mRNA degradation is a crucial cellular process that regulates gene expression by controlling the stability and turnover of messenger RNA molecules.
This complex pathway involves various enzymes and regulatory factors that target mRNA for deadenylation, decapping, and eventual exonucleolytic digestion.
Understanding the mechanisms and dynamics of mRNA degradation is essential for studying cellular homeostasis, developmental regulation, and the dysregulation of gene expression in disease states.
Researchers can enhance the accuracy and reproducibility of their mRNA degradation studies by utilizing the AI-driven comparison and optimization capabilities of PubCompare.ai.
This innovative platform helps scientists easily locate relevant protocols from the literature, preprints, and patents, and identifies the best procedures and products for their research needs.
By improving the quality and consistency of mRNA degradation experiments, PubCompare.ai empowers researchers to advance our knowledge of this fundamental biological process.
Most cited protocols related to «MRNA Degradation»
To generate an in silico library for a single cell, we built a simulator that first selects G genes at random from a relative expression profile (Pbulk) derived from a bulk RNA-Seq experiment to represent the hypothetical relative abundance of a single-cell in cell lysate. These values are rescaled to proportions (i.e. summing to 1), or ρscaled.
These proportions are then used to parameterize a multinomial distribution from which T transcripts are drawn to obtain the transcripts in the library space where we also consider there is αi percentage of the RNA is degraded. Therefore, we have: To this pool of transcripts, a fixed number of spike-in transcripts are added, forming a mixture of simulated “endogenous” and “spike-in” mRNAs where the degradation of spike-in transcripts is represented by βi. Of these, θi percent are selected uniformly at random to simulate incomplete mRNA capture by the reverse transcription process. Finally, the abundances of these cDNAs relative to one another were used to parameterize another multinomial, from which Ri reads are sampled. The read counts are then used to calculate the relative abundance for the spike-in and the endogenous RNA. In this study, we systematically simulated the sc RNA-seq process obtained from bulk RNA-Seq measurements made in Trapnell and Cacchiarelli et al18 by varying the gene number G, capture rate θ, endogenous RNA degradation α, spike-in degradation β, total endogenous transcript count T and total number of reads R. Results based on simulation are shown in Supplemental Figure 4.
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.
TarBase5.0 contains data extracted from a total of 203 scientific papers resulting in 1333 entries describing a regulatory interaction between a miRNA and a target 3′ UTR (summarized in Table 1).
A list of all TarBase5.0 entries
Organism
Number of papers
Number of entries
Microarray data
pSILAC data
Homo sapiens
110
285
328
474
Mus musculus
28
105
13
–
D. melanogaster
23
77
–
–
C. elegans
18
14
–
–
Plants
21
30
–
–
Danio rerio
1
1
–
–
Rat
2
2
–
–
Total
203
514
341
474
The TarBase5.0 data set contains miRNA targets that tested either positive (induces target gene repression) or negative (no influence on target gene expression). For each experiment with a positive outcome the target site is described by the miRNA that binds it, the gene in which it occurs, the nature of the experiments that were conducted to test it, the sufficiency of the site to induce translational repression and/or cleavage, and the paper from which all these data is extracted. Additionally, for each miRNA and protein-coding gene, the database contains links to several other relevant and useful databases such as Ensembl (10 (link)), Hugo (11 (link)), UCSC genome browser (12 (link)) and SwissProt (13 (link)). There are a number of direct and indirect experimental procedures that have been developed to test a possible miRNA–mRNA interaction. The entries in Tarbase5.0 are classified into four categories: TRUE or FALSE in the cases where an assay provides direct experimental evidence, or MICROARRAY and/or pSILAC in the cases that present only indirect evidence from high-throughput techniques to measure miRNA-mediated global transcriptomic or proteomic changes. All of these approaches make use of technology for miRNA knock down or overexpression. To overexpress a miRNA, expression constructs can be engineered using the mature miRNA, the precursor (hairpin) miRNA, or the pri-miRNA sequence for transfection into in vitro or in vivo transformed cells. Also, silencing of a specific miRNA can be accomplished by introducing chemically modified oligonucleotides that are perfectly complimentary to the mature miRNA (antagomirs) (14 (link)). These methods for modifying miRNA expression allow for several types of follow-up techniques to quantify and interpret differences in target gene expression. Below we provide a more detailed description of each of the four categories: TRUE or FALSE: The most commonly used method for providing direct experimental evidence is the reporter gene assay. In its simplest form, an