> Chemicals & Drugs > Nucleic Acid > RNA I

RNA II →

RNA I

Q: What is RNA interference (RNAi) and how does it work?

RNA interference (RNAi) is a powerful biological process where RNA molecules inhibit gene expression or translation by neutralizing targeted mRNA molecules. This technology allows for the silencing of specific genes, making it a valuable tool in both basic research and therapeutic applications.

Q: What are the different types or variations of RNA I?

There are several variations of RNA I, including small interfering RNA (siRNA), microRNA (miRNA), and short hairpin RNA (shRNA). Each type has unique characteristics and applications, such as targeted gene silencing, regulation of gene expression, and exploring specific pathways.

Q: What are some common challenges in optimizing RNA I experiments?

Some common challenges in optimizing RNA I experiments include achieving efficient delivery of RNA molecules, ensuring target specificity, and maintaining consistent and reproducible results. Navigating the vast amount of published protocols and identifying the most effective methods for your research can also be a significant hurdle.

Q: How can PubCompare.ai help with RNA I research?

PubCopmare.ai allows you to screen protocol literature more efficiently and leverage AI to pinpoint critical insights. The platform can help researchers identify the most effective protocols related to RNA I for their specific research goals. The AI-driven analysis can highlight key differences in protocol effectiveness, enabling you to choose the best option for reproducibility and accuracy in your RNA I studies.

Q: What are some common applications of RNA I technology?

RNA I technology has a wide range of applications, including gene function studies, disease modeling, therapeutic target validation, and the development of novel treatments. Researchers can use RNA I to silence specific genes, understand their roles in biological pathways, and explore potential therapeutic interventions for various diseases.

RNA interference (RNAi) is a biological process in which RNA molecules inhibit gene expression or translation, by neutralizing targeted mRNA molecules.
This powerful technology allows for the silencing of specific genes, making it a valuable tool in both basic research and therapeutic applications.
PubCompare.ai leverages AI to help researchers optimize their RNAi experiments, providing access to the best protocols from published literature, preprints, and patents.
With automated comparisons, the platform identifies the most reproducible and accurate methods, elevating the quality and reliability of RNI studies.

Most cited protocols related to «RNA I»

Negative Binomial Generalized Linear Models for Transcriptome Analysis

Cited 810 times

GLMs are an extension of classical linear models to non-normally distributed response data (42 ,43 ). GLMs specify probability distributions according to their mean–variance relationship, for example the quadratic mean–variance relationship specified above for read counts. Assuming that an estimate is available for ϕ_g, so the variance can be evaluated for any value of μ_gi, GLM theory can be used to fit a log-linear model

for each gene (32 (link),41 ). Here x_i is a vector of covariates that specifies the treatment conditions applied to RNA sample i, and β_g is a vector of regression coefficients by which the covariate effects are mediated for gene g. The quadratic variance function specifies the negative binomial GLM distributional family. The use of the negative binomial distribution is equivalent to treating the π_gi as gamma distributed.

McCarthy D.J., Chen Y, & Smyth G.K. (2012). Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Research, 40(10), 4288-4297.

Publication 2012

Cloning Vectors Gamma Rays Genes Genes, vif RNA, immune RNA I

RNAtag-Seq Library Preparation Protocol

Cited 71 times

K562 dUTP libraries were generated as described with rRNA depleted using the RNaseH approach^{22 (link)}. Bacterial dUTP libraries were generated as described^{21 (link)} with rRNA depleted using with RiboZero (Epicentre). In all RiboZero reactions, the maximal amount recommended by the manufacturer per reaction was used to avoid an additional quantification step during library construction and ensure the RNA did not exceed the capacity of the solution. RNAtag-Seq cDNA libraries were generated according to the detailed protocol in the Supplementary Protocol. Briefly, 200-400 ng of total RNA was fragmented, depleted of genomic DNA and dephosphorylated before its ligation to barcoded adaptors with a 5′ phosphate and a 3′ blocking group. DNA adaptors carried 5′-AN₈-3′ barcodes and RNA adaptors 5′-rArN₆-3′ barcodes. Sequences of these barcodes are provided in the Supplementary Protocol and Supplementary Table 2. Barcoded RNAs were pooled and depleted of rRNA using the appropriate RiboZero rRNA depletion kit (Epicentre) for bacterial and K562 pools (8 samples per pool, Supplementary Table 2) and as previously described^{23 (link)} for mouse pools. These pools of barcoded RNAs were converted to Illumina cDNA libraries in three key steps: (i) reverse transcription of the RNA using a primer designed to the constant region of the barcoded adaptor; (ii) degradation of the RNA and ligation of a second adaptor to the single-stranded cDNA; (iii) PCR amplification using primers that target the constant regions of the 3′ and 5′ ligated adaptors and contain the full sequence of the Illumina sequencing adaptors (Fig. 1). Two SPRI cleanup steps are included following adaptor ligations to ensure efficient removal of adaptor dimers (<1% of our sequencing reads represented adaptor dimers). Modifications of the RNAtag-Seq protocol used in generation of mouse libraries are detailed in Appendix A in the Supplementary Protocol. cDNA libraries were sequenced on Illumina MiSeq or HiSeq2500

