Development of the method presented here has been carried out with various sources of funding, including Genome Canada and Genome Quebec, Environment Canada STAGE program and internal NRC funds.

1. 16S rRNA Gene Amplicon Library Preparation by the Fusion Method
<ol>
	<li>Thaw the primers (Forward, F343-IonA-MIDXX: 5&#8217;-CCA TCT CAT CCC TGC GTG TCT CCG ACT CAG XXX XXX XXX XTA CGG RAG GCA GCA G-3&#8217;; reverse, R533-IonP1: 5&#8217;-CCT CTC TAT GGG CAG TCG GTG ATA TTA CCG CGG CTG CTG GC-3&#8217;; bold: Ion Torrent specific adapters; italics: sequencing key for calibrating signal intensity at the beginning of the sequencing run; regular font: template-specific primers; X&#8230;X: 10 bp barcode, see Table 1 for some barcode sequences), the 2x HotStartTaq Plus master mix and the samples. Mix thoroughly before use, except for the master mix that should be mixed gently.</li>
	<li>Prepare a PCR mix (20 &#181;l reactions) with 0.5 &#181;M of the reverse primer, 0.4 mg/ml of Bovine Serum Albumine (BSA) and 1x HotStartTaq Plus Master mix. Add 18.5&#160;&#181;l of the master mix to each PCR tube.</li>
	<li>Add a forward primer (0.5 &#181;l at 20 mM) with a different barcode for each of the samples that will be sequenced together. Add 1&#160;&#181;l of sample (1-10 ng/&#181;l) to the PCR tube.</li>
	<li>Place the tubes in a PCR machine and run the following program: initial denaturation at 95 &#176;C for 5 min; 25 cycles of 95 &#176;C for 30 sec, 55 &#176;C for 30 sec and 72 &#176;C for 45 sec; final elongation at 72&#176;C for 10 min. PCR products can be kept at 4 &#176;C O/N or frozen until used.</li>
</ol>
2. Amplicon Purification, Quantification and Pooling
<ol>
	<li>Prepare a 2% agarose gel using the largest combs available. Add 5&#160;&#181;l of 6x gel loading dye to the reaction and load on the gel, separating each sample by an empty well. Run the gel at 70 V for 50 min.</li>
	<li>Take a picture of the gel and cut the bands (expected size around 250 bp: 190 bp product plus 63 bp adapters and barcodes) using a clean scalpel.</li>
	<li>Weigh the excised bands and proceed with the gel extraction and purification of the PCR products (with e.g., Qiaquick Gel Extraction kit).</li>
	<li>Quantify each PCR product with PicoGreen and make dilutions to obtain a stock solution at 5 x 109 molecules/&#181;l (approximately 1 ng/&#181;l). If some samples show lower amplification, adjust the dilutions to a lower concentration, keeping in mind that the lowest concentration suitable for subsequent procedures is 1.57 x 107 molecules/&#181;l (26 pM).</li>
	<li>Pool 10&#160;&#181;l of each diluted PCR product to obtain an equimolar pool of samples. Prepare one separate pool for each of the planned sequencing run. The pooled PCR products can be kept in the freezer until used.</li>
</ol>
3. Emulsion PCR and Sequencing
<ol>
	<li>Perform emulsion PCR:
	<ol>
		<li>Dilute the pooled PCR products to 26 pM in a volume of 25&#160;&#181;l. Thaw reagents from an Ion PGM template kit.</li>
		<li>Prepare the emulsion PCR mix (reagents provided in the kit) in a PCR hood and add the pooled PCR products and the sphere particles (provided) on the bench. Mix thoroughly and insert the mixture in a filter cartridge and carefully top with emulsion oil (provided).</li>
		<li>Slowly reverse the filter cartridge and load on the automated emulsion PCR apparatus (e.g., Ion One Touch 2 instrument). Select the appropriate program and start the procedure.</li>
	</ol>
	</li>
	<li>After the automated emulsion PCR has completed, remove the supernatant from the collection tubes and retrieve the sphere particles at the bottom of the tubes. Resuspend the spheres in 100 &#181;l of wash solution (provided in the kit) and take a 2.0 &#181;l aliquot for library quantification.</li>
	<li>Perform the library quantification.
	<ol>
		<li>Mix 100 &#181;l of SSPE buffer (150 mM NaCl, 10mM NaPO4, 1mM Na2-EDTA) with 2 &#181;l of Adapter B&#8217;-Fam (5&#8217;-FAM-CTG AGA CTG CCA AGG CAC ACA GGG GAT AGG-3&#8217;) probe and 2 &#181;l of Adapter A-Cy5 (5&#8217;-Cy5-CCA TCT CAT CCC TGC GTG TCT CCG ACT CAG-3&#39;) probe. Add 52 &#181;l of this mixture to the 2.0 &#181;l aliquot taken in step 3.2 and add 2 &#181;l of wash solution (from the template kit) to the remaining 52 &#181;l as a blank for quantitation.</li>
		<li>Incubate at 95 &#176;C for 2 min, then at 37 &#176;C for 2 min. Transfer the mixtures in 1.5 ml tubes.</li>
