> Anatomy > Cell Component > Spliceosomes

Spliceosomes

Spliceosomes are large, dynamic ribonucleoprotein complexes responsible for the removal of introns from pre-mRNA molecules.
They play a crucial role in gene expression by facilitating the splicing process, which helps generate mature mRNA transcripts.
Spliceosomes are composed of small nuclear ribonucleoproteins (snRNPs) and additional accessory proteins, and their assembly and disassembly are tightly regulated.
Understanading spliceosome structure, function, and regulation is essential for research into alternative splicing, RNA processing, and gene expression.
PubCompare.ai can help optimize your spliceosome research workflows by identifying the most reliable protocols from literature, preprints, and patents, and comparing them to ensure reproducibility and accuracy in your studies.

Most cited protocols related to «Spliceosomes»

Comprehensive Spliceosomal Gene Identification

Protocol full text hidden due to copyright restrictions

Open the protocol to access the free full text link

Publication 2018

Genes Homo sapiens Hybrids Mass Spectrometry MLL protein, human Proteins RNA Splicing Factors Saccharomyces cerevisiae Spliceosomes

Automated Annotation of Pyrenophora Genome

CDSs were annotated by a combination of semi-automatic procedures. First, P. anserina open reading frames longer than 20 codons that are evolutionary conserved in N. crassa were retrieved by TBLASTN analysis. Candidates with an e-value lower than 10^-18were conserved as hypothetical exons. Exons separated by less than 200 nucleotides were merged into putative CDSs and putative introns were predicted thanks to the P. anserina consensus sequences defined in the pilot project [24 (link)]. Then, 5' and 3' smaller exons were searched by the same procedure except that open reading frames longer than five codons surrounding putative CDSs were analyzed by BLAST with the homologous N. crassa region. Candidates with an e-value lower than 10^-5were conserved and added to the putative CDSs. CDS and intron predictions were edited with Artemis [86 (link)] and manually corrected after comparison with available ESTs. Finally, ab initio prediction with GeneID [87 (link)] using the N. crassa and Chaetomium globosum parameter files were performed on regions devoid of annotated features. Manual verification was then applied to improve prediction. This resulted in the definition of 10,545 putative CDSs.
A canonic rDNA unit was assembled. A junction sequence between the left arm of chromosome 3 and an rDNA unit was observed, confirming the position of the cluster on this chromosome based on pulse field electrophoresis data [28 (link)]. On the other end of the cluster a junction between an incomplete rDNA repeat and CCCTAA telomeric repeats [88 (link)] was detected showing that the cluster is in a subtelomeric position. Similar to the previously investigated filamentous fungi [89 (link)], 5S rRNAs were detected by comparison with the N. crassa 5S genes. They are encoded by a set of 87 genes, including 72 full-length copies dispersed in the genome. tRNAs were identified with tRNAscan [90 (link)]. A total of 361 genes encode the cytosolic tRNA set, which is composed of 48 different acceptor families containing up to 22 members. This set enabled us to decode the 61 sense codons with the classical wobble rule. Other non-coding RNAs were detected with a combination of the Erpin [91 (link)], Blast [92 (link)] and Yass [93 (link)] programs. Homology search included all RNAs contained in the RFAM V.8 [94 (link)] and ncRNAdb [95 (link)] databases. Any hit from either program with an e-value below 10^-4was retained, producing a list of 28 annotated non-coding RNA genes or elements, including 12 spliceosomal RNAs, 15 snoRNAs (mostly of the C/D box class) and one thiamine pyrophosphateriboswitch.

Espagne E., Lespinet O., Malagnac F., Da Silva C., Jaillon O., Porcel B.M., Couloux A., Aury J.M., Ségurens B., Poulain J., Anthouard V., Grossetete S., Khalili H., Coppin E., Déquard-Chablat M., Picard M., Contamine V., Arnaise S., Bourdais A., Berteaux-Lecellier V., Gautheret D., de Vries R.P., Battaglia E., Coutinho P.M., Danchin E.G., Henrissat B., Khoury R.E., Sainsard-Chanet A., Boivin A., Pinan-Lucarré B., Sellem C.H., Debuchy R., Wincker P., Weissenbach J, & Silar P. (2008). The genome sequence of the model ascomycete fungus Podospora anserina. Genome Biology, 9(5), R77.

