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Small Nuclear RNA

Small nuclear RNAs (snRNAs) are a class of small, non-coding RNAs that play critical roles in various cellular processes, including pre-mRNA splicing.
Accurately and reproducibly studying snRNAs is essential for understanding their biological functions.
PubCompare.ai's AI-driven protocol comparison features can enhance the accuracy and reproducibility of snRNA research by helping researchers easily locate the best protocols from literature, pre-prints, and patents.
Its intelligent comparison tools identify optimal methods and products, streamlining snRNA studies with powerful analysis tools.
PubCompare.ai is a valuable resource for researchers seeking to improve the quality and efficiency of their snRNA investigations.

Most cited protocols related to «Small Nuclear RNA»

1
Comprehensive Human, Mouse, and C. elegans Gene Annotations
Cited 998 times
Human gene annotations were acquired from GENCODE v17 (31 (link)). Protein-coding transcripts were defined as those with ‘protein_coding’ gene biotype and ‘protein_coding’ transcript biotype. The lncRNAs transcripts were defined as those with ‘processed_transcript’, ‘lincRNA’, ‘3prime_overlapping_ncrna’, ‘antisense’, ‘non_coding’, ‘sense_intronic’ or ‘sense_overlapping’ gene biotype. Small non-coding RNA (sncRNA) transcripts were defined as those with ‘snRNA’, ‘snoRNA’, ‘rRNA’, ‘Mt_tRNA’, ‘Mt_rRNA’, ‘misc_RNA’ or ‘miRNA’ gene biotype. Pseudogene transcripts were defined as those with ‘polymorphic_pseudogene’, ‘pseudogene’, ‘IG_C_pseudogene’, ‘IG_J_pseudogene’, ‘IG_V_pseudogene’, ‘TR_V_pseudogene’ or ‘TR_J_pseudogene’ gene biotype.
Mouse and Caenorhabditis elegans gene annotations were extracted from Ensembl Gene Release 72 and LiftOver to mm9/mm10 and ce6/ce10, respectively. Protein-coding, lncRNAs, sncRNAs and pseudogenes were classified using a similar method. Human, mouse and C. elegans circRNA annotations were downloaded from circBase v0.1 (6 (link)).
These transcripts were scanned to find conserved miRNAs target sites using miRanda v3.3a with the ‘-strict’ parameter. The target sites that overlap with any entry of the aforementioned AGO CLIP clusters were considered as the CLIP-supported target sites.
Li J.H., Liu S., Zhou H., Qu L.H, & Yang J.H. (2013). starBase v2.0: decoding miRNA-ceRNA, miRNA-ncRNA and protein–RNA interaction networks from large-scale CLIP-Seq data. Nucleic Acids Research, 42(Database issue), D92-D97.
Publication 2013
Caenorhabditis elegans Clip Gene Annotation Gene Products, Protein Genes Genes, Overlapping Homo sapiens Introns Long Intergenic Non-Protein Coding RNA MicroRNAs Mus Neutrophil Proteins Pseudogenes Ribosomal RNA RNA, Circular RNA, Long Untranslated RNA, Small Untranslated RNA, Untranslated Small Nuclear RNA Small Nucleolar RNA Transfer RNA
2
Comprehensive sncRNA Annotation Pipeline
Cited 184 times
We annotate small non-coding RNAs (sncRNAs) using a variety of mechanisms. Specifically, miRNA annotations are imported directly from miRBase (22 (link)), while tRNAs are identified ab initio using tRNAScan-SE (23 (link)) although they are not included directly in the gene set. For other classes of sncRNA, including small nucleolar RNAs (snoRNAs), small nuclear RNAs (snRNAs) and small Cajal body-specific RNAs (scaRNAs), we use a homology-based, computational pipeline (24 (link)), which first compares sequences of known RNA families in Rfam (25 (link)) to the genome using BLAST (26 (link)). This initial step reduces the genomic search space and excludes sequences with sub-optimal alignments to the genome. We define putative sncRNA models after clustering top BLAST hits and evaluating these predictions by performing sequence and structure searches against covariance models in the Infernal suite of tools (27 (link)).
