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Untranslated Regions

Untranslated Regions are segments of mRNA or DNA that do not encode protein sequences.
These regions, located at the 5' and 3' ends of the mRNA transcript, play crucial roles in regulating gene expression, mRNA stability, and translation efficiency.
They contain important regulatory sequences and motifs that interact with cellular machinery to control various aspects of gene function.
Understanding the structure and function of Untranslated Regions is essential for studying gene regulation and developing targeted therapeutic interventions.
PubCompare.ai's AI-powered platform can help researchers effeciently locate and analyze the most relevant literature on Untranslated Regions, enabling them to optimize their research protocols and enhance the reproducibility of their findings.

Most cited protocols related to «Untranslated Regions»

1
HISAT-genotype: Identifying Allele Sequences
Because allele sequences may only be partially available (e.g., exons
only), HISAT-genotype first identifies two alleles based on the sequences
commonly available for all alleles, e.g. exons. For example, the IMGT/HLA
database includes many sequences for some key exons of HLA genes, but it
contains far fewer complete sequences comprising all exons, introns, and UTRs of
the genes. So far 3,644 alleles have been classified for HLA-A. Although all
alleles of HLA-A have known sequences for exons 2 and 3, only 383 alleles have
full-length sequences available. The sequences for the remaining 3,261 alleles
include either all 8 exons or a subset of them. HLA-B has 4,454 alleles, of
which 416 have full sequences available. HLA-C has 3,290 alleles, with only 590
fully sequenced, HLA-DQA1 has 76 alleles with 53 fully sequenced, HLA-DQB1 has
978 alleles with 69 fully sequenced, and HLA-DRB1 has 1,972 alleles, with only
43 fully sequenced. During this step, HISAT-genotype first chooses
representative alleles from groups of alleles that have the same exon sequences.
Next it identifies alleles in the representative alleles that are highly likely
to be present in a sequenced sample. Then the other alleles from the groups with
the same exons as the representatives are selected for assessment during the
next step. Second, HISAT-genotype further identifies candidate alleles based on
both exons and introns. HISAT-genotype applies the following statistical model
in each of the two steps to find maximum likelihood estimates of abundance
through an Expectation-Maximization (EM) algorithm39 . We previously implemented an EM
solution in our Centrifuge system40 , and we used a similar algorithm in HISAT-genotype, with
modifications to the variable definitions as follows.
Kim D., Paggi J.M., Park C., Bennett C, & Salzberg S.L. (2019). Graph-Based Genome Alignment and Genotyping with HISAT2 and HISAT-genotype. Nature biotechnology, 37(8), 907-915.
Publication 2019
Alleles Exons Genes Genotype HLA-B Antigens HLA-C Antigens HLA-DQA1 HLA-DQB1 antigen HLA-DRB1 Antigen Introns Untranslated Regions
2
Genetic Architecture of Major Depression
Genotyping procedures can be found in the primary reports for each cohort (summarized in Supplementary Table 3). Individual genotype data for all PGC29 samples, GERA, and iPSYCH were processed using the PGC “ricopili” pipeline (URLs) for standardized quality control, imputation, and analysis19 (link). The cohorts from deCODE, Generation Scotland, UK Biobank, and 23andMeD were processed by the collaborating research teams using comparable procedures. SNPs and insertion-deletion polymorphisms were imputed using the 1000 Genomes Project multi-ancestry reference panel (URLs)86 (link). More detailed information on sample QC is provided in the Supplementary Note.
Linkage disequilibrium (LD) score regression (LDSC)22 (link),24 (link) was used to estimate
hSNP2 from GWA summary statistics. Estimates of
hSNP2 on the liability scale depend on the assumed lifetime prevalence of MDD in the population (K), and we assumed K=0.15 but also evaluated a range of estimates of K to explore sensitivity including 95% confidence intervals (Supplementary Fig. 1). LDSC bivariate genetic correlations attributable to genome-wide SNPs (rg) were estimated across all MDD and major depression cohorts and between the full meta-analyzed cohort and other traits and disorders.
