We are grateful to the Banner Sun Health Research Institute Brain and Body Donation Program (BBDP) of Sun City, Arizona for the provision of human brain tissues. The BBDP has been supported by the National Institute of Neurological Disorders and Stroke (U24 NS072026 National Brain and Tissue Resource for Parkinson's Disease and Related Disorders), the National Institute on Aging (P30AG19610 Arizona Alzheimer's Disease Core Center), the Arizona Department of Health Services (contract 211002, Arizona Alzheimer's Research Center), the Arizona Biomedical Research Commission (contracts 4001, 0011, 05-901 and 1001 to the Arizona Parkinson&#39;s Disease Consortium) and the Michael J. Fox Foundation for Parkinson's Research27. This study was also supported by the DHS and the State of Arizona (ADHS grant # ADHS14-052688). We also thank Andrea Schmitt (Banner Research) and Cynthia Lechuga (TGen) for administrative support.

This research has been performed in compliance with all institutional, national and international guidelines for human welfare. Brain tissues were obtained from the Banner Sun Health Research Institute Brain and Body Donation Program in Sun City, AZ. The operations of the Brain and Body Donation Program are approved by the Western Institutional Review Board (WIRB protocol #20120821). All subjects or their legal representatives signed the informed consent. Commercial (non-brain) biospecimens were purchased from Proteogenex.
1. RNase R Treatment
NOTE: In the following steps, the reaction volume is adjusted to a total volume of 50 &#181;L. This is the minimum sample volume to be used in the RNA cleanup &#38; concentrator kit (see Table of Materials). Additionally, the optimized protocol described here is for an input amount of 4 &#181;g of total RNA. A longer incubation time for RNase R treatment is recommended for an input amount &#62;4 &#181;g.
<ol>
	<li>Dilute total RNA to 4 &#181;g in 39 &#181;L RNase-free water in a microcentrifuge tube and mix well by pipetting.</li>
	<li>In a separate tube, dilute the RNase R to a working concentration of 2 U/&#181;L with 1x RNase R Reaction Buffer. Make only enough for immediate use.</li>
	<li>Pipette 39 &#181;L of total RNA and 5 &#181;L of 10x RNase R Reaction Buffer into a 1.5 mL reaction tube and mix well by pipetting (50 &#181;L will be the total reaction volume). Next, add 6 &#181;L of RNase R (2 U/&#181;L).</li>
	<li>Adjust the pipette to the full reaction volume (50 &#181;L) and mix well by pipetting up and down 10 times.</li>
	<li>Place the tube in a 37 &#176;C water bath for 10 min. Make sure that the full reaction volume is immersed in the water bath.</li>
	<li>Place the tube on ice and immediately proceed with RNA cleanup &#38; concentration (section 2).</li>
</ol>
2. Purifying RNA Using an RNA Cleanup and Concentrator Kit
NOTE: When using high quality RNA (RIN&#62;8, DV200&#62;80%), RNase R treatment may result in loss of approximately 60% of RNA. Using a 4 &#181;g input, it is estimated that 2&#8211;2.5 &#181;g of treated RNA is left after section 1.
<ol>
	<li>Before starting, prepare the RNA Wash Buffer by adding 48 mL of 100% ethanol to the buffer concentrate and mix well by pipetting. Place purification columns into collection tubes (see Table of Materials) and place in a tube rack. 
	NOTE: Use the following centrifugation settings for all following steps: 10,000&#8211;16,000 x g. If DNase I treatment has already been performed, skip DNase I treatment at this stage.</li>
	<li>Add 2 volumes of RNA Binding Buffer to the RNase R treated sample, and mix well by pipetting (total volume: 150 &#181;L).</li>
	<li>Add 1 volume of 100% ethanol to the RNA Binding Buffer and RNase R treated sample mixture, and mix well by pipetting (total volume: 300 &#181;L).</li>
	<li>Transfer the entire volume to the column and centrifuge the column for 30 s. Discard flow through.</li>
	<li>Add 400 &#181;L of RNA Prep Buffer directly to the column, centrifuge the column for 30 s, and discard flow through.</li>
	<li>Add 700 &#181;L of RNA Wash Buffer directly to the column, centrifuge the column for 30 s, and discard flow through.</li>
	<li>Add 400 &#181;L of RNA Wash Buffer directly to the column, centrifuge the column for 2 min, and transfer the column to a fresh RNase-free 1.5 mL tube.</li>
	<li>Add 11 &#181;L of RNase-free water directly to the column by holding the pipette tip right above the column filter and ensuring that water lands only on the column filter.</li>
	<li>Incubate the column for 1 min at room temperature and centrifuge for 1 min.</li>
	<li>Before discarding the column, check for flow through in the RNase-free tube. If elution was successful, store sample at -80 &#176;C or immediately proceed with library preparation. The final total elution volume of approximately 10 &#181;L is used for library construction. 
	NOTE: Stopping point: Leave RNA at -80 &#176;C for up to 7 days before continuing with library preparation.</li>
</ol>
3. circRNA Library Prep
NOTE: See Table of Materials for kit, which contains most reagents used in this section.
<ol>
	<li>rRNA depletion and fragmentation
	<ol>
		<li>Transfer 10 &#181;L of purified RNA from step 2.10 to a clean well in a new 96-well 0.3 mL PCR plate. To the well, add 5 &#181;L of rRNA Binding Buffer followed by 5 &#181;L rRNA Removal Mix. Gently pipette up and down 10 times to mix.</li>
		<li>Seal plate and incubate for 5 min at 68 &#176;C on a pre-programmed, pre-heated thermocycler block. After completion of the 5 min incubation, place plate on bench and incubate at room temperature for 1 min.</li>
		<li>Remove seal from plate. Add 35 &#181;L of vortexed room temperature rRNA removal beads to sample. Adjust the pipette to 45 &#181;L and pipette up and down 10&#8211;20x to mix thoroughly. Incubate plate for 1 min at room temperature.</li>
		<li>Transfer plate to a magnetic stand and incubate on the stand for 1 min or until the solution clears. Transfer all of the supernatant (~45 &#181;L) to new well on the same plate, or new plate (depending on how many samples you are working with).</li>
		<li>Vortex the RNA cleanup beads (see Table of Materials) until well dispersed, and add 99 &#181;L of beads to each sample. Pipette up and down 10x to mix. Incubate the plate at room temperature for 10 min.</li>
		<li>Transfer the plate to the magnetic stand and incubate an additional 5 min or until the solution clears. Remove and discard all of the supernatant from the well.</li>
		<li>With the plate still on the magnetic stand, add 200 &#181;L of freshly prepared 80% EtOH to the well without disrupting the beads. Incubate for 30 s, then remove and discard ethanol. Repeat for a total of 2 washes.</li>
		<li>Add 11 &#181;L of Elution Buffer to each well and pipette up and down 10 times to mix. Incubate at room temperature for 2 min, and then transfer to the magnetic stand until the solution clears (1&#8211;5 min).</li>
