This work was supported by the National Center for Scientific Research (CNRS), The French Alternative Energies and Atomic Energy Commission (CEA) and Paris-Sud University. All library preparation, Illumina sequencing and bioinformatics analyses for this study were performed at the I2BC Next-Generation Sequencing (NGS) facility. The members of the I2BC NGS facility are acknowledged for critical reading of the manuscript and helpful suggestions.

1. Isolation of small RNAs
<ol>
	<li>Extract total RNA using phenol-based reagents or any other method. Verify if the RNA is of good quality.</li>
	<li>Pre-run a 15% TBE-urea gel (see Table of Materials) for 15 min at 200 V.</li>
	<li>While the gel is pre-running, mix 5-20 &#181;g of total RNA in a 5-15 &#181;L volume with an equal volume of formamide loading dye (see Table of Materials; 95% deionized formamide, 0.025% bromophenol blue, 0.025% xylene cyanol, 5 mM EDTA pH 8) in a 200 &#181;L PCR tube. Likewise, mix 10 &#181;L (200 ng) of small-RNA ladder (see Table of Materials) with an equal volume of formamide loading dye. Incubate for 5 min at 65 &#176;C in a thermocycler with heated lid, then place the tubes immediately on ice.</li>
	<li>Load the ladder and the sample on the same gel with at least one lane between them and run at 200 V until the bromophenol blue (dark blue) has migrated about two-third of the gel length (approximately 40 min).</li>
	<li>Prepare a system to elute the RNA from gel as follows: puncture the bottom of a nuclease-free 0.5 mL micro centrifuge tube with a 21-gauge needle. Place the punctured 0.5 mL tube in a nuclease-free round-bottom 2 mL micro centrifuge tube.</li>
	<li>Remove the gel, and incubate at room temperature with 3 &#956;L nucleic acid gel stain (10,000 x concentrate; see Table of Materials) in 30 mL&#160;water for 10-15 min.</li>
	<li>View the gel on a &#39;Dark Reader&#39; trans illuminator (it is strongly recommended to avoid UV as this might damage the RNA) and cut out the sample RNA between the 17 nt and the 29 nt ladder bands. Transfer the gel piece to the 0.5 mL tube from step 1.5.</li>
	<li>Centrifuge the 0.5 mL tube in the 2 mL tube in a micro centrifuge at maximum speed for 2 min. Remove the 0.5 mL tube, which should be empty now.</li>
	<li>Add 300 &#181;L of nuclease-free 0.3 M NaCl to the 2 mL tube containing the crushed gel and rotate for at least 2 h at room temperature or at 4 &#176;C overnight (16 h).</li>
	<li>Transfer the suspension of crushed gel pieces to a spin column (see Table of Materials) and centrifuge for 2 min at maximum speed in a micro centrifuge.</li>
	<li>Add 1 &#181;L (20 &#181;g/&#181;L) of glycogen (see Table of Materials) and 950 &#181;L of room temperature 100% ethanol. Incubate for at least 30 min at -80 &#176;C.</li>
	<li>Centrifuge for 20 min at maximum speed in a micro centrifuge at 4 &#176;C. Remove the supernatant, wash the pellet with 800 &#181;L of cold 80 % ethanol. Centrifuge again for 5 min at 4 &#176;C, and carefully remove all supernatant. Resuspend RNA pellet in 15 &#181;L of nuclease free water. Typically, ~5-20 ng of small RNA should be recovered, depending on the amount of input total RNA (~1 ng of small RNA per 1 &#181;g of input total RNA).</li>
	<li>Recommended additional step: Check the quantity and quality of the recovered sRNA (e.g., by capillary gel electrophoresis using a small RNA kit; see Table of Materials).</li>
</ol>
2. Preparation of preadenylated 3&#39;&#160;HD adapter
NOTE: Preadenylation of 3&#39;&#160;HD adapter was done in a manner similar to the protocol described by Chen et al18. Note that preadenylated adapter can be ordered directly (/5rApp/ modification), but this is quite expensive.
<ol>
	<li>Order 5&#39; phosphorylated, 3&#39; blocked 3&#39; HD adapter oligonucleotide. See Table 1 for sequence and modifications. Note that &#39;3AmMO&#39;&#160;is a 3&#39;&#160;amino modifier group, most suppliers can produce oligonucleotide with this modification. Dilute in nuclease free water to 100 &#181;M.</li>
	<li>Set up a 100 &#181;L reaction containing the following reagents: 10 &#181;L of oligo (100 &#181;M), 10 &#181;L of T4 RNA ligase buffer (10x), 10 &#181;L of ATP (10 mM), 40 &#181;L of 50% PEG8000, 5 &#181;L of T4 RNA ligase 1 (50 units), 25 &#181;L of nuclease-free water. Incubate overnight at 20 &#176;C.</li>
	<li>Perform a classical phenol-chloroform extraction of the preadenylated oligonucleotide followed by ethanol precipitation. Add 100 &#181;L of acid (pH 4.5) phenol:chloroform and vortex. Spin for 5 min at room temperature,&#160;maximum speed. Carefully transfer 90 &#181;L of the upper phase to a new tube; add 10 &#181;L of 3 M sodium acetate pH 5.2, 1 &#181;L of ultra-pure glycogen and 250 &#181;L of cold 100% ethanol.</li>
	<li>Keep at -20 &#176;C for at least 30 min. Centrifuge for 30 min at 4 &#176;C maximum speed. Remove the supernatant, wash the pellet once with 500 &#181;L cold 80% ethanol. As an alternative to phenol-chloroform extraction and ethanol precipitation, a nucleotide removal kit can be used (see Table of Materials). Resuspend in 25&#160;&#181;L water.</li>
	<li>Measure the concentration of the adapter using a kit for specific detection of single-stranded DNA (see Table of Materials). Dilute to 80 ng/&#181;L (10 &#181;M).&#160;</li>
