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QIIME2

This is the readme file for the Qiime2 pipeline to use for the analysis of 16S amplicons for example! You can activate Qiime2 by the following command:

source activate qiime2-2018.2

Import demultiplex and trim sequences

This step only imports Forwrd reads forward.fastq.gz, Reverse reads reverse.fastq.gz and the Barcodes barcodes.fastq.gz file in the EMP format

qiime tools import --type EMPPairedEndSequences --input-path . --output-path amp_nov_2018.qza

Now comes demultiplexing! In here demultiplex samples based on barcodes first before you do primer/adaptor removal. In the map file you can specify a selected number of samples to make a small file.

qiime demux emp-paired --m-barcodes-file DG280F_map.tsv --m-barcodes-column BarcodeSequence --i-seqs ../amp_nov_2018.qza --o-per-sample-sequences DG280F.qza

It's time to cutadapt trimming, as it is implemented in qiime2

qiime cutadapt trim-paired --i-demultiplexed-sequences DG280F.qza --p-front-f AATCGNTANGGGCCGTGA --p-adapter-f AGATCGGAAGAGCACACGTC --p-front-r GACCACTTGAAGAGCTGGT --p-adapter-r AGATCGGAAGAGCGTCGTGT --o-trimmed-sequences DG280F_trimmed.qza --output-dir cutadapt_di --verbose > cutadapt_trim.log

Summarizing samples

Here, we summarize the demultiplexed samples and their individual sequence counts and also to look at the sequence quality plot to choose parameters for trimming in the following steps.

qiime demux summarize --i-data Amp170209.demux.qza --o-visualization Amp170209.demux.qzv

Then, we can visialize the summary file in a web browser by:

qiime tools view Amp170209.demux.qzv

DADA2 analysis

After the demultiplexing step, the quality control, 'ASV' selection, chimera checking and many other qulaity steps are dobe by the pipleline DADA2 along with extracting the representative sequnces and building a table for the representatives. All of this is done by just one command:

qiime dada2 denoise-paired --i-demultiplexed-seqs DG280F_trimmed.qza --p-trim-left-f 20 --p-trim-left-r 20 --p-trunc-len-f 200 --p-trunc-len-r 200 --o-table DG280F_table.qza --o-representative-sequences DG280F_rep-seqs.qza --p-n-threads 6

Note that the --p-trim-left 10 is to remove the forward primer and --p-trunc-len is to remove the reverse primer and also based on how the quality of the sequences looked like from the previous step. Amp170209.rep-seqs-dada2.qza contains the representative sequences and Amp170209.table-dada2.qza contains their respective abundance.

Visualizing files

We can visialize the different characteristics of the reprentstavive sequences and the table by the following commands:

qiime feature-table tabulate-seqs --i-data Amp170209.rep-seqs-dada2.qza --o-visualization Amp170209.rep-seqs-dada2.qzv

qiime feature-table summarize --i-table Amp170209.table-dada2.qza --o-visualization Amp170209.table-dada2.qzv --m-sample-metadata-file Comb_4ITruns_map.txt

qiime tools view Amp170209.table-dada2.qzv
qiime tools view Amp170209.rep-seqs-dada2.qzv

Taxonomic classifications

Now, we can classify the representative sequences to their respective taxonomic unit using the already existing reference sequences such as the SILVA database and CREST and so on.

qiime feature-classifier classify-consensus-vsearch --i-query All_Asg_16S_rep_seqs.qza --i-reference-reads ~/Files/Database/Qiime2/Silva_128_Q2/16S_SILVA128_99_otus.qza --i-reference-taxonomy ~/Files/Database/Qiime2/Silva_128_Q2/16S_SILVA128_99_taxa.qza --p-threads 5 --output-dir Silva_classified

qiime feature-classifier classify-consensus-vsearch --i-query All_Asg_16S_rep_seqs.qza --i-reference-reads ~/Files/Database/Qiime2/Crest_Q2/Crest_97_otus.qza --i-reference-taxonomy ~/Files/Database/Qiime2/Crest_Q2/Crest_97_taxa.qza --p-threads 5 --output-dir Crest_classified

Bar plots

The Qiime barplots can be plotted using the following commnad:

qiime taxa barplot --i-table ../All_Asg_16S_table.qza --i-taxonomy classification.qza --m-metadata-file ../Comb_4ITruns_map.txt --o-visualization silva_taxa-bar-plots.qzv

Exporting classifications and table

qiime tools export classification.qza --output-dir .
qiime tools export All_Asg_16S_table.qza --output-dir .

The classfication table has a name taxonomy.tsv as an output with taxonomy for each ASV and the table out put is in biom format!

Krona plots

Here, we would combine the taxonomy classifications and the abundance table from the biom table to make Krona plots

biom convert -i All_Asg-table.biom -o All_Asg_asv_table.tab --to-tsv
krona_qiime.py ../taxonomy.tsv ../../All_Asg_asv_table.tab

The above biom command will create a normal ASV abundance table. then, we combine the the taxonomic classification of the ASV to their abundance using my own krona_qiime.py command. This will create text files for each sample in the analysis. then we would combine the text files to make the krona plots.

ktImportText text_files/Str* text_files/Sec* -o Piran_silva.html