		<li>Add 1.0 ml of TEX buffer (10 mM Tris, 1 mM EDTA, 0.01% Triton X-100, pH 8.0), mix and centrifuge at 15,500 x g for 3 min at RT. Remove the supernatant and leave 20 &#181;l in each tube. Repeat this washing procedure two times.</li>
		<li>Add 180 &#181;l of TEX buffer, resuspend the pelleted particles and transfer into 500 &#181;l Qubit tubes.</li>
		<li>Measure fluorescence for FAM and Cy5 using a Qubit apparatus and calculate the ratio of positive spheres using the calculator available on the Ion Community website.</li>
	</ol>
	</li>
	<li>Enrich the remainder of the sphere particles for positive spheres (containing amplified DNA) using an automated enrichment system (e.g., Ion One Touch Enrichment System).
	<ol>
		<li>Pipet reagents in the appropriate wells of the provided 8-well strip: Well 1: sphere particles from step 3.2; Well 2: pre-washed MyOne Streptavidin C1 beads (13 &#181;l); Well 3, 4 and 5: wash solution (provided in the template kit); Well 6: empty; Well 7: melt-off solution (125 mM NaOH, 0.1% Tween 20); Well 8: empty.</li>
		<li>Install a tip on the pipetting arm and a 0.2 ml tube for sample collection. Start the instrument. At the end of the enrichment, add 10 &#181;l of neutralizing solution (provided). The enriched beads can be kept a 4 &#176;C for up to 15 days.</li>
	</ol>
	</li>
	<li>Initialize the sequencing instrument:
	<ol>
		<li>Connect to the sequencing instrument and prepare a sequencing run plan, specifying the details of the sequencing run.</li>
		<li>Install the wash solution #2 (provided in the Ion PGM sequencing kit), the wash solution #1 (350 &#181;l 100 mM NaOH) and the auto-pH buffer solution (provided).</li>
		<li>Start the initialization procedure and when prompted add the dATP, dGTP dCTP and dTTP nucleotides (provided) in their respective 50 ml tubes. Screw the tubes onto the sequencing machine and continue the initialization procedure. When the initialization is completed, start the run procedure immediately.</li>
	</ol>
	</li>
	<li>Prepare samples for sequencing and load the chip:
	<ol>
		<li>Collect the enriched spheres from step 3.4 at the bottom of the tube by centrifuging at 15,500 x g for 1.5 min. Remove the supernatant leaving exactly 3 &#181;l.</li>
		<li>Add 3 &#181;l of sequencing primer (provided in the sequencing kit), mix thoroughly by pipetting up and down to resuspend the particles. Incubate at 95 &#176;C for 2 min, then at 37 &#176;C for 2 min.</li>
		<li>Add 1 &#181;l of polymerase (provided), mix well and incubate at RT for 5 min. Proceed to load the enzyme-sphere mixture on the sequencing chip within 30 min.</li>
		<li>During incubation, load the sequencing chip on the sequencer and perform a chip quality check. Remove the liquid from the chip by centrifugation and load the sample in the chip by slowly pipeting down the enzyme-sphere mix (7 &#181;l) and avoiding the introduction of bubbles in the chip.</li>
		<li>Centrifuge the chip three times for 1 min in a microcentrifuge. Change the orientation of the chip at each centrifugation and mix by pipetting up and down in between each run.</li>
		<li>Load the chip in the instrument and start the run.</li>
	</ol>
	</li>
</ol>
4. Basic Sequence Data Analysis
<ol>
	<li>Connect to the sequencing data server and download the fastq file. Create a tab-delimited &#8220;oligos&#8221; file containing the primer and barcode information (see example in Table 1). Download the Greengenes reference files from the Mothur website (<a href="http://www.mothur.org/wiki/Taxonomy_outline">http://www.mothur.org/wiki/Taxonomy_outline</a>).</li>
	<li>Launch Mothur and perform analyses:
	<ol>
		<li>Convert the fastq file to a fasta and a quality file using the following command: fastq.info(fastq=test.fastq)</li>
		<li>Determine the group membership of each sequence and trim the sequences using the following command: trim.seqs(fasta=test.fasta, oligos=barcode.txt, qfile=test.qual, qwindowaverage=20, qwindowsize=50, minlength=150, keepforward=T)</li>
		<li>The various options can be modified according to the stringency desired</li>
		<li>Classify the sequences in the greengenes taxonomy using the following command: classify.seqs(fasta=test.trim.fasta, method=wang, group=test.groups, template=gg_99_otus.fasta, taxonomy=gg_99_otus.tax, cutoff=50)</li>
	</ol>
	</li>
</ol>