LDSC was also used to partition
hSNP2 by genomic features24 (link),46 (link). We tested for enrichment of
hSNP2 based on genomic annotations partitioning
hSNP2 proportional to bp length represented by each annotation. We used the “baseline model” which consists of 53 functional categories. The categories are fully described elsewhere46 (link), and included conserved regions47 (link), USCC gene models (exons, introns, promoters, UTRs), and functional genomic annotations constructed using data from ENCODE 87 (link) and the Roadmap Epigenomics Consortium88 (link). We complemented these annotations by adding introgressed regions from the Neanderthal genome in European populations89 (link) and open chromatin regions from the brain dorsolateral prefrontal cortex. The open chromatin regions were obtained from an ATAC-seq experiment performed in 288 samples (N=135 controls, N=137 schizophrenia, N=10 bipolar, and N=6 affective disorder)90 . Peaks called with MACS91 (link) (1% FDR) were retained if their coordinates overlapped in at least two samples. The peaks were re-centered and set to a fixed width of 300bp using the diffbind R package92 (link). To prevent upward bias in heritability enrichment estimation, we added two categories created by expanding both the Neanderthal introgressed regions and open chromatin regions by 250bp on each side.
We used LDSC to estimate rg between major depression and a range of other disorders, diseases, and human traits22 (link). The intent of these comparisons was to evaluate the extent of shared common variant genetic architectures in order to suggest hypotheses about the fundamental genetic basis of major depression (given its extensive comorbidity with psychiatric and medical conditions and its association with anthropometric and other risk factors). Subject overlap of itself does not bias rg. These rg are mostly based on studies of independent subjects and the estimates should be unbiased by confounding of genetic and non-genetic effects (except if there is genotype by environment correlation). When GWA studies include overlapping samples, rg remains unbiased but the intercept of the LDSC regression is an estimate of the correlation between association statistics attributable to sample overlap. These calculations were done using the internal PGC GWA library and with LD-Hub (URLs)60 (link).
Wray N.R., Ripke S., Mattheisen M., Trzaskowski M., Byrne E.M., Abdellaoui A., Adams M.J., Agerbo E., Air T.M., Andlauer T.F., Bacanu S.A., Bækvad-Hansen M., Beekman A.T., Bigdeli T.B., Binder E.B., Blackwood D.H., Bryois J., Buttenschøn H.N., Bybjerg-Grauholm J., Cai N., Castelao E., Christensen J.H., Clarke T.K., Coleman J.R., Colodro-Conde L., Couvy-Duchesne B., Craddock N., Crawford G.E., Crowley C.A., Dashti H.S., Davies G., Deary I.J., Degenhardt F., Derks E.M., Direk N., Dolan C.V., Dunn E.C., Eley T.C., Eriksson N., Escott-Price V., Kiadeh F.F., Finucane H.K., Forstner A.J., Frank J., Gaspar H.A., Gill M., Giusti-Rodríguez P., Goes F.S., Gordon S.D., Grove J., Hall L.S., Hannon E., Hansen C.S., Hansen T.F., Herms S., Hickie I.B., Hoffmann P., Homuth G., Horn C., Hottenga J.J., Hougaard D.M., Hu M., Hyde C.L., Ising M., Jansen R., Jin F., Jorgenson E., Knowles J.A., Kohane I.S., Kraft J., Kretzschmar W.W., Krogh J., Kutalik Z., Lane J.M., Li Y., Li Y., Lind