The GENCODE lncRNAs set version 7 (Harrow et al. 2012 ) was downloaded from the official GENCODE ftp repository: ftp://ftp.sanger.ac.uk/pub/GENCODE/ . The protein-coding set used to define the lncRNAs category was extracted from the whole GENCODE 7 annotation (GENCODE.v7.annotation.gtf.gz on the ftp) and corresponds to transcripts having both gene and transcript biotypes annotated as “protein_coding” with the “known” status. This results in a protein-coding set of 20,646 genes, 76,006 transcripts, and 743,827 exons. Then, lncRNA and protein-coding genomic coordinates were intersected at the exons, introns, and gene levels using both the Bedtools suite (Quinlan and Hall 2010 (link)) and custom scripts. In an initial filtering step, we removed all lncRNAs that were shorter than 200 nt or overlapped a protein-coding exon on the same strand. The resulting set was divided into categories “intergenic” and “genic.” An lncRNA not intersecting any protein-coding loci was defined as intergenic and then subclassified according to its transcription orientation with the closest protein-coding gene (same sense, convergent, or divergent). The genic lncRNA set was classified as exonic if at least one of its exons intersects a protein-coding exon by at least 1 bp. lncRNAs intersecting a protein-coding exon on the same strand were discarded from all analyses. Otherwise, lncRNAs were classified as “intronic,” i.e., completely contained within protein-coding introns (sense or antisense) or overlapping (sense or antisense), i.e., when the protein-coding transcript was located within the intron of the lncRNA. For each category, a best mRNA partner is defined according to (1) its closer proximity to the lncRNA (intergenic category) or (2) a higher number of nucleotides intersecting with the candidate lncRNA (genic category). Finally, for the comparative analysis with the lncRNAs sets, we defined a stringent set of mRNAs which corresponds to transcripts having both gene and transcripts annotated as “protein_coding” with status “known,” a “ccdsid” tag and no match with “{start/stop}_NF” (Not Found). This results in a stringent protein coding set of 17,998 genes, 30,046 transcripts, and 319,048 exons. A full breakdown of subclassifications of lncRNA can be found in Supplemental Table S1. Furthermore, the number of genes used in every analysis is detailed in Supplemental Table S9.
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Amino Acid
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Intein
Intein
Inteins are self-splicing protein segments that are able to excise themselves from a host protein and join the flanking sequences together.
Thses genetic elements are found in a variety of organisms, including bacteria, archaea, and some eukaryotes.
Inteins play a role in protein splicing and can be useful tools for protein engineering and biotechnology applications, such as protein purification and the generation of cyclic proteins.
Researchers can utilize PubCompare.ai's AI-driven platform to easily locate and compare intein-related protocols from published literature, preprints, and patents, optimizing their research with powerful analysis tools and expertise.
Thses genetic elements are found in a variety of organisms, including bacteria, archaea, and some eukaryotes.
Inteins play a role in protein splicing and can be useful tools for protein engineering and biotechnology applications, such as protein purification and the generation of cyclic proteins.
Researchers can utilize PubCompare.ai's AI-driven platform to easily locate and compare intein-related protocols from published literature, preprints, and patents, optimizing their research with powerful analysis tools and expertise.
Most cited protocols related to «Intein»
Catabolism
Exons
Gene Products, Protein
Genes
Genome
Intein
Introns
Nucleotides
Proteins
RNA, Long Untranslated
RNA, Messenger
SET protein, human
Staphylococcal Protein A
Strains
Transcription, Genetic
The following strategies were used when recruiting variants from the databases into our positive group: (i) based on the National Center for Biotechnology Information Reference Sequence (RefSeq) database release 59 (23 (link)), we only included variants within the splicing consensus regions (−3 to +8 at the 5′ splice site and −12 to +2 at the 3′ splice site) at the exon/intron boundaries of protein-coding genes; (ii) within the consensus regions, all variants at GT-AG sites were excluded, because these sites are so invariant that almost all mutations that occur at these sites affect splicing and most tools can predict their impact with very high accuracy (22 (link),24 ); (iii) only single nucleotide substitutions (i.e. SNVs) were retained; (iv) variants were excluded if information provided by the database did not contain biological evidence (e.g. merely computational predictions or statistical associations); and (v) to avoid duplication, variants present in more than one database were only counted once. The first three criteria were also applied to the recruitment of negative variants from the 1000 Genomes Project phase 1 data. Furthermore, additional filtering strategies were implemented: we chose variants within genes that have only one annotated transcript in RefSeq database release 59 (this only applies to recruitment of negative variants) (23 (link)), and we only chose variants with minor allele frequency >0.05 in combined populations of European ancestry. The rationale is that as individuals of European ancestry are the most commonly studied subjects; if a common variant alters splicing, it is highly likely the alternatively spliced transcript has been reported in this population. In contrast, a common variant in a gene without alternative transcripts reported is unlikely to alter splicing of that gene. For the additional test set, we chose the variants reported in the work of Houdayer et al. that are (i) within splicing consensus regions defined above; (ii) single nucleotide substitutions; and (iii) not in our dataset (22 (link)). All variants were annotated using ANNOVAR, a software package that performs functional annotation of genetic variants from high-throughput sequencing data (25 (link)) and based on human reference sequence assembly GRCh37/hg19.
