We would like to thank members of the laboratories of Andrew Fire and Mark Kay for guidance and suggestions.

Animal experiments were authorized by the Institutional Animal Care and Use Committee of the University of Washington.
Small RNA library preparation
1. RNA isolation
<ol>
	<li>Isolate RNA from a biological source using a standard RNA isolation reagent, or a kit that enriches for microRNAs. For tissues, it is best to start with samples snap-frozen in liquid nitrogen and ground to a powder using a pre-chilled mortar and pestle.</li>
	<li>Measure each sample&#8217;s RNA integrity on an instrument that can quantify RNA and provide an RNA integrity number (RIN). RINs should be &#62;7.</li>
</ol>
2. 3&#39; adaptor ligation
<ol>
	<li>Prepare a ligation reaction in PCR strip tubes, by combining: 11 &#956;L of RNA (1-3 &#956;g, using the same amount for each sample), 1.5 &#956;L of 10x T4 RNA ligase reaction buffer, ATP-free, 1 &#956;L of poly-ethylene glycol (PEG), and 0.5 &#956;L of 3&#39;-linker (100 &#956;M Universal miRNA cloning linker). 
	NOTE: The absence of ATP helps enrich for miRNAs and minimizes cloning of mRNA degradation products. PEG acts as a molecular crowding agent, enhancing successful ligation. The Universal miRNA cloning linker has a 3&#39; blocking group (amine) to prevent self-ligation, circularization, and ligation to RNA at the 5&#39; end.</li>
	<li>Heat the samples at 95 &#176;C on a thermocycler for 30-40 s. Cool on ice for 1 min. Add 1 &#956;L of T4 RNA ligase 2 and incubate at room temperature for 2 h. Prepare the gel (step 2.3) while the samples are incubating. 
	NOTE: Incubation at room temperature helps prevent linker-linker ligation. We have also successfully used T4 RNA ligase 1.</li>
	<li>Prepare 30 mL of a 15% polyacrylamide gel with 8 M urea (for a 20x20 cm gel): 14.4 g of urea, 3 mL of 10x Tris-buffered ethylenediaminetetraacetic acid (TBE), 11.2 mL of 40% 19:1 acrylamide, and H2O to 30 mL. Solution is best dissolved at 42 &#176;C. Immediately before casting, add 150 &#956;L of 10% ammonium persulfate (APS) and 30 &#956;L of tetramethylethylenediamine (TEMED) for polymerization.</li>
	<li>Pour between 0.8 mm separated glass plates in a plastic cast and insert comb. Once the gel is solidified (about 20 min), add 0.5x TBE to tank and wash wells of residual urea by pipetting vigorously.</li>
	<li>Pre-run the gel at a constant 375 V for 25 min without samples so urea can enter the gel, then wash the wells again. 
	NOTE: The amount of voltage may need to be reduced depending on the type of power supply and electrophoresis system that is used.</li>
	<li>Once the samples are done ligating, add 15 &#956;L of acrylamide loading dye to the samples (for a 1:1 ratio), then denature for 5 min at 95 &#176;C on a thermocycler.</li>
	<li>Prepare 25 ng/&#956;L of 37 and 44 bp size markers, diluted with one part acrylamide loading dye. Sequences are listed in Table 1.</li>
	<li>Load the samples on the polyacrylamide gel, leaving at least one lane in between each sample. Load 20 &#956;L of at least two sets of markers, in an asymmetrical pattern to keep track of gel orientation.</li>
	<li>Run the gel at a constant 375 V for the first 15 min and then increase to a constant 425 V for the remaining run. Run until bromophenol blue is about 1-4 cm from the bottom, which takes approximately 2 h. 
	NOTE: If necessary, the gel may be run at a lower constant voltage for a longer period of time until the bromophenol blue is about 1-4 cm from the bottom.</li>
	<li>Remove the gel from the glass plates using a plate separator, and place the gel on a plastic page protector. Dilute 5 &#956;L of ethidium bromide in 500 &#956;L of distilled water, and pipette onto marker lanes just above the top light blue marker (see Figure 1A). 
	CAUTION: Use gloves for ethidium bromide and dispose of waste in accordance with local regulations. Let sit for 5 min.</li>
	<li>Under ultraviolet (UV) light, cut the gels from upper to lower marker in each lane using clean razor blade (see Figure 1A). Transfer to a 4 x 4 cm square of laboratory sealing film, then cut the gel with about 4 cuts horizontally and 3 vertically to produce 12 small squares (see Figure 1B).</li>
	<li>Pipette 400 &#956;L of 0.3 M NaCl on to the sealing film square, and funnel gel pieces into 1.5 mL siliconized tubes (see Figure 1C). Agitate the samples on a nutator at 4 &#176;C overnight. 
	NOTE: Other low-retention 1.5 mL tubes can be substituted for siliconized tubes.</li>
	<li>After at least 12 h of agitation at 4 &#176;C, retrieve the samples and put them on ice, along with a conical tube of 100% ethanol.</li>
	<li>Transfer 400 &#956;L of supernatant to a new tube, and then add 1 mL of 100% ethanol and 1 &#956;L of 15 mg/mL glycogen coprecipitant. Ensure to collect as much supernatant as possible, spinning down at 4 &#176;C and pipetting more as necessary. Place at -80 &#176;C for 1 h, or -20 &#176;C for 2 h or longer. Glycogen coprecipitant improves pellet visibility and recovery.</li>
	<li>Spin at 4 &#176;C at 17,000 x g for 20-30 min. Remove all traces of ethanol and let pellet air dry for 5 min.</li>