expression vector containing a reporter gene [i.e. Luciferase or Green Fluorescent Protein (GFP)] is first modified by cloning the predicted target 3′UTR downstream of the reporter gene, and then transfected into a cell line of interest in the absence and presence of the cognate miRNA. Despite the general utility of this approach to assay for 3′UTR-mediated effects on reporter protein expression, it is not informative for the precise location of the miRNA response element (MRE) or number of miRNA target sites in the 3′UTR. Integration of the reporter gene assay with site directed mutagenesis of the predicted MRE (and, further, restoring the complementarity of the miRNA–MRE interaction by mutating the mature miRNA sequence) yields a much more specific and direct result. To measure effects on reporter mRNA levels, the most commonly applied technique is quantitative RT-PCR (qRT-PCR). Measuring effects on both protein and mRNA levels can help provide information about the mode of miRNA-mediated silencing: mRNA translational repression or immediate RISC-mediated mRNA cleavage and degradation. A miRNA–MRE interaction is reported as TRUE or FALSE based on the results of the reporter gene assay. MICROARRAY and/or pSILAC: These high-throughput approaches measure global changes in the transcriptome (15 (link)) or proteome (8 (link),16 (link)) given the presence or absence of a miRNA. Despite their power for large-scale analysis, these techniques only provide indirect evidence about a miRNA's targets since it is not possible to distinguish between primary direct targets and secondary indirect targets. Other high-throughput methods like degradome sequencing (17 (link),18 (link)) are also immensely useful but only in the scenarios where a miRNA induces RISC-mediated mRNA cleavage. In order to facilitate user interaction, the query function is divided into several functionally related subgroups. The initial screen of the TarBase5.0 user interface allows users to query based on miRNA, gene and organism. For more advanced queries, the user can utilize the extended query options. In this case, the search menus are arranged into four functionally related groups. The first group contains the fields with information about the miRNA–target interaction: the validity of the interaction (field ‘Support Type’, either true or false), the function of the interaction which can be either translational repression or mRNA cleavage (field ‘DataType’), the sufficiency of a single target site to exert the specific function (field ‘S_S_S’) and the number of miRNA response elements present in the specific UTR (field ‘MRE’). The second group contains the fields that refer to the experimental methods that led to the reported result. The field ‘Direct Support’ refers to experimental procedures that provide direct evidence regarding the miRNA–target interaction (i.e. reporter gene assays) while ‘Indirect Support’ refers to experimental procedures that provide more global, system-wide miRNA-mediated effects (i.e. microarrays). The third group corresponds to biological properties of the miRNA or target gene: biological functions (field ‘Protein Type’), specific expression profiles (field ‘miRNA Expression’) or the physiological processes in which this interaction is involved (field ‘Event or Pathology’). The fourth and final group contains some general query features such as the scientific paper (searchable by Author or PMID). The results are presented in a similar format as the query fields. By default, the results screen (Figure 2) shows only the repression type, the miRNA identifier, the target gene identified by the HGNC symbol (if it is a human gene), the common gene name, the Refseq isoform id (particularly relevant in cases of gene variants or SNP haplotypes), the affected biological processes and the paper containing the information presented. Users can opt to view more detailed information by clicking on the ‘+’ box so that the expanded results view is opened (Figure 2). The additional information is divided into three categories: miRNA information, gene information and experimental conditions.
Example of a result screen for a TarBase query. The context-specific links to other resources are indicated by the blue arrows.
The ‘miRNA information’ category contains the properties of the specific miRNA such as the miRNA's sequence [extracted from miRBase (19 (link))], the number and sequences of the MREs, their locations within the gene's 3′UTR, and the affected tissues (extracted from the paper). The ‘Gene information’ category gathers mostly biological properties of the target gene like the protein type, Ensembl and SwissProt IDs and chromosome location, providing direct links to Ensembl, SwissProt and the UCSC browser respectively. Moreover, expression profiles and tumor involvement are also provided for human genes (information extracted from the Ensembl eGenetics database). Finally, the ‘Experimental conditions’ category provides the nature of the direct or indirect evidence for the miRNA–target gene interaction. The cell lines used to carry out the specific experiment are also presented in order to render the experimental conditions more complete and reproducible.
