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DNA Insertion Elements

DNA Insertion Elements are genetic sequences that can be integrated into a host genome, often playing a role in gene regulation, genome evolution, and genetic diversity.
These mobile genetic elements can replicate and insert themselves throughout the genome, leading to insertional mutagenesis and potentially altering gene expression.
Understanding the mechanisms and impact of DNA Insertion Elements is crucial for advancing research in areas such as genetics, genomics, and evolutionary biology.
Leveraging PubCompare.ai's AI-driven protocol comparison tool can optimize your DNA Insertion Element research by helping you identify the most effective methods and products from the literature, enhancing reproducibility and accuracy in your studies.

Most cited protocols related to «DNA Insertion Elements»

1
Metagenomic Assembly with metaSPAdes
In addition to single-nucleotide variants and small indels, strain variation is often manifested as highly diverged regions, insertions of mobile elements, rearrangements, large deletions, parallel gene transfer, etc. The green edges in the assembly graph shown in Figure 3 result from an additional copy of a mobile element in a rare strain2 (compared with the abundant strain1), while the blue edge corresponds to a horizontally transferred gene (or a highly diverged genomic region) in a rare strain3 (compared to the abundant strain1). Such edges fragment contigs corresponding to the abundant strain1; for example, the green edges in Figure 3 (bottom right) break the edge c into three shorter edges. We note that the edges in the assembly graph are condensed; that is, they represent nonbranching paths formed by k-mers.
We refer to edges originating from rare strain variants within the assembly graph of a strain mixture as filigree edges. Traditional genome assemblers use a global threshold on read coverage to remove the low-coverage edges (that typically result from sequencing errors) from the assembly graph during the graph simplification step. However, this approach does not work well for metagenomic assemblies, since there is no global threshold that (1) removes edges corresponding to rare strains and (2) preserves edges corresponding to rare species. Similarly to IDBA-UD and MEGAHIT, metaSPAdes analyzes the coverage ratios between adjacent edges in the assembly graph, classifying edges with low-coverage ratios as potential filigree edges.
We denote the coverage of an edge e in the assembly graph as cov(e) and define the coverage cov(v) of a vertex v as the maximum of cov(e) over all edges e incident to v. Given an edge e incident to a vertex v and a threshold ratio (the default value is 10), a vertex v predominates an edge e if its coverage is significantly higher than the coverage of the edge e; that is, if ratio · cov(e) < cov(v). An edge (v,w) is weak if it is predominated by either v or w. Note that filigree edges are often classified as weak since their coverage is much lower than the coverage of adjacent edges resulting from abundant strains.
metaSPAdes disconnects all weak edges from their predominating vertices in the assembly graph. Disconnection of a weak edge (v,w) in the assembly graph from its starting vertex v (ending vertex w) is simply a removal of its first (last) k-mer rather than removal of the entire condensed edge. We emphasize that, in contrast to IDBA-UD and MEGAHIT, we disconnect rather than remove weak edges in the assembly graph since our goal is to preserve the information about rare strains whenever possible, that is, when it does not lead to a deterioration of the consensus backbone.
Nurk S., Meleshko D., Korobeynikov A, & Pevzner P.A. (2017). metaSPAdes: a new versatile metagenomic assembler. Genome Research, 27(5), 824-834.
