- Open Access
Decreasing miRNA sequencing bias using a single adapter and circularization approach
© The Author(s). 2018
- Received: 9 February 2018
- Accepted: 18 July 2018
- Published: 3 September 2018
The ability to accurately quantify all the microRNAs (miRNAs) in a sample is important for understanding miRNA biology and for development of new biomarkers and therapeutic targets. We develop a new method for preparing miRNA sequencing libraries, RealSeq®-AC, that involves ligating the miRNAs with a single adapter and circularizing the ligation products. When compared to other methods, RealSeq®-AC provides greatly reduced miRNA sequencing bias and allows the identification of the largest variety of miRNAs in biological samples. This reduced bias also allows robust quantification of miRNAs present in samples across a wide range of RNA input levels.
- Sequencing bias
- Small RNA sequencing
- Small RNA library preparation
MicroRNAs (miRNAs) are small non-coding RNAs of approximately 21–23 nucleotides that control the expression of most genes and are involved in many biological processes. Dysregulation of miRNA expression or biogenesis has been implicated in cancer and other diseases . The ability to accurately measure absolute amounts of all miRNAs is important for understanding their biological functions as well as for discovering miRNA biomarkers, developing miRNA-mimic drugs, and identifying miRNA therapeutic targets.
Next-generation sequencing (NGS) is the only approach that allows both discovery of novel miRNA sequences—including sequence variants such as isomiRs—and expression profiling of miRNAs. It is widely believed that NGS will increasingly displace other expression profiling methods in both research and clinical diagnostic applications . Besides its high cost, the major problem preventing the adoption of NGS for routine expression profiling of miRNAs is its poor accuracy (bias) in quantifying many miRNA sequences: most individual miRNAs are underrepresented as a proportion of total sequence reads, in some cases by as much as 104-fold relative to their true abundance within a sample [3–7]. In cases where sequencing bias leads to some miRNAs being under-detected or undetectable in particular samples, NGS can provide misleading or useless results [5, 7, 8]. Indeed, it is likely that many important miRNAs remain to be discovered because they are not incorporated by current library preparation methods .
Here we describe a new, more effective approach to reducing ligation bias. In our approach (Fig. 1b), we incorporate the sequences of both standard sequencing adapters into a single adapter. This single adapter is ligated to the 3′ end of the miRNA and the miRNA–adapter ligation product is then circularized via intramolecular ligation. Before circularization of the miRNA–adapter ligation product, a blocking oligonucleotide is ligated to the 5′-preadenylated end of the remaining unligated adapter to inhibit adapter circularization, which would be a template for adapter-dimer amplification. RT-PCR amplification of the resulting circles generates a library suitable for use in standard next-generation platforms. With this approach, the inefficient, intermolecular 5′-adapter ligation step is replaced by a highly efficient intramolecular ligation.
To assess the value of this approach, we first identified eight miRNAs for which detection with a two-adapter ligation approach is accompanied by a wide range (~ 216-fold) of bias levels (Additional file 1: Figure S1). Using this set of eight miRNAs we developed a highly efficient ligation and circularization protocol that minimized differences in ligation and circularization efficiency among these eight miRNAs, with < 20% variation in single-adapter ligation efficiency and < 25% variation in circularization efficiency (Additional file 1: Figure S2).
The development of small RNA library preparation kits has been focused mainly on the detection of miRNAs. This narrow focus has hindered the detection of other classes of small RNAs. Different classes of small RNAs contain features that could further increase their bias in detection by current techniques (e.g., piRNAs have 2′-OMe modifications at their 3′ ends). To further characterize the ability of RealSeq®-AC to efficiently detect other classes of small RNAs, we profiled an RNA reference sample (Universal miRNA Reference kit, Agilent) comprised of RNAs from nine human tissues and cell lines, including adult testis and a testis cell line. It has been demonstrated that piRNAs play important roles during spermatogenesis, where their levels of expression are increased . This sample thus contains a high percentage of piRNAs with 2′-OMe modifications at their 3′ ends. We found that, of the reads that map to small RNAs, the percentage of piRNA reads is higher for RealSeq-AC (17.9%) than for the three other kits tested (QIAseq 7.1%, NEXTFlex™ 5.5%, and TruSeq® 6.2%) (Additional file 1: Figure S4). The apparently larger bias of other kits against non-miRNA small RNAs compared to RealSeq®-AC may contribute to the fact that a larger percentage of their small-RNA reads map to miRNAs.
