Absolute quantification RNA sequencing (AQRNA-seq) is a technology developed to quantify the landscape of all small RNAs in biological mixtures. Here, both the library preparation and data processing steps of AQRNA-seq are demonstrated, quantifying changes in the transfer RNA (tRNA) pool in Mycobacterium bovis BCG during starvation-induced dormancy.
AQRNA-seq provides a direct linear relationship between sequencing read counts and small RNA copy numbers in a biological sample, thus enabling accurate quantification of the pool of small RNAs. The AQRNA-seq library preparation procedure described here involves the use of custom-designed sequencing linkers and a step for reducing methylation RNA modifications that block reverse transcription processivity, which results in an increased yield of full-length cDNAs. In addition, a detailed implementation of the accompanying bioinformatics pipeline is presented. This demonstration of AQRNA-seq was conducted through a quantitative analysis of the 45 tRNAs in Mycobacterium bovis BCG harvested on 5 selected days across a 20-day time course of nutrient deprivation and 6 days of resuscitation. Ongoing efforts to improve the efficiency and rigor of AQRNA-seq will also be discussed here. This includes exploring methods to obviate gel purification for mitigating primer dimer issues after PCR amplification and to increase the proportion of full-length reads to enable more accurate read mapping. Future enhancements to AQRNA-seq will be focused on facilitating automation and high-throughput implementation of this technology for quantifying all small RNA species in cell and tissue samples from diverse organisms.
Next-generation sequencing (NGS), also known as massively parallel sequencing, is a DNA sequencing technology that involves DNA fragmentation, ligation of adaptor oligonucleotides, polymerase chain reaction (PCR)-based amplification, sequencing of the DNA, and reassembly of the fragment sequences into a genome. The adaptation of NGS to sequence RNA (RNA-seq) is a powerful approach to identify and quantify RNA transcripts and their variants1. Innovative developments in RNA library preparation workflows and bioinformatic analysis pipelines, coupled with advancements in laboratory instrumentation, have expanded the repertoire of RNA-seq applications, progressing beyond exome sequencing into advanced functional omics like non-coding RNA profiling2, single cell analysis3, spatial transcriptomics4,5, alternative splicing analysis6, among others. These advanced RNA-seq methods reveal complex RNA functions through quantitative analysis of the transcriptome in normal and diseased cells and tissue.
Despite these advances in RNA-seq, several key technical features limit the quantitative power of the method. While most RNA-seq methods allow precise and accurate quantification of changes in the levels of RNAs between experimental variables (i.e., biological samples and/or physiological states), they cannot provide quantitative comparisons of the levels of RNA molecules within a sample. For example, most RNA-seq methods cannot accurately quantify the relative number of copies of individual tRNA isoacceptor molecules in a cellular pool of expressed tRNAs. As highlighted in the companion publication7, this limitation to RNA-seq arises from several features of RNA structure and the biochemistry of library preparation. For example, the activity of the ligation enzymes used to attach the 3'- and 5'-end sequencing linkers to RNA molecules is strongly influenced by the identity of the terminal nucleotides of the RNA and the sequencing linkers. This leads to large variations in efficiencies of linker ligations and profound artifactual increases in sequencing reads8,9,10.
A second set of limitations arise from the inherent structural properties of RNA molecules. Specifically, RNA secondary structure formation and dynamic changes in the dozens of post-transcriptional RNA modifications of the epitranscriptome can cause polymerase fall-off or mutation during reverse transcription. These errors result in incomplete or truncated cDNA synthesis or altered RNA sequence. While both of these phenomena can be exploited to map secondary structures or some modifications, they degrade the quantitative accuracy of RNA-seq if subsequent library preparation steps fail to capture truncated cDNAs or if data processing throws out mutated sequences not matching a reference dataset11,12. Furthermore, the immense chemical, length, and structural diversity of RNA transcripts, as well as the lack of tools to uniformly fragment long RNAs, diminishes the applicability of most RNA-seq methods to all RNA species13.