		<li>Transfer 8.5 &#181;L of the supernatant from the well to a new well on the same plate or to a new plate. Add 8.5 &#181;L of the Elute, Primer, Fragment High mix to each well containing sample. Pipette up and down 10 times to mix thoroughly.</li>
		<li>Seal plate and incubate for 8 min at 94 &#176;C on a pre-programmed, pre-heated thermocycler block. Remove from thermocycler when it reaches 4 &#176;C and centrifuge briefly. 
		NOTE: Proceed immediately to the Synthesize First Strand cDNA protocol.</li>
	</ol>
	</li>
	<li>Synthesize cDNA
	<ol>
		<li>For each sample being prepared, mix 9 &#181;L First Strand Synthesis Mix with 1 &#181;L of reverse transcriptase (see Table of Materials). Add 8 &#181;L of the mixture to the sample. Pipette up and down 6 times to mix.
		<ol>
			<li>Seal plate and incubate on a pre-programmed, pre-heated thermocycler block using the following parameters: 25 &#176;C for 10 min, 42 &#176;C for 15 min, 70 &#176;C for 15 min, 4 &#176;C hold. Proceed immediately to second strand synthesis.</li>
		</ol>
		</li>
		<li>Add 5 &#181;L of Resuspension buffer to each sample followed by 20 &#181;L of Second Strand Marking master mix. Pipette the entire volume up and down 6 times.</li>
		<li>Seal plate and incubate on a pre-programmed, pre-heated thermocycler block set to 16 &#176;C for 1 h. Following incubation, remove the plate from the thermocycler and let it equilibrate to room temperature.</li>
		<li>Vortex PCR purification beads (see Table of Materials) and add 90 &#181;L beads to each well of sample. Pipette up and down 10 times to mix thoroughly. Incubate at room temperature for 10 min.</li>
		<li>Transfer the bead/sample mix to the magnetic stand and incubate for 5 min or until liquid clears. Remove and discard supernatant.</li>
		<li>Add 200 &#181;L of 80% EtOH to each sample. Incubate samples on the magnetic stand at room temperature for 30 s. Discard supernatant. Repeat 1x.</li>
		<li>Allow beads to dry at room temperature for 6 min, and then remove from magnetic stand.</li>
		<li>Resuspend beads in 19.5 &#181;L of Resuspension buffer. Pipette up and down 10 times to mix thoroughly. Incubate at room temperature for 2 min, then transfer to the magnetic stand and incubate for an additional 1 min or until the liquid clears.</li>
		<li>Transfer 17.5 &#181;L of supernatant to new well/new plate. 
		NOTE: If not proceeding immediately, samples can be stored at -20 &#176;C for up to 7 days.</li>
	</ol>
	</li>
	<li>Library preparation
	<ol>
		<li>Add 12.5 &#181;L of A-Tailing Mix to each well containing supernatant. Pipette the entire volume up and down 10 times to mix.</li>
		<li>Incubate the reaction on a pre-programmed, pre-heated thermocycler block set to 37 &#176;C using the following parameters: 37 &#176;C for 30 min, 70 &#176;C for 5 min, 4 &#176;C hold. When samples reach 4 &#176;C, proceed immediately to adapter ligation.</li>
		<li>To each sample add 2.5 &#181;L of Resuspension buffer, 2.5 &#181;L of a unique RNA adapter, and 2.5 &#181;L of Ligation Mix. Pipette up and down 10x to mix.</li>
		<li>Incubate samples on a pre-programmed, pre-heated thermocycler block at 30 &#176;C for 10 min.</li>
		<li>Add 5 &#181;L of Stop Ligation buffer to each sample and pipette up and down to mix.</li>
		<li>Add 42 &#181;L of mixed PCR purification beads to each sample and mix thoroughly. Follow steps 3.2.6 through 3.2.10, but change the resuspension volume to 52 &#181;L and final elution volume to 50 &#181;L.</li>
		<li>Repeat the PCR purification bead protocol again with the 50 &#181;L elution from step 3.3.6, but change the resuspension volume to 22 &#181;L and final elution volume to 20 &#181;L. 
		NOTE: If not proceeding immediately, samples can be stored at -20 &#176;C for up to 7 days.</li>
		<li>Add 5 &#181;L of PCR Primer Cocktail and 25 &#181;L of PCR Master Mix to each sample. Mix by pipetting up and down 10 times. Incubate the reaction on a pre-programmed, pre-heated thermocycler block using the following parameters: 98 &#176;C for 30 s; then 8 cycles of 98 &#176;C for 10 s, 60 &#176;C for 30 s, and 72 &#176;C for 30 s; then 72 &#176;C for 5 min, then 4 &#176;C hold. 
		NOTE: Optimization of the total number of PCR cycles may be needed to generate sufficient amounts of library for sequencing.</li>
		<li>Follow the protocol for PCR bead purification (steps 3.2.4 through 3.2.9), except add 50 &#181;L of well-mixed PCR purification beads and change the resuspension volume to 32.5 &#181;L with a final elution volume of 30 &#181;L. 
		NOTE: Samples should be stored at -20 &#176;C.</li>
	</ol>
	</li>
	<li>Quantification and Quality Control using a nucleic acid analyzer
	<ol>
		<li>Allow tapes and reagents to equilibrate at room temperature for 30 min.</li>
		<li>Mix 2 &#181;L of library with 2 &#181;L of HS D1000 buffer, and add to a compatible well plate.</li>
		<li>Seal tightly with compatible foil seal, and vortex for 1 min at 2,000 rpm.</li>
		<li>Spin down and load plate on the analyzer following software prompts. 
		NOTE: Libraries should be approximately 260 bp in size.</li>
	</ol>
	</li>
</ol>
4. Data Analysis Workflow
<ol>
	<li>Sequence RNA-seq libraries (see Table of Materials) to generate 82 bp paired-end reads. Convert raw sequencing data in the form of basecall files (.bcl) to FASTQs using the bcl2fastq tool (v0.2.19).</li>
	<li>Detect circRNAs. 
	NOTE: Based on previously reported evidence that an ensemble circRNA detection approach performs better compared to using a single detection tool21,22, we suggest using multiple tools for circRNA detection. Here, circRNAs were identified using six existing circRNA prediction algorithms: find_circ, CIRI, CIRCexplorer, Mapsplice, KNIFE, and DCC, applying the recommended parameter settings for each algorithm.
	<ol>
		<li>Download and install each circRNA detection algorithm on a Linux high performance computing cluster using the instructions provided by the developers.</li>
		<li>Align RNA-seq FASTQs against the reference genome (GRCh37), utilizing the aligner recommended for each tool.</li>
		<li>Following alignment, execute circRNA detection algorithms by applying their respective recommended parameter settings.</li>
		<li>Each tool will output a multi-column results file with the list of detected circRNAs, extract the circRNA co-ordinates and the number of supporting reads from this in order to quantify the number of candidates detected in each sample/test condition.</li>
	</ol>
	</li>
	<li>Convert the circRNA coordinates output by CIRI, Mapsplice, and DCC to 0-based coordinates to be consistent with the other three algorithms.</li>
	<li>Select circRNAs with two or more supporting reads or downstream analyses and comparisons. Table 1 summarizes all the parameters evaluated in our study along with the total number of sequencing reads generated for each sample.</li>
	<li>For each sample/test condition, count the number of detected circRNAs normalized to the number of mapped reads generated for that library, per million. Summarize the results across the various tools/samples in box plots, as detailed in the Representative Results.</li>
</ol>