Shishkin A.A., Giannoukos G., Kucukural A., Ciulla D., Busby M., Surka C., Chen J., Bhattacharyya R.P., Rudy R.F., Patel M.M., Novod N., Hung D.T., Gnirke A., Garber M., Guttman M, & Livny J. (2015). Simultaneous generation of many RNA-seq libraries in a single reaction. Nature methods, 12(4), 323-325.

Publication 2015

Bacteria cDNA Library deoxyuridine triphosphate DNA, Complementary Genome Ligation Mice, House Oligonucleotide Primers Phosphates Reverse Transcription Ribosomal RNA RNA RNA, immune RNA Degradation RNA I

Generative Model for Single-Cell RNA-Seq

Cited 27 times

Census is motivated by a generative model of single-cell (sc) RNA-Seq similar to the one developed by Kim et al.⁴⁷. When performing sc-RNA-seq, each individual cell is lysed to recover its endogenous RNA molecules, some fraction of which may be degraded or lost. Lysis thus involves an RNA recovery rate α. Spike-in transcripts are then added into the cell lysate. Note that spike-in transcripts are added to the lysate as naked RNA, and thus may be degraded at different rates from the endogenous RNA. We denote the ladder recovery rate as β. The RNA counts in the lysate can be written:

Cell lysate : {\begin{matrix} Y_{ij}^{l} & \approx & α_{i} Y_{ij}^{c} \\ S_{ij}^{l} & \approx & β_{i} S_{. j} \end{matrix},

where Y^l, S^l, S, are the transcript counts of endogenous RNA in cell lysate, spike-in transcript counts in cell lysate and the spike-in transcript counts added into the cell lysate. The first subscript in all variables (here and below) corresponds to cell while the second subscript corresponds to gene index. Note that we are not able to directly observe

Y_{ij}^{c}

, the true transcript counts for gene j in cell i and thus α is an unknown variable.
The RNA molecules and spike-in transcripts will then be subjected to reverse transcription and amplified to make a cDNA library. The expected number of cDNA molecules generated from each RNA molecules is denoted by θ. The cDNA counts can be written:

cDNA : {\begin{matrix} Y_{ij}^{d} & = & Y_{ij}^{l} \cdot θ_{i} \\ S_{ij}^{d} & = & S_{ij}^{l} \cdot θ_{i} \end{matrix},

where Y^d, S^d, are the cDNA counts of endogenous RNA, spike-in cDNA counts successfully converted from the corresponding transcript counts Y^l, S^l in cell lysate under a uniform capture rate θ, which for current protocols is less than 1.
Our model generates sequencing reads from the cDNA. The relative cDNA abundances are calculated as

\frac{Y_{ij}^{d}}{\sum_{j = 1}^{n} (Y_{ij}^{d} + S_{ij}^{d})}

for endogenous RNA, or

\frac{S_{ij}^{d}}{\sum_{j = 1}^{n} (Y_{ij}^{d} + S_{ij}^{d})}

for spike-in RNA.
The model then generates γ reads per cDNA molecule on average; with sufficient sequencing, γ will be larger than 1; we expect each cDNA molecule to generate at least one sequencing read. This process can be regarded as a multinomial sampling of R reads

(R_{i} = γ \sum_{j = 1}^{n} (Y_{ij}^{d} + S_{ij}^{d}))

from the distribution of relative cDNA abundances mentioned above which can be represented as:

Read counts : {\begin{matrix} Y_{ij}^{r} \sim multinomial (\frac{Y_{ij}^{d}}{\sum_{j = 1}^{n} (Y_{ij}^{d} + S_{ij}^{d})}, R_{i}^{e}) \\ S_{. j}^{r} \sim multinomial (\frac{S_{ij}^{d}}{\sum_{j = 1}^{n} (Y_{ij}^{d} + S_{ij}^{d})}, R_{i}^{s}) \end{matrix},

where

R_{i}^{e}, R_{i}^{s}

, denotes the reads sampled for cDNA from the endogenous RNA or spike- in RNA in cell i,

Y_{ij}^{r}, S_{. j}^{r}

denotes the reads sampled for cDNA from the endogenous RNA j or spike-in RNA j in cell i.
The model described here is essentially a special case of the model in Kim et al., and differs mainly in that their model describes transcript-level capture rates and sequencing rates with beta and gamma distributions, respectively. In contrast, we simply use global constants for these rates. As Census does not make use of variance estimates from the generative model, this simpler model is sufficient for calculating key statistics (e.g. mode of the transcript counts) needed to convert relative to absolute abundances.

Qiu X., Hill A., Packer J., Lin D., Ma Y.A, & Trapnell C. (2017). Single-cell mRNA quantification and differential analysis with Census. Nature methods, 14(3), 309-315.

Publication 2017

cDNA Library Cells DNA, Complementary Gamma Rays Genes Reverse Transcription RNA I Single-Cell RNA-Seq

Automated Transcription Start Site Prediction

Cited 24 times

A whole genome alignment of the four C. jejuni strains was computed with Mauve[83] (link). Based on this global alignment a common genomic coordinate system, the SuperGenome was defined into which all positional information can be projected that relates to the single genomes [29] (link). This resulted in a consensus sequence with the coordinates of the complete alignment and a mapping of each position of each single genome to a position in the alignment. Next, all genome-specific data (expression height graphs derived from mapped read data, genomic annotations, and sequences) were mapped to the common coordinate system.
Our automated TSS prediction approach, which uses this SuperGenome mapping for comparative analyses, consists of several steps: The initial detection of TSS in the single strains is based on the localization of positions, where a significant number of reads start. Thus, for each position i in the RNA–seq graph corresponding to the TEX+ library the algorithm calculates e(i)-e(i-1), where e(i) is the expression height at position i (Figure S15 in Text S1). In addition, the factor of height change is calculated, i.e. e(i)/e(i-1). To evaluate if the reads starting at this position are originating from primary transcripts, the enrichment factor is calculated as e_TEX+(i)/e_TEX−(i). For all positions where these values exceed the threshold (see Supplementary Material) a TSS candidate is annotated.
The TSS prediction procedure is applied to both replicates of each strain. TSS candidates, which are not detected in both replicates with a maximal positional difference of one nucleotide, are discarded. Afterwards, TSS candidates that are in close vicinity are grouped into a cluster and only the TSS candidate with the highest expression is kept. In the next step, the TSS candidates of each strain are mapped to the SuperGenome to assign each TSS to the corresponding TSS in the other strains. The final TSS annotations are then characterized on the SuperGenome level with respect to their occurrence in the different strains and in which strains they appear to be enriched. In the context of the individual strains the TSS are further classified according to their location relative to annotated genes. For this we used a similar classification scheme as previously described [4] (link). Thus, for each TSS it is decided if it is the primary or secondary TSS of a gene, if it is an internal TSS, an antisense TSS or if it cannot be assigned to one of these classes (orphan). A TSS is classified as primary or secondary if it is located ≤300 bp upstream of a gene. The TSS with the strongest expression considering all strains is classified as primary. All other TSS that are assigned to the same gene are classified as secondary. Internal TSS are located within an annotated gene on the sense strand and antisense TSS are located inside a gene or within ≤100 bp on the antisense strand. These assignments are indicated by a 1 in the respective column of Tables S4, S5, S6, S7, S8, S9. Orphan TSS, which are not in the vicinity of an annotated gene, are indicated by “0” in all four columns.
To validate our automated TSS detection we applied it to the previously generated dRNA–seq data of Helicobacter pylori grown under five different conditions [4] (link). In this study, we had manually annotated the TSS based on enrichment patterns in the TEX+ compared to TEX- libraries. We used these hand-curated TSS positions as benchmark and compared it to the results of the automated detection. We allowed a difference of up to one nucleotide when comparing an automatically detected TSS to a manually annotated TSS. With this threshold, the automated approach achieves a sensitivity of 82% and a precision rate of 75%. The parameters used for the TSS annotation in C. jejuni were selected according to this benchmarking with the manual TSS set of H. pylori (see also Supplementary Methods in Text S1).