The AQRNA-seq (absolute quantification RNA sequencing) method has been developed to remove several of these technical and biological constraints that limit quantitative accuracy7. By minimizing sequence-dependent biases in capture, ligation, and amplification during RNA sequencing library preparation, AQRNA-seq achieves superior linearity compared to other methods, accurately quantifying 75% of a reference library of 963 miRNAs within 2-fold accuracy. This linear correlation of sequencing read count and RNA abundance is also observed in an analysis of a variable length pool of RNA oligonucleotide standards and in reference to orthogonal methods like northern blotting. Establishing linearity between sequencing read count and RNA abundance enables AQRNA-seq to achieve accurate, absolute quantification of all RNA species within a sample.
Here is a description of the protocol for AQRNA-seq library preparation workflow and the accompanying downstream data analytics pipeline. The method was applied to elucidate the dynamics of tRNA abundance during starvation-induced dormancy and subsequent resuscitation in the Mycobacterium bovis bacilli de Calmette et Guérin (BCG) model of tuberculosis. Results were presented for the exploratory visualization of the sequencing data, along with subsequent clustering and differential expression analyses that unveiled discernable patterns in tRNA abundance associated with various phenotypes.
NOTE: Figure 1 provides a graphical illustration of the procedures involved in AQRNA-seq library preparation. Detailed information regarding the reagents, chemicals, and columns/kits used in the procedure can be found in the Table of Materials. It is recommended to perform a comprehensive evaluation of purity, integrity, and quantity of the input RNA samples using (i) 3% agarose gel electrophoresis, (ii) automated electrophoresis tools for sample quality control of biomolecules (see Table of Materials), and (iii) UV-Visible spectrophotometry and/or fluorometric quantification. It is mandatory to keep all reactions and master mixes on ice, unless otherwise specified. Transport reagents (e.g., enzymes) in cool boxes to and from -20 °C storage to preserve their shelf lives and avoid multiple freeze-thaw cycles of library intermediates.
1. Dephosphorylation of RNAs
NOTE: Removal of the 5'-phosphate (P; donor) prevents self-ligation to the 3'-hydroxyl (OH; acceptor) of RNAs. Linkers will not self-ligate as their 3' end is modified to incorporate either a dideoxycytidine (Linker 1) or a spacer (Linker 2). Linkers can only be ligated by joining their 5'-P to the 3'-OH of the RNAs or cDNAs.
2. Ligation of Linker 1 to the 3' end of RNAs
3. Removal of post-transcriptional methylations by AlkB demethylase
NOTE: AlkB is a bacterial enzyme that removes methyl groups from some, but not all, methylated nucleotides in DNA and RNA. Removal of several types of methylated ribonucleotides in RNA prevents fall-off of the reverse transcriptase to allow more full-length reads and identification of modification sites. This step needs to be controlled at a low pH to prevent unexpected degradation of RNAs.
4. Removal of excess Linker 1
NOTE: It is recommended to save an aliquot (1.2 µL) of each sample after purification. When necessary, these aliquots can be used for checking the efficiency of RecJf digestion by running a commercial nucleic acid analyzer. Proceed with the purified samples immediately to reverse transcription.
5. Reverse transcription (RT) reaction
NOTE: The following RT (see Table of Materials) reaction setup follows the manufacturer's protocol, with minor modifications to allow for AQRNA-seq compatibility.
6. RNA hydrolysis
7. Ligation of Linker 2 to the 3' end of cDNAs
8. Removal of excess Linker 2
9. PCR amplification of the cDNAs with sequencing primers
10. Gel purification
11. Library sequencing
12. Data analytics pipeline
NOTE: Figure 2 provides a graphical illustration of simplified procedures involved in the data analytics pipeline, which takes raw sequence reads (in FASTQ format) as the input and generates an abundance matrix with rows representing members of small RNA species of interest and columns representing samples. For paired-end sequencing, each sample corresponds to two FASTQ files, one for the forward reads and the other for the reverse reads. The complete data analytics pipeline, with all the associated scripts and a manual with extensive annotations for each step, is available at GitHub (https://github.com/Chenrx9293/AQRNA-seq-JoVE.git).