Frankish A., Diekhans M., Ferreira A.M., Johnson R., Jungreis I., Loveland J., Mudge J.M., Sisu C., Wright J., Armstrong J., Barnes I., Berry A., Bignell A., Carbonell Sala S., Chrast J., Cunningham F., Di Domenico T., Donaldson S., Fiddes I.T., García Girón C., Gonzalez J.M., Grego T., Hardy M., Hourlier T., Hunt T., Izuogu O.G., Lagarde J., Martin F.J., Martínez L., Mohanan S., Muir P., Navarro F.C., Parker A., Pei B., Pozo F., Ruffier M., Schmitt B.M., Stapleton E., Suner M.M., Sycheva I., Uszczynska-Ratajczak B., Xu J., Yates A., Zerbino D., Zhang Y., Aken B., Choudhary J.S., Gerstein M., Guigó R., Hubbard T.J., Kellis M., Paten B., Reymond A., Tress M.L, & Flicek P. (2018). GENCODE reference annotation for the human and mouse genomes. Nucleic Acids Research, 47(Database issue), D766-D773.
Publication 2018
Coiled Bodies Genes Genome MicroRNAs RNA RNA, Small Untranslated Sequence Alignment Small Nuclear RNA Small Nucleolar RNA Transfer RNA
3
miRNA Sequencing and Expression Analysis
Cited 122 times
For miRNA sequencing, 5 µg of total RNA form each sample was ligated with both a 5′ adapter and 3′ adapter for reverse transcription using Superscript II at 42°C for 1 h and 70°C for 15 min. Subsequently, the reverse transcribed products were amplified using the following PCR program: a 15-cycle reaction at 98°C for 30 sec, followed by 15 cycles of 98°C for 10 sec, 72°C for 15 sec, and then 72°C for 10 min. After obtaining a ∼92-bp DNA band on 6% PAGE gels, the PCR products were ethanol precipitated and purified using Spin-X filter columns. Finally, miRNA libraries were sequenced on the Illumina Cluster Station and Genome Analyzer II following the manufacturer's protocol.
Low quality reads were trimmed and adapter sequences were accurately clipped with the aid of a dynamic programming algorithm before subsequent statistical analysis. After elimination of the duplicate reads, the remaining reads of at least 18 nt were mapped to a human reference genome (hg19) using SOAP V2.0. To remove tags originating from protein-coding genes, repeat sequences, rRNA, tRNA, snRNA, and snoRNA, we also mapped the short read tags to UCSC RefGene, RepeatMasker and NCBI Refseq, as well as our in-house ncRNA annotation datasets compiled from the NCBI GenBank database (http://www.ncbi.nih.gov). The same pipeline used for DGE mRNA differential expression analysis was also used for miRNA expression analysis.
Zhou L., Chen J., Li Z., Li X., Hu X., Huang Y., Zhao X., Liang C., Wang Y., Sun L., Shi M., Xu X., Shen F., Chen M., Han Z., Peng Z., Zhai Q., Chen J., Zhang Z., Yang R., Ye J., Guan Z., Yang H., Gui Y., Wang J., Cai Z, & Zhang X. (2010). Integrated Profiling of MicroRNAs and mRNAs: MicroRNAs Located on Xq27.3 Associate with Clear Cell Renal Cell Carcinoma. PLoS ONE, 5(12), e15224.
Publication 2010
DNA, A-Form Ethanol Gels Gene Products, Protein Genome Genome, Human Homo sapiens MicroRNAs Repetitive Region Reverse Transcription Ribosomal RNA RNA, Messenger RNA, Untranslated Small Nuclear RNA Small Nucleolar RNA Transfer RNA
4
Multimodal Profiling of Human Brain
Cited 87 times
A full description of the materials and methods is available in the supplementary materials. Briefly, we precisely dissected multiple brain regions (HIP, STR, AMY, cerebellum, thalamus, and 11 neocortical areas) in more than 60 postmortem human brains ranging in age from 5 PCW to 64 PY. We then applied bulk tissue RNA-seq, scRNA-seq and snRNA-seq, smRNA-seq, DNA methylation assay, or ChIP-seq to generate multimodal datasets, often from the same brain. After applying stringent quality control checks and independent analysis of each dataset, we performed integrated analyses to gain insights into human brain development, function, and disease.