</ol>
3. 5&#39; linker ligation
<ol>
	<li>Re-suspend the pellet by pipetting in 6.5 &#956;L of nuclease-free water. Letting the pellet sit in water for a few minutes first will help with re-suspension.</li>
	<li>After spinning down the pellet and resuspending in water, add 0.5 &#956;L of 100 &#956;M 5&#39;-linker (with barcodes; see Table 1), 1 &#956;L of T4 RNA ligase buffer, 1 &#956;L of 10 mM ATP, and 1 &#956;L of PEG. Heat at 90 &#176;C for 30 s, then place on ice. Add 1 &#956;L of T4 RNA ligase 1 and let incubate at room temperature for 2 h.</li>
	<li>Add 400 &#956;L of 0.3 M NaCl, followed by 400 &#956;L of acid phenol/chloroform. Vortex 30 s - 1 min (solution will look cloudy), and then spin at 4 &#176;C for 10-15 min at max speed in a microcentrifuge (~17,000 x g). Draw off the top layer and place in new 1.5 mL tube. 
	NOTE: Avoid pipetting any of the bottom layer.</li>
	<li>Add 350 &#956;L of chloroform, vortex briefly, and then spin at 4 &#176;C for 10 min at max speed (~17,000 x g). Draw off the top later and place in new 1.5 mL tube. Add 1.5 &#956;L of glycogen coprecipitant and 1 mL of 100% ethanol. 
	NOTE: Again, avoid pipetting any of the bottom layer.</li>
	<li>Vortex briefly, then place at -80 &#176;C for at least 1 h, or -20 &#176;C overnight.</li>
</ol>
4. Reverse transcription (RT)
<ol>
	<li>Turn on the 42 &#176;C heat block. Spin the samples at 4 &#176;C and ~17,000 x g for 20-30 min. Remove all supernatant and let the pellet air dry for 5 min.</li>
	<li>Re-suspend pelleted sample in 8.25 &#956;L of nuclease-free water, then add: 0.5 &#956;L of 100 &#956;M RT primer (Table 1), and 5 &#956;L of 2x RT Reaction Mix from a cDNA synthesis kit. Incubate at 42 &#176;C for 3 min.</li>
	<li>Add 1.5 &#956;L of 10x RT enzyme to each sample and incubate at 42 &#176;C for 30 min in a thermocycler. Place at -20 &#176;C or continue with hydrolysis and neutralization. 
	NOTE: Several RT kits can be utilized for steps 4.2 and 4.3.</li>
	<li>Perform alkaline hydrolysis and neutralization: Make 1 mL of 150 mM potassium hydroxide (KOH) solution (150 &#956;L of 1 M KOH, 20 &#956;L of 1 M Tris Base pH 7.5, and 830 &#956;L of H2O) and 1 mL of 150 mM hydrochloric acid (HCl) (150 &#956;L of 1 M HCl and 850 &#956;L of H2O).</li>
	<li>Before adding to the samples, determine the amount of HCl needed to neutralize the KOH solution. Generally, around 20-24 &#956;L of HCl will neutralize the 25 &#956;L of KOH. Check the combination on a pH strip to ensure it is in the right range (pH 7.0 to 9.5).</li>
	<li>Hydrolyze the samples by adding 25 &#956;L of 150 mM KOH solution and incubate for 10 min at 95 &#176;C.</li>
	<li>Neutralize the samples by adding the amount of 150 mM HCl determined in step 4.4, to obtain a final sample pH between 7.0 and 9.5.</li>
</ol>
5. PCR amplification
<ol>
	<li>After neutralization, prepare a PCR reaction with: 29.5 &#956;L of water, 5 &#956;L of 10x Taq buffer, 1 &#956;L of dNTP, 2 &#956;L of 25 &#956;M forward primer (Table 1), 2 &#956;L of 25 &#956;M reverse primer (Table 1), 0.5 &#956;L of Taq, and 10 &#956;L of the reverse transcribed cDNA from step 4.6.</li>
	<li>Run the following PCR reaction: 94 &#176;C for 2 min, then 20 cycles of 94 &#176;C for 45 s, 50 &#176;C for 75 s and 72 &#176;C for 60 s.</li>
	<li>Run a second PCR reaction of about 2-4 more cycles using 5 &#956;L of product from step 5.2. Mix: 34.8 &#956;L of water, 5 &#956;L of 10x Taq buffer, 1 &#956;L of dNTP, 1 &#956;L of 25 &#956;M forward primer (Table 1), 1 &#956;L of 25 &#956;M reverse primer (Table 1), and 0.2 &#956;L of Taq polymerase. Follow the same thermocycler parameters outlined in step 5.2. 
	NOTE: The purpose of doing two PCR reactions &#8211; with the first for 20 cycles and the second for just 2-4 more &#8211; is to ensure that the amount of cDNA amplified is in a dynamic range (i.e., not a saturated amount).</li>
</ol>
6. Agarose gel purification
<ol>
	<li>Prepare a 4% agarose gel with low-melting agarose. Load 40 &#956;L or more of the PCR product on the gel, along with loading dye. Load 100 bp and 25 bp size markers. 
	NOTE: The 25 bp ladder helps to distinguish amplified product from linker-linker ligation products. Low-melting agarose gels must be cast with greater care than traditional agarose gels, so closely follow the instructions from the manufacturer.</li>
	<li>For gel extraction, select the cycle number that is visible on the gel but is not saturated (usually 22&#8211;24 cycles). Choose similar intensity bands when running multiple samples.</li>
	<li>Cut the band that is above the 125 bp band (the darker band on 25 bp ladder; see Figure 1D). Using a gel extraction kit, follow the manufacturer&#8217;s instructions for adding buffer based on a 4% gel, then shake to dissolve agarose in buffer at room temperature. 
	NOTE: Dissolving at 55 &#176;C increases the potential for linker-linker ligation.</li>
	<li>Follow the manufacturer&#8217;s gel extraction instructions and elute in 30 &#956;L of elution buffer or water. If the product looked weak on the gel, then reduce elution to 20 &#956;L.</li>