Mycobacterium bovis BCG (bacilli de Calmette et Guérin) strain 1173P2 undergoing exponential growth were subject to a time series (0, 4, 10, and 20 days) of nutrient starvation, followed by a 6-day resuscitation in nutrient-rich medium as previously presented in Hu et al.7. Small RNAs were isolated from bacterial culture, with three biological replicates, at each of the five designated time points. Illumina libraries were constructed using the above-described AQRNA-seq library preparation workflow (Figure 1), followed by sequencing on a sequencer at the BioMicro Center of the Massachusetts Institute of Technology. The sequencing data was then processed using the AQRNA-seq data analytics pipeline (Figure 2) customized for quantification of tRNA abundance.
After the PCR amplification of the cDNA library with the sequencing primers, the presence of PCR products with a size of 175 base pairs (bp) was observed in all samples (Figure 3A), suggesting the formation of primer dimers. To mitigate the carry-over of primer dimers, PCR products exceeding 195 bp in size were excised from the gel and purified (Figure 3B).
Quality-filtered and trimmed sequence reads were mapped to a custom reference sequence library including the 45 tRNA isoacceptors, the internal standard, and control sequences (i.e., 23S rRNA, 16S rRNA, 5S rRNA, rnpB, and ssr). tRNA isoacceptors accounted for 10.5% to 40.2% of the total mapped reads of a given sample and showed a much higher abundance than the control sequences (Figure 4). Importantly, the relatively low read proportions of tRNA isoacceptors could be attributed to the higher relative abundance of the internal standards. Therefore, the read proportions of tRNA isoacceptors relative to internal standards (Figure 4, pink vs green color blocks) can be controlled by the operator, through fine-tuning the amount of internal standard spiked into the reaction.
The raw tRNA abundance data was normalized using the median of ratios method implemented with the DESeq2 package version (hereafter referred to as v) 1.36.016 in R Statistical Programming Environment (hereafter referred to as R) v 4.2.117. After normalization, a quantitative landscape of tRNA isoacceptors in Mycobacterium bovis BCG during a time-course of nutrient starvation and resuscitation is achieved (Figure 5).
To reveal distinct clusters of samples with different phenotypes based on patterns in tRNA isoacceptor abundance, Principal Component Analysis (PCA) was performed on the normalized tRNA abundance data using the stats package v 4.2.117 in R (Figure 6). The analysis distinguished samples of starvation day 0 and resuscitation day 6 from samples of starvation days 4, 10, and 20, suggesting a considerable difference in the tRNA landscape of Mycobacterium bovis BCG grown in nutrient-deprived medium and nutrient-rich medium.
To profile the dynamics of the abundance of each tRNA isoacceptor across the five designated time points, differential expression analysis was performed on the normalized tRNA abundance data using the DESeq2 package v 1.36.0 in R (Figure 7). The analysis revealed that 17 of the 20 isoacceptor families contained isoacceptors that were differentially expressed (i.e., significantly up- or down-regulated) in at least one of the time points, suggesting a potential role of the regulation of tRNA pool in the persistent state of Mycobacterium bovis BCG during tuberculosis.
Figure 1: Schematic of the AQRNA-seq library preparation workflow. The key steps outlined in the workflow are listed in the center of the schematic and connected to their respective graphical illustrations by dotted lines. The detailed description of each step can be found in the Protocol section. Please click here to view a larger version of this figure.
Figure 2: Schematic of the AQRNA-seq data analytics pipeline. The key steps outlined in the pipeline are listed in the center of the schematic and connected to their respective graphical illustrations by dotted lines. The detailed description of each step is available at GitHub (https://github.com/Chenrx9293/AQRNA-seq-JoVE.git). Please click here to view a larger version of this figure.
Figure 3: Agarose gel electrophoresis of the cDNA fragments after PCR amplification with sequencing primers. (A) Image of the gel prior to gel extraction and purification. Lanes 7 and 14 from the left side contain 5 µL of the 50 bp DNA ladder, while the other lanes contain 20 µL of each of the 15 samples. Size localization of the PCR products indicate their highest concentration within the range from 175 bp (primer dimers) to 300 bp (two primers + 120 bp 5S rRNA). (B) Image of the gel after gel extraction and purification. For each sample, the gel block between 200 bp and 400 bp was excised to minimize the contamination of primer dimers in the sequencing library. Please click here to view a larger version of this figure.