Li M., Santpere G., Kawasawa Y.I., Evgrafov O.V., Gulden F.O., Pochareddy S., Sunkin S.M., Li Z., Shin Y., Zhu Y., Sousa A.M., Werling D.M., Kitchen R.R., Kang H.J., Pletikos M., Choi J., Muchnik S., Xu X., Wang D., Lorente-Galdos B., Liu S., Giusti-Rodríguez P., Won H., de Leeuw C.A., Pardiñas A.F., Hu M., Jin F., Li Y., Owen M.J., O’Donovan M.C., Walters J.T., Posthuma D., Reimers M.A., Levitt P., Weinberger D.R., Hyde T.M., Kleinman J.E., Geschwind D.H., Hawrylycz M.J., State M.W., Sanders S.J., Sullivan P.F., Gerstein M.B., Lein E.S., Knowles J.A, & Sestan N. (2018). Integrative functional genomic analysis of human brain development and neuropsychiatric risks. Science (New York, N.Y.), 362(6420), eaat7615.
Publication 2018
Autopsy Biological Assay Brain Cerebellum Chromatin Immunoprecipitation Sequencing Dietary Fiber DNA Methylation Homo sapiens Human Development Multimodal Imaging RNA-Seq Single-Cell RNA-Seq Small Nuclear RNA Thalamus Tissues
5
Integrative Single-Nucleus Consensus Co-Expression Analysis
Cited 40 times
We developed a novel co-expression network analysis approach to single-cell data by integrating snRNA-seq and bulk-tissue RNA-seq datasets and called this approach Single-nucleus Consensus Weighted Gene Co-expression Network Analysis (scWGCNA). scWGCNA is based on a co-expression network analysis approach called Weighted Gene Co-expression Network Analysis (WGCNA), implemented using the WGCNA R package (v1.69)53 ,54 . For scWGCNA, we used multiple transcriptomic datasets comprising of our snRNA-seq data, Mathys et al. snRNA-seq data, bulk-tissue RNA-seq data from our UCI cohort and bulk tissue RNA-seq data from ROSMAP cohort57 (link). First, we integrated our snRNA-seq and Mathys et al. snRNA-seq datasets using iNMF approach, and then constructed metacells in a fashion similar to our CCAN analysis of chromatin accessibility data, in which we apply a bootstrapped aggregation process to single-nucleus transcriptomes. During metacell computation, we only pool cells within the same cell-type, and within the same AD diagnosis stage, in order to retain these metadata for scWGCNA. We then employed a signed consensus WGCNA approach58 within a given cell-type, by calculating component-wise values for topological overlap for each dataset. First, bi-weighted mid-correlations were calculated for all pairs of genes, and then a signed similarity matrix was created. In the signed network, the similarity between genes reflects the sign of the correlation of their expression profiles. The signed similarity matrix was then raised to power β, varies between cell-types, to emphasize strong correlations and reduce the emphasis of weak correlations on an exponential scale. The resulting adjacency matrix was then transformed into a topological overlap matrix. Modules were defined using specific module cutting parameters which included minimum module size of 100 genes, deepSplit = 4 and threshold of correlation = 0.2. Modules with correlation greater than 0.8 were merged together. We used first principal component of the module, called the module eigengene, to correlate with diagnosis and other variables. Hub genes were defined using intra-modular connectivity (kME) parameter of the WGCNA package. Gene-set enrichment analysis was done using EnrichR.
Morabito S., Miyoshi E., Michael N., Shahin S., Martini A.C., Head E., Silva J., Leavey K., Perez-Rosendahl M, & Swarup V. (2021). Single-nucleus chromatin accessibility and transcriptomic characterization of Alzheimer’s Disease. Nature genetics, 53(8), 1143-1155.
Publication 2021
Cells Chromatin Debility Diagnosis Dietary Fiber Gene Expression Profiling Gene Modules Genes Nucleus Solitarius RNA-Seq Single-Cell Analysis Small Nuclear RNA Tissues Transcriptome

Most recents protocols related to «Small Nuclear RNA»

1
Quantitative Analysis of Coding, Long Non-coding, and miRNA Transcripts
RNA was isolated using TRIzol Reagent (Invitrogen), according to the manufacturer’s instructions. RNA concentration and purity were determined by measuring absorbance using a NanoDrop Spectrophotometer ND-1000 (Thermo Fisher Scientific). RNA integrity was evaluated by agarose gel electrophoresis or Agilent RNA 6 000 Pico kit (Agilent).
To test the expression of coding and long non-coding transcripts, total RNA was first treated with TURBO DNA-free kit (Life Technologies), following the manufacturer’s protocol, to remove potential DNA contaminants. RNA samples were then used to synthesize cDNA, using random hexamers (Life Technologies) and SuperScript® III Reverse Transcriptase kit (Life Technologies). qPCR reactions were performed using cDNA, iQ SYBR Green Supermix (Bio-Rad) and respective primers, according to the following conditions: 3 min at 95 °C and forty cycles of 30 s at 95 °C, 30 s at 58 °C and 30 s at 72 °C. β-actin was used as a reference gene. Primer sequences are described in Additional file 1: Table SII.