Publication 2008

Biological Evolution Chaetomium globosum Chromosomes Chromosomes, Human, Pair 3 Codon Consensus Sequence Cytosol DNA, Ribosomal Electrophoresis Exons Expressed Sequence Tags Fungus, Filamentous Genes Genes, vif Genome Introns Nucleotides Open Reading Frames Pulse Rate RNA RNA, Ribosomal, 5S RNA, Untranslated Sense Codon Small Nucleolar RNA Spliceosomes Telomere Thiamine Transfer RNA Triglyceride Storage Disease with Ichthyosis

Spliceosome Assembly and Manipulation

U6 was depleted from Saccharomyces cerevisiae splicing extracts and splicing activity reconstituted with synthetic U6 snRNA, essentially as described^{28 (link)}. Spliceosomes were assembled on modified model pre-mRNA substrates: UBC4, for experiments probing branching, or ACT1, for experiments probing exon ligation, both synthesized by splint-mediated ligation^{49 (link)}. Oligonucleotides containing specific 5′ or 3′ splice site modifications were synthesized in house, as described previously^{50 (link)}. Assembled spliceosomes were isolated by affinity purification with Prp19p (ref. ^{36 (link)}), washed to remove ATP, and chased as described^{28 (link)}, at room temperature, in the absence of ATP, at pH 7.0, 8.0, or 8.5. All experiments were repeated with at least two independent extract preparations. Data were quantified using ImageQuant TL (Amersham Biosciences).

Fica S.M., Tuttle N., Novak T., Li N.S., Lu J., Koodathingal P., Dai Q., Staley J.P, & Piccirilli J.A. (2013). RNA catalyzes nuclear pre-mRNA splicing. Nature, 503(7475), 229-234.

Publication 2013

Chromatography, Affinity Exons Ligation mRNA Precursor Oligonucleotides Saccharomyces cerevisiae Splice Acceptor Site Spliceosomes Splints U6 small nuclear RNA

Mapping RBP Binding and Splicing Dynamics

RBP binding or splicing maps were generated using eCLIP-normalized (reads per million) read densities overlapped with alternatively spliced (AS) regions from rMATS JunctionCountsOnly files from the same cell type using the RBP-Maps methodology^{24 (link)} (Supplementary Text, Supplementary Fig. 14). Analyses described used only events with rMATS P < 0.05, FDR < 0.1, and |ΔΨ| > 0.05 in knockdown versus control RNA-seq.
Correlation between splicing maps was defined as the Pearson correlation (R) between a vector that contained both included-upon knockdown and excluded-upon knockdown RBP-responsive event eCLIP enrichment for each RBP. If an RBP had fewer than the minimum required number of events (100 for skipped exons or 50 for alternative 5′ or 3′ splice site events) for either knockdown-included or knockdown-excluded events, the correlation was calculated only using the other event type.
To generate cross-RBP splicing maps, the above approach was modified by taking the set of differentially included (or excluded) skipped exons identified in knockdown of RBP A and calculating the eCLIP splicing map separately for every other RBP within the same binding class (determined in Fig. 2a) as RBP A, including the normalization against a background of eCLIP signal for native skipped exon events (as shown for HNRNPC knockdown-included, RBFOX2 knockdown-excluded, and TIA1 knockdown-included skipped exons in Extended Data Fig. 8b, Fig. 5d, and Extended Data Fig. 8e, respectively). The average across all RBPs was then used to calculate the average cross-RBP enrichment (Extended Data Fig. 8a).
To calculate the number of RBPs bound per exon, the set of spliceosomal RBPs was taken from manual annotation of RBP functions (described above and listed in Supplementary Data 1). The number of reproducible (IDR) peaks at each position relative to splice sites was summed across all RBPs and divided by the total number of skipped or constitutive exons.