Papadopoulos G.L., Reczko M., Simossis V.A., Sethupathy P, & Hatzigeorgiou A.G. (2008). The database of experimentally supported targets: a functional update of TarBase. Nucleic Acids Research, 37(Database issue), D155-D158.
HepG2 cells with stably expressed shRNAs against IGF2BPs or shNS were seeded into 6-well plates to get 50% confluency after 24 hours. Cells were treated with 5 μg/ml actinomycin D and collected at indicated time points. The total RNA was extracted by miRNeasy Kit (Qiagen) and analyzed by RT-PCR and RNA-seq. For RNA sequencing, equal amount of ERCC RNA spike-in control (Thermo Fisher Scientific) was added to the total RNA samples as internal controls before library construction. Sequencing libraries were prepared using NEBNext Ultra Directional RNA Library Prep Kit. RNA stability profiling was generated from two biological replicates. The turnover rate and half-life of mRNA was estimated according to previously published paper44 . Since actinomycin D treatment results in transcription stalling, the change of mRNA concentration at a given time (dC/dt) is proportional to the constant of mRNA decay (Kdecay) and mRNA concentration (C), leading to following equation:
Thus the mRNA degradation rate Kdecay was estimated by:
To calculate the mRNA half-life (t1/2), when 50% of mRNA is decayed (ie. C/C0=1/2), the equation was:
From where:
Huang H., Weng H., Sun W., Qin X., Shi H., Wu H., Zhao B.S., Mesquita A., Liu C., Yuan C.L., Hu Y.C., Hüttelmaier S., Skibbe J.R., Su R., Deng X., Dong L., Sun M., Li C., Nachtergaele S., Wang Y., Hu C., Ferchen K., Greis K.D., Jiang X., Wei M., Qu L., Guan J.L., He C., Yang J, & Chen J. (2018). Recognition of RNA N6-methyladenosine by IGF2BP Proteins Enhances mRNA Stability and Translation. Nature cell biology, 20(3), 285-295.
General pre-processing of reads: All samples were sequenced by illumine Hiseq2000 with single end 100-bp read length. For libraries that generated from small RNA (PAR-CLIP and ribosome profiling), the adapters was trimmed by using FASTX-Toolkit34 (link). The deep sequencing data were mapped to Human genome version hg19 by Tophat version 2.035 (link) without any gaps and allowed for at most two mismatches. RIP and Ribosome profiling were analyzed by DESeq36 (link) to generate RPKM (reads per kilobase, per million reads). mRNA lifetime data were analyzed by Cuffdiff version 2.037 (link) to calculate RPKM. Data analysis for each experiment: (1) for m6A profiling, the m6A-enriched regions in each m6A-IP sample were extracted by using the model-based analysis of ChIP-seq (MACS) peak-calling algorithm38 (link), with the corresponding m6A-Input sample serving as the input control. For each library, the enriched peaks with p < 1e-5 were used for further analysis; (2) for RIP, enrichment fold was calculated as log2(IP/input); (3) PAR-CLIP data were analyzed by PARalyzerv1.1 with default settings39 (link); (4) for ribosome profiling, only genes with RPKM>1 were used for analysis and the change fold was calculated as log2(siYTHDF2/siControl); (5) for mRNA lifetime profiling: RKPM were converted to attomole by linear-fitting of the RNA spike-in. The degradation rate of RNA k was estimated by where t is transcription inhibition time (h), At and A0 represent mRNA quantity (attomole) at time t and time 0. Two k values were calculated: time 3 h versus time 0 h, and time 6 h versus time 0 h. The final lifetime was calculated by using the average of k3h and k6h. Integrative data analysis and statistics: PAR-CLIP targets were defined as reproducible gene targets among three biological replicates (3,251). RIP targets (2528) were genes with log2(IP/input) >1. The overlap of PAR-CLIP and RIP targets were defined as CLIP+IP targets (1,277). And non-targets (3,905) should meet the conditions: (1) complementary set of PAR-CLIP targets; (2) RIP enrichment fold <0. For the comparison of PAR-CLIP and m6A peaks, at least 1 bp overlap was applied as the criteria of overlap peaks. Two biological replicates were conducted for ribosome profiling and mRNA lifetime profiling, respectively. And genes with sufficient expression level (RPKM>1) were subjected to further analysis. The change fold that used in the main text is the average of the two log2(siYTHDF2/siControl) values. Nonparametric Mann-Whitney U test (Wilcoxon rank-sum test, two sided, significance level = 0.05) was applied in ribosome profiling data analysis as previous reported22 (link). For the analysis of cell viability (Extended Data Fig.8e), RPF of ribosome profiling data were analyzed by Cuffidff version 2.0 for differential expression test, and the genes that differentially expressed (p<0.05) were subjected to Ingeuity Pathway Analysis (IPA, Ingenuity System). RPF was chosen since it may better reflect the translation status of each gene. Data accession: All the raw data and processed files have been deposited in the Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo). m6A profiling data are accessible under GSE 46705 (GSM1135030 and GSM1135031 are input samples while GSM1135032 and GSM1135033 are IP samples). All other data are accessible under GSE 49339.
