<ahref="./installation.html"class="sidebar-item-text sidebar-link"><spanclass="chapter-number">1</span> <spanclass="chapter-title">Installation of the <em>OBITools</em></span></a>
<ahref="./inupt.html"class="sidebar-item-text sidebar-link"><spanclass="chapter-number">4</span> <spanclass="chapter-title">Specifying the data input to <em>OBITools</em> commands</span></a>
<ahref="./common_options.html"class="sidebar-item-text sidebar-link"><spanclass="chapter-number">6</span> <spanclass="chapter-title">Options common to most of the <em>OBITools</em> commands</span></a>
<ahref="./comm_reformat.html"class="sidebar-item-text sidebar-link"><spanclass="chapter-number">9</span> <spanclass="chapter-title">File format conversions</span></a>
<ahref="./comm_computation.html"class="sidebar-item-text sidebar-link"><spanclass="chapter-number">11</span> <spanclass="chapter-title">Computations on sequences</span></a>
<ahref="./comm_sampling.html"class="sidebar-item-text sidebar-link"><spanclass="chapter-number">12</span> <spanclass="chapter-title">Sequence sampling and filtering</span></a>
<li><ahref="#wolves-diet-based-on-dna-metabarcoding"id="toc-wolves-diet-based-on-dna-metabarcoding"class="nav-link active"data-scroll-target="#wolves-diet-based-on-dna-metabarcoding"><spanclass="toc-section-number">3.1</span> Wolves’ diet based on DNA metabarcoding</a></li>
<li><ahref="#step-by-step-analysis"id="toc-step-by-step-analysis"class="nav-link"data-scroll-target="#step-by-step-analysis"><spanclass="toc-section-number">3.2</span> Step by step analysis</a>
<li><ahref="#recover-full-sequence-reads-from-forward-and-reverse-partial-reads"id="toc-recover-full-sequence-reads-from-forward-and-reverse-partial-reads"class="nav-link"data-scroll-target="#recover-full-sequence-reads-from-forward-and-reverse-partial-reads"><spanclass="toc-section-number">3.2.1</span> Recover full sequence reads from forward and reverse partial reads</a></li>
<li><ahref="#assign-each-sequence-record-to-the-corresponding-samplemarker-combination"id="toc-assign-each-sequence-record-to-the-corresponding-samplemarker-combination"class="nav-link"data-scroll-target="#assign-each-sequence-record-to-the-corresponding-samplemarker-combination"><spanclass="toc-section-number">3.2.3</span> Assign each sequence record to the corresponding sample/marker combination</a></li>
<li><ahref="#dereplicate-reads-into-uniq-sequences"id="toc-dereplicate-reads-into-uniq-sequences"class="nav-link"data-scroll-target="#dereplicate-reads-into-uniq-sequences"><spanclass="toc-section-number">3.2.4</span> Dereplicate reads into uniq sequences</a></li>
<li><ahref="#denoise-the-sequence-dataset"id="toc-denoise-the-sequence-dataset"class="nav-link"data-scroll-target="#denoise-the-sequence-dataset"><spanclass="toc-section-number">3.2.5</span> Denoise the sequence dataset</a></li>
<li><ahref="#taxonomic-assignment-of-sequences"id="toc-taxonomic-assignment-of-sequences"class="nav-link"data-scroll-target="#taxonomic-assignment-of-sequences"><spanclass="toc-section-number">3.2.6</span> Taxonomic assignment of sequences</a></li>
<li><ahref="#assign-each-sequence-to-a-taxon"id="toc-assign-each-sequence-to-a-taxon"class="nav-link"data-scroll-target="#assign-each-sequence-to-a-taxon"><spanclass="toc-section-number">3.2.7</span> Assign each sequence to a taxon</a></li>
<li><ahref="#generate-the-final-result-table"id="toc-generate-the-final-result-table"class="nav-link"data-scroll-target="#generate-the-final-result-table"><spanclass="toc-section-number">3.2.8</span> Generate the final result table</a></li>
<li><ahref="#looking-at-the-data-in-r"id="toc-looking-at-the-data-in-r"class="nav-link"data-scroll-target="#looking-at-the-data-in-r"><spanclass="toc-section-number">3.2.9</span> Looking at the data in R</a></li>
<h2data-number="3.1"class="anchored"data-anchor-id="wolves-diet-based-on-dna-metabarcoding"><spanclass="header-section-number">3.1</span> Wolves’ diet based on DNA metabarcoding</h2>