To test the expression of miRNAs, TaqMan miRNA assays (Applied Biosystems) were used, according to the manufacturer’s protocol. Briefly, complementary DNA (cDNA) was synthesized using total RNA, TaqMan MicroRNA Reverse Transcription Kit and gene specific stem-loop Reverse Transcription primers (hsa-miR-29a-3p. hsa-miR-29b-3p and hsa-miR-29c-3p, Applied Biosystems; Additional file 1: Table SIII). qPCR was carried out in a thermal cycler (MyCycler Thermal Cycler, Bio-Rad) using cDNA, SsoAdvanced™ Universal Probes Supermix (Bio-Rad) and TaqMan probes (Applied Biosystems), according to the following conditions: 10 min at 95 °C, 40 cycles of 15 s at 95 °C and 1 min at 60 °C. Small nuclear RNA U6 was used as a reference gene.
All RT-qPCR reactions were performed in duplicate and data was analyzed using Bio-Rad CFX Manager software (Bio-Rad). Relative expression levels were calculated using the quantification cycle (Ct) method, according to MIQE guidelines [36 (link)].
Moura S.R., Freitas J., Ribeiro-Machado C., Lopes J., Neves N., Canhão H., Rodrigues A.M., Barbosa M.A, & Almeida M.I. (2023). Long non-coding RNA H19 regulates matrisome signature and impacts cell behavior on MSC-engineered extracellular matrices. Stem Cell Research & Therapy, 14, 37.
Publication 2023
Actins Biological Assay DNA, Complementary Electrophoresis, Agar Gel Genes MicroRNAs MIRN29a microRNA, human MIRN29B1 microRNA, human Oligonucleotide Primers Reverse Transcription RNA-Directed DNA Polymerase SKP1 protein, human Small Nuclear RNA Stem, Plant SYBR Green I Transcription, Genetic trizol
2
Zebrafish Embryo Manipulation and Rescue
Zebrafish (Danio rerio) adults and embryos were maintained at 28.5 °C under standard protocols (41 (link)). The following strains were used: the mixed wild-type ABxTU, Tg(wt1b:EGFP)li1 (42 (link)), Tg(cmlc2:GFP), and Tg(bactin:arl13bGFP)hsc5Tg (43 (link)) to label proximal pronephros, heart, and cilia, respectively. MO, designed to target the 5′ Stem-Loop (5’SL) (5′-GATGTTCTCAGTTAACCTTCATTGA-3′) or the Stem II (SII) (5′-AAACCACCCCCAGACAAGGAA-GGTT-3′) regions of u4atac_chr11, were provided by GeneTools, LCC and injected into the yolk at the one-cell stage (quantity ranging from 0.016 to 0.066 pmol (i.e., 135 to 556 pg) per embryo for 5’SL MO and 0.002 to 0.006 pmol (i.e., 17 to 51 pg) per embryo for SII MO). A five-mismatch MO (5′-GATcTTgTCAcTTAACCTTgATTgA-3′) was used as a negative control. For rescue experiments, snRNA molecules were synthesized using the MAXIscript T7 Transcription Kit (ThermoFisher Scientific); DNA templates of human U4atac snRNA sequence were PCR-amplified from previously described plasmids (9 (link)) (SI Appendix, Table S3). snRNA molecules were purified with Clean and Concentrator kit (Zymo Research), and 65 pg were coinjected with u4atac MO per embryo.
Khatri D., Putoux A., Cologne A., Kaltenbach S., Besson A., Bertiaux E., Guguin J., Fendler A., Dupont M.A., Benoit-Pilven C., Qebibo L., Ahmed-Elie S., Audebert-Bellanger S., Blanc P., Rambaud T., Castelle M., Cornen G., Grotto S., Guët A., Guibaud L., Michot C., Odent S., Ruaud L., Sacaze E., Hamel V., Bordonné R., Leutenegger A.L., Edery P., Burglen L., Attié-Bitach T., Mazoyer S, & Delous M. (2023). Deficiency of the minor spliceosome component U4atac snRNA secondarily results in ciliary defects in human and zebrafish. Proceedings of the National Academy of Sciences of the United States of America, 120(9), e2102569120.