	<li>Recommended additional step: Verify the efficacy of preadenylation by migrating 1 &#181;L of 10 &#181;M adapter on a 15% TBE-urea gel (Table of Materials) along with untreated oligonucleotide. The preadenylated adapter should migrate slightly slower than untreated oligonucleotide. If desired,&#160;the preadenylated adapter can be gel-purified; proceed as described above (steps 1.2-1.12).</li>
</ol>
3. Library preparation - Protocol TS5
NOTE: We present here the modified TS protocol &#39;TS5&#39;&#160;that we described previously12 and that can be performed either with reagents from the kit or with self-provided reagents. It should be noted that we obtained similar or even slightly better results with a different protocol, &#39;TS7&#39;. However, with TS7 it is more difficult to eliminate adapter dimers. We have therefore preferred to describe TS5 in detail, but TS7 can be followed by simply replacing the adapters. For TS7 use the &#39;MidRand-Like (MRL)&#39;&#160;adapter sequences (Table 1). Note that here the randomized regions are in the middle of the adapters. Primers for reverse transcription and PCR will hybridize to the sequences downstream of the randomized region in the 3&#39;&#160;adapter and upstream of the randomized region in the 5&#39;&#160;adapter. Sequencing will start from the first randomized nucleotide in the 5&#39; adapter.
<ol>
	<li>3&#39; adapter ligation.
	<ol>
		<li>Combine 1 &#181;L of preadenylated 3&#39;&#160;HD adapter (10 &#181;M) with 1 &#181;L of purified small RNA (~0.1-1 &#181;M) in a 0.2 mL micro centrifuge tube. Incubate for 2 min at 72 &#176;C in a thermo cycler, then put directly on ice.</li>
		<li>Add 4 &#181;L of 50 % PEG 8000 (viscous solution; pipet slowly), 1 &#181;L of RNA ligase buffer (10x), 1 &#181;L of H2O, 1 &#181;L of T4 RNA ligase2 truncated, and 1 &#181;L of RNase inhibitor. Incubate overnight at 16 &#176;C.</li>
	</ol>
	</li>
	<li>Elimination of unligated 3&#39;&#160;adapter
	<ol>
		<li>Add 10 &#181;L of nuclease-free water and mix well. Add 6 &#181;L of 3 M NaOAc pH 5.2 or &#39;Adapter Depletion Solution&#39;&#160;from the Nf kit (Table of Materials) and mix well. Add 40 &#181;L of magnetic purification beads (Table of Materials) and 60 &#181;L of isopropanol and mix well. Incubate for 5 min at room temperature.</li>
		<li>Put the sample in a magnetic rack until the solution appears clear. Remove and discard the supernatant.</li>
		<li>Add 180 &#181;L of freshly prepared 80 % ethanol. Incubate for ~30 s, then remove. Take care to use freshly prepared 80% ethanol and do not incubate with 80% ethanol for extended periods.</li>
		<li>Briefly spin the tube and remove residual liquid that may have collected at the bottom of the well. Let the beads dry for 2 min, then resuspend in 22 &#181;L of 10 mM Tris pH 8 or resuspension buffer from the Nf kit. Incubate for 2 min, then magnetize the sample until the solution appears clear.</li>
		<li>Add 6 &#181;L of 3 M NaOAc pH 5.2 or &#8216;Adapter Depletion Solution&#8217; from the Nf kit (Table of Materials)&#160;to a new tube. Transfer 20 &#181;L of the supernatant from the previous step to this new tube and mix by pipetting. Add 40 &#181;L of magnetic beads and 60 &#181;L of 100% isopropanol and mix well by pipetting. Incubate for 5 min.</li>
		<li>Magnetize the sample until the solution appears clear, then remove and discard the supernatant.</li>
		<li>Add 180 &#181;L of freshly prepared 80 % ethanol. Incubate for ~30 s, then remove. Take care to use freshly prepared 80% ethanol and do not incubate for with 80% ethanol for extended periods.</li>
		<li>Briefly spin the tube and remove residual liquid that may have collected at the bottom of the well. Let the beads dry for 2 min, and resuspend in 10 &#181;L of nuclease-free water. Incubate for 2 min, then magnetize the sample until the solution appears clear.</li>
		<li>Transfer 9 &#181;L of supernatant to a new tube. Add 1 &#181;L of T4 RNA ligase buffer (10x) and 1 &#181;L of water. Alternatively, add 2 &#181;L of ligase buffer from the TS kit.</li>
	</ol>
	</li>
	<li>Ligation of 5&#39;&#160;adapter.
	<ol>
		<li>Add 1 &#181;L of 5&#39;&#160;HD adapter (10 &#181;M; Table 1) to a 200 &#181;L PCR tube in a thermo cycler with heated lid. Incubate for 2 min at 70 &#176;C, then put the tube directly on ice.</li>
		<li>Add 1 &#181;L of 10 mM ATP and 1 &#181;L of T4 RNA ligase 1. Mix well by gently pipetting. Add 3 &#181;L of this mix to the 3&#39;&#160;ligated RNA from step 3.2.9 and mix by pipetting. Incubate for 1 h at 28 &#176;C.</li>
	</ol>
	</li>
	<li>Reverse transcription (RT)
	<ol>
		<li>Transfer 6 &#181;L of 3&#39;&#160;and 5&#39;&#160;adapter ligated RNA to a new 200 &#181;L PCR tube (keep the remaining ~8 &#181;L at -80 &#176;C for later use if necessary). Add 1 &#181;L of RT primer (10 &#181;M; Table 1) and mix by pipetting. Incubate for 2 min at 70 &#176;C, then put the tube directly on ice.</li>
		<li>Add the following reagents for RT: 2 &#181;L of 5x first strand buffer, 0.5 &#181;L of 12.5 mM dNTP mix, 1 &#181;L of 100 mM DTT, 1 &#181;L of RNase inhibitor, and 1 &#181;L of reverse transcriptase (Table of Materials). Incubate for 1 h at 50 &#176;C.</li>
	</ol>
	</li>
	<li>PCR amplification
	<ol>
		<li>Add the following reagents to the 12.5 &#181;L of RT reaction mixture: 10 &#181;L of PCR polymerase buffer, 2 &#181;L of universal P5 primer (10 &#181;M; Table 1), 2 &#181;L of P7-index primer (10 &#181;M; Table 1), 1 &#181;L of 12.5 mM dNTPs, 0.5 &#181;L of DNA polymerase (Table of Materials), and 22 &#181;L of water. Keep the reaction on ice until use.</li>