<ol>
	<li>Salzman, J., Gawad, C., Wang, P. L., Lacayo, N., Brown, P. O. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Circular+RNAs+are+the+predominant+transcript+isoform+from+hundreds+of+human+genes+in+diverse+cell+types.">Circular RNAs are the predominant transcript isoform from hundreds of human genes in diverse cell types.</a> PloS One. 7 (2), e30733 (2012).</li><li>Jeck, W. 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=Circular+RNAs+are+abundant,+conserved,+and+associated+with+ALU+repeats.">Circular RNAs are abundant, conserved, and associated with ALU repeats.</a> RNA. 19 (2), 141-157 (2013).</li><li>Memczak, 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=Circular+RNAs+are+a+large+class+of+animal+RNAs+with+regulatory+potency.">Circular RNAs are a large class of animal RNAs with regulatory potency.</a> Nature. 495 (7441), 333-338 (2013).</li><li>Sanger, H. L., Klotz, G., Riesner, D., Gross, H. J., Kleinschmidt, A. K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Viroids+are+single-stranded+covalently+closed+circular+RNA+molecules+existing+as+highly+base-paired+rod-like+structures.">Viroids are single-stranded covalently closed circular RNA molecules existing as highly base-paired rod-like structures.</a> Proceedings of the National Academy of Sciences of the United States of America. 73 (11), 3852-3856 (1976).</li><li>Nigro, J. 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=Scrambled+exons.">Scrambled exons.</a> Cell. 64 (3), 607-613 (1991).</li><li>Salzman, J., Chen, R. E., Olsen, M. N., Wang, P. L., Brown, P. O. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cell-type+specific+features+of+circular+RNA+expression.">Cell-type specific features of circular RNA expression.</a> PLoS Genetics. 9 (9), e1003777 (2013).</li><li>Du, W. W., 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=Foxo3+circular+RNA+promotes+cardiac+senescence+by+modulating+multiple+factors+associated+with+stress+and+senescence+responses.">Foxo3 circular RNA promotes cardiac senescence by modulating multiple factors associated with stress and senescence responses.</a> European Heart Journal. 38 (18), 1402-1412 (2016).</li><li>Capel, B., 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=Circular+transcripts+of+the+testis-determining+gene+Sry+in+adult+mouse+testis.">Circular transcripts of the testis-determining gene Sry in adult mouse testis.</a> Cell. 73 (5), 1019-1030 (1993).</li><li>Hansen, T. B., 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=miRNA-dependent+gene+silencing+involving+Ago2-mediated+cleavage+of+a+circular+antisense+RNA.">miRNA-dependent gene silencing involving Ago2-mediated cleavage of a circular antisense RNA.</a> The EMBO Journal. 30 (21), 4414-4422 (2011).</li><li>Hansen, T. B., 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=Natural+RNA+circles+function+as+efficient+microRNA+sponges.">Natural RNA circles function as efficient microRNA sponges.</a> Nature. 495 (7441), 384-388 (2013).</li><li>Li, Z., 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=Exon-intron+circular+RNAs+regulate+transcription+in+the+nucleus.">Exon-intron circular RNAs regulate transcription in the nucleus.</a> Nature Structural &amp; Molecular Biology. 22 (3), 256-264 (2015).</li><li>Tan, W. L., 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+landscape+of+circular+RNA+expression+in+the+human+heart.">A landscape of circular RNA expression in the human heart.</a> Cardiovascular Research. 113 (3), 298-309 (2016).</li><li>Zhong, Z., Lv, M., Chen, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Screening+differential+circular+RNA+expression+profiles+reveals+the+regulatory+role+of+circTCF25-miR-103a-3p/miR-107-CDK6+pathway+in+bladder+carcinoma.">Screening differential circular RNA expression profiles reveals the regulatory role of circTCF25-miR-103a-3p/miR-107-CDK6 pathway in bladder carcinoma.</a> Scientific Reports. 6, 30919 (2016).</li><li>Gao, Y., Wang, J., Zhao, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=CIRI:+an+efficient+and+unbiased+algorithm+for+de+novo+circular+RNA+identification.">CIRI: an efficient and unbiased algorithm for de novo circular RNA identification.</a> Genome Biology. 16, 4-014-0571-0573 (2015).</li><li>Wang, 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=MapSplice:+accurate+mapping+of+RNA-seq+reads+for+splice+junction+discovery.">MapSplice: accurate mapping of RNA-seq reads for splice junction discovery.</a> Nucleic Acids Research. 38 (18), e178 (2010).</li><li>Szabo, L., 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=Statistically+based+splicing+detection+reveals+neural+enrichment+and+tissue-specific+induction+of+circular+RNA+during+human+fetal+development.">Statistically based splicing detection reveals neural enrichment and tissue-specific induction of circular RNA during human fetal development.</a> Genome Biology. 16, 126-015-0690-0695 (2015).</li><li>Cheng, J., Metge, F., Dieterich, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Specific+identification+and+quantification+of+circular+RNAs+from+sequencing+data.">Specific identification and quantification of circular RNAs from sequencing data.</a> Bioinformatics. 32 (7), 1094-1096 (2016).</li><li>Zhang, X. O., 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=Complementary+sequence-mediated+exon+circularization.">Complementary sequence-mediated exon circularization.</a> Cell. 159 (1), 134-147 (2014).</li><li>Rybak-Wolf, A., 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=Circular+RNAs+in+the+mammalian+brain+are+highly+abundant,+conserved,+and+dynamically+expressed.">Circular RNAs in the mammalian brain are highly abundant, conserved, and dynamically expressed.</a> Molecular Cell. 58 (5), 870-885 (2015).</li><li>Ji, P., 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=Expanded+Expression+Landscape+and+Prioritization+of+Circular+RNAs+in+Mammals.">Expanded Expression Landscape and Prioritization of Circular RNAs in Mammals.</a> Cell Reports. 26 (12), 3444-3460 (2019).</li><li>Hansen, T. B., Ven&#248;, M. T., Damgaard, C. K., Kjems, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comparison+of+circular+RNA+prediction+tools.">Comparison of circular RNA prediction tools.</a> Nucleic Acids Research. 44 (6), e58 (2016).</li><li>Zeng, X., Lin, W., Guo, M., Zou, Q. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+comprehensive+overview+and+evaluation+of+circular+RNA+detection+tools.">A comprehensive overview and evaluation of circular RNA detection tools.</a> PLoS Comput Biol. 13 (6), e1005420 (2017).</li><li>Sekar, 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=ACValidator:+a+novel+assembly-based+approach+for+in+silico+validation+of+circular+RNAs.">ACValidator: a novel assembly-based approach for in silico validation of circular RNAs.</a> bioRxiv. , (2019).</li><li>Westholm, J. O., 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=Genome-wide+analysis+of+drosophila+circular+RNAs+reveals+their+structural+and+sequence+properties+and+age-dependent+neural+accumulation.">Genome-wide analysis of drosophila circular RNAs reveals their structural and sequence properties and age-dependent neural accumulation.</a> Cell Reports. 9 (5), 1966-1980 (2014).</li><li>Szabo, L., Salzman, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Detecting+circular+RNAs:+bioinformatic+and+experimental+challenges.">Detecting circular RNAs: bioinformatic and experimental challenges.</a> Nature Reviews Genetics. 17 (11), 679-692 (2016).</li><li>Zheng, Y., Ji, P., Chen, S., Hou, L., Zhao, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Reconstruction+of+full-length+circular+RNAs+enables+isoform-level+quantification.">Reconstruction of full-length circular RNAs enables isoform-level quantification.</a> Genome Medicine. 11 (1), 2 (2019).</li><li>Beach, T. 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=Arizona+study+of+aging+and+neurodegenerative+disorders+and+brain+and+body+donation+program.">Arizona study of aging and neurodegenerative disorders and brain and body donation program.</a> Neuropathology. 35 (4), 354-389 (2015).</li></ol>