P.A., Liu X., Lu L., MacIntyre D.J., MacKinnon D.F., Maier R.M., Maier W., Marchini J., Mbarek H., McGrath P., McGuffin P., Medland S.E., Mehta D., Middeldorp C.M., Mihailov E., Milaneschi Y., Milani L., Mill J., Mondimore F.M., Montgomery G.W., Mostafavi S., Mullins N., Nauck M., Ng B., Nivard M.G., Nyholt D.R., O’Reilly P.F., Oskarsson H., Owen M.J., Painter J.N., Pedersen C.B., Pedersen M.G., Peterson R.E., Pettersson E., Peyrot W.J., Pistis G., Posthuma D., Purcell S.M., Quiroz J.A., Qvist P., Rice J.P., Riley B.P., Rivera M., Mirza S.S., Saxena R., Schoevers R., Schulte E.C., Shen L., Shi J., Shyn S.I., Sigurdsson E., Sinnamon G.C., Smit J.H., Smith D.J., Stefansson H., Steinberg S., Stockmeier C.A., Streit F., Strohmaier J., Tansey K.E., Teismann H., Teumer A., Thompson W., Thomson P.A., Thorgeirsson T.E., Tian C., Traylor M., Treutlein J., Trubetskoy V., Uitterlinden A.G., Umbricht D., Van der Auwera S., van Hemert A.M., Viktorin A., Visscher P.M., Wang Y., Webb B.T., Weinsheimer S.M., Wellmann J., Willemsen G., Witt S.H., Wu Y., Xi H.S., Yang J., Zhang F., Arolt V., Baune B.T., Berger K., Boomsma D.I., Cichon S., Dannlowski U., de Geus E., DePaulo J.R., Domenici E., Domschke K., Esko T., Grabe H.J., Hamilton S.P., Hayward C., Heath A.C., Hinds D.A., Kendler K.S., Kloiber S., Lewis G., Li Q.S., Lucae S., Madden P.A., Magnusson P.K., Martin N.G., McIntosh A.M., Metspalu A., Mors O., Mortensen P.B., Müller-Myhsok B., Nordentoft M., Nöthen M.M., O’Donovan M.C., Paciga S.A., Pedersen N.L., Penninx B.W., Perlis R.H., Porteous D.J., Potash J.B., Preisig M., Rietschel M., Schaefer C., Schulze T.G., Smoller J.W., Stefansson K., Tiemeier H., Uher R., Völzke H., Weissman M.M., Werge T., Winslow A.R., Lewis C.M., Levinson D.F., Breen G., Børglum A.D, & Sullivan P.F. (2018). Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nature genetics, 50(5), 668-681.
Publication 2018
ATAC-Seq Brain Chromatin DNA Library Dorsolateral Prefrontal Cortex Europeans Exons Genetic Diversity Genetic Polymorphism Genome Genome-Wide Association Study Genotype Genotyping Techniques Homo sapiens Hypersensitivity INDEL Mutation Introns Mood Disorders Neanderthals Reproduction Schizophrenia Single Nucleotide Polymorphism Unipolar Depression Untranslated Regions
3
Randomized Control Sequences for miRNA Analysis
Control sequences for the randomized experiment were constructed by assembling 100 sets of 73 miRNAs each generated by random shuffling of each D. melanogaster miRNA. Each of these sets of 73 randomized miRNAs was independently searched against all D. melanogaster and D. pseudoobscura 3' UTRs as in the reference experiment. Results and counts were then averaged over all 100 random sets, and were compared with the results of the actual miRNA scan. For the functional analysis, GO classes for known D. melanogaster genes were obtained from FlyBase and conserved hits for the real and random miRNAs for each class are counted. The Z-scores are generated from the actual miRNA counts, averaged random miRNA counts and their standard deviations.
Note that recent work by Stark et al. [85 ] and also by Rajewski and Socci [86 ] addresses similar issues to those described in this work.
Enright A.J., John B., Gaul U., Tuschl T., Sander C, & Marks D.S. (2003). MicroRNA targets in Drosophila. Genome Biology, 5(1), R1.