Dugar G., Herbig A., Förstner K.U., Heidrich N., Reinhardt R., Nieselt K, & Sharma C.M. (2013). High-Resolution Transcriptome Maps Reveal Strain-Specific Regulatory Features of Multiple Campylobacter jejuni Isolates. PLoS Genetics, 9(5), e1003495.

Full text: Click here

Publication 2013

BP 100 Consensus Sequence DNA Library Genes Genome Helicobacter pylori Hypersensitivity Nucleotides Orphaned Children RNA, immune RNA I Strains

Competitive Gene Set Analysis Framework

Cited 22 times

One of the advantages of competitive gene set tests is that they can be applied just as easily to any genewise test statistic, no matter how complex. There is no need to be limited to two-group comparisons, for example. To be as general as possible, we assume throughout this article a linear model setup similar to that described previously (8 (link),24 ). Suppose that a gene expression experiment has been conducted resulting in log-expression values y_gi for genes g = 1, … , G and RNA samples i = 1, … , n. We assume a linear model for the expected value of each expression value given the experimental design,

where the x_ij are covariates or design variables specifying which treatment condition is associated with each RNA sample, and the α_gj are unknown regression coefficients representing expression log-fold changes (logFCs) between conditions in the experiment.
Each gene is assumed to have its own variance, . Expression values from different arrays are assumed to be independent, but expression values for different genes from the same RNA sample are generally not. The correlations cor(y_gi,y_g′i) = ρ_g,g′ are generally non-zero. Note that the ρ_g,g′ here represent residual correlations between genes across replicate samples, after the treatment effects μ_gi have been removed.

Wu D, & Smyth G.K. (2012). Camera: a competitive gene set test accounting for inter-gene correlation. Nucleic Acids Research, 40(17), e133.

Publication 2012

DNA Replication Gene Expression Genes Genetic Testing RNA I

Most recents protocols related to «RNA I»

Extraction and Analysis of mRNA and miRNA from Cells and Cartilage

In this study, mRNA was isolated from cultured cells and knee articular cartilage tissues. The cultured cells were rinsed with PBS and lysed in RNA-Solv^® Reagent (Omega Bio-tek, Norcross, GA, USA). The knee cartilage samples were placed in paired RNase-Free 1.5 EP tubes with four ground beads (5 mm in diameter) and frozen with liquid nitrogen. Subsequently, the tissues were pulverized and homogenized using Tissuelyser-24 (Jingxin, Shanghai, China). The TissueLyser was operated twice for 30 s at 45 Hz. The above tissue powder (50–100 mg) was lysed in Omega RNA-Solv^® Reagent and RNA was isolated using the E.Z.N.A.^® Total RNA Kit I (Omega Bio-tek) according to manufacturer’s protocol. MiRNA levels were extracted using a miRNA Isolation Kit (Ambion). RNA was stored at − 80 °C. Reverse transcription was performed using 1.0 µg total RNA and then used to prepare cDNA using miRNA and HiFiScript cDNA kits (CWBIO, Beijing, China), which were used to investigate the expression of miRNA and mRNA, respectively. All qPCRs were performed in a 20 µL volume using appropriate primers (1 µL; Sangon Biotech, Shanghai, China), cDNA (1 µL), and a ROX-containing UltraSYBR Mixture (CWBIO) with an ABI 7500 Sequencing Detection instrument (Applied Biosystems, CA, USA). The thermocycler settings were as follows: 40 cycles of 95 °C for 5 s and 60 °C for 24 s. U6 was used as an internal control for microRNA, whereas β-actin served as the control for messenger RNA. The cycle threshold (Ct) values were collected and normalized to the level of U6 or β-actin, with three samples per group. The relative mRNA level of each target gene was calculated by using the 2^−ΔΔCt method. Primer sequences are shown in Table 1.Table 1