The authors have nothing to disclose.

In this study, two commercially available library preparation kits, pre-treatment options, and input RNA amounts were tested in order to optimize a circRNA enrichment protocol for construction of circRNA sequencing libraries. Based on this study&rsquo;s assessments, a number of key aspects and critical steps in creating circRNA sequencing libraries are apparent. Our evaluation confirms the utility of RNase R pre-treatment, as reflected by the increased number of circRNAs detected. Overall, a higher diversity of circRNAs when using the Illumina TruSeq library kit with RNase R pre-treatment and 4 &micro;g of input RNA was observed. These results align with previous findings that the RNase R enrichment step is beneficial for detection of circRNAs2.
Additional key aspects of circRNA library construction include the amount of total RNA that is available for sequencing as well as the type of tissue that the RNA extracted from. Although a 4 &micro;g input of total RNA was found to yield the highest number of detected circRNAs, the majority of RNAseq studies utilize &lt;=1 &micro;g of total RNA such that obtaining higher amounts may be challenging, particularly for analysis of human specimens. Identification of circRNAs remains feasible for lower input amounts, but it is relevant to acknowledge that the specificity of the analysis may be impacted. This study further highlights the higher number of circRNAs that are detected in human brain compared to other tissues, as previously reported19,20. It is thus critical to acknowledge the differential expression of circRNAs across different tissue types. Furthermore, additional research in the context of disease will be important for shedding light into how circRNAs may be involved in pathogenic processes.
The performance of the two assessed RNA library preparation kits also highlights that although different commercially-available kits may demonstrate significant similarities, differences are still observed when analyzing circRNAs. Two major findings from this comparison include decreased rRNA depletion and a lower number of circRNAs identified using one approach. While one possibility is that a higher abundance of rRNA in a sample may interfere with creating sequence-able circRNA library molecules, this finding emphasizes the need to assess seemingly similar kits, particularly when reagents are proprietary.
 Although the data presented here provides insights into the existence and abundance of circRNAs in various tissue types, this study has a few technical limitations. Firstly, while RNase R treatment reduces the population of linear RNAs in a sample, it is not well understood if this depletion step introduces any biases in circRNA detection and whether it may deplete circRNAs. Previous studies have reported that in some cases, circRNAs are sensitive to RNase R2,24,25. Secondly, it is unclear if increasing the total RNA input above 4 &micro;g will result in a linear increase in the number of identified circRNAs. As previously mentioned, available total RNA is often limited in research studies so lower input amounts were considered here. Of note, circRNAs can still be detected when using lower inputs but it is important to acknowledge that lower inputs are associated with detection of a lower number of circRNAs. Thirdly, the optimized protocol presented here utilizes RNAs extracted from a specific set of tissues. Given the variable distribution of circRNA expression across different tissue types, the association between total RNA input amounts and the number of identified circRNAs may differ across tissues.
With increasing interests in understanding the biological role of circRNAs, new strategies are also being developed to better enable characterization and identification of circRNAs. One new bioinformatics approach enables identification of circRNAs that may be lowly expressed through reconstruction of full-length circRNAs, and also enables quantification of expression of specific circRNA isoforms26. This approach takes advantage of features described as reverse overlap (RO) reads that may occur on the 3&rsquo; or 5&rsquo; ends of circRNA library molecules. Development of new strategies for identifying circRNAs, encompassing both laboratory approaches and bioinformatics tools, will contribute to the field&rsquo;s understanding of the function and impact of circRNAs.

Circular RNAs (CircRNAs) are endogenous, non-coding RNAs that have gained attention given their pervasive expression in the eukaryotic transcriptome1,2,3. They are formed when exons back-splice to each other and hence were initially considered to be splicing artifacts4,5. However, recent studies have demonstrated that circRNAs exhibit cell type, tissue, and developmental stage specific expression3,6 and are evolutionarily conserved2,3. Furthermore, they are involved in mediation of protein-protein interactions7, micro-RNA (miRNA) binding3,8,9,10, and regulation of parental gene transcription11.
In classical RNA sequencing (RNA-seq), circRNAs may be completely lost during library construction as a result of poly-A selection for mRNAs or may be difficult to isolate given their low abundance. However, recent circRNA characterization studies have incorporated a pre-treatment step using RNase R in order to enrich for circRNAs2,12,13. RNase R is an exoribonuclease that digests linear RNAs, leaving behind circular RNA structures. CircRNA enrichment protocols were optimized by generating and comparing data from two commercially available whole transcriptome library construction kits, with and without an RNase R pre-treatment step, and using varying amounts of total RNA input (1 to 4 &#181;g). The optimized protocol was next used to evaluate the abundance of circRNAs across five different brain regions (cerebellum [BC], inferior parietal lobe [IP], middle temporal gyrus [MG], occipital cortex [OC] and superior frontal gyrus [SF]) and four other tissue types (liver [LV], lung [LU], lymph node [LN] and pancreas [PA]). RNA-seq libraries were paired end sequenced and data was analyzed using six different circRNA prediction algorithms: find_circ3, CIRI14, Mapsplice15, KNIFE16, DCC17, and CIRCexplorer18. Based on our analysis, the highest number of unique circRNAs was detected when using a stranded total RNA library preparation kit with RNase R pre-treatment and 4 &#181;g total input RNA. The optimized protocol is described here. As previously reported19,20, the highest enrichment of circRNAs was observed in the brain compared to other tissue types.

<table>
	<tbody>
		<tr>
			<td>1000 &#181;L pipette tips</td>
			<td>Rainin</td>
			<td>GP-L1000F</td>
			<td></td>
		</tr>
		<tr>
			<td>20 &#181;L pipette tips</td>
			<td>Rainin</td>
			<td>SR L 10F</td>
			<td></td>
		</tr>
		<tr>
			<td>200 &#181;L pipette tips</td>
			<td>Rainin</td>
			<td>SR L 200F</td>
			<td></td>
		</tr>
		<tr>
			<td>2200 TapeStation Accessories (foil covers)</td>
			<td>Agilent Technologies</td>
			<td>5067-5154</td>
			<td></td>
		</tr>
		<tr>
			<td>2200 TapeStation Accessories (tips)</td>
			<td>Agilent Technologies</td>
			<td>5067-5153</td>
			<td></td>
		</tr>
		<tr>
			<td>Adhesive Film for Microplates</td>
			<td>VWR</td>
			<td>60941-064</td>
			<td></td>
		</tr>
		<tr>
			<td>AMPure XP Beads 450 mL</td>
			<td>Beckman Coulter</td>
			<td>A63882</td>
			<td>PCR purification</td>
		</tr>
		<tr>
			<td>Eppendorf twin.tec 96-Well PCR Plates</td>
			<td>VWR</td>
			<td>951020401</td>
			<td></td>
		</tr>
		<tr>
			<td>High Sensitivity D1000 reagents</td>
			<td>Agilent Technologies</td>
			<td>5067-5585</td>
			<td></td>
		</tr>
		<tr>
			<td>High Sensitivity D1000 ScreenTape</td>
			<td>Agilent Technologies</td>
			<td>5067-5584</td>
			<td></td>
		</tr>
		<tr>
			<td>HiSeq 2500 Sequencing System</td>
			<td>Illumina</td>
			<td>SY-401-2501</td>
			<td></td>
		</tr>
		<tr>
			<td>HiSeq 3000/4000 PE Cluster Kit</td>
			<td>Illumina</td>
			<td>PE-410-1001</td>
			<td></td>
		</tr>
		<tr>
			<td>HiSeq 3000/4000 SBS Kit (150 cycles)</td>
			<td>Illumina</td>
			<td>FC-410-1002</td>
			<td></td>
		</tr>
		<tr>
			<td>HiSeq 4000 Sequencing System</td>
			<td>Illumina</td>
			<td>SY-401-4001</td>
			<td></td>
		</tr>
		<tr>
			<td>HiSeq PE PE Rapid Cluster Kit v2</td>
			<td>Illumina</td>
			<td>PE-402-4002</td>
			<td></td>
		</tr>
		<tr>
			<td>HiSeq Rapid SBS Kit v2 (50 cycle)</td>
			<td>Illumina</td>
			<td>FC-402-4022</td>
			<td></td>
		</tr>
		<tr>
			<td>Kapa Total RNA Kit</td>
			<td>Roche</td>
			<td>KK8400</td>
			<td></td>
		</tr>
		<tr>
			<td>Molecular biology grade ethanol</td>
			<td>Fisher Scientific</td>
			<td>BP28184</td>
			<td></td>
		</tr>
		<tr>
			<td>Qubit Assay Tubes</td>
			<td>Supply Center by Thermo Fischer</td>
			<td>Q32856</td>
			<td></td>
		</tr>
		<tr>
			<td>Qubit dsDNA High Sense Assay Kit</td>
			<td>Supply Center by Thermo Fischer</td>
			<td>Q32854</td>
			<td></td>
		</tr>
		<tr>
			<td>RNA cleanup and concentrator - 5</td>
			<td>Zymo</td>
			<td>RCC-100</td>
			<td>Contains purification columns, collection tubes</td>
		</tr>
		<tr>
			<td>RNAClean XP beads</td>
			<td>Beckman Coulter Genomics</td>
			<td></td>
			<td>RNA Cleanup beads</td>
		</tr>
		<tr>
			<td>Rnase R</td>
			<td>Lucigen</td>
			<td>RNR07250</td>
			<td></td>
		</tr>
		<tr>
			<td>SuperScript II Reverse Transcriptase 10,000 units</td>
			<td>ThermoFisher (LifeTech)</td>
			<td>18064014</td>
			<td></td>
		</tr>
		<tr>
			<td>TapeStation 2200</td>
			<td>Agilent Technologies</td>
			<td></td>
			<td>Nucleic Acid analyzer</td>
		</tr>
		<tr>
			<td>TElowE</td>
			<td>VWR</td>
			<td>10128-588</td>
			<td></td>
		</tr>
		<tr>
			<td>TruSeq Stranded Total RNA Library Prep Kit</td>
			<td>Illumina</td>
			<td>20020596</td>
			<td>Kit used in section 3</td>
		</tr>
		<tr>
			<td>Two-Compartment Divided Tray</td>
			<td>VWR</td>
			<td>3054-1004</td>
			<td></td>
		</tr>
		<tr>
			<td>UltraPure Water</td>
			<td>Supply Center by Thermo Fischer</td>
			<td>10977-015</td>
			<td></td>
		</tr>
		<tr>
			<td>Universal control RNA</td>
			<td>Agilent</td>
			<td>740000</td>
			<td></td>
		</tr>
	</tbody>
</table>