Publication 2003
Childbirth Classes MicroRNAs Radionuclide Imaging Untranslated Regions
4
Quantitative miRNA Target Site Identification and Repression Modeling
AGO2–miRNA complexes were generated by adding synthetic miRNA duplexes to lysate from cells that over-expressed recombinant AGO2, and then these complexes were purified on the basis of affinity to the miRNA seed. RNA libraries were generated by in vitro transcription of synthetic DNA templates. For AGO-RBNS, purified AGO2–miRNA complex was incubated with a large excess of library molecules, and after reaching binding equilibrium, library molecules bound to AGO2–miRNA complex were isolated and prepared for high-throughput sequencing. Examination of k-mers enriched within the bound library sequences identified miRNA target sites, and relative KD values for each of these sites were simultaneously determined by maximum likelihood estimation, fitting to AGO-RBNS results obtained over a 100-fold range in AGO2–miRNA concentration.
Intracellular miRNA-mediated repression was measured by performing RNA-seq on HeLa cells that had been transfected with a synthetic miRNA duplex. For sites that were sufficiently abundant in endogenous 3′ UTRs, efficacy was measured on the basis of their influence on levels of endogenous mRNAs of HeLa cells. Site efficacy was also evaluated using massively parallel reporter assays, which provided information for the rare sites as well as the more abundant ones. The biochemical and biochemical+ models of miRNA-mediated repression were constructed and fit using the measured KD values and the repression of endogenous mRNAs was observed after transfecting miRNAs into HeLa cells. The CNN was built using TensorFlow, trained using the measured KD values and the repression observed in the HeLa transfection experiments, and tested on the repression of endogenous mRNAs observed after transfecting miRNAs into HEK293T cells. Results were also tested on external datasets examining either intracellular binding of miRNAs by CLIP-seq or repression of endogenous mRNAs after miRNAs had been transfected, knocked down, or knocked out. The details of each of these methods are described in the supplementary materials.
McGeary S.E., Lin K.S., Shi C.Y., Pham T.M., Bisaria N., Kelley G.M, & Bartel D.P. (2019). The biochemical basis of microRNA targeting efficacy. Science (New York, N.Y.), 366(6472), eaav1741.
Publication 2019
Biological Assay cDNA Library Cells Cross-Linking and Immunoprecipitation Followed by Deep Sequencing EIF2C2 protein, human GPER protein, human HeLa Cells MicroRNAs Protoplasm Repression, Psychology RNA, Messenger RNA-Seq Transcription, Genetic Transfection Untranslated Regions
5
Phylogenetic Analysis of SARS-CoV-2 Genomes
Whole genome sequences were downloaded from GISAID (full table of GISAID acknowledgements hosted here: https://cov-lineages.org/gisaid_acknowledgements.html, last accessed: 29 June 2021). Using the tools available at grapevine (https://github.com/COG-UK/grapevine), these sequences were mapped against the canonical SARS-CoV-2 reference genome (Genbank IDNC_045512.2) using minimap2 v2.17 (Li 2018 (link)). Genome sequences were trimmed to the region defined by positions 265–29,674, which correspond to the untranslated regions (UTRs), and the missing 5ʹ and 3ʹ UTRs subsequently masked as N’s. Early data releases were based on complete trees containing all sequences, constructed with IQ-TREE v1.6.2, using ultrafast bootstrapping (Minh et al. 2020 (link)). However, as the number of sequences increased, an ‘allocate-and-graft’ method was adopted. This involved using the previous list of designated sequences to provisionally allocate new sequences to the most likely major viral lineage (A, B, B.1 and B.1.1, and later B.1.1.7 and B.1.177). A separate alignment for each of these major lineages was constructed and a tree for each was estimated using FastTree (Price, Dehal, and Arkin 2010 (link)), with a representative from the basal polytomy of that lineage as an outgroup. The lineage trees were then grafted together to construct the global phylogeny. More recently, this potential circularity during sequence designation has been avoided by inferring a large maximum parsimony tree de novo using FastTree (Price, Dehal, and Arkin 2010 (link)). At time of writing, we continue to estimate large maximum parsimony trees as a guide and for specific lineage cases build smaller maximum likelihood trees that include the diversity of interest using IQ-TREE v2.0 (with -blmin 0.0000000001 -m GTR+G -bb 1000 and all other parameters as default; Minh et al. 2020 (link)).