	<li>Measure cDNA concentration using a sensitive technique, and prepare sample library for sequencing. Preparation will depend on the type of sequencing used. 
	NOTE: Minimum requirements for a sequencing library are typically a 10 &#956;L volume of 10 &#956;M product. If concentrations are too low, pool and ethanol precipitate samples to bring library to the desired concentration.</li>
	<li>Sequence the samples using available equipment. A common example would be to run samples using a kit for 50 bp single reads, to obtain approximately 15-25 million reads in a FASTQ output format.</li>
</ol>
Small RNA sequence alignment and bioinformatics
7. Data upload
<ol>
	<li>Download FASTQ files generated from each sequencing run. Download a list of microRNA hairpin sequences from miRbase.org8,9,10.</li>
	<li>Generate a Galaxy account at www.usegalaxy.org and upload a FASTQ file of sequence reads to this account.</li>
	<li>Upload a text file of barcode sequences to the Galaxy account, such as barcodes.txt, which is available as a text file (Supplementary Table 1).</li>
	<li>Upload a FASTA file of microRNA hairpins to the Galaxy account from a database like miRBase.org. Examples of mouse (mousehairpins.fa) or human (humanhairpins.fa) microRNA precursors are provided in Supplementary Table 2 and Supplementary Table 3.</li>
</ol>
8. Adaptor removal, barcode sort, and trim
<ol>
	<li>In the left-hand tab, navigate to Genomic file manipulation &#62; FASTA/FASTQ &#62; Clip adapter sequences.</li>
	<li>In Input file in FASTA or FASTQ format, enter FASTQ file from the drop-down list. Change Minimum sequence length to 18. Change Source to Enter custom sequence. Enter CTGTAGGC. Keep all other default parameters. Click Execute. 
	NOTE: Sequence reads that are shorter than 18 nucleotides are difficult to map uniquely to microRNAs and contain many degradation products.</li>
	<li>In the left-hand tab, navigate to Genomic file manipulation &#62; FASTA/FASTQ &#62; Barcode Splitter. 
	NOTE: Galaxy functions and headers are updated periodically, so the search function may be necessary to find an equivalent tool or its location. Commercial kits using indexed primers are often already sorted by barcode. Therefore, this step and the barcode trim step are not necessary if starting from a commercial kit.</li>
	<li>For Barcodes to use, point to barcodes.txt. For Library to split, use Clip on data file produced in the previous step. In Number of allowed mismatches, enter 1. Click Execute.</li>
	<li>Trim the first 4 nucleotides: navigate to Text Manipulations &#62; Trim leading or trailing characters. For Input dataset, click on the folder icon, which is a dataset collection. Select the batch file of samples, which includes the label Barcode splitter on data. In Trim from the beginning up to this position, enter 5. In Is input dataset in FASTQ format? enter Yes. Click Execute. Execution may take several minutes.</li>
</ol>
9. Alignment of reads to microRNAs
<ol>
	<li>In Galaxy, navigate to Genomics Analysis &#62; RNA-Seq &#62; Sailfish transcript quantification11.</li>
	<li>For the question Select a reference transcriptome from your history or use a built-in index? select Use one from the history. Enter the uploaded file mousehairpins.fa from the drop-down list. In FASTA/Q file, click the folder icon to use a dataset collection and select the file that includes Trim on collection. Click Execute. Execution may take several minutes.</li>
	<li>In the history tab on the right, click on Sailfish on collection&#8230; which is a list with 19 items. Click on each individual file and click the disk icon to save to local computer. Individually downloaded files first need to be uncompressed. They also may need to be re-saved with a .txt extension for the purpose of importing into a spreadsheet.</li>
	<li>Open each spreadsheet file in and re-name the NumReads column to the treatment condition. Merge the columns together to generate a matrix of microRNAs in the first column and read counts per condition in subsequent columns.</li>
	<li>To calculate differentially expressed microRNAs for each treatment condition, use the file with raw microRNA read counts as input for programs such as DESeq212. DESeq2 is present in Galaxy in the Genomics Analysis &#62; RNA-seq &#62; DESeq2 tab.</li>
	<li>Convert raw reads to normalized microRNA read counts. Counts are normalized to the library sequence depth of the library through the following calculation: [(raw reads/total microRNA reads) * (1,000,000 &#8211; number of microRNAs counted) + 1]. 
	NOTE: This calculation provides a normalized reads per million (RPM) mapped microRNAs that can be compared across datasets and biological conditions. The output is a tab delimited file. Sailfish provides a tpm output column, though this value is normalized by microRNA hairpin length, which is not necessary in this context.</li>
	<li>If relevant, repeat alignment with custom input sequences (e.g., a vector) to identify reads that map to delivered constructs, like shRNAs.</li>
</ol>