Van Nostrand E.L., Freese P., Pratt G.A., Wang X., Wei X., Xiao R., Blue S.M., Chen J.Y., Cody N.A., Dominguez D., Olson S., Sundararaman B., Zhan L., Bazile C., Bouvrette L.P., Bergalet J., Duff M.O., Garcia K.E., Gelboin-Burkhart C., Hochman M., Lambert N.J., Li H., McGurk M.P., Nguyen T.B., Palden T., Rabano I., Sathe S., Stanton R., Su A., Wang R., Yee B.A., Zhou B., Louie A.L., Aigner S., Fu X.D., Lécuyer E., Burge C.B., Graveley B.R, & Yeo G.W. (2020). A large-scale binding and functional map of human RNA-binding proteins. Nature, 583(7818), 711-719.

Publication 2020

Cells Cloning Vectors Exons Microtubule-Associated Proteins RNA-Seq Splice Acceptor Site Spliceosomes TIA1 protein, human

Identification of Human Genome U12-Dependent Splice Sites

We extracted all the nonredundant 5′ GT splice sites in the entire human genome using the CDS tags in the NCBI RefSeq Database Build 36.2. Each 5′ splice site on the genome is counted once, even if it is used multiple times in alternatively spliced transcripts. The analysis was performed with the PrimePower HPC2500/Solaris 9 supercomputer (Fujitsu Ltd., Tokyo, Japan). Using the JMP-IN Ver. 5.1.2 software (SAS Institute, Cary, NC, USA), we statistically determined a threshold for each variable using the default settings.
In humans, ∼0.1–0.3% of introns are spliced by the minor U12-dependent spliceosome (2 (link),11 (link),12 (link)), and ∼70% of the U12-dependent introns have GT-AG terminal dinucleotides (13 (link)). Previous in silico analyses of the human genome identified 275 (12 (link)), 469 (14 (link)) and 487 (13 (link)) GT-AG U12-dependent introns. We thus eliminated 487 U12-dependent 5′ GT splice sites from our analysis, according to the U12 Intron Database (http://genome.imim.es/cgi-bin/u12db/u12db.cgi). Our training and validation data sets (see ‘Results’ section) did not include any of the known U12-dependent splice sites.

Sahashi K., Masuda A., Matsuura T., Shinmi J., Zhang Z., Takeshima Y., Matsuo M., Sobue G, & Ohno K. (2007). In vitro and in silico analysis reveals an efficient algorithm to predict the splicing consequences of mutations at the 5′ splice sites. Nucleic Acids Research, 35(18), 5995-6003.

Publication 2007

Dinucleoside Phosphates Genome Genome, Human Homo sapiens Introns Spliceosomes

Most recents protocols related to «Spliceosomes»

Comprehensive Functional Annotation of Transcriptome

When comparing the open-reading frames in the transcriptomic data with their genomic counterparts, we failed to observe obvious spliceosomal introns. Therefore, Prodigal v. 2.6.3 (Hyatt et al. 2010 (link)), a bacterial gene prediction tool, was used to predict gene models and proteins for both P. canceri genome assemblies. TransDecoder v.5.3.0 (https://github.com/TransDecoder/TransDecoder) was used to identify candidate coding regions from all transcriptome assemblies generated in this study and the published transcriptome of M. mackini (Burki et al. 2013 (link)). Functional annotation of the predicted proteins was performed based on the following strategy. The predicted proteome was used as a query against the NCBI nr database (May 2020) to retrieve the top scoring hits (BLAST suite v. 2.9.0+). Interpro (IPR) domains were assigned using Interproscan v.5.30-69.0 (Jones et al. 2014 (link)). The online version of eggNOG-mapper v2 (Huerta-Cepas et al. 2017 (link)) was used for orthology assignments of the predicted proteins and K numbers were assigned on the GhostKoala web server (https://www.genome.jp/kegg/tool/map_pathway.html). The subcellular localization of each protein was determined with targetP v.2 (Almagro Armenteros et al. 2019 ) searching the non-plant organism group, MitoFates with fungal settings and DeepLoc-1.0 with default settings (Almagro Armenteros et al. 2017 (link)).

Onuț-Brännström I., Stairs C.W., Campos K.I., Thorén M.H., Ettema T.J., Keeling P.J., Bass D, & Burki F. (2023). A Mitosome With Distinct Metabolism in the Uncultured Protist Parasite Paramikrocytos canceri (Rhizaria, Ascetosporea). Genome Biology and Evolution, 15(3), evad022.