Publication 2017
Debility DNA Insertion Elements Fast Green FCF Gene Deletion Gene Rearrangement Genes Gene Transfer, Horizontal Genome INDEL Mutation Metagenome Nucleotides Strains Vertebral Column
2
Insights into Hepatitis E Virus Protein Modifications
The sequences of all the PPRs were identified with reference to the 11,938 sequences of Orthohepevirus A (including 338 complete HEV genomes) available in the Virus Pathogen Resource (VIPR) database.5 Selected sequences were systematically searched to identify insertions so that they could be used, together with those identified by PacBio sequencing, for further analysis. The compositions of HEV PPR insertions/duplications were determined and their post-translational modifications predicted by analyzing a range of parameters. Potential ubiquitination sites were identified using the BDM-PUB server6 with a threshold of >0.3 average potential score. Potential phosphorylation sites were identified using the NetPhos 3.1 server7 with a threshold of >0.5 average potential score. Potential acetylation sites were identified using the Prediction of Acetylation on Internal Lysines (PAIL) server8 with a threshold of >0.2 average potential score. Potential N-linked glycosylation sites were identified using the NetNGlyc 1.0 server9 with a threshold of >0.5 average potential score. Potential methylation sites were identified using the BPB-PPMS server10 with a threshold of >0.5 average potential score. Nuclear export signal (NES) sites were identified using the Wregex server11 with parameters NES/CRM1 and Relaxed. Nuclear localization signal (NLS) sites were identified using SeqNLS12 with a 0.86 cut-off. The amino acid composition (proportions of amino acids), physico-chemical composition, and net load were analyzed with R. Principal component analysis (PCA) is a mathematical algorithm that reduces the dimensionality of the data while retaining most of the variation in a data set. PCA allows to identify new variables, the principal components, which are linear combinations of the original variables (Ringner, 2008 (link)). PCA was done (excluding the amino acid composition due to redundancy with physico-chemical properties) to summarize and visualize the information on the variables in our data set (Abdi and Williams, 2010 (link)); each variable was then studied independently. An in-house R-pipeline based on the amino acid sequences and the results of each analysis was used to generate bar plots for amino acid composition. The amino acid compositions were assigned to one of two categories: sequences with insertions/duplications (including insertions of human genome and HEV genome duplications) and sequences without insertions/duplications. The other parameters were assigned to one of three categories: sequences with insertions, those with duplications, and sequences without insertion/duplication.
Lhomme S., Nicot F., Jeanne N., Dimeglio C., Roulet A., Lefebvre C., Carcenac R., Manno M., Dubois M., Peron J.M., Alric L., Kamar N., Abravanel F, & Izopet J. (2020). Insertions and Duplications in the Polyproline Region of the Hepatitis E Virus. Frontiers in Microbiology, 11, 1.
Publication 2020
Acetylation Amino Acids Amino Acid Sequence chemical composition chemical properties DNA Insertion Elements Genome Genome, Human Insertion Mutation Lysine Methylation Nuclear Export Signals Nuclear Localization Signals Pathogenicity Phosphorylation Protein Glycosylation Sequence Insertion Ubiquitination Virus
3
Versatile Expression Vector Cloning
Expression clones were generated by PCR and ligation-independent cloning (LIC) into one or more of a set of vectors. The first choice of vector is pNIC28-Bsa4. It is derived from the pET28a vector (Merck), with the expression of the cloned gene driven by the T7-LacO system. Proteins cloned in this vector are fused to an amino-terminal tag of 23 residues (MHHHHHHSSGVDLGTENLYFQ∗SM) including a hexahistidine (His6) and a TEV-protease cleavage site (marked with *). Additional features include cloning sites for ligation-independent cloning (LIC) separated by a “stuffer” fragment that includes the SacB gene. The SacB protein (levansucrase) converts sucrose into a toxic product, allowing selection for recombinant plasmids on agar plates containing 5% sucrose.
Several alternative expression vectors have been used with selected targets (Table 2). pNIC-CTHF appends a C-terminal tag including a TEV-protease cleavage site followed by His6 and a flag epitope. Larger fusion tags include E. coli thioredoxin (combined with hexahistidine and a TEV cleavage site), GST, and a reversible streptavidin binding tag (derived from vector pBEN-SBP-SET1, Stratagene). Baculovirus expression vectors were constructed based on pFastBac (invitrogen), incorporating the same arrangement of LIC2 cloning sites as the bacterial vectors. We have recently adopted a highly charged, globular domain termed the Z-basic tag (Hedhammar and Hober, 2007 (link)), which may provide substantial enrichment of the tagged protein on cation-exchange columns. The Z-basic domain is flanked by a His6 tag and a TEV cleavage site.
An important consideration in vector construction is the ease of cloning the same gene fragment into multiple contexts. LIC requires short (12–16 bp) extensions at both ends of the insert that overlap vector sequences flanking the cloning sites. The vectors used in the SGC can be divided into three LIC classes (Table 2). All vectors within a class utilize the same extensions, so the same PCR fragment can be cloned in parallel into any vector within the class. In practice, cloning a gene into a series of vectors with a variety of N-terminal or C-terminal tags requires at most two PCR reactions (and two pairs of primers). We found this to be nearly as convenient as and more economical than the Gateway system, while minimizing the insertion of extraneous sequences into the expressed proteins.