Publication 2023
Adult Cells Cilia DNA Sequence Embryo Heart Homo sapiens Plasmids Pronephros Small Nuclear RNA Stem, Plant Strains Transcription, Genetic Zebrafish
3
Analyzing Human PFC snRNA-seq Data
The snRNA-seq datasets (raw or gene expression matrices) of the human PFC used in this study were downloaded from the NCBI Gene Expression Omnibus database (GEO)1 under accession numbers GSE157827 (Lau et al., 2020 (link)), GSE174367 (Morabito et al., 2021 (link)), containing 24 AD and 17 control samples in total. After merging all the datasets, cells with less than 200 unique molecular identifiers (UMIs), more than 5,000 UMIs, or mitochondrial counts greater than 20% were filtered out. Genes expressed in fewer than three cells were also filtered out. Seurat (version 4.0)2 was used for a wide variety of single-cell analyses, including normalization, scaling, batch correction, dimensionality reduction, clustering, and visualization. We used the harmony package3 for the batch correction. The expression of known marker genes in the CNS was used as a reference for the annotation of different cell types. All statistical analyses were conducted using the R software (version 4.0.2).
Fan L.Y., Yang J., Li M.L., Liu R.Y., Kong Y., Duan S.Y., Guo G.Y., Yang J.H, & Xu Y.M. (2023). Single-nucleus transcriptional profiling uncovers the reprogrammed metabolism of astrocytes in Alzheimer’s disease. Frontiers in Molecular Neuroscience, 16, 1136398.
Publication 2023
Cells Gene Expression Genes Homo sapiens Mitochondrial Inheritance Single-Cell Analysis Small Nuclear RNA
4
Acutely Purified Cortical Nuclei RNA-seq Analysis
Nuclei were acutely purified from the cerebral cortex of 2 CrT+/y and 2 CrT−/y animals (PND100) as described previously, with minor modifications [27 (link)]. The whole cortex of the right cerebral hemisphere was manually dissected for each mouse. Tissue dissection was performed with extreme caution to avoid cross contamination with underlying brain tissue. For every sample, 15000 nuclei were loaded into a Chromium Single Cell A Chip (10 × Genomics) and processed following the manufacturer’s instructions. Single-nuclei RNA-seq libraries were prepared using the Chromium Single Cell 3’ Library & Gel Bead kit v2 and i7 Multiplex kit (10 × Genomics). Pooled libraries were then loaded on a HiSeq2500 instrument (Illumina) and sequenced to obtain 75 bp paired-end reads. snRNA-seq data can be accessed at the GEO repository (GSE216766). Sequenced samples were processed using the Cell Ranger (v.3.1.0) pipeline (10 × Genomics). Downstream analyses were performed using the R package Seurat (v3.1.4). To explore the heterogeneity of the cortex, we identified 7 major cell populations. DEGs between the two groups were identified using the Wilcoxon Rank Sum test and Bonferroni correction (adjusted p-value < 0.1). GO analysis for each differentially expressed gene list was performed as described above. The overlap between snRNA-seq and bulk RNA-seq data was assessed using a Fisher Exact Test.
Ghirardini E., Sagona G., Marquez-Galera A., Calugi F., Navarron C.M., Cacciante F., Chen S., Di Vetta F., Dadà L., Mazziotti R., Lupori L., Putignano E., Baldi P., Lopez-Atalaya J.P., Pizzorusso T, & Baroncelli L. (2023). Cell-specific vulnerability to metabolic failure: the crucial role of parvalbumin expressing neurons in creatine transporter deficiency. Acta Neuropathologica Communications, 11, 34.
Publication 2023
Animals Brain Cell Nucleus Cells Chromium Cortex, Cerebral Dietary Fiber Dissection DNA Chips DNA Library Genes Genetic Heterogeneity Mus Nucleus Solitarius RNA-Seq Small Nuclear RNA Tissues
5
Comparative snRNA-seq Analysis of Ovarian Aging
We accessed data sets for comparative snRNA-seq analysis of ovarian tissue biopsies collected from four women at 23, 27, 28, and 29 years of age (young adult) versus four women at 49, 51, 52, and 54 years of age (peri/postmenopausal), which were deposited by Jin et al. [53 (link)] (available through National Center for Biotechnology Information Gene Expression Omnibus Accession No. GSE202601; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE202601); all tissues were flash-frozen autopsy samples of sudden death individuals with normal ovarian histology. After preprocessing of the data using 10xGenomics Cell Ranger 7.0.0 software, further filtering and analysis were performed in Seurat using R-Studio (for quality control) and lines of code we have published previously [14 (link)] (https://github.com/hanrico/Ovarian-scRNA-seq). For quality control, genes expressed in no <3 nuclei were retained for analysis, and nuclei expressing 200–6,000 genes, with no more than 15% of these genes being mitochondrial in nature, were included in the downstream analyses.