Publication 2023

FCER2 protein, human Gene Expression Profiling Genes, Bacterial Genome Introns Plants Protein Annotation Proteins Proteome Spliceosomes Transcriptome

Purification of Spliceosomal C and C* Complexes

HeLa S3 cells were obtained from the Helmholtz Zentrum für Infektionsforschung, Braunschweig, and tested negative for mycoplasma. HeLa nuclear extracts were prepared according to Dignam et al. (46 (link)) and dialyzed twice for 2.5 hours against 50 volumes of Roeder D buffer [20 mM Hepes-KOH (pH 7.9), 0.2 mM EDTA (pH 8.0), 1.5 mM MgCl₂, 100 mM KCl, 10% (v/v) glycerol, 0.5 mM dithiothreitol (DTT), and 0.5 mM phenylmethylsulfonyl fluoride]. For purification of spliceosomal C complexes assembled on MINX or PM5 pre-mRNA, dialyzed nuclear extracts were preincubated for 10 min at 30°C with 1 μM of dominant-negative mutant of PRP16 protein (dnPRP16) (8 (link)). For both C and C* purifications, m7G(5′)ppp(5′)G-capped PM5, MINX, or MINX GG pre-mRNAs (5 nM) were preincubated with 20 nM MS2-MBP fusion protein for 30 min on ice before addition to the splicing reaction. Splicing reactions were carried out at 30°C with 40% (v/v) nuclear extract in splicing buffer [3 mM MgCl₂, 65 mM KCl, 20 mM Hepes-KOH (pH 7.9), 2 mM ATP, and 20 mM creatine phosphate]. Splicing was carried out for 1.5 hours for purification of C complexes assembled on MINX and PM5 pre-mRNA, 3 hours for C* complexes assembled on PM5, and 1 hour for C* complexes assembled on MINX GG pre-mRNA. Splicing reactions were then chilled on ice, centrifuged for 30 min at 12,000 rpm to remove aggregates, and loaded onto an MBP Trap HP column (GE Healthcare) after the addition of 100 mM NaCl. The column was washed with G-150 buffer [20 mM Hepes-KOH (pH 7.9), 1.5 mM MgCl₂, and 150 mM NaCl], and the complexes were eluted with G-150 buffer containing 1 mM maltose. Eluted complexes were loaded onto a 36-ml linear 5 to 20% (w/v) sucrose gradient prepared in G-150 buffer and centrifuged at 27,200 rpm for 9 hours at 4°C in a Surespin 630 (Thermo Fisher Scientific) rotor, and fractions were harvested from the bottom of the gradient. RNA from peak gradient fractions was separated on denaturing 4 to 12% NuPAGE gels (Life Technologies) and visualized by staining with SYBER Gold (Thermo Fischer Scientific).

Dybkov O., Preußner M., El Ayoubi L., Feng V.Y., Harnisch C., Merz K., Leupold P., Yudichev P., Agafonov D.E., Will C.L., Girard C., Dienemann C., Urlaub H., Kastner B., Heyd F, & Lührmann R. (2023). Regulation of 3′ splice site selection after step 1 of splicing by spliceosomal C* proteins. Science Advances, 9(9), eadf1785.

Publication 2023

Buffers Dithiothreitol Edetic Acid Gels Glycerin Gold HeLa Cells HEPES Magnesium Chloride Maltose mRNA Precursor Mycoplasma Phenylmethylsulfonyl Fluoride Phosphocreatine Protein C Proteins Sodium Chloride Spliceosomes Sucrose