identification of circular rnas using rna sequencing

Circular RNAs (circRNAs) are a class of non-coding RNAs involved in functions including micro-RNA (miRNA) regulation, mediation of protein-protein interactions, and regulation of parental gene transcription. In classical next generation RNA sequencing (RNA-seq), circRNAs are typically overlooked as a result of poly-A selection during construction of mRNA libraries, or are found at very low abundance, and are therefore difficult to isolate and detect. Here, a circRNA library construction protocol was optimized by comparing library preparation kits, pre-treatment options and various total RNA input amounts. Two commercially available whole transcriptome library preparation kits, with and without RNase R pre-treatment, and using variable amounts of total RNA input (1 to 4 &#181;g), were tested. Lastly, multiple tissue types; including liver, lung, lymph node, and pancreas; as well as multiple brain regions; including the cerebellum, inferior parietal lobe, middle temporal gyrus, occipital cortex, and superior frontal gyrus; were compared to evaluate circRNA abundance across tissue types. Analysis of the generated RNA-seq data using six different circRNA detection tools (find_circ, CIRI, Mapsplice, KNIFE, DCC, and CIRCexplorer) revealed that a stranded total RNA library preparation kit with RNase R pre-treatment and 4 &#181;g RNA input is the optimal method for identifying the highest relative number of circRNAs. Consistent with previous findings, the highest enrichment of circRNAs was observed in brain tissues compared to other tissue types.