Host cells are derived from BL21(DE3) and Rosetta2 (Merck). A phage-resistant derivative of BL21(DE3) was isolated in our lab and termed BL21(DE3)-R3; this bacterial strain was then transformed with plasmid pRARE2 (isolated from Rosetta2 calls), which carries seven rare-codon tRNA genes. The resulting chloramphenicol-resistant strain BL21(DE3)-R3-pRARE2 is the standard expression host.
Savitsky P., Bray J., Cooper C.D., Marsden B.D., Mahajan P., Burgess-Brown N.A, & Gileadi O. (2010). High-throughput production of human proteins for crystallization: The SGC experience. Journal of Structural Biology, 172(1), 3-13.
Publication 2010
Agar Bacteria Bacteriophages Baculoviridae Cells Chloramphenicol Cloning Vectors Codon Cytokinesis DNA Insertion Elements Epitopes Escherichia coli Eye Gene Expression Genes Genetic Vectors His-His-His-His-His-His levansucrase Ligation Oligonucleotide Primers Plasmids Proteins Sequence Insertion Strains Streptavidin Sucrose TEV protease Thioredoxins Transfer RNA
4
Curated Annotation of Rice Genome
The curated TE annotation of the rice genome (Oryza sativa L. ssp. japonica cv. “Nipponbare” v. MSU7) was created using the standard library (v6.9.5) and RepeatMasker v4.0.8 with parameters “-pa 36 -q -no_is -norna -nolow -div 40 -cutoff 225.” These parameters identified homologous sequences with up to 40% divergence without detecting bacterial insertion elements, small RNA (pseudo) genes, and low complexity DNA. This annotation was used as the curated annotation for the calculation of benchmarking metrics. For genomic regions that cover more than 80% of a TE sequence in the curated library, the region was counted as a complete copy, and those that covered less than 80% were counted as a fragmented copy.
When we obtained a non-redundant test library from a target program (details in the next section), the test library was used to annotate the rice genome with the same RepeatMasker parameters, except that the test library was provided as a custom library. Then, the testing annotation was compared to the curated annotation for calculations of sensitivity, specificity, accuracy, precision, FDR, and F1 measures (Fig. 1). These six metrics were calculated using the script “lib-test.pl” in our EDTA toolkit.
Ou S., Su W., Liao Y., Chougule K., Agda J.R., Hellinga A.J., Lugo C.S., Elliott T.A., Ware D., Peterson T., Jiang N., Hirsch C.N, & Hufford M.B. (2019). Benchmarking transposable element annotation methods for creation of a streamlined, comprehensive pipeline. Genome Biology, 20, 275.
Publication 2019
Bacteria DNA Insertion Elements DNA Library Edetic Acid Genes Genome Homologous Sequences Hypersensitivity Oryza sativa
5
Mapping P-element Insertion Sites
Purified genomic DNA (∼1 µg; QIAGEN DNeasy kit) was digested by BfuCI (NEB) for 8 h. Digested DNA (∼0.5 µg) was self ligated (T4 DNA Ligase, NEB) for 2 h at 25°C in a total volume of 50 µl. For isolating 5′ P-element insertion sequence, primer pairs PGAW2 (CAGATAGATTGGCTTCAGTGGAGACTG) and PGAW3 (CGCATGCTTGTTCGATAGAAGAC) were used. For isolating 3′ P-element insertion sequence, primer pairs PRY4 (ACTGTGCGTTAGGTCCTGTTCGTT) and PRY1 (CCTTAGCATGTCCGTGGGGTTTGAAT) were used. iPCR products were sequenced with Sp1 (ACACAACCTTTCCTCTCAACAA; 5′ insertion sites) or Spep1 (GACACTCAGAATACTATTC; 3′ insertion sites). The PCR protocol for 5′ iPCR was 98°C 75 sec, 35 cycles of 98°C 30 sec, 65.5°C 30 sec, 72°C 2 min, followed by 72°C 7 min. For 3′ iPCR the PCR protocol was 98°C 75 sec, 35 cycles of 98°C 30 sec, 62.5°C 30 sec, 72°C 2 min followed by 72°C 7 min. Phusion Taq (NEB) was used for all PCR reactions.