Wang X., Lu Z., Gomez A., Hon G.C., Yue Y., Han D., Fu Y., Parisien M., Dai Q., Jia G., Ren B., Pan T, & He C. (2013). m6A-dependent regulation of messenger RNA stability. Nature, 505(7481), 117-120.
Stochastic simulations of the stochastic mRNA and protein model described in Protocol S1 were performed by implementing Gillespie's Direct method [30 ] in Matlab (The Mathworks). The parameters governing the mRNA dynamics were taken from those obtained from cell line E-YFP-M1-7x grown under conditions of no doxycycline: rate of gene activation (λ/δ) = 2.44, inactivation (γ/δ) = 2.49, and transcription (μ/δ) = 910. Since we are only interested in steady-state solutions, the mRNA degradation rate δ was chosen as a factor by which all the other rates were scaled. The translation rate μp/δ was set to 100 and three values of the protein degradation rate δp/δ were investigated: 0.02, 0.16, and 2.56. In Figure 7C, the values of δ and δp are reported in physical units for clarity.
Raj A., Peskin C.S., Tranchina D., Vargas D.Y, & Tyagi S. (2006). Stochastic mRNA Synthesis in Mammalian Cells. PLoS Biology, 4(10), e309.
We compare three alternative models for temporal maternal mRNA kinetics. The simplest (null) model assumes no maternal expression of a gene, and has no parameters: The simpler alternative model assumes very low degradation of maternal mRNA, and has only one parameter: initial expression level ( ), The second alternative model, describes dynamic changes in maternal mRNA levels using an exponential decay with 3 parameters: a constant decay rate ( ), initial expression level ( ), and degradation onset time ( ): Solving this function analytically we get: And we solve it looking for the log expression rate:
Fishman L., Modak A., Nechooshtan G., Razin T., Erhard F., Regev A., Farrell J.A, & Rabani M. (2024). Cell-type-specific mRNA transcription and degradation kinetics in zebrafish embryogenesis from metabolically labeled single-cell RNA-seq. Nature Communications, 15, 3104.
To calculate codon optimality, zebrafish CDS sequences were downloaded from Ensembl Biomart and filtered to include only sequences starting with a start codon, ending with a stop codon, and sequence lengths being multiples of three. Codon optimality was calculated using icodon R package34 (link), keeping only the highest value per gene to keep only a single value per gene. Transcript translation efficiencies at different zebrafish developmental stages were taken from ref. 35 (link). A gene was considered m6A modified if evidence for m6A modification was measured by ref. 36 (link) in either m6A-seq or m6A-CLIP-seq data. A gene was considered m5C modified if it contained a location with an m5C level >= 0.25, as measured by ref. 37 (link). Genes’ polyA lengths were taken as the mean length measured by ref. 33 (link). Independent maternal genes’ degradation rates (for maternal-only genes) were calculated from RNA sequencing measured by refs. 8 (link),14 (link),36 (link),51 (link)–53 (link). Only measurements until 6 hpf were used for the calculation.
