples obtained. We also learned about the significance of quality scores, with relation to sequence bases.
Procedure:
To re-install QIIME2:
- Download 64-bit (.pkg installer) version of Miniconda Python 3 – install on my Home page.
- In the Terminal, type: conda update conda, then if needed, type y to start the downloading process. Ensure that all files have been downloaded.
- Next, type: conda install wget, then if needed type y to start the downloading process. Ensure that all files have been downloaded.
- Copy and paste the link from QIIME 2 website to the Terminal to start the cleanup process.
- Activate the QIIME 2 environment in the Terminal by typing: activate qiime2-2019.1
- Test the installation by typing:qiime –helpin the terminal (Check if there were any errors reported when running this command.)
Begin Moving Pictures tutorial:
- Using the same terminal session, create a new directory and change to the new directory by inputting:
mkdir qiime2-moving-pictures-tutorial
cd qiime2-moving-pictures-tutorial
- Open Finder, click Desktop, and File > Show Side Bars and click the link at the bottom with your ID. This will be used alongside the Terminal to monitor your progress.
- Download the sample metadata by inputting (wget):
wget \
-O “sample-metadata.tsv” \
“https://data.qiime2.org/2019.1/tutorials/moving-pictures/sample_metadata.tsv”
- Download the sequence reads by inputting: mkdir emp-single-end-sequences. Then input:
wget \ -O “emp-single-end-sequences/barcodes.fastq.gz” \”https://data.qiime2.org/2019.1/tutorials/moving-pictures/emp-single-end-sequences/barcodes.fastq.gz”
wget \ -O “emp-single-end-sequences/sequences.fastq.gz” \ “https://data.qiime2.org/2019.1/tutorials/moving-pictures/emp-single-end-sequences/sequences.fastq.gz”qiime tools import \ –type EMPSingleEndSequences \ –input-path emp-single-end-sequences \ –output-path emp-single-end-sequences.qza
- To demultiplex sequences (group sequences according to the samples/categories they belong to), input the following:
qiime demux emp-single \ –i-seqs emp-single-end-sequences.qza \ –m-barcodes-file sample-metadata.tsv \ –m-barcodes-column BarcodeSequence \ –o-per-sample-sequences demux.qzaqiime demux summarize \ –i-data demux.qza \ –o-visualization demux.qzv
- To view the qzv file, open QIIME2 View on your browser and drag the qzv file to the designated browser.
- To denoise your sequences, use the DADA2 protocol and input:
qiime dada2 denoise-single \–i-demultiplexed-seqs demux.qza \–p-trim-left 0 \–p-trunc-len 120 \–o-representative-sequences rep-seqs-dada2.qza \–o-table table-dada2.qza \–o-denoising-stats stats-dada2.qza
qiime metadata tabulate \ –m-input-file stats-dada2.qza \ –o-visualization stats-dada2.qzvmv rep-seqs-dada2.qza rep-seqs.qzamv table-dada2.qza table.qza
- To view stats-dada2.qzv, repeat step 6.
- To generate the feature table, input and to view the qzv’s, repeat step 6:
qiime feature-table summarize \–i-table table.qza \–o-visualization table.qzv \–m-sample-metadata-file sample-metadata.tsvqiime feature-table tabulate-seqs \–i-data rep-seqs.qza \–o-visualization rep-seqs.qzv
- Using the Taxonomic Analysis procedure, input:
wget \ -O “gg-13-8-99-515-806-nb-classifier.qza” \ “https://data.qiime2.org/2019.1/common/gg-13-8-99-515-806-nb-classifier.qza”qiime feature-classifier classify-sklearn \ –i-classifier gg-13-8-99-515-806-nb-classifier.qza \ –i-reads rep-seqs.qza \ –o-classification taxonomy.qza qiime metadata tabulate \ –m-input-file taxonomy.qza \ –o-visualization taxonomy.qzv
- To view taxonomy.qzv, repeat step 6.
- Input:
qiime taxa barplot \ –i-table table.qza \ –i-taxonomy taxonomy.qza \ –m-metadata-file sample-metadata.tsv \ –o-visualization taxa-bar-plots.qzv
- To view taxa-bar-plots.qzv, repeat step 6.
Data
Demultiplex Summary
Our version of data sheet:
Sample ID |
Location of Sample |
Year |
Month |
Day |
Soil Texture |
Soil pH |
Soil Water Content |
Nanodrop Read |
LMV |
Baylor Science Building |
2019 |
Jan |
22 |
Sandy Clay Loam |
6.5 |
6.45% |
1.46 |
Conclusion: By learning how to use data to synthesize a visual representation of biodiversity, we can apply the same protocols in our own experimental data/sequences. By gaining insight into the overall composition of organisms (in the context of taxonomy), we also obtain crucial understanding to the system or environment that they live in. We were also able to see how quality scores have a tendency to decrease as we progress in sequence bases and learned the points at which sequences can be cut/excised.
Future Goals: The goal is to develop a protocol in utilizing QIIME2 to interpret data and seeing how we can use QIIME2 in the future to analyze our own experimental data.