Several recent studies comparing NGS and RT-qPCR for miRNA quantification have found poor agreement between these platforms [18–22]. To perform a similar comparison with RealSeq®-AC and RT-qPCR, we identified the 15 most-abundant brain miRNAs according to RealSeq®-AC (Additional file 2: Table S3). From these, we selected four miRNAs that show very disparate abundances when quantified by the various sequencing kits and quantified them by RT-qPCR (Additional file 1: Figure S5 and Additional file 2: Table S4). The rank order of relative abundance of these four miRNAs as determined by RealSeq®-AC was the same as that determined by RT-qPCR, whereas all other kits tested ranked the selected miRNAs differently to RT-qPCR. The kit with the best correlation between quantification by sequencing and by RT-qPCR was RealSeq®-AC (r = 0.89). While RT-qPCR procedures can themselves be biased, these results suggest that the previously reported poor correlation between NGS and RT-qPCR data may have been largely due to bias introduced by the procedures used for sequencing library preparation.
Based on the above results showing its better accuracy in quantification of miRNAs by using a single-adapter and circularization, we tested whether RealSeq®-AC could detect and profile a larger number of different miRNAs in an RNA sample than other small RNA sequencing library preparation kits. Using RealSeq®-AC and TruSeq®, we prepared sequencing libraries from a reference sample of total RNA (Agilent) obtained from nine different human tissues and cell lines. We sequenced both libraries to a coverage of ten million reads and counted the number of miRNAs identified (with ten or more reads of each) by each kit at different sequencing coverages by random subsampling (Additional file 1: Figure S6). At the deepest sequencing coverage of ten million reads, we identified 641 miRNAs using RealSeq®-AC and only 588 miRNAs using TruSeq®. Thus, by using RealSeq®-AC we detected ~ 10% more miRNAs than TruSeq® at the same coverage. Such an increase in coverage allows the reliable quantification of a larger number of miRNAs for differential expression studies and highlights the importance of accuracy in detection even when performing relative profiling measurements.
The level of bias in NGS quantification of miRNAs in currently used library preparation remains very high. This bias hampers the detection of miRNA that could be reliable biomarkers and lowers the reproducibility of sequencing results. It has been shown that the main contributors to library preparation bias are the ligation steps [4, 5], yet almost all commercially available library preparation kits use the same approach of ligating two adapters to the miRNAs, with similar protocols and reagents.
Here we introduce an alternative approach that provides the best accuracy in detection of miRNAs among all tested technologies (Fig. 2). Accurate detection of miRNAs not only provides more accurate relative and absolute quantification, it also enables the discovery of novel miRNAs and other small RNAs. The improved accuracy of RealSeq®-AC is due to two features: i) the use of a single adapter containing the sequences of both 5′ and 3′ standard sequencing adapters in the 3′ ligation step; and ii) replacement of the intermolecular ligation of the 5′ adapter in the two-adapter ligation methods, with a highly efficient and unbiased circularization step.
Due to a high rate of adapter dimer formation, several small RNA library preparation protocols require gel-purification steps to remove adapter dimers. The use of gel purification diminishes the reproducibility of small RNA sequencing experiments: any variability in the efficiency of separation will result in differential recovery of specific small RNA between samples. RealSeq®-AC provides very little formation of empty adapter/adapter dimer, allowing gel-free purification of libraries with as low as 1 ng of input total RNA (Fig. 4). This gel-free purification allows for higher reproducibility of sequencing results between libraries.
RealSeq®-AC differs from previously described single-adapter and circularization methods [25–31] in several respects. (1) The previous single adapter methods use ligation of a standard 3′ sequencing adapter followed by reverse-transcription of the miRNA–adapter ligation products with an RT primer that contains sequences of both adapters used in the two-adapter ligation methods. The resulting cDNA is then circularized and the circular cDNA is PCR-amplified using a pair of PCR primers specific for the standard adapter sequences. In contrast to these methods, RealSeq®-AC incorporates both standard primers into the single adapter rather than the RT primer. (2) In RealSeq®-AC, the combo adapter has RNA nucleotides at its 3′ end that provide circularization by ligating the RNA ends of miRNA–adapter, whereas in the other methods it is the cDNA that is circularized. The efficiency of intramolecular ligation (circularization) is significantly higher between RNA ends than between DNA ends [31, 32]. (3) RealSeq®-AC directly incorporates the miRNA sequences into the sequencing libraries whereas other single-adapter methods incorporate the RT products of these sequences. Thus, any premature stopping of reverse transcription will contaminate the sequencing libraries with truncated copies of RNA sequences. Furthermore, reverse transcriptase can add non-templated nucleotides at the 3′ terminus of the cDNA [25, 30] that may be mistakenly identified as isomiR variants of the miRNAs.