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The authors have nothing to disclose.

Despite the identification of microRNAs over 20 years ago13, the process of microRNA sequencing remains laborious and requires specialized equipment, hindering laboratories from routinely adopting in-house protocols14. Other techniques can simultaneously evaluate microRNAs, like microRNA microarrays and multiplexed expression panels; however, these approaches are limited in that they only quantify the microRNAs present in their probe set. Because of this, they miss importan...

First discovered in 19931, it is now estimated that nearly 2000 microRNAs are present in the human genome2. MicroRNAs are small non-coding RNAs that are typically 21-24 nucleotides long. They are post-transcriptional regulators of gene expression, often binding to complementary sites in the 3-untranslated region (3-UTR) of target genes to repress protein expression and degrade mRNA. Quantifying microRNAs can give valuable insight into gene expression and several protocols have been developed for this purpose3.
We have developed a defined, reproducible, and long-standing protocol for small RNA sequencing, and for analyzing normalized reads using open source bioinformatics tools. Importantly, our protocol enables the simultaneous identification of both endogenous microRNAs and exogenously delivered constructs that produce microRNA-like species, while minimizing reads that map to other small RNA species, including ribosomal RNAs (rRNAs), transfer RNA-derived small RNAs (tsRNAs), repeat-derived small RNAs, and mRNA degradation products. Fortunately, microRNAs are 5-phosphorylated and 2-3 hydroxylated4, a feature that can be leveraged to separate them from these other small RNAs and mRNA degradation products. Several commercial options exist for microRNA cloning and sequencing that are often quicker and easier to multiplex; however, the proprietary nature of kit reagents and their frequent modifications makes comparing sample runs challenging. Our strategy optimizes collecting only the correct size of microRNAs through acrylamide and agarose gel purification steps. In this protocol, we also describe a procedure for aligning sequence reads to microRNAs using open source tools. This set of instructions will be especially useful for novice informatics users, regardless of whether our library preparation method or a commercial method is used.
This protocol has been used in several published studies. For example, it was used to identify the mechanism by which the Dicer enzyme cleaves small hairpin RNAs at a distance of two nucleotides from the internal loop of the stem-loop structure - the so-called &#34;loop-counting rule&#34;5. We also followed these methods to identify the relative abundance of delivered small hairpin RNAs (shRNA) expressed from recombinant adeno-associated viral vectors (rAAVs), to identify the threshold of shRNA expression that can be tolerated prior to liver toxicity associated with excess shRNA expression6. Using this protocol, we also identified microRNAs in the liver that respond to the absence of microRNA-122 - a highly expressed hepatic microRNA - while also characterizing the degradation pattern of this microRNA7. Because we have used our protocol consistently in numerous experiments, we have been able to observe sample preparations longitudinally, and see that there are no discernible batch effects.
In sharing this protocol, our goal is to enable users to generate high quality, reproducible quantification of microRNAs in virtually any tissue or cell line, using affordable equipment and reagents, and free bioinformatics tools.

