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| <h1 id="example-analysis-crohns-disease-microbiome-data">Example analysis - Crohn's disease microbiome data</h1> | ||
| <p>To showcase the potential EURYALE has to expedite the analysis of microbiome data, let's | ||
| try analysing a publicly available gut microbiome dataset (<a href="https://www.ncbi.nlm.nih.gov/bioproject/PRJNA175224">PRJNA175224</a>). | ||
| This dataset contains 7 gut microbiome samples from healthy donors and 4 from donors with Crohn's disease.</p> | ||
| <p>Feel free to download the original dataset to a directory called <code>raw_data</code> and follow along.</p> | ||
| <h2 id="acquiring-databases-and-running-the-pipeline">Acquiring databases and running the pipeline</h2> | ||
| <p>Let's first download the databases and references. | ||
| For this analysis, we'll focus on the taxonomic classification results after de-contamination. | ||
| So, let's download the Human reference genome as well as Kaiju's database:</p> | ||
| <pre><code class="language-bash">nextflow run dalmolingroup/euryale \ | ||
| --download_kaiju \ | ||
| --download_host \ | ||
| --outdir references \ | ||
| -entry download \ | ||
| -profile singularity | ||
| </code></pre> | ||
| <p>Once that's done, let's execute the analysis on the data itself. | ||
| Your samplesheet should look something like this:</p> | ||
| <pre><code class="language-csv">sample,fastq_1,fastq_2 | ||
| SRR579274,raw_data/SRR579274_1.fastq.gz,raw_data/SRR579274_2.fastq.gz | ||
| SRR579275,raw_data/SRR579275_1.fastq.gz,raw_data/SRR579275_2.fastq.gz | ||
| SRR579276,raw_data/SRR579276_1.fastq.gz,raw_data/SRR579276_2.fastq.gz | ||
| SRR579277,raw_data/SRR579277_1.fastq.gz,raw_data/SRR579277_2.fastq.gz | ||
| SRR579278,raw_data/SRR579278_1.fastq.gz,raw_data/SRR579278_2.fastq.gz | ||
| SRR579279,raw_data/SRR579279_1.fastq.gz,raw_data/SRR579279_2.fastq.gz | ||
| SRR579280,raw_data/SRR579280_1.fastq.gz,raw_data/SRR579280_2.fastq.gz | ||
| SRR579281,raw_data/SRR579281_1.fastq.gz,raw_data/SRR579281_2.fastq.gz | ||
| SRR579290,raw_data/SRR579290_1.fastq.gz,raw_data/SRR579290_2.fastq.gz | ||
| SRR579291,raw_data/SRR579291_1.fastq.gz,raw_data/SRR579291_2.fastq.gz | ||
| SRR579292,raw_data/SRR579292_1.fastq.gz,raw_data/SRR579292_2.fastq.gz | ||
| </code></pre> | ||
| <p>And your command should look something like this one:</p> | ||
| <pre><code class="language-bash">nextflow run dalmolingroup/euryale \ | ||
| --input samplesheet.csv \ | ||
| --outdir results \ | ||
| --skip_alignment \ | ||
| --skip_functional \ | ||
| --kaiju_db references/kaiju_db_nr_2023-05-10.tgz \ | ||
| --host_fasta references/Homo_sapiens.GRCh38.dna.primary_assembly.fa.gz \ | ||
| -profile singularity \ | ||
| -r "v1.0.3" | ||
| </code></pre> | ||
| <p>Check the <a href="https://dalmolingroup.github.io/euryale/params/">parameter documentation</a> for a full description | ||
| of possible parameters.</p> | ||
| <ul> | ||
| <li>We're skipping the functional annotation section just to expedite the results, but feel free to include these | ||
| steps in your own analysis.</li> | ||
| </ul> | ||
| <p>Once that's all done, let's check the results we got.</p> | ||
| <h2 id="exploring-the-resulting-data">Exploring the resulting data</h2> | ||
| <p>First, let's open the MultiQC report in our browser and take a look. | ||
| It should be available in <code>results/multiqc/multiqc_report.html</code>.</p> | ||
| <p>We can first see that most reads (80%) in each sample passed | ||
| the quality filter, thankfully:</p> | ||
| <p><img alt="" src="../assets/fastp_filtered_reads_plot.png" /></p> | ||
| <p>We can also see that most samples did not have their reads assigned | ||
| to any particular taxon:</p> | ||
| <p><img alt="" src="../assets/kaiju-topfive-plot.png" /></p> | ||
| <p>This could be due to high host contamination, or could necessitate | ||
| the use of another database, or something else entirely! Either way, it's something worth investigating. | ||
| We can also see there is a strange spike of <em>Enterocloster boltae</em> in one of the samples, which also | ||
| warrants further investigation.</p> | ||
| <p>If we now check the MicroView results, available in <code>results/taxonomy/microview/microview_report.html</code>, | ||
| we can see further points of interest to investigate in our data.</p> | ||
| <p>Let's see the Beta-diversity PCoA, for example:</p> | ||
| <p><img alt="" src="../assets/betadiv_microview.png" /></p> | ||
| <p>The plot shows a somewhat strange division in the dataset: 4 samples stretch further in | ||
| the PC1 than the rest. This could be due to some biological variable or an error in the data generation process. | ||
| Either way, it's worth saving the PCoA table, available in <code>results/taxonomy/microview/microview_tables/</code> and | ||
| crossing the sample names with the metadata of this study.</p> | ||
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