Due to bias in ligation of adapters during sequencing library preparation, current NGS methods underestimate the abundance of most known miRNAs. To address this issue, we have developed a novel single-adapter ligation and circularization-based method for preparation of small RNA sequencing libraries, RealSeq®-AC, that is user-friendly and requires no gel-electrophoresis steps. By greatly reducing incorporation bias, RealSeq®-AC allows detection of a larger variety of miRNAs and other small RNAs in biological samples with more accurate quantification than other available sequencing methods. Its high accuracy in detection also allows a robust quantification with high correlation across several logs of dilution.
Forward PCR primer
Reverse index PCR primer*
Library preparation protocol (RealSeq®-AC)
The 5′-phosphorylated single-adapter was pre-adenylated using a 5′ DNA adenylation kit (NEB) according to the manufacturer’s recommendations.
Single-adapter ligation to 3′ end of miRNAs
Ligation of the pre-adenylated single-adapter to the 3′ end of the miRNAs was performed with either truncated RNA ligase 2 [Rnl2(tr)], Rnl2(tr)K227Q, or Rnl2(tr)KQ (NEB), with similar results obtained with all three enzymes. The reaction included 1 pmol of the miRXplore pool or 1 μg of human brain total RNA (ThermoFisher, AM7962) or 1 μg Universal miRNA Reference Kit (Agilent, 750,700), 1× T4 RNA ligase buffer, 200 units of Rnl2(tr), 40 units of RNase OUT (Life Technologies/ThermoFisher), 15% PEG 8000, and 75 ng of single-adapter, in a 10 μl reaction volume. The reaction mix was incubated for 1 h at 25 °C in a thermocycler followed by 10 min at 65 °C.
To inhibit the amplification of unligated single-adapter, a blocking oligo was ligated to the remaining unligated single-adapters after the ligation of miRNAs was completed. A blocking reaction mix was prepared with 10 μl of the adapter–miRNAs ligation reaction, 2.5 μl of a 10-μM mix of blocking oligo and blocking splint, 400 units of T4 DNA ligase, and 1 unit of T4 polynucleotide kinase (NEB) in 1× T4 RNA ligase buffer in a 20-μl total volume. This reaction mix was incubated for 1 h at 37 °C and 20 min at 65 °C.
To circularize the miRNA–adapter products, 10 units of T4 RNA ligase 1 and 450 μM ATP (sodium salt at pH 7.0 from NEB) were added to the 20 μl reaction mixture from the adapter blocking step for a final reaction volume of 22 μl. This reaction mix was incubated at 37 °C for 1 h.
Reverse transcription of the circular miRNA–adapter templates was performed with SuperScript IV (Invitrogen). The reaction mix included 22 μl from the circularization reaction, 1× SSIV Buffer (Invitrogen), 40 units of RNase OUT (Life Technologies), 1.25 μM RT primer, 5 mM dNTPs, and 200 units of SuperScript IV in a 40 μl total reaction volume. The reaction mix was incubated for 30 min at 50 °C followed by 10 min at 80 °C. PCR was performed with LongAmp® Hot Start Taq DNA polymerase (NEB). The reaction included 40 μl from the RT reaction, 1× LongAmp® Taq Reaction Buffer (NEB), 3 mM dNTPs, 0.7 μM forward PCR primer, 0.7 μM reverse index primer, and 10 units of LongAmp® Hot Start Taq DNA polymerase in a 100 μl reaction volume. The PCR reaction was performed for either 5 cycles for the miRXplore pool, or 7, 10, 13, or 16 cycles for 1 μg, 100 ng, 10 ng or 1 ng 3RNA samples respectively. PCR included a first step at 94 °C for 30 s, and 5, 7, 10, 13, or 16 cycles of 94 °C for 15 s, 62 °C for 30 s, and 70 °C for 15 s, with a final step at 70 °C for 5 min.