O’Toole Á., Scher E., Underwood A., Jackson B., Hill V., McCrone J.T., Colquhoun R., Ruis C., Abu-Dahab K., Taylor B., Yeats C., du Plessis L., Maloney D., Medd N., Attwood S.W., Aanensen D.M., Holmes E.C., Pybus O.G, & Rambaut A. (2021). Assignment of epidemiological lineages in an emerging pandemic using the pangolin tool. Virus Evolution, 7(2), veab064.
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Publication 2021
Genome Patient Discharge SARS-CoV-2 Trees Untranslated Regions

Most recents protocols related to «Untranslated Regions»

1
Dual-Luciferase Assay for miR-382 Binding
We first synthesized wild-type and mutant sequences of the SLIT2 3′UTR (untranslated region) containing the miR-382 binding site. These sequences were cloned into the dual-luciferase vector system pmirGLO (Promega, USA). For the dual-luciferase reporter assay, cells were transfected with the WT- or MUT-SLIT2 luciferase reporter plasmid system together with the indicated components. Cells were cultured for another 48 h and collected. A luciferase assay was then carried out using a dual-luciferase reporter assay system (Promega, USA). Experiments were performed according to the manufacturers’ instructions.
Su H., Yang Y., Lv W., Li X, & Zhao B. (2023). Bone marrow mesenchymal stem cell-derived exosomal microRNA-382 promotes osteogenesis in osteoblast via regulation of SLIT2. Journal of Orthopaedic Surgery and Research, 18, 185.
Full text: Click here
Publication 2023
Binding Sites Biological Assay Cells Cloning Vectors Luciferases Plasmids Promega Untranslated Regions
2
Comprehensive Characterization of Genomic Insertions
Insertion events are known to be caused by various mechanisms and have various consequences [26 (link)]. To characterize and investigate the origins of the detected insertions, we decomposed them into TRs, TEs, tandem duplications (TDs), satellite sequences, dispersed duplications, processed pseudogenes, alternative sequences, “deletions” in GRCh38, and nuclear mitochondrial DNA sequences (NUMTs).
We first applied Tandem Repeats Finder (TRF) [27 ] to all inserted sequences and defined TRs as having (1) element lengths < 50 bp and (2) covering more than 50% of an inserted sequence. After filtering TRs, we identified TEs using RepeatMasker [28 ] if (1) an inserted sequence covered a TE > 50%, (2) the inserted sequence was covered by the TE > 50% (reciprocal overlap), and (3) the total substitutions and indels were < 50% (matching condition).
Previous studies have reported that TDs are understudied but widespread [26 (link), 29 (link)]. After detecting TRs and TEs, we manually reviewed the remaining insertions and found that they contained TDs derived from non-repetitive regions in the reference. We considered these insertions as TDs. To identify this class of insertions, we aligned all insertions except TRs to GRCh38 using BLAT [30 (link)]. We then collected insertions mapped to original breakpoints within 5 bp with > 90% in BLAT identity and defined them as TDs. In this process, missing TRs with long repeat elements were found. Therefore, they were added to the TR callset if (1) an inserted sequence aligned within 500 bp from the insertion breakpoint and (2) the ratio of the total number of matching bases to the insertion length was > 0.5.
To understand the remaining insertions, we manually checked their features by aligning them to the reference using BLAT [30 (link)]. We identified insertions that were aligned from end to end to different chromosomal regions with high identity (> 90%). We defined these insertions as dispersed duplications. Next, we detected insertions aligned to a series of exons and untranslated regions (UTRs) of coding genes with high identity (> 90%) and classified them as processed pseudogenes. We also found other insertions aligned to the alternative sequences (e.g., “alt” or “fix” sequences) on BLAT with high identity (> 90%). We classified them as alternative sequences. Some of the insertions left at this point were thought to have arisen by deletion events in GRCh38 because they were securely aligned to the chimpanzee reference genome (panTro6), although they were classified as insertions when compared with GRCh38 [3 (link)]. We aligned the remaining insertions to the panTro6 assembly and categorized the insertions that lifted over panTro6 with high accuracy (> 90%) within 100 bp of the inserted position on GRCh38 as "deletions” in GRCh38. After this, the remaining insertions were manually reviewed, and features of the genomic regions (segmental duplications or self-chain) were examined.