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The authors have nothing to disclose.

The method presented here is straightforward and inexpensive, and should allow many laboratories to access the power of metagenomic sequencing. Although it varies depending on the sequencing platform used, once the libraries are constructed very little hands-on time is required, with most of the process being automatized. For the sequencing platform used here (Ion Torrent PGM), the complete procedure can be performed within two days of work. At the moment of writing (September 2013), the reagent costs related to the example detailed above were as follows: PCR amplification of 36 samples: $25, gel purification and PicoGreen DNA quantitation of 36 samples: $125, emulsion PCR for one pooled amplicon sample: $150 and sequencing reagents: $250, for a total of $550 or $15 per sample or $0.0015 per quality-filtered read. This price does not include instrument service contract, instrument depreciation, technician salary and laboratory space usage.
One of the most important steps is to pool all the products in an equimolar ratio, in order to retrieve similar number of reads for each of the samples. PicoGreen quantification was used here, but other methods might be suitable, though less accurate (e.g., UV quantification, gel-based quantification). Even by doing the most accurate quantification and pooling, there is some variability in the number of reads per sample, and in the typical run detailed in Table 2, it ranges from 4,380 to 32,750 reads, with an average of 10,338 reads. If processing large number of samples (more than 40-50), single column gel purification can be replaced by gel purification in plate or purification using beads with a stringent size cutoff (e.g., AMPure beads).
To date, the most used next-generation sequencing technology for the 16S rRNA gene is 454. The Ion Torrent sequencing technology used in this protocol is conceptually very similar to 454 and both technologies are prone to the same type of sequencing errors. Not surprisingly, it was shown that Ion Torrent sequencing resulted in sequencing results very similar to 454 sequencing10. Recently, many researchers have explored the use of Illumina technology for 16S rRNA gene amplicon sequencing18,19. In any case, it would be easy to adapt the current protocol for other benchtop sequencers like the Illumina MiSeq or the 454 GS Junior by changing the fusion primer sequences to match the adapters and barcodes needed for these sequencing technologies, like in the method recently described for the Illumina MiSeq19. Alternatively, researchers could follow steps 1 and 2 of the protocol detailed here and send the pooled amplicons to a sequencing center where the emulsion PCR and sequencing would be performed.
The 16S rRNA gene reads were trimmed and classified using Mothur, but many other analyses can be performed on 16S rRNA gene amplicons. For instance, beta diversity can be evaluated by calculating the Unifrac distances between each sample pair using the procedure outlined at <a href="http://unifrac.colorado.edu/">http://unifrac.colorado.edu/</a>20. Alpha diversity indices and number of operational taxonomical units of each sample can be calculated using tools within QIIME like AmpliconNoise 21 or using the procedure outlined by Huse et al.22 and available within Mothur.
The primers used here amplified the variable regions 3 and 4 from the 16S rRNA gene, but many other regions could be targeted. In present study, 16S rRNA genes were amplified from plant material and the choice of primer was made to avoid amplification of chloroplast 16S rRNA gene23,24. There are a wide variety of other primers available that vary in term of the product length, taxonomic power and usefulness25,26. However, in all cases 200-400 bp reads of the 16S rRNA gene cannot be reliably classified at the species level, and analyses are limited to the genus and higher taxonomical levels. Other genes could be more appropriate if species level information is needed, like the cpn60 and rpoB genes27,28. Future drastic drops in the cost of sequencing and increases in the power of analytical tools might make it feasible to replace 16S rRNA gene sequencing by shotgun metagenomics, but until then 16S rRNA gene sequencing remains the gold standard of environmental microbiology.

Metagenomic sequencing is a very powerful technology as it targets the entirety of the genetic information contained in an environmental sample. There are different flavors of metagenomic sequencing, including shotgun sequencing, large-insert libraries and amplicon sequencing. Amplicon sequencing offers the advantage of being relatively inexpensive, fast and able to produce reads from a single genomic region that can be generally aligned. In addition, the data analysis workflow for amplicon sequencing is mostly standardized. However, since it is based on PCR, it has all the biases related to incomplete specificity, incomplete coverage and primer biases1,2, which makes this approach semi-quantitative at best. Several genomic regions can be targeted for amplicon sequencing including functional genes, but the most popular options are to use marker genes such as the 16S rRNA gene to generate a community profile. Traditionally, 16S rRNA gene amplicon sequencing was carried out using labor intensive techniques that included cloning in E. coli, colony picking and plasmid extraction followed by Sanger sequencing on the isolated plasmids, and, consequently, most studies analyzed fewer than 100 clones per sample. Next-generation sequencing brought two major advances: massive parallelization of the sequencing reactions and, most importantly, clonal separation of templates without the need to insert gene fragments in a host. This has simplified tremendously the sequencing of 16S rRNA gene amplicons, which is now back as a routine feature of many environmental microbiology studies, resulting in a &#8220;renaissance&#8221; for 16S rRNA gene amplicon sequencing 3.
Since the advent of Roche 454 sequencing in 20054, several other next-generation sequencing technologies have appeared on the market (e.g., Illumina, Solid, PacBio). More recently, the introduction of bench-top sequencers brought to small labs the sequencing capacity once exclusive to large sequencing centers. Five benchtop machines are currently available: the 454 GS Junior, the Ion Torrent Personal Genome Machine (PGM) and Proton, and the Illumina MiSeq and NextSeq 500. While all these sequencers offer less reads per run and fewer bases per dollar than most full-scale sequencers, they are more flexible, rapid, and their low acquisition and run costs makes them affordable for small academic laboratories. Benchtop sequencers are particularly well suited for amplicon, small genome and low-complexity metagenome sequencing in environmental microbiology studies, because this type of studies generally does not require an extreme depth of sequencing. For example, it is generally agreed that for 16S rRNA gene sequencing studies the number of reads per sample is not paramount, as ~1,000 reads can generate the same patterns as multi-million reads datasets5. Having said that, benchtop next-generation sequencers still generate large amounts of sequence data, with maximal yields of ~35 Mbp (454 GS Junior), ~2 Gbp (Ion Torrent PGM), ~10-15 Gbp (Ion Torrent Proton), ~10 Gbp (Illumina MiSeq) and ~100 Gbp (Illumina Next Seq 500), which is more than enough for most environmental microbiology studies.
Next-generation sequencing of 16S rRNA amplicons using benchtop sequencers has been recently applied to a wide variety of environments. For example, the Ion Torrent PGM has been used for community analyses of uranium mine tailings that had particularly high pH and low permeability6, of recirculating aquaculture systems7, of hydrocarbon-contaminated Arctic soils8,9, of oil sands mining affected sediments and biofilms from the Athabasca River10,11, of the rhizosphere of willows planted in contaminated soils12, of the human and animal bodies13-16 and of anaerobic digesters17.
In this contribution we detail our approach to sequence 16S rRNA gene amplicons in-house using a benchtop next-generation sequencer (the Ion Torrent PGM). After DNA extraction, 16S rRNA genes are amplified using domain-level bacterial primers that contain sequencing adapters and unique, sample-specific sequences (barcodes). The amplicons are purified, quantified and pooled at an equimolar ratio. The pooled samples are then clonally amplified in an emulsion PCR and sequenced. Resulting sequences are analyzed using publicly available bioinformatics tools (e.g., Mothur).