<p>The data used in this tutorial correspond to the analysis of four wolf scats, using the protocol published in <spanclass="citation"data-cites="Shehzad2012-pn">Shehzad et al. (<ahref="references.html#ref-Shehzad2012-pn"role="doc-biblioref">2012</a>)</span> for assessing carnivore diet. After extracting DNA from the faeces, the DNA amplifications were carried out using the primers <code>TTAGATACCCCACTATGC</code> and <code>TAGAACAGGCTCCTCTAG</code> amplifiying the <em>12S-V5</em> region <spanclass="citation"data-cites="Riaz2011-gn">(<ahref="references.html#ref-Riaz2011-gn"role="doc-biblioref">Riaz et al. 2011</a>)</span>, together with a wolf blocking oligonucleotide.</p>
<p>The complete data set can be downloaded here: <ahref="wolf_diet.tgz">the tutorial dataset</a></p>
<p>Once the data file is downloaded, using a UNIX terminal unarchive the data from the <code>tgz</code> file.</p>
<divclass="cell">
<divclass="sourceCode cell-code"id="cb1"><preclass="sourceCode bash code-with-copy"><codeclass="sourceCode bash"><spanid="cb1-1"><ahref="#cb1-1"aria-hidden="true"tabindex="-1"></a><spanclass="fu">tar</span> zxvf wolf_diet.tgz</span></code><buttontitle="Copy to Clipboard"class="code-copy-button"><iclass="bi"></i></button></pre></div>
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<p>That command create a new directory named <code>wolf_data</code> containing every required data files:</p>
<ul>
<li><p><code>fastq <fastq></code> files resulting of aGA IIx (Illumina) paired-end (2 x 108 bp) sequencing assay of DNA extracted and amplified from four wolf faeces:</p>
<ul>
<li><code>wolf_F.fastq</code></li>
<li><code>wolf_R.fastq</code></li>
</ul></li>
<li><p>the file describing the primers and tags used for all samples sequenced:</p>
<li><code>wolf_diet_ngsfilter.txt</code> The tags correspond to short and specific sequences added on the 5' end of each primer to distinguish the different samples</li>
<h3data-number="3.2.1"class="anchored"data-anchor-id="recover-full-sequence-reads-from-forward-and-reverse-partial-reads"><spanclass="header-section-number">3.2.1</span> Recover full sequence reads from forward and reverse partial reads</h3>
<p>When using the result of a paired-end sequencing assay with supposedly overlapping forward and reverse reads, the first step is to recover the assembled sequence.</p>
<p>The forward and reverse reads of the same fragment are <em>at the same line position</em> in the two fastq files obtained after sequencing. Based on these two files, the assembly of the forward and reverse reads is done with the <code>obipairing</code> utility that aligns the two reads and returns the reconstructed sequence.</p>
<spanid="cb3-5"><ahref="#cb3-5"aria-hidden="true"tabindex="-1"></a><spanclass="op">></span> results/wolf.fastq </span></code><buttontitle="Copy to Clipboard"class="code-copy-button"><iclass="bi"></i></button></pre></div>
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<p>The <code>--min-identity</code> and <code>--min-overlap</code> options allow discarding sequences with low alignment quality. If after the aligment, the overlaping parts of the reads is shorter than 10 base pairs or the similarity over this aligned region is below 80% of identity, in the output file, the forward and reverse reads are not aligned but concatenated, and the value of the <code>mode</code> attribute in the sequence header is set to <code>joined</code> instead of <code>alignment</code>.</p>
<p>Unaligned sequences (:py<codeclass="interpreted-text"role="mod">mode=joined</code>) cannot be used. The following command allows removing them from the dataset:</p>
<p>The <code>-p</code> requires a go like expression. <code>annotations.mode != "join"</code> means that if the value of the <code>mode</code> annotation of a sequence is different from <code>join</code>, the corresponding sequence record will be kept.</p>
<h3data-number="3.2.3"class="anchored"data-anchor-id="assign-each-sequence-record-to-the-corresponding-samplemarker-combination"><spanclass="header-section-number">3.2.3</span> Assign each sequence record to the corresponding sample/marker combination</h3>