		<li>Run the following PCR program: 98 &#176;C for 30 s, 11 cycles (98 &#176;C for 10 s, 60 &#176;C for 30 s and 72 &#176;C for 15 s) and 72 &#176;C for 10 min. Keep the reaction at 4 &#176;C when finished. 
		NOTE: Concerning the number of PCR cycles: we typically perform 11 cycles, but this should be optimized by the user. Try to perform the smallest possible number of PCR cycles.</li>
	</ol>
	</li>
	<li>Gel purification
	<ol>
		<li>Run 5, 10 and 20 &#181;L of PCR product on a native 6% TBE gel (see Table of Materials) along with a suitable ladder (see Table of Materials). Run the gel for about 1 h at 145 V (until the bromophenol blue reaches the bottom; this dye migrates at the 65 bp position).</li>
		<li>Remove the gel, and incubate with nucleic acid gel stain (see Table of Materials) in water for 10-15 min.</li>
		<li>View the gel on a &#34;Dark Reader&#34;&#160;trans illuminator (it is strongly recommended to avoid UV as this might damage the RNA) and cut out the library band at 150 bp. Prepare a system to elute the RNA from gel as described in step 1.5 and transfer the gel piece to the 0.5 mL tube.</li>
		<li>Centrifuge in a micro centrifuge at maximum speed for 2 min. Remove the 0.5 mL tube, which should be empty now.</li>
		<li>Add 300 &#181;L of nuclease-free water to the 2 mL tube containing the crushed gel and rotate for at least 2 h at room temperature or at 4 &#176;C overnight.</li>
		<li>Transfer the suspension of crushed gel pieces in water to a spin column and centrifuge for 2 min at maximum speed.</li>
		<li>Add 1 &#181;L of (20 &#181;g/&#181;L) glycogen, 30 &#181;L of 3 M NaOAc and 975 &#181;L of ice cold 100% ethanol. Centrifuge for 20 min at max speed at 4 &#176;C.</li>
		<li>Resuspend the pellet in 20 &#181;L of 10 mM Tris pH8. Use 1 &#181;L for concentration measurement and 1 &#181;L for quality control.</li>
	</ol>
	</li>
</ol>
4. Data analysis
NOTE: The data analysis procedure described below is based on the Linux operating system Ubuntu 16.04 LTS.
<ol>
	<li>Treatment of raw sequence files
	<ol>
		<li>Download the FASTQ sequence file(s) generated during the sequencing run. If required, perform demultiplexing with bcl2fastq2 (version V2.2.18.12; a manual can be downloaded from the following link: http://emea.support.illumina.com/sequencing/sequencing_software/bcl2fastq-conversion-software/documentation.html). 
		Use the following command: 
		nohup Pathway_of_bcl2fastq/bcl2fastq --runfolder-dir Pathway_of_Run --ignore-missing-bcl --output-dir Pathway_of_Output_Directory --barcode-mismatches 1 --aggregated-tiles AUTO -r 16 -d 16 -p 16 -w 16</li>
		<li>Remove adapter sequences using Cutadapt19 version 1.15. A manual can be downloaded here: https://cutadapt.readthedocs.io/en/stable/guide.html. 
		Use the following command: 
		Pathway_to_cutadapt/cutadapt -a TGGAATTCTCGGGTGCCAAGG&#160; -n 5 -O 4 -m 10 -j 0 --nextseq-trim 10 -o Output_File_Read1_cutadapt.fastq.gz Input_File_Read1.fastq.gz 
		Note that the sequence in the command corresponds to the 3&#8217; HD adapter without the 4 random nucleotides; these will therefore not be removed during this step.</li>
		<li>Use seqtk (https://github.com/lh3/seqtk) for the removal of the terminal random nucleotides in the sequencing reads. Use the following command (variable names in bold): 
		seqtk trimfq -b 4 -e 4 Output_File_Read1_cutadapt.fastq &#62; Output_File_Read1_trimmed.fastq</li>
		<li>Use the following awk command in order to discard the sequences shorter than 10 nt: 
		awk &#39;BEGIN {FS = &#34;\t&#34; ; OFS = &#34;\n&#34;} {header = $0 ; getline seq ; getline qheader ; getline qseq ; if (length(seq) &#62;= 10) {print header, seq, qheader, qseq}}&#39; Output_File_Read1_trimmed.fastq &#62; Length_Filtered.fastq</li>
	</ol>
	</li>
	<li>Mapping of the trimmed sequences
	<ol>
		<li>Download the database corresponding to the organism of study from miRBase as follows. Go to http://www.mirbase.org/ftp.shtml and download the &#8216;mature.fa&#8217; file. Note that the sequences are indicated in RNA notation. Replace the U residues by T with the following command: 
		sed -i &#39;/^&#62;/! s/U/T/g&#39; mature.fa 
		NOTE: This will yield a complete list of all miRNAs in miRBase, originating from a variety of organisms.</li>
		<li>Select the miRNA sequences of your organism of interest with the following command: 
		awk &#39;name_of_the_organism{print; nr[NR+1]; next}; NR in nr&#39; mature.fa &#62; mature_name_of_the_organism_mirs.fa</li>
		<li>Map the reads to the above-created file using Bowtie220 (version 2.3.0) allowing no mismatches. First, build an index for your file with the following command: 
		bowtie2-build mature_name_of_the_organism_mirs.fa mature_name_of_the_organism_mirs</li>
		<li>Align the sequencing reads to the database, requiring that a read maps entirely to a miRNA of the database, without any mismatches. To this end, use the following tool: 
		bowtie2 -N 0 -L 10 --score-min C,0,0 --end-to-end --time -x mature_name_of_the_organism_mirs -U Length_Filtered.fastq -S Length_Filtered_ALIGNMENT.sam 
		NOTE: The option: --score-min C,0,0 ensures that alignment is without any mismatches. For an explanation of the various parameters in the tool, please visit the following website: http://bowtie-bio.sourceforge.net/bowtie2/manual.shtml</li>
		<li>To discard the reads that did not align, use the following command: 
		samtools view -F 4 Length_Filtered_ALIGNMENT.sam &#62; Reads_aligned_to_Mirs.sam 
		NOTE: As a result of these steps, you should now have obtained the aligned reads, corresponding to miRNAs.</li>
	</ol>
	</li>
</ol>