<table border="0" cellpadding="0" cellspacing="0" width="544">
	<tbody>
		<tr height="20">
			<td height="20" style="height:20px;width:271px;">Reagent</td>
			<td style="width:145px;">Company</td>
			<td style="width:128px;">Catalog Number</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:271px;">Ion 314 Chip Kit v2</td>
			<td>Life Technologies</td>
			<td style="width:128px;">4482261</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:271px;">Ion PGM Sequencing 200 Kit v2</td>
			<td>Life Technologies</td>
			<td style="width:128px;">4482006</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:271px;">Ion PGM Template OT2 200 Kit</td>
			<td>Life Technologies</td>
			<td style="width:128px;">4480974</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">HotStarTaq Plus Master Mix Kit</td>
			<td>Qiagen</td>
			<td>203646</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Primers and probes</td>
			<td style="width:145px;">IDT</td>
			<td>NA</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Qiaquick Gel Extraction Kit</td>
			<td>Qiagen</td>
			<td>28704</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">BSA 20 mg/ml</td>
			<td>Roche</td>
			<td>10,711,454,001</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Dynabeads MyOne Streptavidin C1</td>
			<td>Life Technologies</td>
			<td>65001</td>
		</tr>
	</tbody>
</table>

next-generation sequencing of 16s ribosomal rna gene amplicons

One of the major questions in microbial ecology is &#8220;who is there?&#8221; This question can be answered using various tools, but one of the long-lasting gold standards is to sequence 16S ribosomal RNA (rRNA) gene amplicons generated by domain-level PCR reactions amplifying from genomic DNA. Traditionally, this was performed by cloning and Sanger (capillary electrophoresis) sequencing of PCR amplicons. The advent of next-generation sequencing has tremendously simplified and increased the sequencing depth for 16S rRNA gene sequencing. The introduction of benchtop sequencers now allows small labs to perform their 16S rRNA sequencing in-house in a matter of days. Here, an approach for 16S rRNA gene amplicon sequencing using a benchtop next-generation sequencer is detailed. The environmental DNA is first amplified by PCR using primers that contain sequencing adapters and barcodes. They are then coupled to spherical particles via emulsion PCR. The particles are loaded on a disposable chip and the chip is inserted in the sequencing machine after which the sequencing is performed. The sequences are retrieved in fastq format, filtered and the barcodes are used to establish the sample membership of the reads. The filtered and binned reads are then further analyzed using publically available tools. An example analysis where the reads were classified with a taxonomy-finding algorithm within the software package Mothur is given. The method outlined here is simple, inexpensive and straightforward and should help smaller labs to take advantage from the ongoing genomic revolution.