Data generated using a commercially available universal control RNA (UC) and using two library preparation kits, both of which include a ribo-depletion step in their protocols, was first assessed. Using an analytical workflow (Data analysis workflow, section 4), overall, a higher number of circRNAs was detected in the TruSeq datasets compared to the Kapa ones (Figure 1). Although the ribosomal RNA (rRNA) percentages were below 5% in datasets from both kits for lower input amounts (1, 2 ug), Kapa datasets had higher rRNA content for 4, 5, and 10 ug inputs (Table 2). Hence, based on the number of detected circRNAs and rRNA depletion efficiency, further experiments were performed using the TruSeq kit.
Next, the significance of RNase R pre-treatment was tested by comparing the data generated from RNase R pre-treated and non-pre-treated libraries. To this end, total RNA was extracted from the MG of healthy elderly individuals and sequencing data generated from libraries with (N = 3) and without (N = 3) pre-treatment using RNase R23 was compared. A higher number of circRNAs was consistently identified in the pre-treated libraries compared to the non-pre-treated ones (Figure 2). This is expected since pre-treatment removes linear RNAs, thus enriching for circRNA species.
Thirdly, the amount of input RNA that would be optimal for detecting a higher diversity of circRNAs was tested. Libraries were prepared using 1, 2, and 4 &#181;g of total input RNA which was extracted from MG, OC, and SF brain regions, and as well as UC RNA. Comparing the abundance of circRNAs detected from each library, the highest diversity of circRNA species was observed when using 4 &#181;g input RNA compared to 2 and 1 &#181;g (Figure 3), as reflected by the number of unique circRNAs identified. One caveat to note is that although various incubation times during RNase R treatment were not tested, a trend whereby an increasing number of circRNAs were detected across total RNA inputs of 1 to 4 &#181;g was observed when controlling for all other parameters.
This optimized protocol was then applied across multiple tissue types to compare circRNA abundances. Five brain regions, including BC, MG, OC, IP, and SF, from four healthy elderly individuals, were tested, along with four other tissue types, including LV, LU, LN and PA, from six healthy donors. Overall, a higher abundance of circRNAs was observed in the brain compared to other tissue types (Figure 4), as has been previously reported19,20.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/59981/59981fig01.jpg" /> 
Figure 1: CircRNA detection using TruSeq vs. Kapa total RNA kits. Sequencing data was generated for UC RNA using two separate total RNA library preparation kits, each with 1, 2, 4, 5, and 10 &#181;g input RNA and ribonuclease R (RNase R) pre-treatment. The number of circRNAs detected by the tools in each sample was normalized to the number of mapped reads, per million (Y-axis). <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/59981/59981fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/59981/59981fig02.jpg" /> 
Figure 2: CircRNA detection with and without RNase R pre-treatment. Sequencing data generated using the TruSeq kit was used to compare the impact of RNase R pre-treatment. RNA was extracted from the middle temporal gyrus (MG) of healthy elderly controls for this analysis. The normalized number of circRNAs detected (Y-axis) was calculated similarly to Figure 1. RNase R+ = pre-treated with RNase R, RNase R- = not pre-treated with RNase R. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/59981/59981fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/59981/59981fig03.jpg" /> 
Figure 3: CircRNA detection using varying amount of input RNA. Using RNA extracted from MG, occipital cortex (OC), and superior frontal gyrus (SF), as well as UC RNA, the number of unique circRNAs detected when using 1, 2, and 4 &#181;g of input RNA, each whose library was constructed using the TruSeq kit and RNase R pre-treatment, was compared. The normalized number of circRNAs detected (Y-axis) was calculated similarly to Figure 1. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/59981/59981fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/59981/59981fig04.jpg" /> 
Figure 4: CircRNA detection in brain vs. other tissue types. CircRNA enriched datasets using RNA extracted from various brain regions including cerebellum (BC), inferior parietal lobe (IP), MG, OC, and SF, as well as four other tissue types including liver (LV), lung (LU), lymph node (LN), and pancreas (PA) were generated. CircRNA enrichment was carried out using the Illumina TruSeq kit with RNase R pre-treatment and 4 &#181;g of total input RNA. Box plots represent the number of circRNAs detected by at least three of the six tools across the samples from each brain region/tissue type. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/59981/59981fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Test #</td>
			<td>Parameter evaluated</td>
			<td>Test conditions</td>
			<td>Sample source</td>
			<td>Input amounts/conditions/samples tested</td>
			<td>Total number of sequencing reads</td>
		</tr>
		<tr>
			<td rowspan="10">1</td>
			<td rowspan="10">Library preparation kit</td>
			<td rowspan="10">Illumina TruSeq Stranded Total RNA vs. the Roche Kapa Total RNA kits</td>
			<td rowspan="10">UC</td>
			<td>TruSeq: 1 &#181;g</td>
			<td>8,91,46,128</td>
		</tr>
		<tr>
			<td>TruSeq: 2 &#181;g</td>
			<td>7,93,90,202</td>
		</tr>
		<tr>
			<td>TruSeq: 4 &#181;g</td>
			<td>6,66,12,238</td>
		</tr>
		<tr>
			<td>TruSeq: 5 &#181;g</td>
			<td>7,88,56,902</td>
		</tr>
		<tr>
			<td>TruSeq: 10 &#181;g</td>
			<td>6,61,06,874</td>
		</tr>
		<tr>
			<td>Kapa: 1 &#181;g</td>
			<td>8,83,95,496</td>
		</tr>
		<tr>
			<td>Kapa: 2 &#181;g</td>
			<td>10,66,59,272</td>
		</tr>
		<tr>
			<td>Kapa: 4 &#181;g</td>
			<td>10,62,34,954</td>
		</tr>
		<tr>
			<td>Kapa: 5 &#181;g</td>
			<td>7,47,75,914</td>
		</tr>
		<tr>
			<td>Kapa: 10 &#181;g</td>
			<td>11,00,68,504</td>
		</tr>
		<tr>
			<td rowspan="6">2</td>
			<td rowspan="6">Pre-treatment</td>
			<td rowspan="6">RNase R pre-treated vs. non pre-treated</td>
			<td rowspan="6">MG</td>
			<td>Pair1: MG_1 (RNase R+)</td>
			<td>10,76,09,934</td>
		</tr>
		<tr>
			<td>Pair1: MG_5 (RNase R-)</td>
			<td>9,62,15,516</td>
		</tr>
		<tr>
			<td>Pair2: MG_2 (RNase R+)</td>
			<td>9,68,40,790</td>
		</tr>
		<tr>
			<td>Pair2: MG_6 (RNase R-)</td>
			<td>10,16,09,754</td>
		</tr>
		<tr>
			<td>Pair3: MG_3 (RNase R+)</td>
			<td>11,15,76,344</td>
		</tr>
		<tr>
			<td>Pair3: MG_7 (RNase R-)</td>
			<td>11,13,14,114</td>
		</tr>
		<tr>
			<td rowspan="12">3</td>
			<td rowspan="12">Total RNA input</td>
			<td rowspan="12">1 &#181;g vs. 2 &#181;g vs. 4 &#181;g</td>
			<td rowspan="12">MG, OC, SF, UC</td>
			<td>MG: 1 &#181;g</td>
			<td>12,00,94,758</td>
		</tr>
		<tr>
			<td>MG: 2 &#181;g</td>
			<td>11,64,75,728</td>
		</tr>
		<tr>
			<td>MG: 4 &#181;g</td>
			<td>12,13,15,232</td>
		</tr>
		<tr>
			<td>OC: 1 &#181;g</td>
			<td>11,11,18,120</td>
		</tr>
		<tr>
			<td>OC: 2 &#181;g</td>
			<td>11,53,25,492</td>
		</tr>
		<tr>
			<td>OC: 4 &#181;g</td>
			<td>11,49,13,266</td>
		</tr>
		<tr>
			<td>SF: 1 &#181;g</td>
			<td>12,27,24,142</td>
		</tr>
		<tr>
			<td>SF: 2 &#181;g</td>
			<td>9,39,33,288</td>
		</tr>
		<tr>
			<td>SF: 4 &#181;g</td>
			<td>12,33,31,474</td>
		</tr>
		<tr>
			<td>UC: 1 &#181;g</td>
			<td>9,24,48,120</td>
		</tr>
		<tr>
			<td>UC: 2 &#181;g</td>
			<td>12,58,15,354</td>
		</tr>
		<tr>
			<td>UC: 4 &#181;g</td>
			<td>12,56,92,534</td>
		</tr>
		<tr>
			<td rowspan="37">4</td>
			<td rowspan="37">Tissue types</td>
			<td rowspan="37">Brain regions vs. other tissue types</td>
			<td rowspan="37">BC, MG, OC, SF, IP, LU, LV, LN, PA</td>
			<td>BC_1</td>
			<td>10,72,08,904</td>
		</tr>
		<tr>
			<td>BC_2</td>
			<td>11,18,33,362</td>
		</tr>
		<tr>
			<td>BC_3</td>
			<td>9,61,25,856</td>
		</tr>
		<tr>
			<td>BC_4</td>
			<td>9,62,77,094</td>
		</tr>
		<tr>
			<td>IP_1</td>
			<td>9,86,56,506</td>
		</tr>
		<tr>
			<td>IP_2</td>
			<td>11,35,95,746</td>
		</tr>
		<tr>
			<td>IP_3</td>
			<td>12,87,81,536</td>
		</tr>
		<tr>
			<td>IP_4</td>
			<td>9,59,81,446</td>
		</tr>
		<tr>
			<td>MG_1</td>
			<td>10,76,09,934</td>
		</tr>
		<tr>
			<td>MG_2</td>
			<td>9,68,40,790</td>
		</tr>
		<tr>
			<td>MG_3</td>
			<td>11,15,76,344</td>
		</tr>
		<tr>
			<td>MG_4</td>
			<td>10,05,39,028</td>
		</tr>
		<tr>
			<td>OC_1</td>
			<td>8,85,47,042</td>
		</tr>
		<tr>
			<td>OC_2</td>
			<td>12,09,83,142</td>
		</tr>
		<tr>
			<td>OC_3</td>
			<td>10,26,55,452</td>
		</tr>
		<tr>
			<td>OC_4</td>
			<td>10,84,49,330</td>
		</tr>
		<tr>
			<td>SF_1</td>
			<td>8,76,21,824</td>
		</tr>
		<tr>
			<td>SF_2</td>
			<td>14,50,57,894</td>
		</tr>
		<tr>
			<td>SF_3</td>
			<td>11,01,52,030</td>
		</tr>
		<tr>
			<td>SF_4</td>
			<td>9,67,24,472</td>
		</tr>
		<tr>
			<td>LN_1</td>
			<td>15,02,94,816</td>
		</tr>
		<tr>
			<td>LN_2</td>
			<td>8,33,30,187</td>
		</tr>
		<tr>
			<td>LN_3</td>
			<td>11,30,96,032</td>
		</tr>
		<tr>
			<td>LN_4</td>
			<td>10,78,38,278</td>
		</tr>
		<tr>
			<td>LU_1</td>
			<td>16,05,27,595</td>
		</tr>
		<tr>
			<td>LU_2</td>
			<td>8,94,30,799</td>
		</tr>
		<tr>
			<td>LU_3</td>
			<td>9,14,51,858</td>
		</tr>
		<tr>
			<td>LV_1</td>
			<td>9,72,18,369</td>
		</tr>
		<tr>
			<td>LV_2</td>
			<td>10,54,16,880</td>
		</tr>
		<tr>
			<td>LV_3</td>
			<td>8,86,53,148</td>
		</tr>
		<tr>
			<td>LV_4</td>
			<td>8,61,02,943</td>
		</tr>
		<tr>
			<td>LV_5</td>
			<td>12,87,88,483</td>
		</tr>
		<tr>
			<td>LV_6</td>
			<td>11,87,76,622</td>
		</tr>
		<tr>
			<td>PA_1</td>
			<td>8,79,20,160</td>
		</tr>
		<tr>
			<td>PA_2</td>
			<td>7,82,36,741</td>
		</tr>
		<tr>
			<td>PA_3</td>
			<td>10,21,24,209</td>
		</tr>
		<tr>
			<td>PA_4</td>
			<td>11,53,22,926</td>
		</tr>
	</tbody>
</table>
Table 1: Sample and test conditions summary. Summary of all parameters and test conditions evaluated in this study, along with the total number of sequencing reads generated for each sample. UC = universal control, MG = middle temporal gyrus, OC = occipital cortex, BC = cerebellum, IP = inferior parietal lobe, SF = superior frontal gyrus, LU = lung, LV = liver, LN = lymph node, PA = pancreas, RNase R = ribonuclease R, RNase R+ = pre-treated with RNase R, RNase R- = not pre-treated with RNase R.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Sample source</td>
			<td>Input amounts/samples tested</td>
			<td>Percent rRNA</td>
		</tr>
		<tr>
			<td rowspan="10">UC</td>
			<td>TruSeq: 1 &#181;g</td>
			<td>5.53%</td>
		</tr>
		<tr>
			<td>TruSeq: 2 &#181;g</td>
			<td>4.11%</td>
		</tr>
		<tr>
			<td>TruSeq: 4 &#181;g</td>
			<td>4.38%</td>
		</tr>
		<tr>
			<td>TruSeq: 5 &#181;g</td>
			<td>3.21%</td>
		</tr>
		<tr>
			<td>TruSeq: 10 &#181;g</td>
			<td>3.74%</td>
		</tr>
		<tr>
			<td>Kapa: 1 &#181;g</td>
			<td>5.57%</td>
		</tr>
		<tr>
			<td>Kapa: 2 &#181;g</td>
			<td>4.56%</td>
		</tr>
		<tr>
			<td>Kapa: 4 &#181;g</td>
			<td>9.67%</td>
		</tr>
		<tr>
			<td>Kapa: 5 &#181;g</td>
			<td>12.69%</td>
		</tr>
		<tr>
			<td>Kapa: 10 &#181;g</td>
			<td>15.59%</td>
		</tr>
	</tbody>
</table>
Table 2: rRNA percentages in TruSeq vs. Kapa libraries.