Primer sequences for qPCR

Gene	Primers
MiR-760	Forward: UUCUCCGAACGUGUCACGUTT Reverse: ACGUGACACGUUCGGAGAATT
MMP3	Forward: AGTCTTCCAATCCTACTGTTGCT Reverse: TCCCCGTCACCTCCAATCC
MMP13	Forward: ACTGAGAGGCTCCGAGAAATG Reverse: GAACCCCGCATCTTGGCTT
ADAMTS4	Forward: GAGGAGGAGATCGTGTTTCCA Reverse: CCAGCTCTAGTAGCAGCGTC
COL2A1	Forward: TGGACGATCAGGCGAAACC Reverse: GCTGCGGATGCTCTCAATCT
Aggrecan	Forward: ACTCTGGGTTTTCGTGACTCT Reverse: ACACTCAGCGAGTTGTCATGG
HBEGF	Forward: ATCGTGGGGCTTCTCATGTTT Reverse: TTAGTCATGCCCAACTTCACTTT
CBL	Forward: TGGTGCGGTTGTGTCAGAAC Reverse: GGTAGGTATCTGGTAGCAGGTC
CAMK2G	Forward: ACCCGTTTCACCGACGACTA Reverse: CTCCTGCGTGGAGGTTTTCTT
MAP2K1	Forward: CAATGGCGGTGTGGTGTTC Reverse: GATTGCGGGTTTGATCTCCAG
ADCY1	Forward: AGGCACGACAATGTGAGCATC Reverse: TTCATCGAACTTGCCGAAGAG
RPS6KA3	Forward: CGCTGAGAATGGACAGCAAAT Reverse: TCCAAATGATCCCTGCCCTAAT
U6	Forward: CTCGCTTCGGCAGCACA Reverse: AACGCTTCACGAATTTGCGT
β-actin	Forward: AGATGTGGATCAGCAAGCAG Reverse: GCGCAAGTTAGGTTTTGTCA

Zhu Y., Zhang C., Jiang B, & Dong Q. (2023). MiR-760 targets HBEGF to control cartilage extracellular matrix degradation in osteoarthritis. Journal of Orthopaedic Surgery and Research, 18, 186.

Full text: Click here

Publication 2023

Actins angiogenin Cartilage Cartilages, Articular Cultured Cells DNA, Complementary Freezing Genes isolation Knee Joint MicroRNAs Nitrogen Oligonucleotide Primers Powder Reverse Transcription RNA, Messenger RNA I Tissues

Arabidopsis Immune Response Gene Expression

Protocol full text hidden due to copyright restrictions

Open the protocol to access the free full text link

Publication 2023

Arabidopsis Deoxyribonuclease I DNA, Complementary Gene Expression Genes Real-Time Polymerase Chain Reaction Reverse Transcription RNA, immune RNA I Seedlings Sterility, Reproductive SYBR Green I trizol

Quantitative mRNA Expression Analysis

Total mRNA was isolated from A20 cells and primary B lymphocytes using an RNeasy Mini Kit (Qiagen) following the manufacturer’s instructions. RNA concentrations were measured using a NanoDrop 2000 (Thermo Scientific) and 1 µg of DNAse I treated RNA was used with the High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems). cDNA was amplified on a QuantStudio 3 Real-Time PCR System (Applied Biosystems) using PowerUp SYBR Green Master Mix (Applied Biosystems). PCR amplification was performed by initial activation for 2 min at 50°C, followed by a 95°C 2 min melt step. The initial melt steps were then followed by 40 cycles of 95°C for 15 s, 60°C for 15 s, and 72°C for 30 s. Data were analyzed with the instrument software v1.3.1 (Applied Biosystems) and analysis of each target was carried out using the comparative Ct method.

Emrich S.M., Yoast R.E., Zhang X., Fike A.J., Wang Y.H., Bricker K.N., Tao A.Y., Xin P., Walter V., Johnson M.T., Pathak T., Straub A.C., Feske S., Rahman Z.S, & Trebak M. (2023). Orai3 and Orai1 mediate CRAC channel function and metabolic reprogramming in B cells. eLife, 12, e84708.