<table>
	<tbody>
		<tr>
			<td>100 bp DNA ladder</td>
			<td>NEB</td>
			<td>N3231</td>
			<td></td>
		</tr>
		<tr>
			<td>19:1 bis-acrylamide</td>
			<td>Millipore Sigma</td>
			<td>A9926</td>
			<td></td>
		</tr>
		<tr>
			<td>25 bp DNA step ladder</td>
			<td>Promega</td>
			<td>G4511</td>
			<td></td>
		</tr>
		<tr>
			<td>Acid phenol/chloroform</td>
			<td>ThermoFisher</td>
			<td>AM9720</td>
			<td></td>
		</tr>
		<tr>
			<td>Acrylamide RNA loading dye</td>
			<td>ThermoFisher</td>
			<td>R0641</td>
			<td></td>
		</tr>
		<tr>
			<td>Ammonium persulfate (APS)</td>
			<td>Biorad</td>
			<td>161-0700</td>
			<td></td>
		</tr>
		<tr>
			<td>Bioanalyzer instrument</td>
			<td>Agilent</td>
			<td>G2991AA</td>
			<td>For assessing RNA quality and concentration</td>
		</tr>
		<tr>
			<td>Chloroform</td>
			<td>Fisher Scientific</td>
			<td>C298-500</td>
			<td></td>
		</tr>
		<tr>
			<td>Ethanol (100%)</td>
			<td>Sigma</td>
			<td>E7023</td>
			<td></td>
		</tr>
		<tr>
			<td>Gel Loading Buffer II</td>
			<td>ThermoFisher</td>
			<td>AM8547</td>
			<td></td>
		</tr>
		<tr>
			<td>GlycoBlue</td>
			<td>ThermoFisher</td>
			<td>AM9516</td>
			<td>Blue color helps in visualizing pellet</td>
		</tr>
		<tr>
			<td>HCl</td>
			<td>Sigma</td>
			<td>320331</td>
			<td></td>
		</tr>
		<tr>
			<td>KOH</td>
			<td>Sigma</td>
			<td>P5958</td>
			<td></td>
		</tr>
		<tr>
			<td>Maxi Vertical Gel Box 20 x 20cm</td>
			<td>Genesee</td>
			<td>45-109</td>
			<td></td>
		</tr>
		<tr>
			<td>miRVana microRNA isolation kit</td>
			<td>ThermoFisher</td>
			<td>AM1560</td>
			<td></td>
		</tr>
		<tr>
			<td>miSeq system</td>
			<td>Illumina</td>
			<td>SY-410-1003</td>
			<td>For generating small RNA sequencing data</td>
		</tr>
		<tr>
			<td>NaCl</td>
			<td>Fisher Scientific</td>
			<td>S271-500</td>
			<td></td>
		</tr>
		<tr>
			<td>Nusieve low-melting agarose</td>
			<td>Lonza</td>
			<td>50081</td>
			<td></td>
		</tr>
		<tr>
			<td>Parafilm (laboratory sealing film)</td>
			<td>Millipore Sigma</td>
			<td>P7793</td>
			<td></td>
		</tr>
		<tr>
			<td>Poly-ethylene glycol 8000</td>
			<td>NEB</td>
			<td>included with M0204</td>
			<td></td>
		</tr>
		<tr>
			<td>ProtoScript II First strand cDNA Synthesis Kit</td>
			<td>NEB</td>
			<td>E6560S</td>
			<td></td>
		</tr>
		<tr>
			<td>QIAquick Gel Extraction kit</td>
			<td>Qiagen</td>
			<td>28704</td>
			<td></td>
		</tr>
		<tr>
			<td>Qubit Fluorometer</td>
			<td>ThermoFisher</td>
			<td>Q33226</td>
			<td>For quantifying DNA concentration</td>
		</tr>
		<tr>
			<td>Qubit RNA HS Assay kit</td>
			<td>ThermoFisher</td>
			<td>Q32855</td>
			<td></td>
		</tr>
		<tr>
			<td>Razor Blades</td>
			<td>Fisher Scientific</td>
			<td>12640</td>
			<td></td>
		</tr>
		<tr>
			<td>Siliconized Low-Retention 1.5 ml tubes</td>
			<td>Fisher Scientific</td>
			<td>02-681-331</td>
			<td></td>
		</tr>
		<tr>
			<td>T4 RNA ligase 1</td>
			<td>NEB</td>
			<td>M0204</td>
			<td></td>
		</tr>
		<tr>
			<td>T4 RNA Ligase 2, truncated</td>
			<td>NEB</td>
			<td>M0242S</td>
			<td></td>
		</tr>
		<tr>
			<td>TapeStation</td>
			<td>Agilent</td>
			<td>G2939BA</td>
			<td>For assessing RNA quality and concentration</td>
		</tr>
		<tr>
			<td>Taq DNA Polymerase</td>
			<td>NEB</td>
			<td>M0273X</td>
			<td></td>
		</tr>
		<tr>
			<td>TEMED</td>
			<td>Biorad</td>
			<td>161-0800</td>
			<td></td>
		</tr>
		<tr>
			<td>Tris Base pH 7.5</td>
			<td>Sigma</td>
			<td>10708976001</td>
			<td></td>
		</tr>
		<tr>
			<td>Tris-buffered EDTA</td>
			<td>Sigma</td>
			<td>T9285</td>
			<td></td>
		</tr>
		<tr>
			<td>Trizol</td>
			<td>ThermoFisher</td>
			<td>15596026</td>
			<td></td>
		</tr>
		<tr>
			<td>UltraPure Ethidium bromide (10 mg/ml)</td>
			<td>Invitrogen</td>
			<td>15585-011</td>
			<td></td>
		</tr>
		<tr>
			<td>Universal miRNA cloning linker</td>
			<td>NEB</td>
			<td>S1315S</td>
			<td></td>
		</tr>
		<tr>
			<td>Urea</td>
			<td>Sigma</td>
			<td>U5378</td>
			<td></td>
		</tr>
	</tbody>
</table>

a complete pipeline for isolating and sequencing micrornas, and analyzing them using open source tools

Half of all human transcripts are thought to be regulated by microRNAs. Therefore, quantifying microRNA expression can reveal underlying mechanisms in disease states and provide therapeutic targets and biomarkers. Here, we detail how to accurately quantify microRNAs. Briefly, this method describes isolating microRNAs, ligating them to adaptors suitable for high-throughput sequencing, amplifying the final products, and preparing a sample library. Then, we explain how to align the obtained sequencing reads to microRNA hairpins, and quantify, normalize, and calculate their differential expression. Versatile and robust, this combined experimental workflow and bioinformatic analysis enables users to begin with tissue extraction and finish with microRNA quantification.