Watch this Scientific Journal Video about Identification of Circular RNAs using RNA Sequencing at JoVE.com

Identification of Circular RNAs using RNA Sequencing

Circular RNAs (circRNAs) are non-coding RNAs that may have roles in transcriptional regulation and mediating interactions between proteins. Following assessment of different parameters for construction of circRNA sequencing libraries, a protocol was compiled utilizing stranded total RNA library preparation with RNase R pre-treatment and is presented here.

Circular RNAs (circRNAs) are a class of non-coding RNAs involved in functions including micro-RNA (miRNA) regulation, mediation of protein-protein ...

identification-of-circular-rnas-using-rna-sequencing

Nanyang Technological University

Research

JoVE Journal

Genetics

12.0K Views.  Translational Genomic Research Institute. Circular RNAs (circRNAs) are non-coding RNAs that may have roles in transcriptional regulation and mediating interactions between proteins. Following assessment of different parameters for construction of circRNA sequencing libraries, a protocol was compiled utilizing stranded total RNA library preparation with RNase R pre-treatment and is presented here.

Video: Identification of Circular RNAs using RNA Sequencing

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 ...

Mapping RNA-RNA Interactions Globally Using Biotinylated Psoralen

Knowing how RNAs interact with themselves and with others is key to understanding RNA based gene regulation in the cell. While examples of RNA-RNA interactions such as microRNA-mRNA interactions have been shown to regulate gene expression, the full extent to which RNA interactions occur in the cell is still unknown. Previous methods to study RNA interactions have primarily focused on subsets of RNAs that are interacting with a particular protein or RNA species. Here, we detail a method named Sequencing of Psoralen crosslinked, Ligated, and Selected Hybrids (SPLASH) that allows genome-wide capture of RNA interactions in vivo in an unbiased manner. SPLASH utilizes in vivo crosslinking, proximity ligation, and high throughput sequencing to identify intramolecular and intermolecular RNA base-pairing partners globally. SPLASH can be applied to different organisms including bacteria, yeast and human cells, as well as diverse cellular conditions to facilitate the understanding of the dynamics of RNA organization under diverse cellular contexts. The entire experimental SPLASH protocol takes about 5 days to complete and the computational workflow takes about 7 days to complete.

Here, we detail the method of Sequencing of Psoralen crosslinked, Ligated, and Selected Hybrids (SPLASH), which enables genome-wide mapping of intramolecular and intermolecular RNA-RNA interactions in vivo. SPLASH can be applied to study RNA interactomes of organisms including yeast, bacteria and humans.

Knowing how RNAs interact with themselves and with others is key to understanding RNA based gene regulation in the cell. While examples of RNA-RNA ...

Transcriptomic Analysis of C. elegans RNA Sequencing Data Through the Tuxedo Suite on the Galaxy Project

Next generation sequencing (NGS) technologies have revolutionized the nature of biological investigation. Of these, RNA Sequencing (RNA-Seq) has emerged as a powerful tool for gene-expression analysis and transcriptome mapping. However, handling RNA-Seq datasets requires sophisticated computational expertise and poses inherent challenges for biology researchers. This bottleneck has been mitigated by the open access Galaxy project that allows users without bioinformatics skills to analyze RNA-Seq data, and the Database for Annotation, Visualization, and Integrated Discovery (DAVID), a Gene Ontology (GO) term analysis suite that helps derive biological meaning from large data sets. However, for first-time users and bioinformatics' amateurs, self-learning and familiarization with these platforms can be time-consuming and daunting. We describe a straightforward workflow that will help C. elegans researchers to isolate worm RNA, conduct an RNA-Seq experiment and analyze the data using Galaxy and DAVID platforms. This protocol provides stepwise instructions for using the various Galaxy modules for accessing raw NGS data, quality-control checks, alignment, and differential gene expression analysis, guiding the user with parameters at every step to generate a gene list that can be screened for enrichment of gene classes or biological processes using DAVID. Overall, we anticipate that this article will provide information to C. elegans researchers undertaking RNA-Seq experiments for the first time as well as frequent users running a small number of samples.

Transcriptomic Analysis of C. elegans RNA Sequencing Data Through the Tuxedo Suite on the Galaxy Project

Galaxy and DAVID have emerged as popular tools that allow investigators without bioinformatics training to analyze and interpret RNA-Seq data. We describe a protocol for C. elegans researchers to perform RNA-Seq experiments, access and process the dataset using Galaxy and obtain meaningful biological information from the gene lists using DAVID.

Next generation sequencing (NGS) technologies have revolutionized the nature of biological investigation. Of these, RNA Sequencing (RNA-Seq) has emerged ...