Library preparation for other kits
Experiments with NEBNext® (NEB), TruSeq® (Illumina), NEXFlex™ (Bioo Scientific), QIAseq (Qiagen), and SMARTer (Takara Bio) were performed following the manufacturers’ recommendations. For all kits, 1 pmol of the miRXplore™ Universal Reference (Miltenyi Biotec) was used as input to test accuracy in detection (Figs. 2 and 3). Brain total RNA (1 μg) was used for Fig. 3 for all kits with the exception of QIAseq, where 500 ng of total RNA was used as per the manufacturer’s recommendations. Libraries were prepared in triplicate for all experiments. To determine concentration and quality of libraries, all libraries were analyzed with an Agilent D1000 ScreenTape on a 2200 TapeStation instrument (Agilent) and then quantified with a Qubit dsDNA BR Assay kit on a Qubit 3.0 instrument.
Experiments to test accuracy of detection (bias) were performed with 1 pmol of the miRXplore™ Universal Reference (Miltenyi Biotec), which contains equimolar amounts of 963 RNAs that match mature miRNAs. Experiments profiling total RNA were performed with 1 μg of reference Human Brain Total RNA (Life Technologies/ThermoFisher) or with 1 μg Universal miRNA Reference Kit (Agilent).
Triplicate libraries for each input and kit, with the exception of brain total RNA with the NEXTFlex™ kit that was performed in duplicates, were pooled at equimolar concentrations and sequenced in Illumina MiSeq or Illumina NextSeq instruments with single-end reads of 36 nucleotides (MiSeq) or 50 nucleotides (NextSeq) following the manufacturer’s recommendations. Libraries were mixed with 5% PhiX.
FastQ files were trimmed of adapter sequences by using Cutadapt (https://doi.org/10.14806/ej.17.1.200) with the following parameters: cutadapt -a TGGAATTCTCGGGTGCCAAGG -m 15. Trimmed reads were aligned to the corresponding reference by using Bowtie2 ; data for Fig. 2 were obtained by alignment to a reference file containing the sequences of all miRNAs included in the miRXplore pool; data for Figs. 3 and 4 were obtained by aligning trimmed reads to a reference containing the sequences of all the high-confidence miRNAs . Data for Additional file 1: Figure S5 were obtained by aligning reads from the Universal miRNA Reference Kit libraries to a reference database of small RNAs (YM500v3 ). Analysis for Fig. 4 included differential quantification between RealSeq®-AC and each of the other kits calculated as the log2-fold difference; these values are represented on the y-axis (differential quantification) and plotted against the accuracy of detection for each of the 276 high-confidence human miRNAs for each of the kits tested, as determined by quantification of each miRNA within the miRXplore™ pool (Fig. 2 data). Data are available at the NCBI GEO with accession number GSE107304 .
Data for Additional file 1: Figure S4 and Additional file 2: Table S4 were generated with miR-ID® assays  for miRNAs hsa-miR-26a-5p, hsa-miR-125b-5p, hsa-miR-16-5p, and hsa-miR-29a-3p with 10 ng reference Human Brain Total RNA following the manufacturer’s recommendations. Absolute quantification to determine the amounts of each miRNA present in the total RNA sample was performed by comparison of the Cq values obtained from brain total RNA with standard dilution curves of the corresponding synthetic miRNA prepared over an 8-log concentration range (200 pM–20 aM).
We thank Colleen McLaughlin for assistance with the RT-qPCR validations of the sequencing data.
This work was supported by the National Human Genome Research Institute Small Business Innovation Research grants R43HG007788 and R44HG007788 to S.A.K.
Availability of data and materials
Data are available at the NCBI GEO with accession number GSE107304 .
SAK created the concept. SBS, BHJ, and SAK conceived and developed experimental plans. SBS, JV, and REH performed experiments. AD supervised the RT-qPCR validations. SBS analyzed the sequencing data. SBS, BHJ, and SAK wrote and/or edited the manuscript. All authors read and approved the final version of the manuscript.
Ethics approval and consent to participate
Consent for publication
All authors are employees and shareholders of Somagenics. S.A.K., S.B.S, A.D., and B.H.J. have filed a patent application on the protocol for single-adapter and circularization.