Full text: Click here

Publication 2023

B-Lymphocytes Cells Deoxyribonucleases DNA, Complementary Reverse Transcription RNA, immune RNA, Messenger RNA I SYBR Green I

Arabidopsis Transcriptome Profiling by RNA-seq

Total RNA was isolated from 12-day-old Arabidopsis seedlings using TRI Reagent (NRC), and DNA was removed by DNase I (Roche) treatment. PolyA RNA was then enriched from 1 μg of DNase I-treated RNA using Oligo d(T)25 Magnetic Beads (NEB S1419S), followed by RNA-seq libraries construction using the NEBNext Ultra Directional RNA Library Prep Kit for Illumina (NEB E7420). RNA libraries were sequenced on an Illumina NovaSeq 6000 platform (PE150 bp), and the sequencing data were analyzed using the pRNASeqTools v.0.8 pipeline. Briefly, raw reads were aligned to the Arabidopsis genome (TAIR 10) using STAR v2.7.6a [56 (link)] with parameters ‘—alignIntronMax 5000—outSAMmultNmax 1—outFilterMultimapNmax 50—outFilterMismatchNoverLmax 0.1’, and counted by featureCounts v2.0.0 [57 (link)]. Normalization was performed by calculating the FPKM (Fragments Per Kilobase Million) for each gene, and differential gene expression analysis was conducted by DESeq2 v1.30.0 with a fold change of 1.5 and adjusted P value < 0.05 as the parameters [55 (link)].

Xu Y., Zhang Y., Li Z., Soloria A.K., Potter S, & Chen X. (2023). The N-terminal extension of Arabidopsis ARGONAUTE 1 is essential for microRNA activities. PLOS Genetics, 19(3), e1010450.

Full text: Click here

Publication 2023

Arabidopsis Deoxyribonuclease I DNA Library Gene Expression Profiling Genes Genome Oligonucleotides RNA, immune RNA, Polyadenylated RNA-Seq RNA I Seedlings

Wastewater Virus Detection Protocol

Protocol full text hidden due to copyright restrictions

Open the protocol to access the free full text link

Publication 2023

Acids Filtration Hypersensitivity Magnesium Chloride RNA, immune RNA I Tissue, Membrane Virus

Top products related to «RNA I»

E z n a total rna kit 1 by Omega Bio-Tek

Sourced in United States, Gabon

The E.Z.N.A.® Total RNA Kit is a laboratory equipment product designed for the isolation and purification of total RNA from a variety of sample types. It utilizes a spin column-based technology to extract and purify high-quality RNA efficiently.

Sepasol rna 1 super g by Nacalai Tesque

Sourced in Japan, Spain, United States, Germany

Sepasol-RNA I Super G is a reagent used for the extraction and purification of RNA from various biological samples. It functions as a complete solution for the isolation of high-quality RNA, enabling efficient and reproducible results.

Total rna kit 1 by Omega Bio-Tek

Sourced in United States, China, Germany

The Total RNA Kit I from Omega Bio-Tek is a laboratory product designed for the isolation and purification of total RNA from various sample types. It provides a reliable and efficient method for extracting high-quality RNA for downstream applications such as gene expression analysis, real-time PCR, and RNA sequencing.

Trizol reagent by Thermo Fisher Scientific

Sourced in United States, China, Japan, Germany, United Kingdom, Canada, France, Italy, Australia, Spain, Switzerland, Netherlands, Belgium, Lithuania, Denmark, Singapore, New Zealand, India, Brazil, Argentina, Sweden, Norway, Austria, Poland, Finland, Israel, Hong Kong, Cameroon, Sao Tome and Principe, Macao, Taiwan, Province of China, Thailand

TRIzol reagent is a monophasic solution of phenol, guanidine isothiocyanate, and other proprietary components designed for the isolation of total RNA, DNA, and proteins from a variety of biological samples. The reagent maintains the integrity of the RNA while disrupting cells and dissolving cell components.

High capacity cdna reverse transcription kit by Thermo Fisher Scientific

Sourced in United States, Germany, United Kingdom, Japan, Lithuania, France, Italy, China, Spain, Canada, Switzerland, Poland, Australia, Belgium, Denmark, Sweden, Hungary, Austria, Ireland, Netherlands, Brazil, Macao, Israel, Singapore, Egypt, Morocco, Palestine, State of, Slovakia

The High-Capacity cDNA Reverse Transcription Kit is a laboratory tool used to convert RNA into complementary DNA (cDNA) molecules. It provides a reliable and efficient method for performing reverse transcription, a fundamental step in various molecular biology applications.

Primescript rt reagent kit by Takara Bio

Sourced in Japan, China, United States, France, Germany, Switzerland, Canada, Sweden, Italy, Puerto Rico, Singapore

The PrimeScript RT reagent kit is a reverse transcription kit designed for the synthesis of first-strand cDNA from RNA templates. The kit includes RNase-free reagents and enzymes necessary for the reverse transcription process.