After purification on gel, with 25 cycles of PCR amplification, the amplification products are usually at a concentration of 0.2-10.0 ng in 50 &#181;l of water. This may vary widely depending on the starting DNA concentration, the type of sample and the purification kit used. It is recommended to keep the number of PCR cycles to the lowest possible to avoid chimera formation and decrease amplification biases, keeping in mind that all samples should be amplified using the same number of cycles. To minimize the number of polyclonal reads and empty spheres and maximize the number of reads, the Qubit ratio should be between 0.1 and 0.3 and the FAM fluorescence should be above 200. Using a 314 chip on an Ion Torrent PGM, the average output is around 0.3-0.5 M good quality reads after filtering of the results in Mothur. Table 2 shows a typical breakdown of the number of reads after each step of the procedure for a run containing 36 multiplexed environmental samples amplified with primers targeting the V3-4 region of the 16S and analyzed using Mothur. In Mothur, the trim.seqs procedure generate a &#8220;*.trim.fasta&#8221; file containing the sequences that passed the quality filters and a &#8220;*.scrap.fasta&#8221; that contains the sequences that did not pass the quality filters along with the reason for rejection in the sequence header. When supplied with barcodes in the &#8220;oligos&#8221; file, this command will also generate a &#8220;*.groups&#8221; file that contains the group membership of every sequence based on the barcode sequence. The classify.seqs procedure generates a &#8220;.tax.summary&#8221; that can be opened in Excel. This file contains the summary of the taxonomic affiliation (in lines) for each of the samples (in columns). This file can be used for downstream statistical analyses and to visualize community composition at various taxonomic levels. The &#8220;.taxonomy&#8221; file contains the detailed taxonomic affiliation for each sequence. The average community composition at the phylum/class level across all 36 samples is shown in Figure 1.
<img alt="Figure 1" fo:content-width="5in" src="/files/ftp_upload/51709/51709fig1highres.jpg" width="500" /> 
Figure 1. Average community composition at the phylum/class level across all samples.
<table border="0" cellpadding="0" cellspacing="0" width="248">
	<colgroup>
		<col />
		<col />
		<col />
	</colgroup>
	<tbody>
		<tr height="20">
			<td height="20" style="height:20px;width:64px;">forward</td>
			<td style="width:139px;">TACGGRAGGCAGCAG</td>
			<td style="width:47px;"></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">barcode</td>
			<td>CTAAGGTAAC</td>
			<td>Sample01</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">barcode</td>
			<td>TAAGGAGAAC</td>
			<td>Sample02</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">barcode</td>
			<td>AAGAGGATTC</td>
			<td>Sample03</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">barcode</td>
			<td>TACCAAGATC</td>
			<td>Sample04</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">barcode</td>
			<td>CAGAAGGAAC</td>
			<td>Sample05</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">barcode</td>
			<td>CTGCAAGTTC</td>
			<td>Sample06</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">barcode</td>
			<td>TTCGTGATTC</td>
			<td>Sample07</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">barcode</td>
			<td>TTCCGATAAC</td>
			<td>Sample08</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">barcode</td>
			<td>TGAGCGGAAC</td>
			<td>Sample09</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">barcode</td>
			<td>CTGACCGAAC</td>
			<td>Sample10</td>
		</tr>
	</tbody>
</table>
Table 1. Example &#8220;oligos&#8221; file for use in Mothur.
<table border="0" cellpadding="0" cellspacing="0" width="660">
	<colgroup>
		<col />
		<col />
		<col />
		<col />
	</colgroup>
	<tbody>
		<tr height="20">
			<td height="20" style="height:20px;width:363px;"></td>
			<td style="width:67px;"># of reads</td>
			<td style="width:123px;">% of previous step</td>
			<td style="width:107px;">Avg. per sample</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;width:363px;">Number of wells</td>
			<td align="center">1,262,519</td>
			<td>-</td>
			<td align="center">35,070</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;width:363px;">Wells with beads</td>
			<td align="center">1,114,108</td>
			<td align="center">88.20%</td>
			<td align="center">30,947</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;width:363px;">Beads with templates</td>
			<td align="center">1,112,746</td>
			<td align="center">99.90%</td>
			<td align="center">30,910</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;width:363px;">Monoclonal beads</td>
			<td align="center">826,805</td>
			<td align="center">74.30%</td>
			<td align="center">22,967</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;width:363px;">Good quality reads (Output from the sequencer)</td>
			<td align="center">782,204</td>
			<td align="center">94.60%</td>
			<td align="center">21,728</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;width:363px;">Pass Mothur filters (min. avg. quality score of 20 over a 50bp window, min. length of 150bp)</td>
			<td align="center">372,168</td>
			<td align="center">47.60%</td>
			<td align="center">10,338</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;width:363px;">Classified at the phylum level in GreenGenes (50% confidence threshold)</td>
			<td align="center">342,171</td>
			<td align="center">91.90%</td>
			<td align="center">9,505</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;width:363px;">Classified at the family level in GreenGenes (50% confidence threshold)</td>
			<td align="center">316,512</td>
			<td align="center">92.50%</td>
			<td align="center">8,792</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;width:363px;">Classified at the genus level in GreenGenes (50% confidence threshold)</td>
			<td align="center">289,899</td>
			<td align="center">91.60%</td>
			<td align="center">8,053</td>
		</tr>
	</tbody>
</table>
Table 2. Number of reads produced from a typical run for 36 environmental samples multiplexed on one Ion Torrent 314 chip.

Watch this Scientific Journal Video about Next-generation Sequencing of 16S Ribosomal RNA Gene Amplicons at JoVE.com

Next-generation Sequencing of 16S Ribosomal RNA Gene Amplicons

Characterizing microbial community has been a longstanding goal in environmental microbiology. Next-generation sequencing methods now allow for the characterization of microbial communities at an unprecedented depth with minimal cost and labor. We detail here our approach to sequence bacterial 16S ribosomal RNA genes using a benchtop sequencer.

One of the major questions in microbial ecology is &#8220;who is there?&#8221; This question can be answered using various tools, but one of the ...

next-generation-sequencing-of-16s-ribosomal-rna-gene-amplicons

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Biology

83.2K Views.  National Research Council Canada. Characterizing microbial community has been a longstanding goal in environmental microbiology. Next-generation sequencing methods now allow for the characterization of microbial communities at an unprecedented depth with minimal cost and labor. We detail here our approach to sequence bacterial 16S ribosomal RNA genes using a benchtop sequencer. 

Video: Next-generation Sequencing of 16S Ribosomal RNA Gene Amplicons

Fluorescence-microscopy Screening and Next-generation Sequencing: Useful Tools for the Identification of Genes Involved in Organelle Integrity

This protocol describes a fluorescence microscope-based screening of Arabidopsis seedlings and describes how to map recessive mutations that alter the subcellular distribution of a specific tagged fluorescent marker in the secretory pathway. Arabidopsis is a powerful biological model for genetic studies because of its genome size, generation time, and conservation of molecular mechanisms among kingdoms. The array genotyping as an approach to map the mutation in alternative to the traditional method based on molecular markers is advantageous because it is relatively faster and may allow the mapping of several mutants in a really short time frame. This method allows the identification of proteins that can influence the integrity of any organelle in plants. Here, as an example, we propose a screen to map genes important for the integrity of the endoplasmic reticulum (ER). Our approach, however, can be easily extended to other plant cell organelles (for example see1,2), and thus represents an important step toward understanding the molecular basis governing other subcellular structures.