To select these parameters, violin plots and scatter plots depicting mitochondrial gene percentage, “nFeature-RNA,” and “nCount-RNA” were prepared and visualized in Seurat [14 (link)].
Once filtering was completed, the remaining nuclei from each sample within each age group were integrated using the canonical correlation analysis integration tool in Seurat. After successful integration, the preprocessed data were merged and regressed for batch effects. Column normalization and log transformation were conducted before principal component analysis. To further analyze the data generated by Cell Ranger 7.0.0, elbow plots were generated in Seurat, which assisted in the determination of the ideal number of principal components to include in downstream Uniform Manifold Approximation and Projection (UMAP) dimensional reduction. To maintain consistency with the analysis completed by Jin et al. [53 (link)], UMAP projections were prepared with the first 15 principal components and a setting of 0.1 for resolution. For gene expression analysis in 10xGenomics Loupe Browser, data for the four samples from each age group were aggregated together using Cell Ranger Aggregate.
Following aggregation of the young adult age group data, the 24,593 nuclei maintained in the output file were reduced to 24,068 nuclei for Loupe Browser; following aggregation of the peri-/postmenopausal age group data, the 20,096 nuclei maintained in the Cell Ranger Aggregate file were reduced to 19,853 nuclei for Loupe Browser. The following parameters were used for quality filtering of aggregated data for Loupe Browser: minimum thresholds for features expressed, 200; maximum thresholds for features expressed, 6,000; maximum mitochondrial unique molecular identified counts, 15%; number of principal components, 15; minimum distance, 0.2; number of neighbors, 8.
MacDonald J.A., Sheehan H.C., Piasecki A., Faustino L.R., Hauschildt C., Stolzenbach V., Woods D.C, & Tilly J.L. (2023). Characterization of Oogonial Stem Cells in Adult Mouse Ovaries with Age and Comparison to In Silico Data on Human Ovarian Aging. Stem Cells and Development, 32(5-6), 99-114.
Publication 2023
Age Groups Autopsy Biopsy Cell Nucleus Cells Elbow Freezing Gene Expression Gene Expression Profiling Genes Genes, Mitochondrial Mitochondrial Inheritance Ovary Single-Cell RNA-Seq Small Nuclear RNA Sudden Death Tissues Woman Young Adult

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More about "Small Nuclear RNA"

Small nuclear RNAs (snRNAs), a class of small, non-coding RNAs, play crucial roles in various cellular processes, including pre-mRNA splicing.
Accurately and reproducibly studying these snRNAs is essential for understanding their biological functions.
PubCompare.ai's AI-driven protocol comparison features can enhance the accuracy and reproducibility of snRNA research by helping researchers easily locate the best protocols from literature, pre-prints, and patents.
Its intelligent comparison tools identify optimal methods and products, streamlining snRNA studies with powerful analysis tools.
PubCompare.ai is a valuable resource for researchers seeking to improve the quality and efficiency of their snRNA investigations.
Synonyms and related terms for snRNAs include small nuclear ribonucleic acids, spliceosomal RNAs, and small nuclear RNPs (snRNPs).
Commonly used techniques for snRNA research include RT-qPCR, Northern blotting, and high-throughput sequencing methods like HiSeq 2500.
Relevant reagents and kits include TRIzol, TRIzol reagent, TaqMan MicroRNA Reverse Transcription Kit, PrimeScript RT reagent kit, MiScript SYBR Green PCR Kit, MiScript II RT Kit, TaqMan MicroRNA Assays, and MiRNeasy Mini Kit.
Key subtopics in snRNA research encompass snRNA biogenesis, snRNA-mediated splicing, snRNA localization and trafficking, snRNA post-transcriptional modifications, and the roles of snRNAs in various disease states.
PubCompare.ai's AI-powered tools can streamline investigations into these critical aspects of snRNA biology, enhancing the accuracy and reproducibility of snRNA studies.