Cross-Linking and Mass Spectrometry of Spliceosomes

Purified spliceosomal PM5 C* complexes were cross-linked with 150 μM BS3 for 30 min at 20°C. After a buffer exchange to decrease the sucrose concentration to below 2% and a concentration step in an Amicon Ultracell-50 centrifugal filter unit with a 50-kDa cutoff (Merck Millipore), the BS3 cross-linked spliceosomes were subjected to 5 to 20% sucrose gradient centrifugation as described above. Spliceosomes from peak fractions were pelleted by ultracentrifugation in an S100-AT4 rotor (Thermo Fisher Scientific) and analyzed as described previously (47 (link)). Briefly, peptides generated after in-solution tryptic digestion were reverse phase–extracted and fractionated by gel filtration on a Superdex Peptide PC3.2/30 column (GE Healthcare). Fifty-microliter fractions corresponding to an elution volume of 1.2 to 1.8 ml were analyzed in triplicate on Thermo Fisher Scientific Q Exactive HF, Q Exactive HF-X, and Orbitrap Fusion Tribrid mass spectrometers. Protein-protein cross-links were identified by pLink 2.3.9 search engine (http://pfind.org/software/pLink) according to the recommendations of the developer (48 (link)). For simplicity, the cross-link score is represented as a negative value of the common logarithm of the original pLink score [i.e., score = −log₁₀(pLink score)]. For the model building, a maximum distance of 30 Å between the Cα atoms of the cross-linked lysines was allowed.

Publication 2023

Buffers Centrifugation Digestion Gel Chromatography Lysine Peptides Proteins S100 Proteins Spliceosomes Sucrose Trypsin Ultracell Ultracentrifugation

Modeling the Human Spliceosomal PM5 C* Complex

To generate a model of the human PM5 C* complex, we first rigid-body–fitted previously published protein and RNA structures from the human spliceosomal C* and P complexes using UCSF Chimera (51 (link)). To obtain a better fit into the EM density map, individual RNAs and proteins and domains thereof were subsequently refitted using UCSF Chimera and manually readjusted in Coot (52 (link)). After an initial round of real-space refinement in Phenix (53 (link)) and a manual optimization in Coot to improve the fit, the map was searched for unassigned elements. Guided by the composition of the PM5 C* complex (fig. S1 and table S1) and CXMS data (data S8), candidate proteins that were cross-linked to the already modeled parts of the complex were selected. Published experimental models or AlphaFold-predicted models (29 (link), 30 (link)) of these candidates were examined and docked using UCSF Chimera. The individual models were manually checked and rebuilt in Coot if the resolution of the map allowed it. Using this approach, we were able to extend the models of several proteins including NKAP, PRP22, SDE2, and SYF2. Furthermore, it was possible to build partial models of ESS2 and NOSIP, as well as to rigid-body dock parts of CXORF56, PPIL3, and TLS1 not localized/modeled in previously published spliceosomal complexes. All AlphaFold models of various structural domains and helices of C* proteins (CXORF56, FAM50A, NKAP, NOSIP, SDE2, TLS1, and ESS2) that were fitted into our PM5 hC* density belong to the confident and very confident classes (as defined by the AlphaFold program). Although some single-stranded stretches (e.g., FAM50A amino acids 118 to 128 and 154 to 168 and NOSIP amino acids 121 to 158) belong to lower confidence classes, they fit well into our hC* EM density and, in some cases, even better into unassigned EM density of the previously published hP complex (see fig. S11). In addition, their location in PM5 C* is supported by protein cross-linking. For PRP22, AlphaFold-predicted models of some of the peripheral parts of its helicase domain (e.g., amino acids 107 to 154, 386 to 501, and 530 to 556) had a lower confidence score, but they fit well into the PM5 hC*, as well as hP densities, and were further supported by protein cross-linking data (see fig. S6). Several nucleotides of the intronic RNA (the PPT loop) were built de novo. The RNA fragment that mimics the 3′ exon was docked using a corresponding RNA element from the S. cerevisiae P complex structure and rebuilt in Coot. The model, excluding its parts located in the less well-resolved peripheral parts of the map, was iteratively refined in Phenix and inspected/adjusted in Coot. The model was validated in Phenix using a cryo-EM validation package (table S4). A summary of the appropriate existing atomic coordinate models used as templates and the procedures used to generate the model is provided in table S5. PyMOL (https://pymol.org/2/) and UCSF Chimera were used to generate the figures.