<ol>
	<li>Ghildiyal, M., Zamore, P. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Small+silencing+RNAs:+an+expanding+universe.">Small silencing RNAs: an expanding universe.</a> Nature Reviews Genetics. 10, 94-108 (2009).</li><li>Chang, T. C., Mendell, J. T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=microRNAs+in+vertebrate+physiology+and+human+disease.">microRNAs in vertebrate physiology and human disease.</a> Annual Review of Genomics and Human Genetics. 8, 215-239 (2007).</li><li>Zhuang, F., Fuchs, R. T., Robb, G. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Small+RNA+expression+profiling+by+high-throughput+sequencing:+implications+of+enzymatic+manipulation.">Small RNA expression profiling by high-throughput sequencing: implications of enzymatic manipulation.</a> Journal of Nucleic Acids. 2012, 360358 (2012).</li><li>van Dijk, E. L., Jaszczyszyn, Y., Thermes, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Library+preparation+methods+for+next-generation+sequencing:+tone+down+the+bias.">Library preparation methods for next-generation sequencing: tone down the bias.</a> Experimental Cell Research. 322, 12-20 (2014).</li><li>Munafo, D. B., Robb, G. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Optimization+of+enzymatic+reaction+conditions+for+generating+representative+pools+of+cDNA+from+small+RNA.">Optimization of enzymatic reaction conditions for generating representative pools of cDNA from small RNA.</a> RNA. 16, 2537-2552 (2010).</li><li>Hafner, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=RNA-ligase-dependent+biases+in+miRNA+representation+in+deep-sequenced+small+RNA+cDNA+libraries.">RNA-ligase-dependent biases in miRNA representation in deep-sequenced small RNA cDNA libraries.</a> RNA. 17, 1697-1712 (2011).</li><li>Sorefan, K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Reducing+ligation+bias+of+small+RNAs+in+libraries+for+next+generation+sequencing.">Reducing ligation bias of small RNAs in libraries for next generation sequencing.</a> Silence. 3, 4 (2012).</li><li>Sun, G., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+bias-reducing+strategy+in+profiling+small+RNAs+using+Solexa.">A bias-reducing strategy in profiling small RNAs using Solexa.</a> RNA. 17, 2256-2262 (2011).</li><li>Jayaprakash, A. D., Jabado, O., Brown, B. D., Sachidanandam, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Identification+and+remediation+of+biases+in+the+activity+of+RNA+ligases+in+small-RNA+deep+sequencing.">Identification and remediation of biases in the activity of RNA ligases in small-RNA deep sequencing.</a> Nucleic Acids Research. 39, 141 (2011).</li><li>Zhuang, F., Fuchs, R. T., Sun, Z., Zheng, Y., Robb, G. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Structural+bias+in+T4+RNA+ligase-mediated+3'-adapter+ligation.">Structural bias in T4 RNA ligase-mediated 3'-adapter ligation.</a> Nucleic Acids Research. 40, 54 (2012).</li><li>Fuchs, R. T., Sun, Z., Zhuang, F., Robb, G. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Bias+in+ligation-based+small+RNA+sequencing+library+construction+is+determined+by+adaptor+and+RNA+structure.">Bias in ligation-based small RNA sequencing library construction is determined by adaptor and RNA structure.</a> PLoS One. 10, 0126049 (2015).</li><li>Dard-Dascot, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Systematic+comparison+of+small+RNA+library+preparation+protocols+for+next-generation+sequencing.">Systematic comparison of small RNA library preparation protocols for next-generation sequencing.</a> BMC Genomics. 19, 118 (2018).</li><li>Van Nieuwerburgh, F., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantitative+bias+in+Illumina+TruSeq+and+a+novel+post+amplification+barcoding+strategy+for+multiplexed+DNA+and+small+RNA+deep+sequencing.">Quantitative bias in Illumina TruSeq and a novel post amplification barcoding strategy for multiplexed DNA and small RNA deep sequencing.</a> PLoS One. 6, 26969 (2011).</li><li>Harrison, B., Zimmerman, S. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Polymer-stimulated+ligation:+enhanced+ligation+of+oligo-+and+polynucleotides+by+T4+RNA+ligase+in+polymer+solutions.">Polymer-stimulated ligation: enhanced ligation of oligo- and polynucleotides by T4 RNA ligase in polymer solutions.</a> Nucleic Acids Research. 12, 8235-8251 (1984).</li><li>Song, Y., Liu, K. J., Wang, T. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Elimination+of+ligation+dependent+artifacts+in+T4+RNA+ligase+to+achieve+high+efficiency+and+low+bias+microRNA+capture.">Elimination of ligation dependent artifacts in T4 RNA ligase to achieve high efficiency and low bias microRNA capture.</a> PLoS One. 9, 94619 (2014).</li><li>Zhang, Z., Lee, J. E., Riemondy, K., Anderson, E. M., Yi, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High-efficiency+RNA+cloning+enables+accurate+quantification+of+miRNA+expression+by+deep+sequencing.">High-efficiency RNA cloning enables accurate quantification of miRNA expression by deep sequencing.</a> Genome Biology. 14, 109 (2013).</li><li>Barberan-Soler, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Decreasing+miRNA+sequencing+bias+using+a+single+adapter+and+circularization+approach.">Decreasing miRNA sequencing bias using a single adapter and circularization approach.</a> Genome Biology. 19, 105 (2018).</li><li>Chen, Y. R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+cost-effective+method+for+Illumina+small+RNA-Seq+library+preparation+using+T4+RNA+ligase+1+adenylated+adapters.">A cost-effective method for Illumina small RNA-Seq library preparation using T4 RNA ligase 1 adenylated adapters.</a> Plant Methods. 8, 41 (2012).</li><li>Martin, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cutadapt+removes+adapter+sequences+from+high-throughput+sequencing+reads.">Cutadapt removes adapter sequences from high-throughput sequencing reads.</a> EMBnet. , (2011).</li><li>Langmead, B., Trapnell, C., Pop, M., Salzberg, S. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Ultrafast+and+memory-efficient+alignment+of+short+DNA+sequences+to+the+human+genome.">Ultrafast and memory-efficient alignment of short DNA sequences to the human genome.</a> Genome Biology. 10, 25 (2009).</li><li>Shore, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Small+RNA+Library+Preparation+Method+for+Next-Generation+Sequencing+Using+Chemical+Modifications+to+Prevent+Adapter+Dimer+Formation.">Small RNA Library Preparation Method for Next-Generation Sequencing Using Chemical Modifications to Prevent Adapter Dimer Formation.</a> PLoS One. 11, 0167009 (2016).</li></ol>