Schematic of steps involved in library preparation 
An overall schematic of small RNA extraction, sequencing, and alignment is outlined in Figure 2. 
Liver samples from one male and one female mouse were collected and snap frozen in liquid nitrogen. Total RNA was extracted and evaluated for quality and concentration.
Small RNA sequencing yields sufficient RNA for s...

Watch this Scientific Journal Video about A Complete Pipeline for Isolating and Sequencing MicroRNAs, and Analyzing Them Using Open Source Tools at JoVE.com

A Complete Pipeline for Isolating and Sequencing MicroRNAs, and Analyzing Them Using Open Source Tools

Here, we describe a step-by-step strategy for isolating small RNAs, enriching for microRNAs, and preparing samples for high-throughput sequencing. We then describe how to process sequence reads and align them to microRNAs, using open source tools.

Half of all human transcripts are thought to be regulated by microRNAs. Therefore, quantifying microRNA expression can reveal underlying mechanisms in ...

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Video: A Complete Pipeline for Isolating and Sequencing MicroRNAs, and Analyzing Them Using Open Source Tools

Detection of Copy Number Alterations Using Single Cell Sequencing

Detection of genomic changes at single cell resolution is important for characterizing genetic heterogeneity and evolution in normal tissues, cancers, and microbial populations. Traditional methods for assessing genetic heterogeneity have been limited by low resolution, low sensitivity, and/or low specificity. Single cell sequencing has emerged as a powerful tool for detecting genetic heterogeneity with high resolution, high sensitivity and, when appropriately analyzed, high specificity. Here we provide a protocol for the isolation, whole genome amplification, sequencing, and analysis of single cells. Our approach allows for the reliable identification of megabase-scale copy number variants in single cells. However, aspects of this protocol can also be applied to investigate other types of genetic alterations in single cells.

Single cell sequencing is an increasingly popular and accessible tool for addressing genomic changes at high resolution. We provide a protocol that uses single cell sequencing to identify copy number alterations in single cells.

Detection of genomic changes at single cell resolution is important for characterizing genetic heterogeneity and evolution in normal tissues, cancers, and ...

Robust DNA Isolation and High-throughput Sequencing Library Construction for Herbarium Specimens

Herbaria are an invaluable source of plant material that can be used in a variety of biological studies. The use of herbarium specimens is associated with a number of challenges including sample preservation quality, degraded DNA, and destructive sampling of rare specimens. In order to more effectively use herbarium material in large sequencing projects, a dependable and scalable method of DNA isolation and library preparation is needed. This paper demonstrates a robust, beginning-to-end protocol for DNA isolation and high-throughput library construction from herbarium specimens that does not require modification for individual samples. This protocol is tailored for low quality dried plant material and takes advantage of existing methods by optimizing tissue grinding, modifying library size selection, and introducing an optional reamplification step for low yield libraries. Reamplification of low yield DNA libraries can rescue samples derived from irreplaceable and potentially valuable herbarium specimens, negating the need for additional destructive sampling and without introducing discernible sequencing bias for common phylogenetic applications. The protocol has been tested on hundreds of grass species, but is expected to be adaptable for use in other plant lineages after verification. This protocol can be limited by extremely degraded DNA, where fragments do not exist in the desired size range, and by secondary metabolites present in some plant material that inhibit clean DNA isolation. Overall, this protocol introduces a fast and comprehensive method that allows for DNA isolation and library preparation of 24 samples in less than 13 h, with only 8 h of active hands-on time with minimal modifications.

This article demonstrates a detailed protocol for DNA isolation and high-throughput sequencing library construction from herbarium material including rescue of exceptionally poor-quality DNA.

Herbaria are an invaluable source of plant material that can be used in a variety of biological studies. The use of herbarium specimens is associated with ...

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Next-generation sequencing (NGS) is quickly revolutionizing how research into the genetic determinants of constitutional disease is performed. The technique is highly efficient with millions of sequencing reads being produced in a short time span and at relatively low cost. Specifically, targeted NGS is able to focus investigations to genomic regions of particular interest based on the disease of study. Not only does this further reduce costs and increase the speed of the process, but it lessens the computational burden that often accompanies NGS. Although targeted NGS is restricted to certain regions of the genome, preventing identification of potential novel loci of interest, it can be an excellent technique when faced with a phenotypically and genetically heterogeneous disease, for which there are previously known genetic associations. Because of the complex nature of the sequencing technique, it is important to closely adhere to protocols and methodologies in order to achieve sequencing reads of high coverage and quality. Further, once sequencing reads are obtained, a sophisticated bioinformatics workflow is utilized to accurately map reads to a reference genome, to call variants, and to ensure the variants pass quality metrics. Variants must also be annotated and curated based on their clinical significance, which can be standardized by applying the American College of Medical Genetics and Genomics Pathogenicity Guidelines. The methods presented herein will display the steps involved in generating and analyzing NGS data from a targeted sequencing panel, using the ONDRISeq neurodegenerative disease panel as a model, to identify variants that may be of clinical significance.