<p>Each sequence record is assigned to its corresponding sample and marker using the data provided in a text file (here <code>wolf_diet_ngsfilter.txt</code>). This text file contains one line per sample, with the name of the experiment (several experiments can be included in the same file), the name of the tags (for example: <code>aattaac</code> if the same tag has been used on each extremity of the PCR products, or <code>aattaac:gaagtag</code> if the tags were different), the sequence of the forward primer, the sequence of the reverse primer, the letter <code>T</code> or <code>F</code> for sample identification using the forward primer and tag only or using both primers and both tags, respectively (see <code>obimultiplex</code> for details).</p>
<spanid="cb7-4"><ahref="#cb7-4"aria-hidden="true"tabindex="-1"></a><spanclass="op">></span> results/wolf.ali.assigned.fastq</span></code><buttontitle="Copy to Clipboard"class="code-copy-button"><iclass="bi"></i></button></pre></div>
</div>
<p>This command creates two files:</p>
<ul>
<li><code>unidentified.fastq</code> containing all the sequence records that were not assigned to a sample/marker combination</li>
<li><code>wolf.ali.assigned.fastq</code> containing all the sequence records that were properly assigned to a sample/marker combination</li>
</ul>
<p>Note that each sequence record of the <code>wolf.ali.assigned.fastq</code> file contains only the barcode sequence as the sequences of primers and tags are removed by the <code>obimultiplex</code> program. Information concerning the experiment, sample, primers and tags is added as attributes in the sequence header.</p>
<p>For instance, the first sequence record of <code>wolf.ali.assigned.fastq</code> is:</p>
<p>The same DNA molecule can be sequenced several times. In order to reduce both file size and computations time, and to get easier interpretable results, it is convenient to work with unique <em>sequences</em> instead of <em>reads</em>. To <em>dereplicate</em> such <em>reads</em> into unique <em>sequences</em>, we use the <code>obiuniq</code> command.</p>
<tableclass="table">
<colgroup>
<colstyle="width: 86%">
</colgroup>
<tbody>
<trclass="odd">
<td>Definition: Dereplicate reads into unique sequences</td>
</tr>
<trclass="even">
<td><oltype="1">
<li>compare all the reads in a data set to each other</li>
<li>group strictly identical reads together</li>
<li>output the sequence for each group and its count in the original dataset (in this way, all duplicated reads are removed)</li>
<p>Definition adapted from <spanclass="citation"data-cites="Seguritan2001-tg">Seguritan and Rohwer (<ahref="references.html#ref-Seguritan2001-tg"role="doc-biblioref">2001</a>)</span></p></td>
<p>For dereplication, we use the <code>obiuniq</code> command with the <code>-m sample</code>. The <code>-m sample</code> option is used to keep the information of the samples of origin for each uniquesequence.</p>
<spanid="cb9-3"><ahref="#cb9-3"aria-hidden="true"tabindex="-1"></a><spanclass="op">></span> results/wolf.ali.assigned.uniq.fasta</span></code><buttontitle="Copy to Clipboard"class="code-copy-button"><iclass="bi"></i></button></pre></div>
</div>
<p>Note that <code>obiuniq</code> returns a fasta file.</p>
<p>The first sequence record of <code>wolf.ali.assigned.uniq.fasta</code> is:</p>
<li><codeclass="interpreted-text"role="mod">"merged_sample":{"29a_F260619":1}</code>: this sequence have been found once in a single sample called <strong>29a_F260619</strong></li>
<li><code>"count":1</code> : the total count for this sequence is <mathdisplay="inline"xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mn>1</mn><annotationencoding="application/x-tex">1</annotation></semantics></math></li>
<spanid="cb11-3"><ahref="#cb11-3"aria-hidden="true"tabindex="-1"></a><spanclass="op">></span> results/wolf.ali.assigned.simple.fasta</span></code><buttontitle="Copy to Clipboard"class="code-copy-button"><iclass="bi"></i></button></pre></div>
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<p>The first five sequence records of <code>wolf.ali.assigned.simple.fasta</code> become:</p>
<h3data-number="3.2.5"class="anchored"data-anchor-id="denoise-the-sequence-dataset"><spanclass="header-section-number">3.2.5</span> Denoise the sequence dataset</h3>