Base Sequence
Biopharmaceuticals
Europeans
Exons
Gene Annotation
Genes
Genetic Diversity
Genome
Homo sapiens
Intein
Mutation
Nucleotides
Splice Acceptor Site
Splice Donor Site
Strains
Using genome-protein alignments generated by the Prot_map program, the PSF program produces a list of alignments possessing the following properties for each protein. First, the identity in blocks of alignment exceeds a certain value. Second, a substantial portion of protein sequence is included in the alignment. Third, the genomic location of alignment differs from that of parent gene. And fourth, at least one of four events is observed: damage to an ORF - there is one or more frameshifts or internal stop codons; a single exon with a close poly-A site - the poly-A site is too close to a 3' end of an alignment, while the carboxyl terminus of the protein sequence is aligned to the last amino acid, and a single exon covers 95% of protein sequence; loss of introns - protein coverage by alignment is at least 95%, and the number of exons is fewer than in the parent gene by a certain number; or the protein sequence is not preserved - the ratio of non-synonymous to synonymous replacements exceeds a certain threshold (Ka/Ks > 0.5). Ka/Ks is calculated relative to the parent gene by the method presented by Nei and Gojobori [18 (link)].
Amino Acids
Amino Acid Sequence
Codon, Terminator
Exons
Frameshift Mutation
Genes
Genome
Intein
Parent
Poly A
Proteins
Surgical Replantation
Our method of searching for pseudogenes can work with two types of initial information available. One type contains exon-intron structures of annotated genes and their protein sequences for a genome under analysis. To get such information, we can execute a gene finding pipeline, such as Fgenesh++. In this case, we run Prot_map program with a set of protein sequences to find possible significant genome-protein alignments that do not correspond to a location of a gene for mapped protein. Another type of initial data can be a set of known proteins for a given organism. Having such data, we can restore gene structure of a given protein using the Prot_map program. For each mapped protein, we can select the best scoring mapping and the computed exon-intron structure as the 'parent' gene structure of this protein. If the alignment of a protein with its own parent has obvious internal stop codons or frameshifts, this locus could be included in the list of potential pseudogenes, but we need to keep in mind more trivial explanations, such as sequencing errors. Such loci cannot be analyzed on the basis of their Ka/Ks or checked for intron losses. In any case, for each of two cases we have a set of protein sequences, their parent gene structures, and protein-genome alignments for further analysis to identify pseudogenes.