Targeted next-generation sequencing is a time- and cost-efficient approach that is becoming increasingly popular in both disease research and clinical diagnostics. The protocol described here presents the complex workflow required for sequencing and the bioinformatics process used to identify genetic variants that contribute to disease.

Next-generation sequencing (NGS) is quickly revolutionizing how research into the genetic determinants of constitutional disease is performed. The ...

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Conventional next-generation sequencing techniques (NGS) have allowed for immense genomic characterization for over a decade. Specifically, NGS has been used to analyze the spectrum of clonal mutations in malignancy. Though far more efficient than traditional Sanger methods, NGS struggles with identifying rare clonal and subclonal mutations due to its high error rate of ~0.5&#8211;2.0%. Thus, standard NGS has a limit of detection for mutations that are &#62;0.02 variant allele fraction (VAF). While the clinical significance for mutations this rare in patients without known disease remains unclear, patients treated for leukemia have significantly improved outcomes when residual disease is &#60;0.0001 by flow cytometry. In order to mitigate this artefactual background of NGS, numerous methods have been developed. Here we describe a method for Error-corrected DNA and RNA Sequencing (ECS), which involves tagging individual molecules with both a 16 bp random index for error-correction and an 8 bp patient-specific index for multiplexing. Our method can detect and track clonal mutations at variant allele fractions (VAFs) two orders of magnitude lower than the detection limit of NGS and as rare as 0.0001 VAF.

Next-generation sequencing (NGS) is a powerful tool for genomic characterization that is limited by the high error rate of the platform (~0.5&#8211;2.0%). We describe our methods of error-corrected sequencing that allow us to obviate the NGS error rate and detect mutations at variant allele fractions as rare as 0.0001.

Conventional next-generation sequencing techniques (NGS) have allowed for immense genomic characterization for over a decade. Specifically, NGS has been ...

A Rapid and Facile Pipeline for Generating Genomic Point Mutants in C. elegans Using CRISPR/Cas9 Ribonucleoproteins

The clustered regularly interspersed palindromic repeats (CRISPR)-CRISPR-associated protein 9 (Cas9) prokaryotic adaptive immune defense system has been co-opted as a powerful tool for precise eukaryotic genome engineering. Here, we present a rapid and simple method using chimeric single guide RNAs (sgRNA) and CRISPR-Cas9 Ribonucleoproteins (RNPs) for the efficient and precise generation of genomic point mutations in C. elegans. We describe a pipeline for sgRNA target selection, homology-directed repair (HDR) template design, CRISPR-Cas9-RNP complexing and delivery, and a genotyping strategy that enables the robust and rapid identification of correctly edited animals. Our approach not only permits the facile generation and identification of desired genomic point mutant animals, but also facilitates the detection of other complex indel alleles in approximately 4 - 5 days with high efficiency and a reduced screening workload.

A Rapid and Facile Pipeline for Generating Genomic Point Mutants in C. elegans Using CRISPR/Cas9 Ribonucleoproteins

Here, we present a method to engineer the genome of C. elegans using CRISPR-Cas9 ribonucleoproteins and homology dependent repair templates.

The clustered regularly interspersed palindromic repeats (CRISPR)-CRISPR-associated protein 9 (Cas9) prokaryotic adaptive immune defense system has been ...

Ultralow Input Genome Sequencing Library Preparation from a Single Tardigrade Specimen

Tardigrades are microscopic animals that enter an ametabolic state called anhydrobiosis when facing desiccation and can return to their original state when water is supplied. The genomic sequencing of microscopic animals such as tardigrades risks bacterial contamination that sometimes leads to erroneous interpretations, for example, regarding the extent of horizontal gene transfer in these animals. Here, we provide an ultralow input method to sequence the genome of the tardigrade, Hypsibius dujardini, from a single specimen. By employing rigorous washing and contaminant exclusion along with an efficient extraction of the 50 ~ 200 pg genomic DNA from a single individual, we constructed a library sequenced with a DNA sequencing instrument. These libraries were highly reproducible and unbiased, and an informatics analysis of the sequenced reads with other H. dujardini genomes showed a minimal amount of contamination. This method can be applied to unculturable tardigrades that could not be sequenced using previous methods.

Contamination during the genomic sequencing of microscopic organisms remains a large problem. Here, we show a method to sequence the genome of a tardigrade from a single specimen with as little as 50 pg of genomic DNA without whole genome amplification to minimize the risk of contamination.

Tardigrades are microscopic animals that enter an ametabolic state called anhydrobiosis when facing desiccation and can return to their original state ...

Isolating, Sequencing and Analyzing Extracellular MicroRNAs from Human Mesenchymal Stem Cells

Extracellular and circulating RNAs (exRNA) are produced by many cell types of the body and exist in numerous bodily fluids such as saliva, plasma, serum, milk and urine. One subset of these RNAs are the posttranscriptional regulators &#8211; microRNAs (miRNAs). To delineate the miRNAs produced by specific cell types, in vitro culture systems can be used to harvest and profile exRNAs derived from one subset of cells. The secreted factors of mesenchymal stem cells are implicated in alleviating numerous diseases and is used as the in vitro model system here. This paper describes the process of collection, purification of small RNA and library generation to sequence extracellular miRNAs. ExRNAs from culture media differ from cellular RNA by being low RNA input samples, which calls for optimized procedures. This protocol provides a comprehensive guide to small exRNA sequencing from culture media, showing quality control checkpoints at each step during exRNA purification and sequencing.