<p>To have a set of sequences assigned to their corresponding samples does not mean that all sequences are <em>biologically</em> meaningful i.e. some of these sequences can contains PCR and/or sequencing errors, or chimeras.</p>
<h4class="unnumbered anchored"data-anchor-id="tag-the-sequences-for-pcr-errors-sequence-variants">Tag the sequences for PCR errors (sequence variants)</h4>
<p>The <code>obiclean</code> program tags sequence variants as potential error generated during PCR amplification. We ask it to keep the <spanclass="title-ref">head</span> sequences (<code>-H</code> option) that are sequences which are not variants of another sequence with a count greater than 5% of their own count (<code>-r 0.05</code> option).</p>
<spanid="cb13-3"><ahref="#cb13-3"aria-hidden="true"tabindex="-1"></a><spanclass="op">></span> results/wolf.ali.assigned.simple.clean.fasta </span></code><buttontitle="Copy to Clipboard"class="code-copy-button"><iclass="bi"></i></button></pre></div>
</div>
<p>One of the sequence records of <code>wolf.ali.assigned.simple.clean.fasta</code> is:</p>
<p>The dataset contains <mathdisplay="inline"xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mn>4313</mn><annotationencoding="application/x-tex">4313</annotation></semantics></math> sequences variant corresponding to 42452 sequence reads. Most of the variants occur only a single time in the complete dataset and are usualy named <em>singletons</em></p>
<spanid="cb17-2"><ahref="#cb17-2"aria-hidden="true"tabindex="-1"></a><spanclass="kw">|</span><spanclass="ex">obicount</span></span></code><buttontitle="Copy to Clipboard"class="code-copy-button"><iclass="bi"></i></button></pre></div>
<p>In that dataset sigletons corresponds to <mathdisplay="inline"xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mn>3511</mn><annotationencoding="application/x-tex">3511</annotation></semantics></math> variants.</p>
<p>Using <em>R</em> and the <code>ROBIFastread</code> package able to read headers of the fasta files produced by <em>OBITools</em>, we can get more complete statistics on the distribution of occurrencies.</p>
<divclass="cell">
<divclass="sourceCode cell-code"id="cb19"><preclass="sourceCode r code-with-copy"><codeclass="sourceCode r"><spanid="cb19-1"><ahref="#cb19-1"aria-hidden="true"tabindex="-1"></a><spanclass="fu">library</span>(ROBIFastread)</span>
<spanid="cb19-11"><ahref="#cb19-11"aria-hidden="true"tabindex="-1"></a><spanclass="fu">xlab</span>(<spanclass="st">"number of occurrencies of a variant"</span>) </span></code><buttontitle="Copy to Clipboard"class="code-copy-button"><iclass="bi"></i></button></pre></div>
<spanid="cb20-5"><ahref="#cb20-5"aria-hidden="true"tabindex="-1"></a><spanclass="fu">xlab</span>(<spanclass="st">"sequence lengths in base pair"</span>)</span></code><buttontitle="Copy to Clipboard"class="code-copy-button"><iclass="bi"></i></button></pre></div>
<h4class="unnumbered anchored"data-anchor-id="keep-only-the-sequences-having-a-count-greater-or-equal-to-10-and-a-length-shorter-than-80-bp">Keep only the sequences having a count greater or equal to 10 and a length shorter than 80 bp</h4>
<p>Based on the previous observation, we set the cut-off for keeping sequences for further analysis to a count of 10. To do this, we use the <codeclass="interpreted-text"role="doc">obigrep <scripts/obigrep></code> command. The <code>-p 'count>=10'</code> option means that the <code>python</code> expression :py<codeclass="interpreted-text"role="mod">count>=10</code> must be evaluated to :py<codeclass="interpreted-text"role="mod">True</code> for each sequence to be kept. Based on previous knowledge we also remove sequences with a length shorter than 80 bp (option -l) as we know that the amplified 12S-V5 barcode for vertebrates must have a length around 100bp.</p>
<spanid="cb21-2"><ahref="#cb21-2"aria-hidden="true"tabindex="-1"></a><spanclass="op">></span> results/wolf.ali.assigned.simple.clean.c10.l80.fasta</span></code><buttontitle="Copy to Clipboard"class="code-copy-button"><iclass="bi"></i></button></pre></div>
</div>
<p>The first sequence record of <code>results/wolf.ali.assigned.simple.clean.c10.l80.fasta</code> is:</p>
<h3data-number="3.2.6"class="anchored"data-anchor-id="taxonomic-assignment-of-sequences"><spanclass="header-section-number">3.2.6</span> Taxonomic assignment of sequences</h3>