The authors have nothing to disclose.

Small RNA library preparation remains challenging due to bias, mainly introduced during adapter ligation steps. RNAs with a 2&#39;-OMe modification at their 3&#39;&#160;end such as plant miRNAs, piRNA in insects, nematodes and mammals, and small interfering RNAs (siRNA) in insects and plants are particularly difficult to study because the 2&#39;-OMe modification inhibits 3&#39;&#160;adapter ligation. A number of solutions have been proposed in the literature to improve sRNA library preparation protocols, but most commerc...

Small RNAs (sRNAs) are involved in the control of a diversity of biological processes1. Eukaryotic regulatory sRNAs are typically between 20 and 30 nt in size; the three major types are microRNAs (miRNA), piwi-interacting RNAs (piRNA) and small interfering RNAs (siRNA). Aberrant miRNA expression levels have been implicated in a variety of diseases2. This underscores the importance of miRNAs in health and disease and the requirement for accurate, quantitative research tools to detect sRNAs in general.
Next-generation sequencing (NGS) is a widely used method to study sRNAs. Main advantages of NGS as compared with other approaches, such as quantitative PCR or microarray techniques (qPCR), are that it does not need a priori knowledge of the sRNA sequences and can therefore be used to discover novel RNAs, and in addition it suffers less of background signal and saturation effects. Further, it can detect single nucleotide differences and has a higher throughput than microarrays. However, NGS also has some drawbacks; the cost of a sequencing run remains relatively high and the multistep process required to convert a sample into a library for sequencing may introduce biases. In a typical sRNA library preparation process, a 3&#39;&#160;adapter is first ligated to the sRNA (often gel-purified from total RNA) using a truncated version of RNA ligase 2 (RNL2) and a preadenylated 3&#39;&#160;adapter (Figure 1) in the absence of ATP. This increases the efficiency of sRNA-adapter ligation and reduces the formation of side reactions such as sRNA circularization or concatemerization. Subsequently, a 5&#8217; adapter is ligated by RNA ligase 1 (RNL1), followed by reverse transcription (RT) and PCR amplification. All these steps may introduce bias3,4. Consequently, read numbers may not reflect actual sRNA expression levels leading to artificial, method-dependent expression patterns. Specific sRNAs may be either over- or underrepresented in a library, and strongly underrepresented sRNAs may escape detection. The situation is particularly complicated with plant miRNAs, siRNAs in insects and plants, and piRNAs in insects, nematodes and mammals, in which the 3&#39;&#160;terminal nucleotide has a 2&#39;-O-methyl (2&#39;-OMe) modification1. This modification strongly inhibits 3&#39;&#160;adapter ligation5, making library preparation for these types of RNA a difficult task.
Previous work demonstrated that adapter ligation introduces serious bias, due to RNA sequence/structure effects6,7,8,9,10,11. Steps downstream of adapter ligation such as reverse transcription and PCR do not significantly contribute to bias6,11,12. Ligation bias is likely due to the fact that adapter molecules with a given sequence will interact with sRNA molecules in the reaction mixture to form co-folds, that may either lead to favorable or unfavorable configurations for ligation (Figure 2). Data from Sorefan et al7 suggest that RNL1 prefers a single stranded context, while RNL2 prefers a double strand for ligation. The fact that the adapter/sRNA co-fold structures are determined by the specific adapter and sRNA sequences explains why specific sRNA are over- or underrepresented with a given adapter set. It is also important to note that within a series of sRNA libraries to be compared, the same adapter sequences should be used. Indeed, it has previously been observed that changing adapters by the introduction of different barcode sequences alters miRNA profiles in sequencing libraries9,13.
Randomization of adapter sequences near the ligation junction likely reduces these biases. Sorefan and colleagues7 used adapters with 4 random nucleotides at their extremities, designated &#34;High Definition&#34;&#160;(HD) adapters, and showed that the use of these adapters lead to libraries that better reflect true sRNA expression levels. More recent work confirmed these observations and revealed that the randomized region does not need to be adjacent to the ligation junction11. This novel type of adapters was named &#34;MidRand&#34;&#160;adapters. Together, these results demonstrate that improved adapter design can reduce bias.
Instead of modifying the adapters, bias can be suppressed through the optimisation of reaction conditions. Polyethylene glycol (PEG), a macromolecular crowding agent known to increase ligation efficiency14, has been shown to significantly reduce bias15,16. Based on these results, several &#34;low bias&#34;&#160;kits appeared on the market. These include kits that use PEG in the ligation reactions, either in combination with classical adapters or HD adapters. Other kits avoid ligation altogether, and use 3&#39;&#160;polyadenylation and template switching for 3&#39;&#160;and 5&#39;&#160;adapter addition, respectively12. In yet another strategy, 3&#39;&#160;adapter ligation is followed by a circularization step, thus omitting 5&#39;&#160;adapter ligation17.
In a previous study, we searched for a sRNA library preparation protocol with the lowest possible levels of bias and the best detection of 2&#39;-OMe RNAs12. We tested some of the above-mentioned &#39;low bias&#39;&#160;kits, which had a better detection of 2&#39;-OMe RNAs than the standard protocol (TS). Surprisingly however, upon modification (the use of randomized adapters, PEG in the ligation reactions and removal of excess 3&#39;&#160;adapter by purification) the latter outperformed the other protocols for the detection of 2&#39;-OMe RNAs. Here, we describe in detail a protocol based on the TS protocol, &#39;TS5&#39;, which had the best overall detection of 2&#39;-OMe RNAs. The protocol can be followed using reagents from the TS kit and one reagent from the &#39;Nf&#39;&#160;kit or, to save money, using homemade reagents, with equal performance. We also provide a detailed protocol for the purification of sRNA from total RNA and the preparation of preadenylated 3&#39;&#160;adapter. &#160;