Publication 2023

Amino Acids Chimera citrate carrier DNA Helicases Exons Helix (Snails) Homo sapiens Human Body Introns Muscle Rigidity Nucleotides Parts, Body Proteins Spliceosomes

Preparation of PM5 C* Spliceosomal Complexes

Purified spliceosomal PM5 C* complexes were in-batch cross-linked with 0.1% glutaraldehyde for 1 hour at 4°C and quenched with 100 mM aspartate (pH 8.0) on ice. The sample was buffer-exchanged and concentrated to 1 ml in an Amicon 50-kDa cutoff unit and purified further by a second sucrose density gradient centrifugation step as described above. Fractions were harvested from the bottom of the gradient. The peak fractions containing PM5 C* complexes were pooled, buffer-exchanged, and concentrated in an Amicon 50-kDa cutoff unit to decrease the sucrose below 0.1% and reach a protein concentration of 0.8 g/liter. UltrAUFoil Gold 200 mesh grids with R2/2 holey gold film (Quantifoil) were glow-discharged for 100 s at 15 mA. After applying a 4-μl sample and a wait time of 15 s, the grid was blotted for 3 s with a blot force of 5 and vitrified by plunging into liquid ethane using a Vitrobot Mark IV (Thermo Fisher Scientific) operated at 4°C and 100% humidity.

Publication 2023

Aspartate Buffers Centrifugation, Density Gradient Ethane Glutaral Gold Humidity Spliceosomes Staphylococcal Protein A Sucrose

Top products related to «Spliceosomes»

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.

Interleukin 3 (il 3) by Thermo Fisher Scientific

Sourced in United States, United Kingdom, Germany, Canada, Israel, Switzerland, France, Macao, Japan

IL-3 is a recombinant protein that supports the growth and differentiation of hematopoietic stem and progenitor cells. It plays a critical role in the regulation of blood cell production.

Qiaamp dna mini kit by Qiagen

Sourced in Germany, United States, France, United Kingdom, Netherlands, Spain, Japan, China, Italy, Canada, Switzerland, Australia, Sweden, India, Belgium, Brazil, Denmark

The QIAamp DNA Mini Kit is a laboratory equipment product designed for the purification of genomic DNA from a variety of sample types. It utilizes a silica-membrane-based technology to efficiently capture and purify DNA, which can then be used for various downstream applications.

Lipofectamine rnaimax by Thermo Fisher Scientific

Sourced in United States, China, United Kingdom, Germany, Japan, France, Canada, Switzerland, Denmark, Belgium, Italy, Australia, Singapore, Spain, Colombia, Sweden, Netherlands, New Zealand, Poland, Pakistan, Lithuania

Lipofectamine RNAiMAX is a transfection reagent designed for efficient delivery of small interfering RNA (siRNA) and short hairpin RNA (shRNA) into a wide range of cell types. It is a cationic lipid-based formulation that facilitates the uptake of these nucleic acids into the target cells.

Gotaq qpcr master mix by Promega

Sourced in United States, Germany, China, Switzerland, United Kingdom, Italy, France, Japan, Australia, Canada, Spain, Singapore, Austria

GoTaq qPCR Master Mix is a ready-to-use solution for real-time quantitative PCR (qPCR) analysis. It contains the GoTaq DNA Polymerase, dNTPs, MgCl2, and reaction buffers optimized for efficient and specific qPCR amplification.

Biomark system by Standard BioTools

Sourced in United States

The BioMark System is a high-throughput microfluidic platform designed for sensitive and accurate gene expression analysis. It enables parallel processing of multiple samples and target genes, facilitating efficient and automated data collection.

Typhoon phosphoimager by GE Healthcare

The Typhoon phosphoimager is a laboratory instrument designed for the detection and quantification of radioactively labeled samples, such as proteins and nucleic acids. The device utilizes a sensitive photomultiplier tube to capture and measure the intensity of radiation emitted from the samples, producing high-resolution digital images for analysis.

Taqman microrna reverse transcription kit by Thermo Fisher Scientific

Sourced in United States, China, Germany, Japan, United Kingdom, Lithuania, Italy, Canada, Australia, Belgium, France, Switzerland, Spain

The TaqMan MicroRNA Reverse Transcription Kit is a laboratory product designed to enable the reverse transcription of microRNA molecules. It provides the necessary reagents and protocols for the conversion of microRNA into complementary DNA (cDNA) for subsequent analysis and quantification.