Rneasy mini kit by Qiagen

Sourced in Germany, United States, United Kingdom, Netherlands, Spain, Japan, Canada, France, China, Australia, Italy, Switzerland, Sweden, Belgium, Denmark, India, Jamaica, Singapore, Poland, Lithuania, Brazil, New Zealand, Austria, Hong Kong, Portugal, Romania, Cameroon, Norway

The RNeasy Mini Kit is a laboratory equipment designed for the purification of total RNA from a variety of sample types, including animal cells, tissues, and other biological materials. The kit utilizes a silica-based membrane technology to selectively bind and isolate RNA molecules, allowing for efficient extraction and recovery of high-quality RNA.

Trizol by Thermo Fisher Scientific

Sourced in United States, Germany, China, Japan, United Kingdom, Canada, France, Italy, Australia, Spain, Switzerland, Belgium, Denmark, Netherlands, India, Ireland, Lithuania, Singapore, Sweden, Norway, Austria, Brazil, Argentina, Hungary, Sao Tome and Principe, New Zealand, Hong Kong, Cameroon, Philippines

TRIzol is a monophasic solution of phenol and guanidine isothiocyanate that is used for the isolation of total RNA from various biological samples. It is a reagent designed to facilitate the disruption of cells and the subsequent isolation of RNA.

Dnase 1 by Thermo Fisher Scientific

Sourced in United States, Germany, Lithuania, Canada, United Kingdom, China, Japan, Switzerland, Italy, France, Spain, Australia, Belgium, Denmark, Argentina, Sao Tome and Principe, Singapore, Poland, Finland, Austria, India, Netherlands, Ireland, Viet Nam

DNase I is an enzyme used in molecular biology laboratories to degrade DNA. It catalyzes the hydrolytic cleavage of phosphodiester linkages in the DNA backbone, effectively breaking down DNA molecules. This enzyme is commonly used to remove contaminating DNA from RNA preparations, allowing for more accurate downstream analysis of RNA.

Steponeplus real time pcr system by Thermo Fisher Scientific

Sourced in United States, Japan, China, Germany, United Kingdom, Switzerland, Canada, Singapore, Italy, France, Belgium, Denmark, Spain, Netherlands, Lithuania, Estonia, Sweden, Brazil, Australia, South Africa, Portugal, Morocco

The StepOnePlus Real-Time PCR System is a compact, flexible, and easy-to-use instrument designed for real-time PCR analysis. It can be used to detect and quantify nucleic acid sequences.

What is RNA interference (RNAi) and how does it work?

What are the different types or variations of RNA I?

What are some common challenges in optimizing RNA I experiments?

How can PubCompare.ai help with RNA I research?

PubCopmare.ai allows you to screen protocol literature more efficiently and leverage AI to pinpoint critical insights. The platform can help researchers identify the most effective protocols related to RNA I for their specific research goals. The AI-driven analysis can highlight key differences in protocol effectiveness, enabling you to choose the best option for reproducibility and accuracy in your RNA I studies.

What are some common applications of RNA I technology?

More about "RNA I"

RNA interference (RNAi) is a powerful biological process in which RNA molecules silence gene expression or translation by neutralizing targeted mRNA molecules.
This technology allows for the targeted silencing of specific genes, making it a invaluable tool in basic research and therapeutic applications.
PubCompare.ai leverages AI to help researchers optimize their RNAi experiments, providing access to the best protocols from published literature, preprints, and patents.
With automated comparisons, the platform identifies the most reproducible and accurate methods, elevating the quality and reliability of RNAi studies.
Researchers can use various RNA extraction kits, such as the E.Z.N.A.® Total RNA Kit I, Sepasol-RNA I Super G, Total RNA Kit I, and TRIzol reagent, to isolate high-quality RNA for their RNAi experiments.
Reverse transcription kits like the High-Capacity cDNA Reverse Transcription Kit and PrimeScript RT reagent kit can then be used to convert the extracted RNA into cDNA for downstream analysis.
The RNeasy Mini Kit and TRIzol can also be employed for RNA purification and extraction.
In addition to these tools, DNase I can be used to remove any contaminating DNA, while the StepOnePlus Real-Time PCR System can be utilized for quantitative analysis of gene expression.
By leveraging these resources and the insights provided by PubCompare.ai, researchers can optimize their RNAi experiments, leading to more reproducible and accurate results that advance our understanding of gene regulation and drive therapeutic development.