Fishman L., Modak A., Nechooshtan G., Razin T., Erhard F., Regev A., Farrell J.A, & Rabani M. (2024). Cell-type-specific mRNA transcription and degradation kinetics in zebrafish embryogenesis from metabolically labeled single-cell RNA-seq. Nature Communications, 15, 3104.
To adapt the pre-trained m6A-BERT for predicting mRNA degradation, we employed a binary classification layer on top of the pre-trained model and fine-tuned it using the training dataset. The output of the classification layer is “1” if the input site sequence is predicted to regulate mRNA degradation” and ‘0’, otherwise. To fine-tune the model, we utilized a data splitting strategy, where 70% of the data was designated as the training set, 20% as the testing set, and the remaining 10% as the validation set. We fine-tuned the model with 100 epochs and used the model with the highest AUC on the validation set for further analysis. The learning rate for fine-tuning was 1e-5. Since m6A-BERT with K = 3, 4, 5, 6 exhibited closely similar performances with minor fluctuations, we consistently reported results of K-mer = 3 in all our experiments, as it consistently yielded the best performance.
Zhang T.H., Jo S., Zhang M., Wang K., Gao S.J, & Huang Y. (2024). Understanding YTHDF2-mediated mRNA Degradation By m6A-BERT-Deg. ArXiv.
To adapt the pre-trained m6A-BERT for predicting mRNA degradation, we employed a binary classification layer on top of the pre-trained model and fine-tuned it using the training dataset. The output of the classification layer is ‘1’ if the input site sequence is predicted to regulate mRNA degradation and ‘0’, otherwise. To fine-tune the model, we utilized a data splitting strategy, where 70% of the data was designated as the training set, 20% as the testing set, and the remaining 10% as the validation set. We fine-tuned the model with 100 epochs and used the model with the highest AUC on the validation set for further analysis. The learning rate for fine-tuning was 1e-5. Since m6A-BERT with K = 3, 4, 5, 6 exhibited closely similar performances with minor fluctuations, we consistently reported results of K-mer = 3 in all our experiments, as it consistently yielded the best performance.
Zhang T.H., Jo S., Zhang M., Wang K., Gao S.J, & Huang Y. (2024). Understanding YTHDF2-mediated mRNA degradation by m6A-BERT-Deg. Briefings in Bioinformatics, 25(3), bbae170.
A two-state mRNA model comprising of reactions including transcription, mRNA degradation, translation and protein degradation was developed to assess the mechanism of mRNA and protein regulation during cooler temperatures [30] (link). The system of ODEs defining the model are as follows: where mRNA denotes mRNA activity and Protein depicts protein activity. k 1 to k 4 refer to the transcription rate, mRNA degradation rate, translation rate and protein degradation rate, respectively. For this model, we assume that both model states are observed and thus measured and all parameters k 1 to k 4 are unknown. The initial conditions are assumed to be mRNA(0) = 2.5 and Protein(0) = 6.5 , both with arbitrary units (a.u.).
Wanika L., Egan J.R., Swaminathan N., Duran-Villalobos C.A., Branke J., Goldrick S, & Chappell M. (2024). Structural and practical identifiability analysis in bioengineering: a beginner's guide. Journal of biological engineering, 18(1).
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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.
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The Agilent 2100 Bioanalyzer is a lab instrument that provides automated analysis of DNA, RNA, and protein samples. It uses microfluidic technology to separate and detect these biomolecules with high sensitivity and resolution.
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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.
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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.
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Actinomycin D is a laboratory-grade chemical compound used in various research applications. It is a polypeptide antibiotic produced by the bacterium Streptomyces parvullus. Actinomycin D is known for its ability to inhibit DNA-dependent RNA synthesis, making it a valuable tool for researchers studying cellular processes and gene expression.
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The NextSeq 500 is a high-throughput sequencing system designed for a wide range of applications, including gene expression analysis, targeted resequencing, and small RNA discovery. The system utilizes reversible terminator-based sequencing technology to generate high-quality, accurate DNA sequence data.
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The MEGACLEAR™ Kit is a nucleic acid purification system designed for the efficient extraction and purification of DNA, RNA, and other nucleic acids from a variety of sample types. The kit utilizes a simple, spin-column-based protocol to facilitate rapid and reliable purification of nucleic acids.