Figure 4: Number of sequence reads successfully mapped to the reference sequence library. The x-axis shows the names of the samples (e.g., D18-69XX) grouped by time point (e.g., Starvation Day 0). For each sample, the read count associated with various target subject categories are represented using color blocks stacked on top of one another. Numbers located at the center of the color blocks represent the proportions of reads corresponding to the respective target subjects within a given sample. Please click here to view a larger version of this figure.
Figure 5: Quantitative landscape of tRNA isoacceptors of Mycobacterium bovis BCG at various time points along the starvation and resuscitation time course. Raw tRNA abundance data was normalized using the median of ratios method. Here, each row depicts normalized tRNA abundances (y-axis) as mean ± standard error for 3 biological replicates at each time point. On the x-axis, isoacceptors from the same family were grouped together and labeled with the corresponding amino acid. Please click here to view a larger version of this figure.
Figure 6: Squared-cosine plot of samples derived from principal component analysis (PCA). PCA was performed based on the normalized tRNA abundance. The squared cosine indicates the importance of the principal components to the samples, and the samples were plotted with respect to the squared cosine of the first two principal components. Samples were labeled using sample IDs and color-coded by time point. Please click here to view a larger version of this figure.
Figure 7: Differential expression of tRNA isoacceptors across different time points. Normalized tRNA abundances were summarized as means (line knots) ± standard error (error bars) across 3 biological replicates. Due to space limitation, the conditions were abbreviated as follows: S0-S20 = starvation days 0-20; R6 = resuscitation day 6. Differential expression analysis was performed for each tRNA isoacceptor, comparing various time points in a pairwise manner using the likelihood ratio test and the Wald test. Compact letters were employed to represent statistical significance, where the abundances of a given tRNA isoacceptor at time points sharing at least one common letter were not significantly different from each other. For instance, the abundance of tRNA-Lys-CTT-1-1 (in the Lysine panel) was significantly down-regulated from S0 to S4 and from S4 to S10, but not from S10 to S20. It was then significantly up regulated from S20 to R6. Please click here to view a larger version of this figure.
Table 1: Oligonucleotides involved in the AQRNA-seq library preparation workflow. The internal standard is RNA, while all other oligonucleotides are DNA. The PCR primers and custom sequencing primers listed are specific for the sequencing platforms. Additional PCR primers can be designed with novel index sequences. Please click here to download this Table.
The AQRNA-seq library preparation workflow is designed to maximize the capture of RNAs within a sample and minimize polymerase fall-off during reverse transcription7. Through a two-step linker ligation, novel DNA oligos (Linker 1 and Linker 2) are ligated in excess to fully complement the RNA within the sample. Excess linkers can be efficiently removed with RecJf, a 5' to 3' exonuclease specific to single-stranded DNAs, leaving the ligated products intact. In addition, AlkB treatment reduces methyl modifications on RNAs18, which may cause polymerase fall-off during reverse transcription, and thus alleviates the issue of artifactual truncated cDNA products. AQRNA-seq represents the first reported method to systematically optimize ligation and amplification efficiencies. AQRNA-seq has been validated for its quantitative accuracy and lack of bias artifacts, and it has been shown to be the most accurate application for miRNA profiling studies.
AQRNA-seq is notably extensive, and operational care is recommended for temperature- and/or time-sensitive steps during library preparation. Specifically, steps prior to cDNA synthesis (Figure 1; steps 1-4), where samples contain inherently unstable single-stranded RNA (ssRNA) or DNA-linker-ligated ssRNA, should be carried out without unnecessary interruptions. RNA hydrolysis (Figure 1; step 6) using strong base at a high temperature is highly efficient in digesting RNA templates after reverse transcription19. However, care needs to be taken to avoid prolonged sample exposure to the strong base and delayed neutralization with strong acid, especially given a large sample count. In general, preparing all reactions on ice and avoiding repeated freeze-thaw cycles for library intermediate products are highly recommended.