Amino Acid Sequence
Codon, Terminator
Exons
Frameshift Mutation
Genes
Genetic Structures
Genome
Intein
Introns
Parent
Proteins
Pseudogenes
SET protein, human
Staphylococcal Protein A
Vaginal Diaphragm
All constructs for constitutive mammalian expression were assembled using Golden Gate cloning (19 (link)) of PCR products into a pCAG-T7 destination vector. hGFAP and Thy1.2 expression constructs were assembled using standard cloning techniques. All cloning strategies can be provided upon request. Functional plasmids used in this study have been deposited with Addgene and annotated sequences of all plasmids are provided in Supplementary Note 1 . hGFAP expression constructs are based on hGFAP-fLuc (20 (link)) (Addgene plasmid 40589). Thy1.2 expression constructs are based on Thy1 promoter construct (21 (link)) (Addgene plasmid 20736). Codon-optimized SSRs Bxb1, B3 and KD (Genscript) and gp41-1 and DnaE split-inteins (IDT) were gene-synthesized. DreO was a generous gift from C. Monetti. For the construction of Co-InCre expression plasmids, codon-optimized Cre (22 (link)) (iCre) was split into a N-terminal (aa 19–59) and a C-terminal (aa 60–343) fragment. Amino acid sequences for gp41-1N and gp41-1C split-intein fragments (16 (link)) were back-translated using Emboss Backtranseq (http://www.ebi.ac.uk/Tools/st/emboss_backtranseq/ ) with mouse codon usage and fused to N- and C-terminal iCre fragments, respectively. The Roxed-Cre expression plasmid was constructed by introducing a rox-flanked STOP cassette [based on the STOP cassette of the CAG-Floxed ZsGreen plasmid (11 (link)), Addgene plasmid 22798] in between iCre codons 177 (aa 59) and 180 (aa 60). A single nucleotide (G) was introduced 5′ of the rox-site to create an in-frame insertion of 33 bp into the iCre open reading frame upon Dre recombination.
Amino Acid Sequence
Cloning Vectors
Codon
Codon Usage
Genes
Intein
Mammals
Mice, Laboratory
Nucleotides
Plasmids
Reading Frames
Recombination, Genetic
Most recents protocols related to «Intein»
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2',5'-oligoadenylate
Cloning Vectors
DNA Restriction Enzymes
Exons
Genes
Genome
Intein
Ligation
Mice, Laboratory
Mutation
Plasmids
Vertebral Column
The predicted miRNA targeting network was constructed by extracting the longest 3′UTR sequence for each of the 2848 protein-coding genes identified in our study from the body-muscle transcriptome dataset we identified in past studies [12 (link), 13 (link)]. We converted the 3′UTR sequences to FASTA format and parsed the file using the miRanda algorithm [51 (link)] using stringent parameters (-strict -sc -1.2). We used either the 5p or the 3p strand identified in our IP-coupled RT-qPCR approach shown in Supplemental Figs. S5 and S6 . All 16 miRNAs used were identified in this study with an FPKM value ≥1 (Supplemental Table S1 ). This value represents the top 5% of miRSVR scores produced by the miRanda algorithm. We also restrict the analysis to pairing scores >150 and energy score < −7. We only used miRNAs that have been previously detected in C. elegans with more than 1000 reads in the miRbase database (www.mirbase.org ) and that are not present in introns of protein-coding genes. The miRanda algorithm produced 118 high-quality predicted targets for 16 miRNAs. The networks were then built using the Cytoscape software [52 (link)] and uploaded to the Network Analyst online software [53 (link)] to produce the images shown in Fig. 5 .
3' Untranslated Regions
Genes
Human Body
Intein
MicroRNAs
Muscle Tissue
Open Reading Frames
Transcriptome
For clarity, the numbering convention for amino acids in Ca(v)1.2 used throughout this investigation refers to the rabbit splice variant CACH2A. A plasmid expressing the human HHT-1 splice variant of α1C was kindly provided by Professor Chris Peers, University of Leeds, UK. Regions of this cDNA that encode intracellular loops of the protein were subcloned into the vector pEYFP-C1 (Clontech).
Plasmids to express split α1C (CFP-[I-II]-N-intein and C-intein-[III-IV]-YFP based on the rabbit splice variant CACH2A) and β2a-CFP were kindly provided by Professor Stanley Colecraft, Columbia University. Adenoviruses that express these proteins were generated using the Clontech Adeno-X system and purified and titered using standard techniques. Point mutations were generated using the QuikChange II Site-Directed Mutagenesis Kit (Agilent).
Plasmids to express split α1C (CFP-[I-II]-N-intein and C-intein-[III-IV]-YFP based on the rabbit splice variant CACH2A) and β2a-CFP were kindly provided by Professor Stanley Colecraft, Columbia University. Adenoviruses that express these proteins were generated using the Clontech Adeno-X system and purified and titered using standard techniques. Point mutations were generated using the QuikChange II Site-Directed Mutagenesis Kit (Agilent).