<table><tbody><tr><td>2100 Bioanalyzer Instrument&amp;nbsp;</td><td>Agilent</td><td>G2939BA</td><td></td></tr><tr><td>Acid-Phenol:Chloroform, pH 4.5 (with IAA, 125:24:1)</td><td>ThermoFisher</td><td>AM9720</td><td></td></tr><tr><td>Adenosine 5&#039;-Triphosphate (ATP)</td><td>Nex England Biolabs</td><td>P0756S</td><td></td></tr><tr><td>Agencourt AMPure XP beads</td><td>Beckman Coulter</td><td>A63880</td><td></td></tr><tr><td>Bioanalyzer High Sensitivity DNA Kit</td><td>Agilent</td><td>5067-4626</td><td></td></tr><tr><td>Bioanalyzer Small RNA Kit</td><td>Agilent</td><td>5067-1548</td><td></td></tr><tr><td>Corning Costar Spin-X centrifuge tube filters</td><td>Sigma Aldrich</td><td>CLS8162-96EA</td><td></td></tr><tr><td>Dark Reader transilluminator</td><td>various suppiers</td><td></td><td></td></tr><tr><td>HotStart PCR Kit, with dNTPs</td><td>Kapa Biosystems</td><td>KK2501</td><td></td></tr><tr><td>NEXTflex small RNA-seq V3 kit</td><td>BIOO Scientific</td><td>NOVA-5132-05</td><td>optional</td></tr><tr><td>Novex TBE gels 6%</td><td>ThermoFisher</td><td>EC6265BOX</td><td></td></tr><tr><td>Novex TBE Urea gels 15%</td><td>ThermoFisher</td><td>EC6885BOX</td><td></td></tr><tr><td>QIAquick Nucleotide Removal Kit</td><td>Qiagen&amp;nbsp;</td><td>28304</td><td></td></tr><tr><td>Qubit 4 Quantitation Starter Kit</td><td>ThermoFisher</td><td>Q33227</td><td></td></tr><tr><td>Qubit ssDNA Assay Kit</td><td>ThermoFisher</td><td>Q10212</td><td></td></tr><tr><td>RNA Gel Loading Dye (2X)</td><td>ThermoFisher</td><td>R0641</td><td></td></tr><tr><td>RNA Gel Loading Dye (2X)</td><td>ThermoFisher</td><td>R0641</td><td></td></tr><tr><td>RNase Inhibitor, Murine&amp;nbsp;</td><td>Nex England Biolabs</td><td>M0314S</td><td></td></tr><tr><td>SuperScript IV Reverse Transcriptase</td><td>ThermoFisher</td><td>18090200</td><td></td></tr><tr><td>SYBR Gold Nucleic Acid Gel Stain</td><td>ThermoFisher</td><td>S11494</td><td></td></tr><tr><td>T4 RNA Ligase 1 (ssRNA Ligase)</td><td>Nex England Biolabs</td><td>M0204S</td><td></td></tr><tr><td>T4 RNA Ligase 2, truncated</td><td>Nex England Biolabs</td><td>M0242S</td><td></td></tr><tr><td>TrackIt 50 bp DNA ladder</td><td>ThermoFisher</td><td>10488043</td><td></td></tr><tr><td>TruSeq Small RNA Library Prep Kit</td><td>Illumina</td><td>RS-200-0012/24/36/48</td><td>optional</td></tr><tr><td>UltraPure Glycogen</td><td>ThermoFisher</td><td>10814010</td><td></td></tr><tr><td>XCell SureLock Mini-Cell</td><td>ThermoFisher</td><td>EI0001</td><td></td></tr><tr><td>XCell SureLock Mini-Cell</td><td>ThermoFisher</td><td>EI0001</td><td></td></tr><tr><td>ZR small RNA ladder</td><td>Zymo Research</td><td>R1090</td><td></td></tr><tr><td colspan="4">the last two numbers correspond to the set of indexes</td></tr></tbody></table>

improving small rna-seq: less bias and better detection of 2'-o-methyl rnas

The study of small RNAs (sRNAs) by next-generation sequencing (NGS) is challenged by bias issues during library preparation. Several types of sRNA such as plant microRNAs (miRNAs) carry a 2&#39;-O-methyl (2&#39;-OMe) modification at their 3&#39;&#160;terminal nucleotide. This modification adds another difficulty as it inhibits 3&#39;&#160;adapter ligation. We previously demonstrated that modified versions of the &#39;TruSeq (TS)&#39;&#160;protocol have less bias and an improved detection of 2&#39;-OMe RNAs. Here we describe in detail protocol &#39;TS5&#39;, which showed the best overall performance. TS5 can be followed either using homemade reagents or reagents from the TS kit, with equal performance.

Critical steps are the isolation of the small RNA fraction of the starting total RNA material (Figure 3) and the desired final library product (Figure 4). Both steps involve polyacrylamide gel purification; small RNA is isolated from 15% TBE urea gels, while the final libraries are isolated from 6% native TBE gels. Small RNA isolated from gel can be analyzed on a small RNA capillary electrophoresis chip (Table of Materials; ...

Watch this Scientific Journal Video about Improving Small RNA-seq: Less Bias and Better Detection of 2'-O-Methyl RNAs at JoVE.com

Improving Small RNA-seq: Less Bias and Better Detection of 2'-O-Methyl RNAs

We present a detailed small RNA library reparation protocol with less bias than standard methods and an increased sensitivity for 2&#39;-O-methyl RNAs. This protocol can be followed using homemade reagents to save cost or using kits for convenience.