A fundamental quest in cell biology is to define the mechanisms that underlie the identity of the organelles that make eukaryotic cells. Here we propose a method to identify the genes responsible for the morphological and functional integrity of plant organelles using fluorescence microscopy and next-generation sequencing tools.

This protocol describes a fluorescence microscope-based screening of Arabidopsis seedlings and describes how to map recessive mutations that alter the ...

Depletion of Ribosomal RNA for Mosquito Gut Metagenomic RNA-seq

The mosquito gut accommodates dynamic microbial communities across different stages of the insect's life cycle. Characterization of the genetic capacity and functionality of the gut community will provide insight into the effects of gut microbiota on mosquito life traits. Metagenomic RNA-Seq has become an important tool to analyze transcriptomes from various microbes present in a microbial community. Messenger RNA usually comprises only 1-3% of total RNA, while rRNA constitutes approximately 90%. It is challenging to enrich messenger RNA from a metagenomic microbial RNA sample because most prokaryotic mRNA species lack stable poly(A) tails. This prevents oligo d(T) mediated mRNA isolation. Here, we describe a protocol that employs sample derived rRNA capture probes to remove rRNA from a metagenomic total RNA sample. To begin, both mosquito and microbial small and large subunit rRNA fragments are amplified from a metagenomic community DNA sample. Then, the community specific biotinylated antisense ribosomal RNA probes are synthesized in vitro using T7 RNA polymerase. The biotinylated rRNA probes are hybridized to the total RNA. The hybrids are captured by streptavidin-coated beads and removed from the total RNA. This subtraction-based protocol efficiently removes both mosquito and microbial rRNA from the total RNA sample. The mRNA enriched sample is further processed for RNA amplification and RNA-Seq.

A ribosomal RNA (rRNA) depletion protocol was developed to enrich messenger RNA (mRNA) for RNA-seq of the mosquito gut metatranscriptome. Sample specific rRNA probes, which were used to remove rRNA via subtraction, were created from the mosquito and its gut microbes. Performance of the protocol can result in the removal of approximately 90-99% of rRNA.

The mosquito gut accommodates dynamic microbial communities across different stages of the insect's life cycle. Characterization of the genetic capacity ...

Targeted DNA Methylation Analysis by Next-generation Sequencing

The role of epigenetic processes in the control of gene expression has been known for a number of years. DNA methylation at cytosine residues is of particular interest for epigenetic studies as it has been demonstrated to be both a long lasting and a dynamic regulator of gene expression. Efforts to examine epigenetic changes in health and disease have been hindered by the lack of high-throughput, quantitatively accurate methods. With the advent and popularization of next-generation sequencing (NGS) technologies, these tools are now being applied to epigenomics in addition to existing genomic and transcriptomic methodologies. For epigenetic investigations of cytosine methylation where regions of interest, such as specific gene promoters or CpG islands, have been identified and there is a need to examine significant numbers of samples with high quantitative accuracy, we have developed a method called Bisulfite Amplicon Sequencing (BSAS). This method combines bisulfite conversion with targeted amplification of regions of interest, transposome-mediated library construction and benchtop NGS. BSAS offers a rapid and efficient method for analysis of up to 10 kb of targeted regions in up to 96 samples at a time that can be performed by most research groups with basic molecular biology skills. The results provide absolute quantitation of cytosine methylation with base specificity. BSAS can be applied to any genomic region from any DNA source. This method is useful for hypothesis testing studies of target regions of interest as well as confirmation of regions identified in genome-wide methylation analyses such as whole genome bisulfite sequencing, reduced representation bisulfite sequencing, and methylated DNA immunoprecipitation sequencing.

Bisulfite amplicon sequencing (BSAS) is a method for quantifying cytosine methylation in targeted genomic regions of interest. This method uses bisulfite conversion paired with PCR amplification of target regions prior to next-generation sequencing to produce absolute quantitation of DNA methylation at a base-specific level.

The role of epigenetic processes in the control of gene expression has been known for a number of years. DNA methylation at cytosine residues is of ...

Amplification, Next-generation Sequencing, and Genomic DNA Mapping of Retroviral Integration Sites

Retroviruses exhibit signature integration preferences on both the local and global scales. Here, we present a detailed protocol for (1) generation of diverse libraries of retroviral integration sites using ligation-mediated PCR (LM-PCR) amplification and next-generation sequencing (NGS), (2) mapping the genomic location of each virus-host junction using BEDTools, and (3) analyzing the data for statistical relevance. Genomic DNA extracted from infected cells is fragmented by digestion with restriction enzymes or by sonication. After suitable DNA end-repair, double-stranded linkers are ligated onto the DNA ends, and semi-nested PCR is conducted using primers complementary to both the long terminal repeat (LTR) end of the virus and the ligated linker DNA. The PCR primers carry sequences required for DNA clustering during NGS, negating the requirement for separate adapter ligation. Quality control (QC) is conducted to assess DNA fragment size distribution and adapter DNA incorporation prior to NGS. Sequence output files are filtered for LTR-containing reads, and the sequences defining the LTR and the linker are cropped away. Trimmed host cell sequences are mapped to a reference genome using BLAT and are filtered for minimally 97% identity to a unique point in the reference genome. Unique integration sites are scrutinized for adjacent nucleotide (nt) sequence and distribution relative to various genomic features. Using this protocol, integration site libraries of high complexity can be constructed from genomic DNA in three days. The entire protocol that encompasses exogenous viral infection of susceptible tissue culture cells to integration site analysis can therefore be conducted in approximately one to two weeks. Recent applications of this technology pertain to longitudinal analysis of integration sites from HIV-infected patients.