In an iPCR reaction, the size of non-genomic DNA (i.e., P-element specific DNA) amplified when mapping GAL4 enhancer traps is 553 bp for the 5′P end and 243 bp for the 3′P end. For all other P-elements, the size of non-genomic DNA in a PCR reaction is 1218 bp for the 5′P end and 243 bp for the 3′P end. Subtracting these numbers from the PCR fragment sizes indicate the extent of the isolated flanking genomic DNA.
Potter C.J, & Luo L. (2010). Splinkerette PCR for Mapping Transposable Elements in Drosophila. PLoS ONE, 5(4), e10168.
Publication 2010
DNA Insertion Elements Genome Inverse PCR Iodine Oligonucleotide Primers Phosphorus T4 DNA Ligase

Most recents protocols related to «DNA Insertion Elements»

1
Metagenomic analysis of antimicrobial resistance
Antimicrobial resistance genes were detected using ABRicate v0.9.959 , using the following databases (Jan 2020): NCBI AMRFinder Plus, Resfinder, and Comprehensive Antibiotic Resistance Database (CARD). Each gene was filtered by coverage (≥ 70%) and identity (≥ 80%). Filtered ARGs were manually classified by type of antibiotic and resistance mechanism (Supplementary Data 2). To determine the abundance of ARGs in the microbiome, htseq-count v0.12.460 (link) was used with a manually curated ARG list, in which one gene ID was selected for genes with multiple synonyms (Supplementary Data 10–12). Taxonomic profiling of contigs was performed with Kraken2 v.2.0861 (link) and Bracken v2.6.062 (link) using contigs against the Kraken ‘standard’ database (build date 23 April 2020)63 . Insertion sequences were identified from filtered contigs using Prokka v.1.14.564 (link) and annotated using ISFinder database65 (link) (update 2019-09-25). Functional profiling of contigs was performed using HUMAnN2 v2.8.266 (link), with functional pathways in each sample classified against UniRef90 and chocophlan databases. Values reported are normalised as counts per million (CPM).
Sukumar S., Wang F., Simpson C.A., Willet C.E., Chew T., Hughes T.E., Bockmann M.R., Sadsad R., Martin F.E., Lydecker H.W., Browne G.V., Davis K.M., Bui M., Martinez E, & Adler C.J. (2023). Development of the oral resistome during the first decade of life. Nature Communications, 14, 1291.
Publication 2023
Antibiotic Resistance, Microbial Antibiotics DNA Insertion Elements Genes Microbicides Microbiome
2
Genetic Context Analysis of Resistomes
The genetic context analysis was done in the host genomes of the latent genes in the core-resistomes. The analysis included 1429 unique resistance gene sequences corresponding to 136 ARGs. For each ARG, the closest known homolog was identified using BLASTx v2.10.1 [36 ] to align the gene sequences against the CARD database [26 ]. Here, CARD was used since it is more comprehensive than ResFinder and includes, in contrast to ResFinder, some genes that are not clinically relevant and/or mobile. Then, genetic regions of up to 10,000 base pairs upstream and downstream of the gene sequences were retrieved using GEnView v0.1.1 [42 (link)] and screened for the presence of genes associated with MGEs and integrons. The genetic regions were translated in all six reading frames using EMBOSS Transeq v6.5.7.0 [43 (link)] and searched with 124 HMMs from MacSyfinder Conjscan v2.0 representing genes involved in conjugation [44 (link)], using HMMER v3.1b2 [45 (link)]. Insertion sequences (ISs) and other mobile ARGs were identified by applying BLASTx v2.10.1 [36 ]. For IS elements, a reference database based on ISFinder [37 , 38 ] was used to find the best among overlapping hits, with the alignment criteria that hits should display >50% coverage and >90% amino acid identity to a known IS transposase, as well as being located within 1,000 base pairs of the latent resistance gene (upstream or downstream). For co-localized mobile ARGs, ResFinder v4.0 was used as a reference database [25 ], with the alignment criterion that hits should display an amino acid identity >90% to a known ARG. Finally, the genetic regions were searched for integrons using Integron Finder v1.5.1 [46 (link)]. After the screening, the genetic contexts were manually investigated and curated.