The study of small RNAs (sRNAs) by next-generation sequencing (NGS) is challenged by bias issues during library preparation. Several types of sRNA such as ...

improving-small-rna-seq-less-bias-better-detection-2-o-methyl

Universidad de Cartagena UDEC

Research

JoVE Journal

Genetics

7.5K Views.  Université Paris-Saclay. We present a detailed small RNA library reparation protocol with less bias than standard methods and an increased sensitivity for 2'-O-methyl RNAs. This protocol can be followed using homemade reagents to save cost or using kits for convenience.

Video: Improving Small RNA-seq: Less Bias and Better Detection of 2'-O-Methyl RNAs

A Method for Measuring RNA N6-methyladenosine Modifications in Cells and Tissues

N6-Methyladenosine (m6A) modifications of RNA are diverse and ubiquitous amongst eukaryotes. They occur in mRNA, rRNA, tRNA, and microRNA. Recent studies have revealed that these reversible RNA modifications affect RNA splicing, translation, degradation, and localization. Multiple physiological processes, like circadian rhythms, stem cell pluripotency, fibrosis, triglyceride metabolism, and obesity are also controlled by m6A modifications. Immunoprecipitation/sequencing, mass spectrometry, and modified northern blotting are some of the methods commonly employed to measure m6A modifications. Herein, we present a northeastern blotting technique for measuring m6A modifications. The current protocol provides good size separation of RNA, better accommodation and standardization for various experimental designs, and clear delineation of m6A modifications in various sources of RNA. While m6A modifications are known to have a crucial impact on human physiology relating to circadian rhythms and obesity, their roles in other (patho)physiological states are unclear. Therefore, investigations on m6A modifications have immense possibility to provide key insights into molecular physiology.

A Method for Measuring RNA N6-methyladenosine Modifications in Cells and Tissues

A modified northern blotting method for measuring N6-methyladenosine (m6A) modifications in RNA is described. The current method can detect modifications in diverse RNAs and controls under various experimental designs.

N6-Methyladenosine (m6A) modifications of RNA are diverse and ubiquitous amongst eukaryotes. They occur in mRNA, rRNA, tRNA, and microRNA. Recent studies ...

Biochemistry

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 ...

Genome-wide Surveillance of Transcription Errors in Eukaryotic Organisms

Accurate transcription is required for the faithful expression of genetic information. Surprisingly though, little is known about the mechanisms that control the fidelity of transcription. To fill this gap in scientific knowledge, we recently optimized the circle-sequencing assay to detect transcription errors throughout the transcriptome of Saccharomyces cerevisiae, Drosophila melanogaster, and Caenorhabditis elegans. This protocol will provide researchers with a powerful new tool to map the landscape of transcription errors in eukaryotic cells so that the mechanisms that control the fidelity of transcription can be elucidated in unprecedented detail.

This protocol provides researchers with a new tool to monitor the fidelity of transcription in multiple model organisms.

Accurate transcription is required for the faithful expression of genetic information. Surprisingly though, little is known about the mechanisms that ...

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.

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 ...

Methylated RNA Immunoprecipitation Assay to Study m5C Modification in Arabidopsis

Secondary base modifications on RNA, such as m5C, affect the structure and function of the modified RNA molecules. Methylated RNA Immunoprecipitation and sequencing (MeRIP-seq) is a method that aims to enrich for methylated RNA and ultimately identify modified transcripts. Briefly, sonicated RNA is incubated with an antibody for 5-methylated cytosines and precipitated with the assistance of protein G beads. The enriched fragments are then sequenced and the potential methylation sites are mapped based on the distribution of the reads and peak detection. MeRIP can be applied to any organism, as it does not require any prior sequence or modifying enzyme knowledge. In addition, besides fragmentation, RNA is not subjected to any other chemical or temperature treatment. However, MeRIP-seq does not provide single-nucleotide prediction of the methylation site as other methods do, although the methylated area can be narrowed down to a few nucleotides. The use of different modification-specific antibodies allows MeRIP to be adjusted for the different base modifications present on RNA, expanding the possible applications of this method.

Methylated RNA Immunoprecipitation Assay to Study m5C Modification in Arabidopsis

The methylated RNA immunoprecipitation assay is an antibody-based method used to enrich for methylated RNA fragments. Coupled with deep sequencing, it leads to the identification of transcripts carrying the m5C modification.

Secondary base modifications on RNA, such as m5C, affect the structure and function of the modified RNA molecules. Methylated RNA Immunoprecipitation and ...

2D-HELS MS Seq: A General LC-MS-Based Method for Direct and de novo Sequencing of RNA Mixtures with Different Nucleotide Modifications

Mass spectrometry (MS)-based sequencing approaches have been shown to be useful in&#160;direct&#160;sequencing&#160;RNA without the need for a complementary DNA (cDNA) intermediate. However, such approaches are rarely applied as a de novo RNA sequencing method, but used mainly as a tool that can assist in quality assurance for confirming known sequences of purified single-stranded RNA samples. Recently, we developed&#160;a direct RNA sequencing method by integrating a 2-dimensional mass-retention time hydrophobic end-labeling strategy into MS-based sequencing (2D-HELS MS Seq). This method is capable of accurately sequencing single RNA sequences as well as mixtures containing up to 12 distinct RNA sequences. In addition to the four canonical ribonucleotides (A, C, G, and U), the method has the capacity to sequence RNA oligonucleotides containing modified nucleotides. This is possible because the modified nucleobase either has an intrinsically unique mass that can help in its identification and its location in the RNA sequence, or can be converted into a product with a unique mass. In this study, we have used RNA, incorporating two representative modified nucleotides&#160;(pseudouridine (&#936;) and 5-methylcytosine (m5C)), to illustrate the application of the method for the de novo sequencing of a single RNA oligonucleotide&#160;as well as a mixture of RNA oligonucleotides, each with a different sequence and/or modified nucleotides. The procedures and protocols described here to sequence these model RNAs will be applicable to other short RNA samples (&#60;35 nt) when using a standard high-resolution LC-MS system, and can also be used for&#160;sequence verification of modified therapeutic RNA oligonucleotides.&#160;In the future, with the development of more robust algorithms and with better instruments, this method could allow sequencing of more complex biological samples.&#160;

Here, we describe a detailed protocol for an LC-MS-based sequencing method that can be used as a direct method to sequence short RNA (&#60;35 nt per run) without a cDNA intermediate, and as a general method to sequence different nucleotide modifications in a single study at single-base precision.