The QuantSeq 3' mRNA-Seq Library Prep Kit is a library preparation kit designed for 3' mRNA sequencing. It enables the generation of sequencing-ready libraries from total RNA samples.
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Lipofectamine 2000 is a cationic lipid-based transfection reagent designed for efficient and reliable delivery of nucleic acids, such as plasmid DNA and small interfering RNA (siRNA), into a wide range of eukaryotic cell types. It facilitates the formation of complexes between the nucleic acid and the lipid components, which can then be introduced into cells to enable gene expression or gene silencing studies.
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The NanoDrop is a spectrophotometer designed for the quantification and analysis of small volume samples. It measures the absorbance of a sample and provides accurate results for DNA, RNA, and protein concentration measurements.
The mRNA degradation process involves several crucial steps: 1) Deadenylation, where the poly(A) tail is removed from the mRNA; 2) Decapping, where the 5' cap structure is cleaved off; and 3) Exonucleolytic digestion, where the mRNA is progressively degraded from both the 5' and 3' ends. This complex pathway is regulated by various enzymes and factors that target the mRNA for turnover, ensuring proper control of gene expression in the cell.
Understanding the mechanisms and dynamics of mRNA degradation is essential for studying cellular homeostasis, developmental regulation, and the dysregulation of gene expression in disease states. Researchers need to have a solid grasp of mRNA degradation pathways to accurately interpret their findings and develop better models of gene regulation.
PubCompare.ai allows researchers to screen protocol literature more efficeintly and leverage AI to pinpoint critical insights. The platform's intelligent analysis tools can help researchers identify the most effective protocols related to mRNA degradation for their specific research goals. By highlighting key differences in protocol effectiveness, PubCompare.ai enables researchers to choose the best option for ensuring reproducibility and accuracy in their mRNA degradation studies.
Some common challenges in studying mRNA degradation include ensuring experimental consistency, accurately measuring mRNA turnover rates, and differentiating the effects of various regulatory factors and enzymes involved in the process. Researchers must also be mindful of potential technical artifacts or biases that could arise from the methods used to analyze mRNA degradation.
More about "MRNA Degradation"
mRNA decay, mRNA turnover, gene expression regulation, cellular homeostasis, developmental processes, disease states, mRNA stability, mRNA deadenylation, mRNA decapping, exonucleolytic digestion, mRNA degradation mechanisms, mRNA degradation dynamics, mRNA degradation protocols, mRNA degradation research, TRIzol reagent, Agilent 2100 Bioanalyzer, RNeasy Mini Kit, Actinomycin D, NextSeq 500, MEGACLEAR™ Kit, QuantSeq 3′ mRNA-Seq Library Prep Kit, Lipofectamine 2000, NanoDrop.
Messanger RNA (mRNA) degradation is a critical cellular process that regulates gene expression by controlling the stability and turnover of mRNA molecules.
This complex pathway involves various enzymes and regulatory factors that target mRNA for deadenylation, decapping, and eventual exonucleolytic digestion.
Understanding the mechanisms and dynamics of mRNA degradation is essential for studying cellular homeostasis, developmental regulation, and the dysregulation of gene expression in disease states.
Researchers can enhance the accuracy and reproducibility of their mRNA degradation studies by utilizing the AI-driven comparison and optimization capabilities of PubCompare.ai.
This innovative platform helps scientists easily locate relevant protocols from the literature, preprints, and patents, and identifies the best procedures and products for their research needs.
By improving the quality and consistency of mRNA degradation experiments, PubCompare.ai empowers researchers to advance our knowledge of this fundamental biological process.
Common techniques used in mRNA degradation research include TRIzol reagent for RNA extraction, Agilent 2100 Bioanalyzer for RNA quality assessment, RNeasy Mini Kit for RNA purification, Actinomycin D for transcription inhibition, NextSeq 500 for RNA sequencing, MEGACLEAR™ Kit for RNA clean-up, QuantSeq 3′ mRNA-Seq Library Prep Kit for 3' mRNA-seq, and Lipofectamine 2000 for transfection.
The NanoDrop spectrophotometer is also frequently used to quantify and assess the purity of RNA samples.