We describe a protocol for amplifying retroviral integration sites from the genomic DNA of infected cells, sequencing the amplified virus-host junctions, and then mapping these sequences to a reference genome. We also describe techniques to quantify the distribution of integration sites relative to various genomic annotations using BEDTools.

Retroviruses exhibit signature integration preferences on both the local and global scales. Here, we present a detailed protocol for (1) generation of ...

Efficient Nucleic Acid Extraction and 16S rRNA Gene Sequencing for Bacterial Community Characterization

There is a growing appreciation for the role of microbial communities as critical modulators of human health and disease. High throughput sequencing technologies have allowed for the rapid and efficient characterization of bacterial communities using 16S rRNA gene sequencing from a variety of sources. Although readily available tools for 16S rRNA sequence analysis have standardized computational workflows, sample processing for DNA extraction remains a continued source of variability across studies. Here we describe an efficient, robust, and cost effective method for extracting nucleic acid from swabs. We also delineate downstream methods for 16S rRNA gene sequencing, including generation of sequencing libraries, data quality control, and sequence analysis. The workflow can accommodate multiple samples types, including stool and swabs collected from a variety of anatomical locations and host species. Additionally, recovered DNA and RNA can be separated and used for other applications, including whole genome sequencing or RNA-seq. The method described allows for a common processing approach for multiple sample types and accommodates downstream analysis of genomic, metagenomic and transcriptional information.

We describe an efficient, robust, and cost effective method for extracting nucleic acid from swabs for characterization of bacterial communities using 16S rRNA gene amplicon sequencing. The method allows for a common processing approach for multiple sample types and accommodates a number of downstream analytic processes.

There is a growing appreciation for the role of microbial communities as critical modulators of human health and disease. High throughput sequencing ...

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

RNA sequencing (RNAseq) is a versatile method that can be utilized to detect and characterize gene expression, mutations, gene fusions, and noncoding RNAs. Standard RNAseq requires 30 - 100 million sequencing reads and can include multiple RNA products such as mRNA and noncoding RNAs. We demonstrate how targeted RNAseq (capture) permits a focused study on selected RNA products using a desktop sequencer. RNAseq capture can characterize unannotated, low, or transiently expressed transcripts that may otherwise be missed using traditional RNAseq methods. Here we describe the extraction of RNA from cell lines, ribosomal RNA depletion, cDNA synthesis, preparation of barcoded libraries, hybridization and capture of targeted transcripts and multiplex sequencing on a desktop sequencer. We also outline the computational analysis pipeline, which includes quality control assessment, alignment, fusion detection, gene expression quantification and identification of single nucleotide variants. This assay allows for targeted transcript sequencing to characterize gene expression, gene fusions, and mutations.

We describe a targeted RNA sequencing-based method that includes preparation of indexed cDNA libraries, hybridization and capture with custom probes and data analysis to interrogate selected transcripts for gene expression, mutations, and gene fusions. Targeted RNAseq permits cost-effective, rapid evaluation of selected transcripts on a desktop sequencer.

RNA sequencing (RNAseq) is a versatile method that can be utilized to detect and characterize gene expression, mutations, gene fusions, and noncoding ...

Guided Protocol for Fecal Microbial Characterization by 16S rRNA-Amplicon Sequencing

The human intestinal microbiome plays a central role in protecting cells from injury, in processing energy and nutrients, and in promoting immunity. Deviations from what is considered a healthy microbiota composition (dysbiosis) may impair vital functions leading to pathologic conditions. Recent and ongoing research efforts have been directed toward the characterization of associations between microbial composition and human health and disease.
Advances in high-throughput sequencing technologies enable characterization of the gut microbial composition. These methods include 16S rRNA-amplicon sequencing and shotgun sequencing. 16S rRNA-amplicon sequencing is used to profile taxonomical composition, while shotgun sequencing provides additional information about gene predictions and functional annotation. An advantage in using a targeted sequencing method of the 16S rRNA gene variable region is its substantially lower cost compared to shotgun sequencing. Sequence differences in the 16S rRNA gene are used as a microbial fingerprint to identify and quantify different taxa within an individual sample.
Major international efforts have enlisted standards for 16S rRNA-amplicon sequencing. However, several studies report a common source of variation caused by batch effect. To minimize this effect, uniformed protocols for sample collection, processing, and sequencing must be implemented. This protocol proposes the integration of broadly used protocols starting from fecal sample collection to data analyses. This protocol includes a column-free, direct-PCR approach that enables simultaneous handling and DNA extraction of large numbers of fecal samples, along with PCR amplification of the V4 region. In addition, the protocol describes the analysis pipeline and provides a script using the latest version of QIIME (QIIME 2 version 2017.7.0 and DADA2). This step-by-step protocol is aimed to guide those interested in initiating the use of 16S rRNA-amplicon sequencing in a robust, reproductive, easy to use, detailed way.