Ikemoto K., Fujimoto H, & Fujimoto A. (2023). Localized assembly for long reads enables genome-wide analysis of repetitive regions at single-base resolution in human genomes. Human Genomics, 17, 21.
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Publication 2023
BP 100 Chromosomes Deletion Mutation DNA, Mitochondrial Exons Gene Deletion Gene Insertion Genes Genome INDEL Mutation Insertion Mutation Mitochondria Pan troglodytes Pseudogenes Repetitive Region Segmental Duplications, Genomic Tandem Repeat Sequences Untranslated Regions
3
Validating miR-124-3p Targeting of EZH2
EZH2-MUT (mutant-type) and EZH2-WT (wild-type) reporter plasmids were created by synthesizing and propagating the anticipated and mutated miR-124-3p target-binding sequences in EZH2 into a luciferase reporter. PCR was used to amplify the wild-type human EZH2 3’-UTR segment from PC3 cells, which contained the anticipated miR-124-3p target locations. The mutant EZH2 3’-UTR sequence was generated by overlap-extension PCR. Next, wild-type and mutant 3’-UTRs were sub-replicated into the psiCHECK-2 luciferase vector. Into 24-well culture plates, PC3 cells were seeded and co-transfected with miR-124-3p or an NC repressor using the Lipofectamine® 2000 system in the luciferase enzyme reporter studies. We extracted the cell transfects after 2 days. The luciferase enzyme activity evaluation was conducted utilizing Dual-Luciferase enzyme reporter assay platform.
Song B.F., Xu L.Z., Jiang K, & Cheng F. (2023). MiR-124-3p inhibits tumor progression in prostate cancer by targeting EZH2. Functional & Integrative Genomics, 23(2), 80.
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Publication 2023
Cells Cloning Vectors enzyme activity Enzyme Assays Enzymes EZH2 protein, human Homo sapiens lipofectamine 2000 Luciferases Mirn124a microRNA, human PC 3 Cell Line Plasmids Untranslated Regions
4
SLAM-seq analysis of mouse transcriptome
Gene and 3′ untranslated region (UTR) annotations were obtained from the UCSC table browser (https://genome.ucsc.edu/cgi-bin/hgTables, mm10 vM14 3′ UTR). Adapters were trimmed from raw reads using cutadapt through the trim_galore wrapper tool with adapter overlaps set to 3 bp for trimming. For Quant-seq, concatenated fastq files were trimmed for adapter sequences, and masked for low-complexity or low-quality sequences using trim_galore, then mapped to the mm10 whole genome using HISAT v.2.2.1 with the default parameters. The number of reads mapped to the 3′ UTR of genes was determined using featureCounts. Raw reads were normalized to CPM. SLAM-seq analysis was performed as previously described58 (link) using the SlamDunk package59 (link). Trimmed reads were further processed with SlamDunk (v.0.3.4 16). The ‘Slamdunk all’ command was executed with the default parameters except ‘-rl 74 -t 8 fastq.gz -n 100 -m -mv 0.2 -o Slamdunk2’, running the full analysis procedure (slamdunk all) and aligning against the mouse genome (GRCm38), filtering for variants with a variant fraction of 0.2. Unless indicated otherwise, reads were filtered for having ≥2 T>C conversions. The remaining parameters were left as defaults.
Analysis of differential gene expression was restricted to genes with ≥10 reads in at least one condition. Differential gene expression calling was performed on raw read counts with ≥2 T>C conversions using DESeq2 with the default settings, and with size factors estimated on corresponding total mRNA reads for global normalization. Downstream analysis was restricted to genes that passed all internal filters for FDR estimation by DESeq2. Plots of differential gene expression were visualized using the ggplot2 package in R with significant genes (P value < 0.05, |log2FC| ≥ 1). Reproducibility of replicates is shown in Supplementary Fig. 3.