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- Witwer KW, Halushka MK. Toward the promise of microRNAs - enhancing reproducibility and rigor in microRNA research. RNA Biol. 2016;13:1103–16.View ArticlePubMedPubMed CentralGoogle Scholar
- Pritchard CC, Cheng HH, Tewari M. MicroRNA profiling: approaches and considerations. Nat Rev Genet. 2012;13:358–69.View ArticlePubMedPubMed CentralGoogle Scholar
- Linsen SE, de Wit E, Janssens G, Heater S, Chapman L, Parkin RK, Fritz B, Wyman SK, de Bruijn E, Voest EE, et al. Limitations and possibilities of small RNA digital gene expression profiling. Nat Methods. 2009;6:474–6.View ArticlePubMedGoogle Scholar
- Tian G, Yin X, Luo H, Xu X, Bolund L, Zhang X, Gan SQ, Li N. Sequencing bias: comparison of different protocols of microRNA library construction. BMC Biotechnol. 2010;10:64.View ArticlePubMedPubMed CentralGoogle Scholar
- Hafner M, Renwick N, Brown M, Mihailovic A, Holoch D, Lin C, Pena JT, Nusbaum JD, Morozov P, Ludwig J, et al. RNA-ligase-dependent biases in miRNA representation in deep-sequenced small RNA cDNA libraries. RNA. 2011;17:1697–712.View ArticlePubMedPubMed CentralGoogle Scholar
- McCormick KP, Willmann MR, Meyers BC. Experimental design, preprocessing, normalization and differential expression analysis of small RNA sequencing experiments. Silence. 2011;2:2.View ArticlePubMedPubMed CentralGoogle Scholar
- Sorefan K, Pais H, Hall AE, Kozomara A, Griffiths-Jones S, Moulton V, Dalmay T. Reducing ligation bias of small RNAs in libraries for next generation sequencing. Silence. 2012;3:4.View ArticlePubMedPubMed CentralGoogle Scholar
- Fuchs RT, Sun Z, Zhuang F, Robb GB. Bias in ligation-based small RNA sequencing library construction is determined by adaptor and RNA structure. PLoS One. 2015;10:e0126049.View ArticlePubMedPubMed CentralGoogle Scholar
- Bicalho Saturnino G, Godinho CPS, Fagundes-Lima D, Silva AC, Weber G. Detection of construction biases in biological databases: the case of miRBase. In: ArXiv e-prints; 2014. https://arxiv.org/abs/1407.6570.
- Jayaprakash AD, Jabado O, Brown BD, Sachidanandam R. Identification and remediation of biases in the activity of RNA ligases in small-RNA deep sequencing. Nucleic Acids Res. 2011;39:e141.View ArticlePubMedPubMed CentralGoogle Scholar
- Zhuang F, Fuchs RT, Sun Z, Zheng Y, Robb GB. Structural bias in T4 RNA ligase-mediated 3′-adapter ligation. Nucleic Acids Res. 2012;40:e54.View ArticlePubMedPubMed CentralGoogle Scholar
- Zhuang F, Fuchs RT, Robb GB. Small RNA expression profiling by high-throughput sequencing: implications of enzymatic manipulation. J Nucleic Acids. 2012;2012:360358.View ArticlePubMedPubMed CentralGoogle Scholar
- Raabe CA, Tang TH, Brosius J, Rozhdestvensky TS. Biases in small RNA deep sequencing data. Nucleic Acids Res. 2014;42:1414–26.View ArticlePubMedGoogle Scholar
- Baran-Gale J, Kurtz CL, Erdos MR, Sison C, Young A, Fannin EE, Chines PS, Sethupathy P. Addressing Bias in small RNA library preparation for sequencing: a new protocol recovers MicroRNAs that evade capture by current methods. Front Genet. 2015;6:352.View ArticlePubMedPubMed CentralGoogle Scholar
- Kozomara A, Griffiths-Jones S. miRBase: annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Res. 2014;42:D68–73.View ArticlePubMedGoogle Scholar
- Dard-Dascot C, Naquin D, d'Aubenton-Carafa Y, Alix K, Thermes C, van Dijk E. Systematic comparison of small RNA library preparation protocols for next-generation sequencing. BMC Genomics. 2018;19:118.View ArticlePubMedPubMed CentralGoogle Scholar
- Yang Q, Hua J, Wang L, Xu B, Zhang H, Ye N, Zhang Z, Yu D, Cooke HJ, Zhang Y, Shi Q. MicroRNA and piRNA profiles in normal human testis detected by next generation sequencing. PLoS One. 2013;8:e66809.View ArticlePubMedPubMed CentralGoogle Scholar
- Chugh P, Dittmer DP. Potential pitfalls in microRNA profiling. Wiley Interdiscip Rev RNA. 2012;3:601–16.View ArticlePubMedPubMed CentralGoogle Scholar