Sampling of library intermediates at key points along the workflow is recommended for quality checks and timely troubleshooting of the process. Analyzing samples on automated electrophoresis tools can determine the success and efficiency of linker ligation (Figure 1; steps 2 and 7). Visualizing the localization of indexed cDNA products after PCR addition of sequencing primers (Figure 1; step 9) via agarose gel electrophoresis can help with assessing the adequacy of PCR cycles as well as identifying possible degradation of samples and/or the presence of primer dimers. The PCR and sequencing primers may be redesigned to be compatible with the operator's sequencing platform of interest.
Due in part to the distinct designs involved in library preparation, the AQRNA-seq data analytics pipeline was primarily constructed comprising custom scripts, incorporating only a handful of existing bioinformatics programs. However, for aligning sequence reads against the reference sequence library, the Basic Local Alignment Search Tool (BLAST)20 was employed due to its various strengths that cater specific needs of AQRNA-seq data analysis. These include (i) the ability to align reads shorter than 20 bp and perform substring alignments, (ii) sensible handling of ambiguous bases (N's), (iii) the slightly higher alignment accuracy, and (iv) the informative output providing substantial details such as mismatches and gaps, which is conducive to diverse downstream analyses.
Notably, there remain several design challenges in both the library preparation workflow and the data analytics pipeline of AQRNA-seq, which warrant further optimizations and represent the focus of the current work-in-progress. First, the superior quantitative accuracy of AQRNA-seq compared to conventional RNA-seq methods was achieved with 50-75 ng of small RNA input, while the workflow is currently tested with reduced amounts of input RNAs to enhance its sensitivity and with longer RNAs to broaden its utility. Second, although post-transcriptional methylation is removed enzymatically with AlkB, there are other RNA modifications that may cause polymerase fall-off during reverse transcription10. This may lead to artifactual truncated cDNA products that could not be effectively differentiated from biologically meaningful 5' degradation of full-length tRNAs. Hence, several possible ways to further improve reverse transcriptase processivity are currently being evaluated for effectiveness and compatibility with AQRNA-seq. Third, the removal of primer dimers through gel extraction and purification is inefficient and invariably results in a loss of the target fragments and increased variability between libraries. Therefore, mitigating potential carry-over of primer dimers without losing target fragments represents an opportunity to further enhance the sensitivity and quantitative accuracy of AQRNA-seq. Importantly, both AlkB treatment as well as gel extraction and purification are labor intensive, making them suboptimal for processing large number of samples. With the improvements achieved in the above areas, automation and high-throughput processing of the AQRNA-seq method will be enabled in future. The ongoing refinement of the data analytics pipeline prioritizes enhancements in efficiency, quantitative accuracy, and flexibility. While the current pipeline effectively trims 3' adaptors from the sequence reads, a subset of reads also possesses 5' adaptor sequences, likely due to inadequate blocking of the 3' end of the linkers during ligation. The presence of 5' adaptor sequences may compromise quantitative accuracy by interfering with sequence read alignment. Therefore, an adequate removal of both 3' and 5' adaptor sequences will be guaranteed by employing additional adaptor trimming tools such as Cutadapt21. To bolster efficiency, the revised pipeline will integrate rational strategies for reducing computing time. This involves a paired end read assembly step to merge forward and reverse reads by identifying a significant overlap, and extraction of unique sequences along with their occurrences in each library. Such strategies are anticipated to considerably reduce the downstream computing time by avoiding redundant analysis of (i) reads from both directions and (ii) duplicated sequences that may dominate the sequence read pool of AQRNA-seq libraries. Moving forward, the synergetic optimization of both the library preparation workflow and data analytics pipeline of AQRNA-seq will likely open diverse opportunities in the rigorous quantitative investigation of all forms of RNA (e.g., transcriptome, tRNA fragments, and rare RNA species like circulating tumor RNAs), mapping of tRNA modifications, and other pivotal areas of biomarker and discovery biology.
The authors have nothing to disclose.