Inda-Díaz J.S., Lund D., Parras-Moltó M., Johnning A., Bengtsson-Palme J, & Kristiansson E. (2023). Latent antibiotic resistance genes are abundant, diverse, and mobile in human, animal, and environmental microbiomes. Microbiome, 11, 44.
Publication 2023
Amino Acids DNA Insertion Elements Genes Genome Hypertelorism, Severe, With Midface Prominence, Myopia, Mental Retardation, And Bone Fragility Integrons Reading Frames Reproduction Transposase
3
Comprehensive Antibiotic Resistance Gene Database
A reference database of antibiotic resistance genes was created consisting of two collections of genes. The first collection comprised 2466 resistance gene sequences present in the ResFinder repository [25 ] (accessed 2019-10-01) that were correctly classified by at least one of the fARGene models. ResFinder was used since it consists of well-established mobile ARGs. The second collection consisted of 74,904 unique sequences of putative resistance genes from the fARGene analysis. As open reading frames of insertion sequence transposases could overlap with ARGs, we used BLASTx 2.2.31 [36 ] with default parameters to align the resistance gene sequences to 7057 transposases from the ISFinder database (accessed 2022-02-08) [37 , 38 ]. Gene sequences with at least 80% identity level and at least 20 amino acid overlap to any transposase were removed from the data set (120 sequences removed).
To reduce redundancy, all gene sequences were clustered at a nucleotide cut-off of 90% using VSEARCH version 2.7.0 [39 ]. The resulting 23,367 centroids were used as representative sequences for each cluster and are hereafter referred to as ARGs. Next, BLASTp version 2.2.31 [36 ] with default parameters was used to align all ARGs to all ResFinder sequences. The ARGs with an identity level of at least 90% and with a match overlap of at least 70% to any sequence in ResFinder were labeled as “established ARGs.” This cut-off is commonly used for assigning reads to resistance genes and, thus, established ARGs will primarily consist of genes typically analyzed in shotgun metagenomic studies [40 (link)]. The ARGs with an identity level below 90% or with a match overlap shorter than 20% were labeled as “latent” ARGs. The ARGs not fulfilling any of these criteria were removed (three sequences removed). In total, we labeled 588 ARGs as established and 22,504 ARGs as latent.
Inda-Díaz J.S., Lund D., Parras-Moltó M., Johnning A., Bengtsson-Palme J, & Kristiansson E. (2023). Latent antibiotic resistance genes are abundant, diverse, and mobile in human, animal, and environmental microbiomes. Microbiome, 11, 44.
Publication 2023
Amino Acids Antibiotic Resistance, Microbial DNA Insertion Elements Genes Genes, vif Metagenome Nucleotides Open Reading Frames Transposase
4
Generating Lentiviral Vectors for Tumor Suppressor Genes
The SLIEW lentiviral vector was previously cloned [15 (link)] by insertion of a luciferase coding sequence into the BAMH1 site at position 8256, of pHR-SINcPPT-SIEW (SIN-SIEW) [16 (link)]. Consensus coding sequences (CCDS) c-DNA for candidate tumour suppressor genes (IDs - Table S10), synthesised (Life technologies) and cloned into pDONR221, were inserted into the BAMH1 site of a modified SIN-SIEW vector by GatewayTM cloning (Invitrogen-Fisher Scientific). Inserts of resulting SIN-SIEW-cDNA clones were validated by PCR (Table S10) and Sanger sequencing (DBS genomics, Durham, UK).
Sinclair P.B., Cranston R.E., Raninga P., Cheng J., Hanna R., Hawking Z., Hair S., Ryan S.L., Enshaei A., Nakjang S., Rand V., Blair H.J., Moorman A.V., Heidenreich O, & Harrison C.J. (2023). Disruption to the FOXO-PRDM1 axis resulting from deletions of chromosome 6 in acute lymphoblastic leukaemia. Leukemia, 37(3), 636-649.