Taqman low density array by Thermo Fisher Scientific

Sourced in United States, Germany, Canada

The TaqMan Low Density Array is a pre-configured and ready-to-use microfluidic card designed for real-time PCR gene expression analysis. It allows for the simultaneous quantification of multiple gene targets in a single sample.

Miscript 2 rt kit by Qiagen

Sourced in Germany, United States, China, Japan, United Kingdom, Netherlands, France, Canada, Italy, Spain, Denmark, Belgium

The MiScript II RT Kit is a laboratory equipment product designed for reverse transcription of microRNA (miRNA) and other small RNA molecules. The kit provides the necessary components for the conversion of RNA into complementary DNA (cDNA), which can be used for downstream applications such as real-time PCR analysis.

What are Spliceosomes and what is their role in gene expression?

Spliceosomes are large, dynamic ribonucleoprotein complexes responsible for the removal of introns from pre-mRNA molecules. They play a crucial role in gene expression by facilitating the splicing process, which helps generate mature mRNA transcripts. Spliceosomes are composed of small nuclear ribonucleoproteins (snRNPs) and additional accessory proteins, and their assembly and disassembly are tightly regulated. Understanding spliceosome structure, function, and regulation is essential for research into alternative splicing, RNA processing, and gene expression.

What are some common challenges in working with Spliceosomes?

Working with Spliceosomes can present some challenges, such as ensuring the reproducibility and accuracy of your experiments. Different protocols and methods may yield varying results, making it difficult to identify the most effective approach for your specific research goals. Additionally, the dynamic and complex nature of Spliceosomes can make it challenging to study their structure and function in detail.

How can PubCompare.ai help with Spliceosome research?

PubCompare.ai can help enhance your spliceosome research workflows by allowing you to more efficiently screen protocol literature and leverage AI to pinpoint critical insights. The platform's AI-driven analysis can help researchers identify the most effective protocols related to Spliceosomes, highlighting key differences in protocol effectiveness and enabling you to choose the best option for reproducibility and accuaracy in your studies.

Are there different types or variations of Spliceosomes?

Yes, there are different types and variations of Spliceosomes. The major types include the U2-dependent spliceosome and the U12-dependent spliceosome, which differ in their snRNP composition and the types of introns they recognize. Additionally, certain organisms may have unique spliceosomal variations, and alternative splicing can lead to the formation of different spliceosomal complexes within the same cell.

What are some specific applications of Spliceosome research?

Spliceosome research has a wide range of applications, including: 1. Understanding gene expression and regulation: Studying the role of Spliceosomes in the splicing process can provide insights into how gene expression is controlled and how alternative splicing contributes to the diversity of the transcriptome. 2. Investigating RNA processing disorders: Aberrant spliceosome function has been implicated in various diseases, such as cancer, neurological disorders, and genetic diseases. Spliceosome research can help identify potential therapeutic targets. 3. Developing new biotechnological tools: The unique properties and dynamics of Spliceosomes can be leveraged to develop novel tools for gene editing, RNA manipulation, and therapeutic applications.

More about "Spliceosomes"

Spliceosomes are large, dynamic ribonucleoprotein (RNP) complexes essential for gene expression.
These molecular machines play a crucial role in RNA processing by removing non-coding introns from pre-messenger RNA (pre-mRNA) molecules, generating mature mRNA transcripts.
Spliceosome assembly and disassembly are tightly regulated processes, involving small nuclear ribonucleoproteins (snRNPs) and additional accessory proteins.
Understanding the structure, function, and regulation of spliceosomes is key for research into alternative splicing, RNA processing, and gene regulation.
Techniques like RT-qPCR (using reagents like TRIzol, GoTaq qPCR Master Mix, and TaqMan MicroRNA Reverse Transcription Kit), microarray analysis (BioMark System), and next-generation sequencing (with QIAamp DNA Mini Kit) can provide insights into spliceosome-mediated gene expression.
Optimizing spliceosome research workflows is essential for reliable and reproducible studies.
PubCompare.ai can help identify the most reliable protocols from literature, preprints, and patents, and compare them to ensure accuracy and consistency in your spliceosome research.
By leveraging this AI-driven platform, you can improove the quality and impact of your studies on this dynamic and essential molecular complex.