The authors of the present work are grateful to the authors of the original paper describing the AQRNA-seq technology7. This work was supported by grants from the National Institutes of Health (ES002109, AG063341, ES031576, ES031529, ES026856) and the National Research Foundation of Singapore through the Singapore-MIT Alliance for Research and Technology Antimicrobial Resistance IRG.
2-ketoglutarate | Sigma-Aldrich | 75890 | Prepare a working solution (1 M) and store it at -20ºC |
2100 Bioanalyzer Instrument | Agilent | G2938C | |
5'-deadenylase (50 U/μL) | New England Biolabs | M0331S (component #: M0331SVIAL) | Store at -20 °C |
Adenosine 5'-Triphosphate (ATP) | New England Biolabs | M0437M (component #: N0437AVIAL) | NEB M0437M contains T4 RNA Ligase 1 (30 U/μL), T4 RNA Ligase Reaction Buffer (10X), PEG 8000 (1X), and ATP (100 mM); prepare a working solution (10 mM) and store it at -20ºC |
AGAROSE GPG/LE | AmericanBio | AB00972-00500 | Store at ambient temperature |
Ammonium iron(II) sulfate hexahydrate | Sigma-Aldrich | F2262 | Prepare a working solution (0.25 M) and store it at -20 °C |
Bioanalyzer Small RNA Analysis | Agilent | 5067-1548 | The Small RNA Analysis is used for checking the quality of input RNAs and the efficiency of enzymatic reactions (e.g., Linker 1 ligation) |
Bovine Serum Albumin (BSA; 10 mg/mL) | New England Biolabs | B9000 | This product was discontinued on 12/15/2022 and is replaced with Recombinant Albumin, Molecular Biology Grade (NEB B9200). |
Chloroform | Macron Fine Chemicals | 4441-10 | |
Demethylase | ArrayStar | AS-FS-004 | Demethylase comes with the rtStar tRNA Pretreatment & First-Strand cDNA Synthesis Kit (AS-FS-004) |
Deoxynucleotide (dNTP) Solution Mix | New England Biolabs | N0447L (component #: N0447LVIAL) | This dNTP Solution Mix contains equimolar concentrations of dATP, dCTP, dGTP and dTTP (10 mM each) |
Digital Dual Heat Block | VWR Scientific Products | 13259-052 | Heating block is used with the QIAquick Gel Extraction Kit |
DyeEx 2.0 Spin Kit | Qiagen | 63204 | Effective at removing short remnants (e.g., oligos less than 10 bp in length) |
Electrophoresis Power Supply | Bio-Rad Labrotories | PowerPac 300 | |
Eppendorf PCR Tubes (0.5 mL) | Eppendorf | 0030124537 | |
Eppendorf Safe-Lock Tubes (0.5 mL) | Eppendorf | 022363611 | |
Eppendorf Safe-Lock Tubes (1.5 mL) | Eppendorf | 022363204 | |
Eppendorf Safe-Lock Tubes (2 mL) | Eppendorf | 022363352 | |
Ethyl alcohol (Ethanol), Pure | Sigma-Aldrich | E7023 | The pure ethanol is used with the Oligo Clean and Concentrator Kit from Zymo Research |
Gel Imaging System | Alpha Innotech | FluorChem 8900 | |
Gel Loading Dye, Purple (6X), no SDS | New England Biolabs | N0556S (component #: B7025SVIAL) | NEB N0556S contains Quick-Load Purple 50 bp DNA Ladder and Gel Loading Dye, Purple (6X), no SDS |
GENESYS 180 UV-Vis Spectrophotometer | Thermo Fisher Scientific | 840-309000 | The spectrophotometer is used for measuring the oligo concentrations using the Beer's law |
HEPES | Sigma-Aldrich | H4034 | Prepare a working solution (1 M; pH = 8 with NaOH) and store it at -20 °C |
