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--- population-diversity:microbiome-analysis-with-qiime2-using-illumina-paired-end-sequence-data [2020/03/30 13:24] – [Importing data into Qiime2] bngina
+++ population-diversity:microbiome-analysis-with-qiime2-using-illumina-paired-end-sequence-data [2020/04/29 13:35] – bngina
@@ Line 11: / Line 11: @@
 We import raw fastq files into qiime2. Depending on the sequencing platform used to generate the data, you will have data that has been de-multiplexed into sample specif fastq files, or not. The MiSeq platform at [[ https://hub.africabiosciences.org/| BecA-ILRI Hub]] will de-multiplex the data and we get sample specific fastq files.
-Read more about different data types and importing them into qiime2 for anlaysis. We will be importing data described as type // Cassava 1.8 paired-end demultiplexed fastq//.
+Read more about [[ https://docs.qiime2.org/2020.2/tutorials/importing/|different data types and importing them into qiime2]] for anlaysis. We will be importing data described as type //Cassava 1.8 paired-end de-multiplexed fastq//.
+To import fastq files;
+<code>
+#i create a directory to store all my artifact i.e '.qza' and the related visualization i.e '.qzv' files.
+mkdir /home/mydir/qiime2_data/
+#import the fastq files
+qiime tools import \
+ --type 'SampleData[PairedEndSequencesWithQuality]' \
+ --input-path /home/mydir/qiime2_data/ \
+ --input-format CasavaOneEightSingleLanePerSampleDirFmt \
+ --output-path /home/mydir/qiime2_data/my_raw_data.qza
+</code>
+We mentioned that qiime2 provides method to summarize and view artifacts by storing them as visualizations, .qzv, files. The ''.qzv''  files can be opened on any browser using [[https://view.qiime2.org/ |qiime viewer]]. To create visualization for the imported data;
+<code>
+qiime demux summarize \
+ --i-data  /home/mydir/qiime2_data/my_raw_data.qza \
+ --o-visualization /home/mydir/qiime2_data/my_raw_data.qzv
+</code>
+==== Qiime2 data filtering and feature (OTU) table construction ====
+Qiime2 provides two pluggins/methods for filtering your sequences to the required quality and length.Sequence Variants are then selected from quality filtered data and the results are feature tables better known as OTU tables and the representative feature sequences. These methods are [[https://www.ncbi.nlm.nih.gov/pubmed/27214047| Dada2]] and [[ https://msystems.asm.org/content/2/2/e00191-16| Deblur]].
+Important to keep in mind is the size of the amplified region covered by the primers used and what is the expected fragment length after read joining? This will determine the trim and truncate length parameters for dada2 and deblur pipelines.
+=== Dada2 ===
+The Dada2 pipeline detects and corrects illumina amplicon sequence data and additionally filter any phiX reads identified in sequencing data and also filter out any chimeric sequences.
+To see usage and parameters that can be adjusted get the help for the pluggin i.e
+<code>
+qiime dada2 --help
+</code>
+The data we are using is paired-end, hence we will use the [[https://docs.qiime2.org/2020.2/plugins/available/dada2/|qiime dada2 denoise-paired]] pluggin.
+A key parameter to be careful is the //''--p-trunc-q''//
+Key parameters in quality control of the sequences are those used to trim the forward, ''--p-trim-left-f''; ''--p-trunc-len-f'' and reverse '' --p-trim-left-r''; ''--p-trunc-len-r'' reads. The //--p-trim-left-[f/r] tell qiime how many bases to trim from the beginning of the sequence, while the //--p-trunc-len[f|r] tell qiime at what position the sequences should be truncated at the end.