This manuscript describes a detailed standardized protocol of high-throughput 16S rRNA-amplicon sequencing. The protocol introduces an integrated, uniformed, feasible, and inexpensive protocol starting from fecal sample collection through data analyses. This protocol enables analysis of large numbers of samples with rigorous standards and several controls.

The human intestinal microbiome plays a central role in protecting cells from injury, in processing energy and nutrients, and in promoting immunity. ...

Tick Microbiome Characterization by Next-Generation 16S rRNA Amplicon Sequencing

In recent decades, vector-borne diseases have re-emerged and expanded at alarming rates, causing considerable morbidity and mortality worldwide. Effective and widely available vaccines are lacking for a majority of these diseases, necessitating the development of novel disease mitigation strategies. To this end, a promising avenue of disease control involves targeting the vector microbiome, the community of microbes inhabiting the vector. The vector microbiome plays a pivotal role in pathogen dynamics, and manipulations of the microbiome have led to reduced vector abundance or pathogen transmission for a handful of vector-borne diseases. However, translating these findings into disease control applications requires a thorough understanding of vector microbial ecology, historically limited by insufficient technology in this field. The advent of next-generation sequencing approaches has enabled rapid, highly parallel sequencing of diverse microbial communities. Targeting the highly-conserved 16S rRNA gene has facilitated characterizations of microbes present within vectors under varying ecological and experimental conditions. This technique involves amplification of the 16S rRNA gene, sample barcoding via PCR, loading samples onto a flow cell for sequencing, and bioinformatics approaches to match sequence data with phylogenetic information. Species or genus-level identification for a high number of replicates can typically be achieved through this approach, thus circumventing challenges of low detection, resolution, and output from traditional culturing, microscopy, or histological staining techniques. Therefore, this method is well-suited for characterizing vector microbes under diverse conditions but cannot currently provide information on microbial function, location within the vector, or response to antibiotic treatment. Overall, 16S next-generation sequencing is a powerful technique for better understanding the identity and role of vector microbes in disease dynamics.

Here we present a next-generation sequencing protocol for 16S rRNA sequencing which enables identification and characterization of microbial communities within vectors. This method involves DNA extraction, amplification and barcoding of samples through PCR, sequencing on a flow-cell, and bioinformatics to match sequence data to phylogenetic information.

In recent decades, vector-borne diseases have re-emerged and expanded at alarming rates, causing considerable morbidity and mortality worldwide. Effective ...

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

The human gut is colonized by trillions of bacteria that support physiologic functions such as food metabolism, energy harvesting, and regulation of the immune system. Perturbation of the healthy gut microbiome has been suggested to play a role in the development of inflammatory diseases, including multiple sclerosis (MS). Environmental and genetic factors can influence the composition of the microbiome; therefore, identification of microbial communities linked with a disease phenotype has become the first step towards defining the microbiome&#8217;s role in health and disease. Use of 16S rRNA metagenomic sequencing for profiling bacterial community has helped in advancing microbiome research. Despite its wide use, there is no uniform protocol for 16S rRNA-based taxonomic profiling analysis. Another limitation is the low resolution of taxonomic assignment due to technical difficulties such as smaller sequencing reads, as well as use of only forward (R1) reads in the final analysis due to low quality of reverse (R2) reads. There is need for a simplified method with high resolution to characterize bacterial diversity in a given biospecimen. Advancements in sequencing technology with the ability to sequence longer reads at high resolution have helped to overcome some of these challenges. Present sequencing technology combined with a publicly available metagenomic analysis pipeline such as R-based Divisive Amplicon Denoising Algorithm-2 (DADA2) has helped advance microbial profiling at high resolution, as DADA2 can assign sequence at the genus and species levels. Described here is a guide for performing bacterial profiling using two-step amplification of the V3-V4 region of the 16S rRNA gene, followed by analysis using freely available analysis tools (i.e., DADA2, Phyloseq, and METAGENassist). It is believed that this simple and complete workflow will serve as an excellent tool for researchers interested in performing microbiome profiling studies.

Described here is a simplified standard operating procedure for microbiome profiling using 16S rRNA metagenomic sequencing and analysis using freely available tools. This protocol will help researchers who are new to the microbiome field as well as those requiring updates on methods to achieve bacterial profiling at a higher resolution.

The human gut is colonized by trillions of bacteria that support physiologic functions such as food metabolism, energy harvesting, and regulation of the ...

A Magnetic-Bead-Based Mosquito DNA Extraction Protocol for Next-Generation Sequencing

A recently published DNA extraction protocol using magnetic beads and an automated DNA extraction instrument suggested that it is possible to extract high quality and quantity DNA from a well-preserved individual mosquito sufficient for downstream whole genome sequencing. However, reliance on an expensive automated DNA extraction instrument can be prohibitive for many laboratories. Here, the study provides a budget-friendly magnetic-bead-based DNA extraction protocol, which is suitable for low to medium throughput. The protocol described here was successfully tested using individual Aedes aegypti mosquito samples. The reduced costs associated with high quality DNA extraction will increase the application of high throughput sequencing to resource limited labs and studies.

Described here is a DNA extraction protocol using magnetic beads to produce high quality DNA extractions from mosquitoes. These extractions are suitable for a downstream next-generation sequencing approach.

A recently published DNA extraction protocol using magnetic beads and an automated DNA extraction instrument suggested that it is possible to extract high ...