<p>Once denoising has been done, the next step in diet analysis is to assign the barcodes to the corresponding species in order to get the complete list of species associated to each sample.</p>
<p>Taxonomic assignment of sequences requires a reference database compiling all possible species to be identified in the sample. Assignment is then done based on sequence comparison between sample sequences and reference sequences.</p>
<spanid="cb25-5"><ahref="#cb25-5"aria-hidden="true"tabindex="-1"></a><spanclass="bu">cd</span> ..</span></code><buttontitle="Copy to Clipboard"class="code-copy-button"><iclass="bi"></i></button></pre></div>
<p>For people have a low speed internet connection, a copy of the <code>taxdump.tar.gz</code> file is provided in the wolf_data directory. The NCBI taxonomy is dayly updated, but the one provided here is ok for running this tutorial.</p>
<p>To build the TAXO directory from the provided <code>taxdump.tar.gz</code>, you need to execute the following commands</p>
<spanid="cb26-4"><ahref="#cb26-4"aria-hidden="true"tabindex="-1"></a><spanclass="bu">cd</span> ..</span></code><buttontitle="Copy to Clipboard"class="code-copy-button"><iclass="bi"></i></button></pre></div>
<p>One way to build the reference database is to use the <code>obipcr</code> program to simulate a PCR and extract all sequences from a general purpose DNA database such as genbank or EMBL that can be amplified <em>in silico</em> by the two primers (here <strong>TTAGATACCCCACTATGC</strong> and <strong>TAGAACAGGCTCCTCTAG</strong>) used for PCR amplification.</p>
<p>The two steps to build this reference database would then be</p>
<li><p>Today, the easiest database to download is <em>Genbank</em>. But this will take you more than a day and occupy more than half a terabyte on your hard drive. In the <code>wolf_data</code> directory, a shell script called <code>download_gb.sh</code> is provided to perform this task. It requires that the programs <code>wget2</code> and <code>curl</code> are available on your computer.</p></li>
<li><p>Use <code>obipcr</code> to simulate amplification and build a reference database based on the putatively amplified barcodes and their recorded taxonomic information.</p></li>
<p>As these steps can take a long time (about a day for the download and an hour for the PCR), we already provide the reference database produced by the following commands so you can skip its construction. Note that as the Genbank and taxonomic database evolve frequently, if you run the following commands you may get different results.</p>
<spanid="cb27-4"><ahref="#cb27-4"aria-hidden="true"tabindex="-1"></a><spanclass="bu">cd</span> ..</span></code><buttontitle="Copy to Clipboard"class="code-copy-button"><iclass="bi"></i></button></pre></div>
</div>
<p>DO NOT RUN THIS COMMAND EXCEPT IF YOU ARE REALLY CONSIENT OF THE TIME AND DISK SPACE REQUIRED.</p>
<spanid="cb28-6"><ahref="#cb28-6"aria-hidden="true"tabindex="-1"></a><spanclass="op">></span> results/v05.pcr.fasta</span></code><buttontitle="Copy to Clipboard"class="code-copy-button"><iclass="bi"></i></button></pre></div>
</div>
<p>Note that the primers must be in the same order both in <code>wolf_diet_ngsfilter.txt</code> and in the <code>obipcr</code> command. The part of the path indicating the <em>Genbank</em> release can change. Please check in your genbank directory the exact name of your release.</p>
<h5class="unnumbered anchored"data-anchor-id="clean-the-database">Clean the database</h5>
<oltype="1">
<li>filter sequences so that they have a good taxonomic description at the species, genus, and family levels (<code>obigrep</code> command command below).</li>
<spanid="cb29-12"><ahref="#cb29-12"aria-hidden="true"tabindex="-1"></a><spanclass="op">></span> results/v05_clean_uniq.indexed.fasta</span></code><buttontitle="Copy to Clipboard"class="code-copy-button"><iclass="bi"></i></button></pre></div>
<p>From now on, for the sake of clarity, the following commands will use the filenames of the files provided with the tutorial. If you decided to run the last steps and use the files you have produced, you'll have to use <code>results/v05_clean_uniq.indexed.fasta</code> instead of <code>wolf_data/db_v05_r117.indexed.fasta</code>.</p>