Mass spectrometry (MS)-based sequencing approaches have been shown to be useful in&#160;direct&#160;sequencing&#160;RNA without the need for a ...

Analyzing tRNA Dynamics Using AQRNA-seq

AQRNA-seq for Quantifying Small RNAs

AQRNA-seq provides a direct linear relationship between sequencing read counts and small RNA copy numbers in a biological sample, thus enabling accurate quantification of the pool of small RNAs. The AQRNA-seq library preparation procedure described here involves the use of custom-designed sequencing linkers and a step for reducing methylation RNA modifications that block reverse transcription processivity, which results in an increased yield of full-length cDNAs. In addition, a detailed implementation of the accompanying bioinformatics pipeline is presented. This demonstration of AQRNA-seq was conducted through a quantitative analysis of the 45 tRNAs in Mycobacterium bovis BCG harvested on 5 selected days across a 20-day time course of nutrient deprivation and 6 days of resuscitation. Ongoing efforts to improve the efficiency and rigor of AQRNA-seq will also be discussed here. This includes exploring methods to obviate gel purification for mitigating primer dimer issues after PCR amplification and to increase the proportion of full-length reads to enable more accurate read mapping. Future enhancements to AQRNA-seq will be focused on facilitating automation and high-throughput implementation of this technology for quantifying all small RNA species in cell and tissue samples from diverse organisms.

Author Spotlight: AQRNA-seq Role in Mapping Small RNAs and Unraveling Protein Translation Mechanisms

Absolute quantification RNA sequencing (AQRNA-seq) is a technology developed to quantify the landscape of all small RNAs in biological mixtures. Here, both the library preparation and data processing steps of AQRNA-seq are demonstrated, quantifying changes in the transfer RNA (tRNA) pool in Mycobacterium bovis BCG during starvation-induced dormancy.

AQRNA-seq provides a direct linear relationship between sequencing read counts and small RNA copy numbers in a biological sample, thus enabling accurate ...

DNAzyme-dependent Analysis of rRNA 2&#8217;-O-Methylation

Guide box C/D small nucleolar RNAs (snoRNAs) catalyze 2&#8217;-O-methylation of ribosomal and small nuclear RNA. However, a large number of snoRNA in higher eukaryotes may promiscuously recognize other RNA species and 2&#8217;-O-methylate multiple targets. Here, we provide step-by-step guide for the fast and non-expensive analysis of the site-specific 2&#8217;-O-methylation using a well-established method employing short DNA oligonucleotides called DNAzymes. These DNA fragments contain catalytic sequences which cleave RNA at specific consensus positions, as well as variable homology arms directing DNAzyme to its RNA targets. DNAzyme activity is inhibited by 2-&#8217;O-methylation of the nucleotide adjacent to the cleavage site in the RNA. Thus, DNAzymes, limited only by the consensus of the cleaved sequence, are perfect tools for the quick analysis of snoRNA-mediated RNA 2&#8217;-O-methylation. We analyzed snoRNA snR13- and snR47-guided 2&#8217;-O-methylation of 25S ribosomal RNA in Saccharomyces cerevisiae to demonstrate the simplicity of the technique and to provide a detailed protocol for the DNAzyme-dependent assay.

Here we present a protocol for DNAzyme-dependent cleavage of RNA. This enables fast and site-dependent analysis of RNA 2&#8217;-O-methylation. This approach can be used for the preliminary or major assessment of snoRNA activity.

Guide box C/D small nucleolar RNAs (snoRNAs) catalyze 2&#8217;-O-methylation of ribosomal and small nuclear RNA. However, a large number of snoRNA in ...

Developmental Biology

Highly Efficient Ligation of Small RNA Molecules for MicroRNA Quantitation by High-Throughput Sequencing

MiRNA cloning and high-throughput sequencing, termed miR-Seq, stands alone as a transcriptome-wide approach to quantify miRNAs with single nucleotide resolution. This technique captures miRNAs by attaching 3&#8217; and 5&#8217; oligonucleotide adapters to miRNA molecules and allows de novo miRNA discovery. Coupling with powerful next-generation sequencing platforms, miR-Seq has been instrumental in the study of miRNA biology. However, significant biases introduced by oligonucleotide ligation steps have prevented miR-Seq from being employed as an accurate quantitation tool. Previous studies demonstrate that biases in current miR-Seq methods often lead to inaccurate miRNA quantification with errors up to 1,000-fold for some miRNAs1,2. To resolve these biases imparted by RNA ligation, we have developed a small RNA ligation method that results in ligation efficiencies of over 95% for both 3&#8217; and 5&#8242; ligation steps. Benchmarking this improved library construction method using equimolar or differentially mixed synthetic miRNAs, consistently yields reads numbers with less than two-fold deviation from the expected value. Furthermore, this high-efficiency miR-Seq method permits accurate genome-wide miRNA profiling from in vivo total RNA samples2.

MicroRNAs (miRNAs) are a widely conserved class of regulatory molecules. Here we describe a miRNA cloning method that relies upon two potent ligation steps followed by high-throughput sequencing. Our method permits accurate genome-wide quantitation of miRNAs.

MiRNA cloning and high-throughput sequencing, termed miR-Seq, stands alone as a transcriptome-wide approach to quantify miRNAs with single nucleotide ...

Biology

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 ...

Improving Small RNA-seq: Less Bias and Better Detection of 2'-O-Methyl RNAs

Podsumowanie

Przeglądaj więcej filmów

Rozdziały w tym wideo

A Method for Measuring RNA N⁶-methyladenosine Modifications in Cells and Tissues