Publication 2023
Clone Cells Cloning Vectors Consensus Sequence DNA, Complementary DNA Insertion Elements Luciferases Open Reading Frames Sequence Insertion Tumor Suppressor Genes
5
Hybrid assembly and annotation of SCLZS63 genome
Genomic DNA of SCLZS63 was obtained using the QIAamp DNA Mini Kit (Qiagen), and the purified DNA was subjected to whole-genome sequencing on a HiSeq 2000 platform (Illumina, San Diego, CA, USA) using a paired-end library with an insert size of 150 bp, followed by the long-read MinION Sequencer (Nanopore, Oxford, UK). The de novo hybrid assembly of the Illumina reads and MinION reads were carried out by using Unicycler v0.4.324 (link). Gene annotation for the assembled genomes was performed with the RAST tools25 (link) and BLASTp/BLASTn searches against the UniProtKB/SwissProt database26 (link). Bacterial precise species were identified using pairwise ANI analysis between strain SCLZS63 and reference genomes of Comamonas with the online software JSpeciesWS (https://jspecies.ribohost.com/jspeciesws/). A > 96% ANI cut-off was used for species circumscription27 (link). Plasmid incompatibility types, antibiotic resistance genes, and insertion elements were predicted using PlasmidFinder 2.1 (95%, minimum threshold for identity; 60%, minimum coverage)28 (link), ResFinder (90%, minimum threshold for identity; 60%, minimum coverage)29 (link), and ISfinder30 (link).
Li Y., Fang C., Wang X., Liu Q., Qiu Y., Dai X, & Zhang L. (2023). A new class A beta-lactamase gene blaCAE-1 coexists with blaAFM-1 in a novel untypable plasmid in Comamonas aquatica. Scientific Reports, 13, 3634.
Publication 2023
Antibiotic Resistance, Microbial Bacteria Comamonas DNA Insertion Elements DNA Library Gene Annotation Genes Genome Hybrids Plasmids Radioallergosorbent Test Strains

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Lipofectamine 2000 is a cationic lipid-based transfection reagent designed for efficient and reliable delivery of nucleic acids, such as plasmid DNA and small interfering RNA (siRNA), into a wide range of eukaryotic cell types. It facilitates the formation of complexes between the nucleic acid and the lipid components, which can then be introduced into cells to enable gene expression or gene silencing studies.
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More about "DNA Insertion Elements"

DNA Insertion Elements, also known as transposable elements or mobile genetic elements, are fascinating genetic sequences that can integrate into a host genome, playing crucial roles in gene regulation, genome evolution, and genetic diversity.
These dynamic entities have the remarkable ability to replicate and insert themselves throughout the genome, leading to insertional mutagenesis and potentially altering gene expression patterns.
Understanding the mechanisms and impact of DNA Insertion Elements is a crucial area of research in fields such as genetics, genomics, and evolutionary biology.
Researchers often leverage techniques like Lipofectamine 2000, a widely used transfection reagent, to study the behavior and effects of these mobile genetic elements.
Additionally, tools like the QIAamp DNA Mini Kit, a reliable DNA extraction kit, and the HiSeq 2000, a powerful next-generation sequencing platform, are commonly employed to analyze the genomic integration and expression patterns of DNA Insertion Elements.
The use of Polybrene, a cationic polymer that enhances viral transduction, and GeneArt, a suite of genetic engineering tools, can further facilitate the manipulation and study of these fascinating genetic elements.
Purification methods, such as the QIAquick PCR Purification Kit, and cloning strategies, like the In-Fusion HD Cloning Kit, are also invaluable for isolating and integrating DNA Insertion Elements into experimental systems.
Researchers often utilize expression vectors like the pcDNA3.1 and pcDNA3.1+ to study the regulatory mechanisms and impact of DNA Insertion Elements on gene expression.
The PrimeSTAR GXL DNA Polymerase, a high-fidelity enzyme, can be employed for accurate amplification and characterization of these mobile genetic elements.
By leveraging the insights and tools available, researchers can optimize their DNA Insertion Element studies, enhance reproducibility, and gain deeper understandings of these dynamic genetic entities and their far-reaching implications in the realms of genetics, genomics, and evolutionary biology.