Hydrochloric acid (HCl) | VWR Scientific Products | BDH3028 | Prepare a working solution (5 M) and store it at ambient temperature |
Isopropyl Alcohol (Isopropanol), Pure | Macron Fine Chemicals | 3032-16 | Isopropanol is used with the QIAquick Gel Extraction Kit |
L-Ascorbic acid | Sigma-Aldrich | A5960 | Prepare a working solution (0.5 M) and store it at -20ºC |
Microcentrifuge | Eppendorf | 5415D | |
NanoDrop 2000 Spectrophotometer | Thermo Fisher Scientific | ND-2000 | |
NEBuffer 2 (10X) | New England Biolabs | M0264L (component #: B7002SVIAL) | NEB M0264L contains RecJf (30 U/μL) and NEBuffer 2 (10X); store at -20 °C |
Nuclease-Free Water (not DEPC-Treated) | Thermo Fisher Scientific | AM9938 | |
Oligo Clean & Concentrator Kit | Zymo Research | D4061 | Store at ambient temperature |
PEG 8000 (50% solution) | New England Biolabs | M0437M (component #: B1004SVIAL) | NEB M0437M contains T4 RNA Ligase 1 (30 U/μL), T4 RNA Ligase Reaction Buffer (10X), PEG 8000 (1X), and ATP (100 mM); prepare a working solution (10 mM) and store it at -20ºC |
Peltier Thermal Cycler | MJ Research | PTC-200 | |
Phenol:choloroform:isoamyl alcohol 25:24:1 pH = 5.2 | Thermo Fisher Scientific | J62336 | |
PrimeScript Buffer (5X) | TaKaRa | 2680A | |
PrimeScript Reverse Transcriptase | TaKaRa | 2680A | |
QIAquick Gel Extraction Kit | Qiagen | 28704 | This kit requires a heating block and isopropanol to work with |
Quick-Load Purple 100 bp DNA Ladder | New England Biolabs | N0551S (component #: N0551SVIAL) | |
Quick-Load Purple 50 bp DNA Ladder | New England Biolabs | N0556S (component #: N0556SVIAL) | NEB N0556S contains Quick-Load Purple 50 bp DNA Ladder and Gel Loading Dye, Purple (6X), no SDS |
RecJf (30 U/μL) | New England Biolabs | M0264L (component #: M0264LVIAL) | NEB M0264L contains RecJf (30 U/μL) and NEBuffer 2 (10X); store at -20 °C |
RNase Inhibitor (murine; 40 U/μL) | New England Biolabs | M0314L (component #: M0314LVIAL) | Store at -20 °C |
SeqAMP DNA Polymerase | TaKaRa | 638509 | TaKaRa 638509 contains SeqAMP DNA Polymerase and SeqAMP PCR Buffer (2X) |
SeqAMP PCR Buffer (2X) | TaKaRa | 638509 | TaKaRa 638509 contains SeqAMP DNA Polymerase and SeqAMP PCR Buffer (2X) |
Shrimp Alkaline Phosphatase (1 U/μL) | New England Biolabs | M0371L (component #: M0371LVIAL) | |
Sodium hydroxide (NaOH) | Sigma-Aldrich | S5881 | Prepare a working solution (5 M) and store it at ambient temperature |
T4 DNA Ligase (400 U/μL) | New England Biolabs | M0202L (component #: M0202LVIAL) | NEB M0202L contains T4 DNA Ligase (400 U/μL) and T4 DNA Ligase Reaction Buffer (10X) |
T4 DNA Ligase Reaction Buffer (10X) | New England Biolabs | M0202L (component #: B0202SVIAL) | NEB M0202L contains T4 DNA Ligase (400 U/μL) and T4 DNA Ligase Reaction Buffer (10X) |
T4 RNA Ligase 1 (30 U/μL) | New England Biolabs | M0437M (component #: M0437MVIAL) | NEB M0437M contains T4 RNA Ligase 1 (30 U/μL), T4 RNA Ligase Reaction Buffer (10X), PEG 8000 (1X), and ATP (100 mM) |
T4 RNA Ligase Reaction Buffer (10X) | New England Biolabs | M0437M (component #: B0216SVIAL) | NEB M0437M contains T4 RNA Ligase 1 (30 U/μL), T4 RNA Ligase Reaction Buffer (10X), PEG 8000 (1X), and ATP (100 mM) |