This protocol demonstrates how to purify extracellular microRNAs from cell culture media for small RNA library construction and next generation sequencing. Various quality control checkpoints are described to allow readers to understand what to expect when working with low input samples like exRNAs.

Extracellular and circulating RNAs (exRNA) are produced by many cell types of the body and exist in numerous bodily fluids such as saliva, plasma, serum, ...

Sequencing of mRNA from Whole Blood using Nanopore Sequencing

Sequencing in remote locations and resource-poor settings presents unique challenges. Nanopore sequencing can be successfully used under such conditions, and was deployed to West Africa during the recent Ebola virus epidemic, highlighting this possibility. In addition to its practical advantages (low cost, ease of equipment transport and use), this technology also provides fundamental advantages over second-generation sequencing approaches, particularly the very long read length, ability to directly sequence RNA, and real-time availability of data. Raw read accuracy is lower than with other sequencing platforms, which represents the main limitation of this technology; however, this can be partially mitigated by the high read depth generated. Here, we present a field-compatible protocol for sequencing of the mRNAs encoding for Niemann-Pick C1, which is the cellular receptor for ebolaviruses. This protocol encompasses extraction of RNA from animal blood samples, followed by RT-PCR for target enrichment, barcoding, library preparation, and the sequencing run itself, and can be easily adapted for use with other DNA or RNA targets.

Nanopore sequencing is a novel technology that allows cost-effective sequencing in remote locations and resource-poor settings. Here, we present a protocol for sequencing of mRNAs from whole blood that is compatible with such conditions.

Sequencing in remote locations and resource-poor settings presents unique challenges. Nanopore sequencing can be successfully used under such conditions, ...

Mass Spectrometry-Based Proteomics Analyses Using the OpenProt Database to Unveil Novel Proteins Translated from Non-Canonical Open Reading Frames

Genome annotation is central to today's proteomic research as it draws the outlines of the proteomic landscape. Traditional models of open reading frame (ORF) annotation impose two arbitrary criteria: a minimum length of 100 codons and a single ORF per transcript. However, a growing number of studies report expression of proteins from allegedly non-coding regions, challenging the accuracy of current genome annotations. These novel proteins were found encoded either within non-coding RNAs, 5' or 3' untranslated regions (UTRs) of mRNAs, or overlapping a known coding sequence (CDS) in an alternative ORF. OpenProt is the first database that enforces a polycistronic model for eukaryotic genomes, allowing annotation of multiple ORFs per transcript. OpenProt is freely accessible and offers custom downloads of protein sequences across 10 species. Using OpenProt database for proteomic experiments enables novel proteins discovery and highlights the polycistronic nature of eukaryotic genes. The size of OpenProt database (all predicted proteins) is substantial and need be taken in account for the analysis. However, with appropriate false discovery rate (FDR) settings or the use of a restricted OpenProt database, users will gain a more realistic view of the proteomic landscape. Overall, OpenProt is a freely available tool that will foster proteomic discoveries.

OpenProt is a freely accessible database that enforces a polycistronic model of eukaryotic genomes. Here, we present a protocol for the use of OpenProt databases when interrogating mass spectrometry datasets. Using OpenProt database for analysis of proteomic experiments allows for discovery of novel and previously undetectable proteins.

Genome annotation is central to today's proteomic research as it draws the outlines of the proteomic landscape. Traditional models of open reading frame ...

RNA Next-Generation Sequencing and a Bioinformatics Pipeline to Identify Expressed LINE-1s at the Locus-Specific Level

Long INterspersed Elements-1 (LINEs/L1s) are repetitive elements that can copy and randomly insert in the genome resulting in genomic instability and mutagenesis. Understanding the expression patterns of L1 loci at the individual level will lend to the understanding of the biology of this mutagenic element. This autonomous element makes up a significant portion of the human genome with over 500,000 copies, though 99% are truncated and defective. However, their abundance and dominant number of defective copies make it challenging to identify authentically expressed L1s from L1-related sequences expressed as part of other genes. It is also challenging to identify which specific L1 locus is expressed due to the repetitive nature of the elements. Overcoming these challenges, we present an RNA-Seq bioinformatic approach to identify L1 expression at the locus specific level. In summary, we collect cytoplasmic RNA, select for polyadenylated transcripts, and utilize strand-specific RNA-Seq analyses to uniquely map reads to L1 loci in the human reference genome. We visually curate each L1 locus with uniquely mapped reads to confirm transcription from its own promoter and adjust mapped transcript reads to account for mappability of each individual L1 locus. This approach was applied to a prostate tumor cell line, DU145, to demonstrate the ability of this protocol to detect expression from a small number of the full-length L1 elements.

Here, we present a bioinformatic approach and analyses to identify LINE-1 expression at the locus specific level.

Long INterspersed Elements-1 (LINEs/L1s) are repetitive elements that can copy and randomly insert in the genome resulting in genomic instability and ...