Adenoviruses
Amino Acids
Cloning Vectors
Conferences
DNA, Complementary
Homo sapiens
Intein
Mutagenesis, Site-Directed
Plasmids
Point Mutation
Proteins
Protoplasm
Rabbits
To perform downstream analyses, we first aligned the methylase sequences using MAFFT (v7.471) [30 (link)]. MAFFT was used to create two different alignments. The first (which we refer to as the compact alignment) used the globalpair and reorder settings, and a maximum iteration count of 1000, while the second (which we refer to as the gappy alignment) used the globalpair and reorder settings, a maximum iteration count of 1000, and an unalignlevel of 0.8. SeaView (v5.0.4) [31 (link)] was used to inspect alignments and to then define four separate site sets: one for the methylase excluding the insertion elements and one each for the three insertion elements. We will refer to the site set containing only the methylases and not the insertion elements as the methylase extein. The methylase extein set was copied and split into three different subsets. Each one contained only the methylase sequences which were invaded by a given insertion element such that there was a subset for intein-containing methylases, a subset for ShiLan domain-containing methylases, and a subset for endonuclease-containing methylases. The alignment of these three extein sub-datasets was the same as in the compact alignment.
DNA Insertion Elements
Endonuclease
Exteins
Intein
Methyltransferase
A predicted protein structure was generated for the PopTart_63 methylase, which contains no insertion elements. This methylase sequence was used as input for the AlphaFold v2.2.4. [33 (link)] Jupyter notebook hosted on Google Colab. The predicted structure was then colored in Chimera [34 (link)] to indicate the insertion sites of the ShiLan domain, intein, and second homing endonuclease. In addition, AlphaFold v2.2.4 was used to generate a predicted structure for the full methylase from the Taj phage. The Taj methylase does not contain the ShiLan domain nor the intein, but does contain the second homing endonuclease. The predicted structure was colored in Chimera to indicate the three insertion sites and the second homing endonuclease domain.
Bacteriophages
Chimera
DNA Insertion Elements
Endonuclease
Intein
Methyltransferase
Proteins
Top products related to «Intein»
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The IMPACT kit is a recombinant protein purification system designed for the efficient expression and purification of target proteins in E. coli. The kit utilizes an intein-based system to achieve self-cleavage and release of the target protein from the affinity tag, enabling simple and effective purification.
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The Chitin column is a chromatography column used for the purification of proteins. It consists of a matrix of chitin, a natural polymer derived from the exoskeleton of crustaceans, which can selectively bind to proteins containing a chitin-binding domain. The column is designed to facilitate the capture, wash, and elution of these target proteins.
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More about "Intein"
Inteins are fascinating self-splicing genetic elements found in a variety of organisms, including bacteria, archaea, and some eukaryotes.
These protein segments are able to excise themselves from a host protein and join the flanking sequences together, a process known as protein splicing.
Inteins play a crucial role in protein engineering and biotechnology applications, such as protein purification and the generation of cyclic proteins.
Researchers can utilize powerful AI-driven platforms like PubCompare.ai to easily locate and compare intein-related protocols from published literature, preprints, and patents.
This allows them to optimize their research by identifying the best protocols and products available.
Chitin resin, IMPACT kits, and T4 DNA ligase are some of the tools commonly used in intein-related research.
Chitin columns and beads can be employed for protein purification, while the QIAprep Spin Miniprep Kit and BigDye Terminator v3.1 may be utilized for DNA manipulation and sequencing.
Propargylamine hydrochloride, PTXB1, and PTYB12 are also associated with intein-based techniques.
By leveraging the insights and expertise provided by AI-powered platforms, researchers can streamline their intein-related studies, leading to more efficient and effective research outcomes.
These protein segments are able to excise themselves from a host protein and join the flanking sequences together, a process known as protein splicing.
Inteins play a crucial role in protein engineering and biotechnology applications, such as protein purification and the generation of cyclic proteins.
Researchers can utilize powerful AI-driven platforms like PubCompare.ai to easily locate and compare intein-related protocols from published literature, preprints, and patents.
This allows them to optimize their research by identifying the best protocols and products available.
Chitin resin, IMPACT kits, and T4 DNA ligase are some of the tools commonly used in intein-related research.
Chitin columns and beads can be employed for protein purification, while the QIAprep Spin Miniprep Kit and BigDye Terminator v3.1 may be utilized for DNA manipulation and sequencing.
Propargylamine hydrochloride, PTXB1, and PTYB12 are also associated with intein-based techniques.
By leveraging the insights and expertise provided by AI-powered platforms, researchers can streamline their intein-related studies, leading to more efficient and effective research outcomes.