<h3data-number="3.2.7"class="anchored"data-anchor-id="assign-each-sequence-to-a-taxon"><spanclass="header-section-number">3.2.7</span> Assign each sequence to a taxon</h3>
<spanid="cb30-3"><ahref="#cb30-3"aria-hidden="true"tabindex="-1"></a><spanclass="op">></span> results/wolf.ali.assigned.simple.clean.c10.l80.taxo.fasta</span></code><buttontitle="Copy to Clipboard"class="code-copy-button"><iclass="bi"></i></button></pre></div>
</div>
<p>The <code>obitag</code> adds several attributes in the sequence record header, among them:</p>
<ul>
<li>obitag_bestmatch=ACCESSION where ACCESSION is the id of hte sequence in the reference database that best aligns to the query sequence;</li>
<li>obitag_bestid=FLOAT where FLOAT*100 is the percentage of identity between the best match sequence and the query sequence;</li>
<li>taxid=TAXID where TAXID is the final assignation of the sequence by <code>obitag</code></li>
<li>scientific_name=NAME where NAME is the scientific name of the assigned taxid.</li>
</ul>
<p>The first sequence record of <code>wolf.ali.assigned.simple.clean.c10.l80.taxo.fasta</code> is:</p>
<spanid="cb31-3"><ahref="#cb31-3"aria-hidden="true"tabindex="-1"></a><spanclass="ex">gcttaaaactcaaaggacttggcggtgctttatatccct</span></span></code><buttontitle="Copy to Clipboard"class="code-copy-button"><iclass="bi"></i></button></pre></div>
<h3data-number="3.2.8"class="anchored"data-anchor-id="generate-the-final-result-table"><spanclass="header-section-number">3.2.8</span> Generate the final result table</h3>
<spanid="cb32-7"><ahref="#cb32-7"aria-hidden="true"tabindex="-1"></a><spanclass="op">></span> results/wolf.ali.assigned.simple.clean.c10.l80.taxo.ann.fasta</span></code><buttontitle="Copy to Clipboard"class="code-copy-button"><iclass="bi"></i></button></pre></div>
</div>
<p>The first sequence record of <code>wolf.ali.assigned.simple.c10.l80.clean.taxo.ann.fasta</code> is then:</p>
<h3data-number="3.2.9"class="anchored"data-anchor-id="looking-at-the-data-in-r"><spanclass="header-section-number">3.2.9</span> Looking at the data in R</h3>
<divclass="sourceCode cell-code"id="cb34"><preclass="sourceCode r code-with-copy"><codeclass="sourceCode r"><spanid="cb34-1"><ahref="#cb34-1"aria-hidden="true"tabindex="-1"></a><spanclass="fu">library</span>(ROBIFastread)</span>
<spanid="cb34-2"><ahref="#cb34-2"aria-hidden="true"tabindex="-1"></a><spanclass="fu">library</span>(vegan)</span></code><buttontitle="Copy to Clipboard"class="code-copy-button"><iclass="bi"></i></button></pre></div>
<divclass="cell-output cell-output-stderr">
<pre><code>Le chargement a nécessité le package : permute</code></pre>
</div>
<divclass="cell-output cell-output-stderr">
<pre><code>Le chargement a nécessité le package : lattice</code></pre>
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<divclass="cell-output cell-output-stderr">
<pre><code>This is vegan 2.6-4</code></pre>
</div>
<divclass="sourceCode cell-code"id="cb38"><preclass="sourceCode r code-with-copy"><codeclass="sourceCode r"><spanid="cb38-1"><ahref="#cb38-1"aria-hidden="true"tabindex="-1"></a><spanclass="fu">library</span>(magrittr)</span>
<spanid="cb38-8"><ahref="#cb38-8"aria-hidden="true"tabindex="-1"></a>diet_tab</span></code><buttontitle="Copy to Clipboard"class="code-copy-button"><iclass="bi"></i></button></pre></div>
Riaz, Tiayyba, Wasim Shehzad, Alain Viari, François Pompanon, Pierre Taberlet, and Eric Coissac. 2011. <span>“<spanclass="nocase">ecoPrimers: inference of new DNA barcode markers from whole genome sequence analysis</span>.”</span><em>Nucleic Acids Research</em> 39 (21): e145. <ahref="https://doi.org/10.1093/nar/gkr732">https://doi.org/10.1093/nar/gkr732</a>.
Seguritan, V, and F Rohwer. 2001. <span>“<spanclass="nocase">FastGroup: a program to dereplicate libraries of 16S rDNA sequences</span>.”</span><em>BMC Bioinformatics</em> 2 (October): 9. <ahref="https://doi.org/10.1186/1471-2105-2-9">https://doi.org/10.1186/1471-2105-2-9</a>.
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