Wang H., Fan Z., Shliaha P.V., Miele M., Hendrickson R.C., Jiang X, & Helin K. (2023). H3K4me3 regulates RNA polymerase II promoter-proximal pause-release. Nature, 615(7951), 339-348.
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Publication 2023
3' Untranslated Regions Gene Expression Gene Expression Profiling Genes Genome Mus RNA, Messenger Untranslated Regions
5
SLAM-seq analysis of mouse transcriptome
Gene and 3′ untranslated region (UTR) annotations were obtained from the UCSC table browser (https://genome.ucsc.edu/cgi-bin/hgTables, mm10 vM14 3′ UTR). Adapters were trimmed from raw reads using cutadapt through the trim_galore wrapper tool with adapter overlaps set to 3 bp for trimming. For Quant-seq, concatenated fastq files were trimmed for adapter sequences, and masked for low-complexity or low-quality sequences using trim_galore, then mapped to the mm10 whole genome using HISAT v.2.2.1 with the default parameters. The number of reads mapped to the 3′ UTR of genes was determined using featureCounts. Raw reads were normalized to CPM. SLAM-seq analysis was performed as previously described58 (link) using the SlamDunk package59 (link). Trimmed reads were further processed with SlamDunk (v.0.3.4 16). The ‘Slamdunk all’ command was executed with the default parameters except ‘-rl 74 -t 8 fastq.gz -n 100 -m -mv 0.2 -o Slamdunk2’, running the full analysis procedure (slamdunk all) and aligning against the mouse genome (GRCm38), filtering for variants with a variant fraction of 0.2. Unless indicated otherwise, reads were filtered for having ≥2 T>C conversions. The remaining parameters were left as defaults.
Analysis of differential gene expression was restricted to genes with ≥10 reads in at least one condition. Differential gene expression calling was performed on raw read counts with ≥2 T>C conversions using DESeq2 with the default settings, and with size factors estimated on corresponding total mRNA reads for global normalization. Downstream analysis was restricted to genes that passed all internal filters for FDR estimation by DESeq2. Plots of differential gene expression were visualized using the ggplot2 package in R with significant genes (P value < 0.05, |log2FC| ≥ 1). Reproducibility of replicates is shown in Supplementary Fig. 3.
Wang H., Fan Z., Shliaha P.V., Miele M., Hendrickson R.C., Jiang X, & Helin K. (2023). H3K4me3 regulates RNA polymerase II promoter-proximal pause-release. Nature, 615(7951), 339-348.
Full text: Click here
Publication 2023
3' Untranslated Regions Gene Expression Gene Expression Profiling Genes Genome Mus RNA, Messenger Untranslated Regions

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More about "Untranslated Regions"

Untranslated Regions (UTRs) are critical regulatory segments of mRNA or DNA that do not encode protein sequences.
These 5' and 3' end regions play pivotal roles in gene expression, mRNA stability, and translation efficiency.
They contain key regulatory sequences and motifs that interact with cellular machinery to control various aspects of gene function.
Understanding the structure and function of UTRs is essential for studying gene regulation and developing targeted therapeutic interventions.
Techniques like the Dual-Luciferase Reporter Assay System, Lipofectamine 2000, and Dual-luciferase reporter assay kit can be used to analyze UTR-mediated gene regulation.
Vectors such as PsiCHECK-2, PmirGLO, and Dual-Glo Luciferase Assay System provide tools for UTR-based reporter assays.
Exploring UTRs can yield insights into post-transcriptional gene control, mRNA localization, and translational regulation - all of which are crucial for optimizing research protocols and enhancing the reproducibility of findings.
PubCompare.ai's AI-powered platform can help researchers efficiently locate and analyze the most relevant literature on Untranslated Regions, empowering them to advance their work and unlock new discoveries.