- Mestdagh P, Hartmann N, Baeriswyl L, Andreasen D, Bernard N, Chen C, Cheo D, D'Andrade P, DeMayo M, Dennis L, et al. Evaluation of quantitative miRNA expression platforms in the microRNA quality control (miRQC) study. Nat Methods. 2014;11:809–15.View ArticlePubMedGoogle Scholar
- Moldovan L, Batte KE, Trgovcich J, Wisler J, Marsh CB, Piper M. Methodological challenges in utilizing miRNAs as circulating biomarkers. J Cell Mol Med. 2014;18:371–90.View ArticlePubMedPubMed CentralGoogle Scholar
- Tam S, de Borja R, Tsao MS, McPherson JD. Robust global microRNA expression profiling using next-generation sequencing technologies. Lab Investig. 2014;94:350–8.View ArticlePubMedGoogle Scholar
- Backes C, Sedaghat-Hamedani F, Frese K, Hart M, Ludwig N, Meder B, Meese E, Keller A. Bias in high-throughput analysis of miRNAs and implications for biomarker studies. Anal Chem. 2016;88:2088–95.View ArticlePubMedGoogle Scholar
- Leshkowitz D, Horn-Saban S, Parmet Y, Feldmesser E. Differences in microRNA detection levels are technology and sequence dependent. RNA. 2013;19:527–38.View ArticlePubMedPubMed CentralGoogle Scholar
- Tosar JP, Rovira C, Naya H, Cayota A. Mining of public sequencing databases supports a non-dietary origin for putative foreign miRNAs: underestimated effects of contamination in NGS. RNA. 2014;20:754–7.View ArticlePubMedPubMed CentralGoogle Scholar
- Kwon YS. Small RNA library preparation for next-generation sequencing by single ligation, extension and circularization technology. Biotechnol Lett. 2011;33:1633–41.View ArticlePubMedGoogle Scholar
- Konig J, Zarnack K, Rot G, Curk T, Kayikci M, Zupan B, Turner DJ, Luscombe NM, Ule J. iCLIP--transcriptome-wide mapping of protein-RNA interactions with individual nucleotide resolution. J Vis Exp. 2011;(50). https://doi.org/10.3791/2638.
- Lamm AT, Stadler MR, Zhang H, Gent JI, Fire AZ. Multimodal RNA-seq using single-strand, double-strand, and CircLigase-based capture yields a refined and extended description of the C. elegans transcriptome. Genome Res. 2011;21:265–75.View ArticlePubMedPubMed CentralGoogle Scholar
- Ingolia NT, Brar GA, Rouskin S, McGeachy AM, Weissman JS. The ribosome profiling strategy for monitoring translation in vivo by deep sequencing of ribosome-protected mRNA fragments. Nat Protoc. 2012;7:1534–50.View ArticlePubMedPubMed CentralGoogle Scholar
- Jackson TJ, Spriggs RV, Burgoyne NJ, Jones C, Willis AE. Evaluating bias-reducing protocols for RNA sequencing library preparation. BMC Genomics. 2014;15:569.View ArticlePubMedPubMed CentralGoogle Scholar
- Heyer EE, Ozadam H, Ricci EP, Cenik C, Moore MJ. An optimized kit-free method for making strand-specific deep sequencing libraries from RNA fragments. Nucleic Acids Res. 2015;43:e2.View ArticlePubMedGoogle Scholar
- Sterling CH, Veksler-Lublinsky I, Ambros V. An efficient and sensitive method for preparing cDNA libraries from scarce biological samples. Nucleic Acids Res. 2015;43:e1.View ArticlePubMedGoogle Scholar
- Brennan CA, Manthey AE, Gumport RI. Using T4 RNA ligase with DNA substrates. Methods Enzymol. 1983;100:38–52.View ArticlePubMedGoogle Scholar
- Langmead B, Salzberg SL. Fast gapped-read alignment with bowtie 2. Nat Methods. 2012;9:357–9.View ArticlePubMedPubMed CentralGoogle Scholar
- Chung IF, Chang SJ, Chen CY, Liu SH, Li CY, Chan CH, Shih CC, Cheng WC. YM500v3: a database for small RNA sequencing in human cancer research. Nucleic Acids Res. 2017;45:D925–31.View ArticlePubMedGoogle Scholar
- Barberan-Soler S, Kazakov SA: Increasing miRNA sequencing accuracy using an RNA circularization approach. NCBI GEO. Datasets. 2018. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE107304.
- Kumar P, Johnston BH, Kazakov SA. miR-ID: a novel, circularization-based platform for detection of microRNAs. RNA. 2011;17:365–80.View ArticlePubMedPubMed CentralGoogle Scholar