Metabolic Labeling and Profiling of Transfer RNAs Using Macroarrays

Transfer RNAs (tRNA) are abundant short non-coding RNA species that are typically 76 to 90 nucleotides in length. tRNAs are directly responsible for protein synthesis by translating codons in mRNA into amino acid sequences. tRNAs were long considered as house-keeping molecules that lacked regulatory functions. However, a growing body of evidence indicates that cellular tRNA levels fluctuate in correspondence to varying conditions such as cell type, environment, and stress. The fluctuation of tRNA expression directly influences gene translation, favoring or repressing the expression of particular proteins. Ultimately comprehending the dynamic of protein synthesis requires the development of methods able to deliver high-quality tRNA profiles. The method that we present here is named SPOt, which stands for Streamlined Platform for Observing tRNA. SPOt consists of three steps starting with metabolic labeling of cell cultures with radioactive orthophosphate, followed by guanidinium thiocyanate-phenol-chloroform extraction of radioactive total RNAs and finally hybridization on in-house printed macroarrays. tRNA levels are estimated by quantifying the radioactivity intensities at each probe spot. In the protocol presented here we profile tRNAs in Mycobacterium smegmatis mc2155, a nonpathogenic bacterium often used as a model organism to study tuberculosis.

We describe an original transfer RNA analysis platform named SPOt (Streamlined Platform for Observing tRNA). SPOt simultaneously measures cellular levels of all tRNAs in biological samples, in only three steps, and in less than 24 hours.

Transfer RNAs (tRNA) are abundant short non-coding RNA species that are typically 76 to 90 nucleotides in length. tRNAs are directly responsible for ...

Retrospective MicroRNA Sequencing: Complementary DNA Library Preparation Protocol Using Formalin-fixed Paraffin-embedded RNA Specimens

&#8211;Archived, clinically classified formalin-fixed paraffin-embedded (FFPE) tissues can provide nucleic acids for retrospective molecular studies of cancer development. By using non-invasive or pre-malignant lesions from patients who later develop invasive disease, gene expression analyses may help identify early molecular alterations that predispose to cancer risk. It has been well described that nucleic acids recovered from FFPE tissues have undergone severe physical damage and chemical modifications, which make their analysis difficult and generally requires adapted assays. MicroRNAs (miRNAs), however, which represent a small class of RNA molecules spanning only up to ~18&#8211;24 nucleotides, have been shown to withstand long-term storage and have been successfully analyzed in FFPE samples. Here we present a 3&#39; barcoded complementary DNA (cDNA) library preparation protocol specifically optimized for the analysis of small RNAs extracted from archived tissues, which was recently demonstrated to be robust and highly reproducible when using archived clinical specimens stored for up to 35 years. This library preparation is well adapted to the multiplex analysis of compromised/degraded material where RNA samples (up to 18) are ligated with individual 3&#39; barcoded adapters and then pooled together for subsequent enzymatic and biochemical preparations prior to analysis. All purifications are performed by polyacrylamide gel electrophoresis (PAGE), which allows size-specific selections and enrichments of barcoded small RNA species. This cDNA library preparation is well adapted to minute RNA inputs, as a pilot polymerase chain reaction (PCR) allows determination of a specific amplification cycle to produce optimal amounts of material for next-generation sequencing (NGS). This approach was optimized for the use of degraded FFPE RNA from specimens archived for up to 35 years and provides highly reproducible NGS data.

Formalin-fixed paraffin-embedded specimens represent a valuable source of molecular biomarkers of human diseases. Here we present a laboratory-based cDNA library preparation protocol, initially designed with fresh frozen RNA, and optimized for the analysis of archived microRNAs from tissues stored up to 35 years.

&#8211;Archived, clinically classified formalin-fixed paraffin-embedded (FFPE) tissues can provide nucleic acids for retrospective molecular studies of ...

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 ...

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture (4C-seq)

The identification of regulatory elements for a given target gene poses a significant technical challenge owing to the variability in the positioning and effect sizes of regulatory elements to a target gene. Some progress has been made with the bioinformatic prediction of the existence and function of proximal epigenetic modifications associated with activated gene expression using conserved transcription factor binding sites. Chromatin conformation capture studies have revolutionized our ability to discover physical chromatin contacts between sequences and even within an entire genome. Circular chromatin conformation capture coupled with next-generation sequencing (4C-seq), in particular, is designed to discover all possible physical chromatin interactions for a given sequence of interest (viewpoint), such as a target gene or a regulatory enhancer. Current 4C-seq strategies directly sequence from within the viewpoint but require numerous and diverse viewpoints to be simultaneously sequenced to avoid the technical challenges of uniform base calling (imaging) with next generation sequencing platforms. This volume of experiments may not be practical for many laboratories. Here, we report a modified approach to the 4C-seq protocol that incorporates both an additional restriction enzyme digest and qPCR-based amplification steps that are designed to facilitate a greater capture of diverse sequence reads and mitigate the potential for PCR bias, respectively. Our modified 4C method is amenable to the standard molecular biology lab for assessing chromatin architecture.

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture 4C-seq

The identification of physical interactions between genes and regulatory elements is challenging but has been facilitated by chromosome conformation capture methods. This modification to the 4C-seq protocol mitigates PCR bias by minimizing over-amplification of PCR templates and maximizes the mappability of reads by incorporating an addition restriction enzyme digest step.

The identification of regulatory elements for a given target gene poses a significant technical challenge owing to the variability in the positioning and ...

Overexpressing Long Noncoding RNAs Using Gene-activating CRISPR

Long noncoding RNA (lncRNA) biology is a new and exciting field of research, with the number of publications from this field growing exponentially since 2007. These studies have confirmed that lncRNAs are altered in almost all diseases. However, studying the functional roles for lncRNAs in the context of disease remains difficult due to the lack of protein products, tissue-specific expression, low expression levels, complexities in splice forms, and lack of conservation among species. Given the species-specific expression, lncRNA studies are often restricted to human research contexts when studying disease processes. Since lncRNAs function at the molecular level, one way to dissect lncRNA biology is to either remove the lncRNA or overexpress the lncRNA and measure cellular effects. In this article, a written and visualized protocol to overexpress lncRNAs in vitro is presented. As a representative experiment, an lncRNA associated with inflammatory bowel disease, Interferon Gamma Antisense 1 (IFNG-AS1), is shown to be overexpressed in a Jurkat T-cell model. To accomplish this, the activating clustered regularly interspaced short palindromic repeats (CRISPR) technique is used to enable overexpression at the endogenous genomic loci. The activating CRISPR technique targets a set of transcription factors to the transcriptional start site of a gene, enabling a robust overexpression of multiple lncRNA splice forms. This procedure will be broken down into three steps, namely (i) guide RNA (gRNA) design and vector construction, (ii) virus generation and transduction, and (iii) colony screening for overexpression. For this representative experiment, a greater than 20-fold enhancement in IFNG-AS1 in Jurkat T cells was observed.

Traditional cDNA-based overexpression techniques have a limited applicability for the overexpression of long noncoding RNAs due to their multiple splice forms with potential functionality. This review reports a protocol using CRISPR technology to overexpress multiple splice variants of a long noncoding RNA.

Long noncoding RNA (lncRNA) biology is a new and exciting field of research, with the number of publications from this field growing exponentially since ...

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, ...

Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets

Pancreatic islets comprise of endocrine cells with distinctive hormone expression patterns. The endocrine cells show functional differences in response to normal and pathological conditions. The goal of this protocol is to generate high-quality, large-scale transcriptome data of each endocrine cell type with the use of a droplet-based microfluidic single-cell RNA sequencing technology. Such data can be utilized to build the gene expression profile of each endocrine cell type in normal or specific conditions. The process requires careful handling, accurate measurement, and rigorous quality control. In this protocol, we describe detailed steps for human pancreatic islets dissociation, sequencing, and data analysis. The representative results of about 20,000 human single islet cells demonstrate the successful application of the protocol.

Here, we present a protocol to generate high-quality, large-scale transcriptome data of single cells from isolated human pancreatic islets using a droplet-based microfluidic single-cell RNA sequencing technology.

Pancreatic islets comprise of endocrine cells with distinctive hormone expression patterns. The endocrine cells show functional differences in response to ...