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Base Sequence

Base Sequnce: The order of nucleotides in a nucleic acid molecule (DNA or RNA).
It is the primary structure of nucleic acids and determines their biological activities.

Most cited protocols related to «Base Sequence»

For SOLiD reads, BWA converts the reference genome to dinucleotide ‘color’ sequence and builds the BWT index for the color genome. Reads are mapped in the color space where the reverse complement of a sequence is the same as the reverse, because the complement of a color is itself. For SOLiD paired-end mapping, a read pair is said to be in the correct orientation if either of the two scenarios is true: (i) both ends mapped to the forward strand of the genome with the R3 read having smaller coordinate; and (ii) both ends mapped to the reverse strand of the genome with the F3 read having smaller coordinate. Smith–Waterman alignment is also done in the color space.
After the alignment, BWA decodes the color read sequences to the nucleotide sequences using dynamic programming. Given a nucleotide reference subsequence b1b2bl+1 and a color read sequence c1c2cl mapped to the subsequence, BWA infers a nucleotide sequence such that it minimizes the following objective function:

where q′ is the Phred-scaled probability of a mutation, qi is the Phred quality of color ci and function g(b, b′)=g(b′, b) gives the color corresponding to the two adjacent nucleotides b and b′. Essentially, we pay a penalty q′ if and a penalty qi if .
This optimization can be done by dynamic programming because the best decoding beyond position i only depends on the choice of . Let be the best decoding score up to i. The iteration equations are


BWA approximates base qualities as follows. Let . The i-th base quality , i=2…l, is calculated as:

BWA outputs the sequence and the quality as the final result for SOLiD mapping.
Publication 2009
Base Sequence Dinucleoside Phosphates Genome Mutation Nucleotides
We obtained 16S sequences from the Greengenes database, which extracts these sequences from public databases using quality filters as described previously (DeSantis et al., 2006 (link)). We only used sequences that had <1% non-ACGT characters. The sequences were checked for chimeras using UCHIME (http://www.drive5.com/uchime/) and ChimeraSlayer (Haas et al., 2011 (link)). We only removed sequences from named isolates if they were classified as chimeric by both tools; we removed other sequences if they were classified as chimeric by either tool or if they were unique to one study, meaning that no similar sequence (within 3% in a preliminary tree) was reported by another study. Quality filtered 16S sequences were aligned based on both primary sequence and secondary structure to archaeal and bacterial covariance models (ssu-align-0.1) using Infernal (Nawrocki et al., 2009 (link)) with the sub option to avoid alignment errors near the ends. The models were built from structure-annotated training alignments derived from the Comparative RNA Website (Cannone et al., 2002 ) as described in detail previously (Nawrocki et al., 2009 (link)). The resulting alignments were adjusted to fit the fixed 7682 character Greengenes alignment through identification of corresponding positions between the model training alignments and the Greengenes alignment. Hypervariable regions were filtered using a modified version of the Lane mask (Lane, 1991 ). A tree of the remaining 408 135 filtered sequences, (tree_16S_all_gg_2011_1) was built using FastTree v2.1.1, a fast and accurate approximately maximum-likelihood method using the CAT approximation and branch lengths were rescaled using a gamma model (Price et al., 2010 (link)). Statistical support for taxon groupings in this tree was conservatively approximated using taxon jackknifing, in which a fraction (0.1%) of the sequences (rather than alignment positions) is excluded at random and the tree reconstructed. We use these support values to help guide selection of monophyletic interior nodes for group naming during manual curation.
For evaluation of NCBI-defined candidate phyla, we added 765 mostly partial length sequences, that failed the Greengenes filtering procedure but were required for the evaluation, to the alignment using PyNAST (Caporaso et al., 2010 (link); based on the 29 November, 2010 Greengenes OTU templates) and generated a second FastTree (tree_16S_candiv_gg_2011_1) using the parameters described above.
Publication 2011
Archaea Bacteria Base Sequence Character Chimera Gamma Rays Trees
In comparative modelling, a 3D protein model of a target sequence is generated by extrapolating experimental information from an evolutionary related protein structure that serves as a template. In SWISS-MODEL, the default modelling workflow consists of the following main steps:

Input data: The target protein can be provided as amino acid sequence, either in FASTA, Clustal format or as a plain text. Alternatively, a UniProtKB accession code (34 (link)) can be specified. If the target protein is heteromeric, i.e. it consists of different protein chains as subunits, amino acid sequences or UniProtKB accession codes must be specified for each subunit.

Template search: Data provided in step 1 serve as a query to search for evolutionary related protein structures against the SWISS-MODEL template library SMTL (30 (link)). SWISS-MODEL performs this task by using two database search methods: BLAST (35 (link),36 (link)), which is fast and sufficiently accurate for closely related templates, and HHblits (37 (link)), which adds sensitivity in case of remote homology.

Template selection: When the template search is complete, templates are ranked according to expected quality of the resulting models, as estimated by Global Model Quality Estimate (GMQE) (30 (link)) and Quaternary Structure Quality Estimate (QSQE) (23 ). Top-ranked templates and alignments are compared to verify whether they represent alternative conformational states or cover different regions of the target protein. In this case, multiple templates are selected automatically and different models are built accordingly. To provide the user with the option to use alternative templates than those selected automatically, all templates are shown in a tabular form with a descriptive set of features. In addition, interactive graphical views facilitate the analysis and comparison of available templates in terms of their three-dimensional structures, sequence similarity and quaternary structure features.

Model building: For each selected template, a 3D protein model is automatically generated by first transferring conserved atom coordinates as defined by the target-template alignment. Residue coordinates corresponding to insertions/deletions in the alignment are generated by loop modelling and a full-atom protein model is obtained by constructing the non-conserved amino acid side chains. SWISS-MODEL relies on the OpenStructure computational structural biology framework (38 (link)) and the ProMod3 modelling engine to perform this step. For more detailed information on model building we refer to a dedicated section in Results.

Model quality estimation: To quantify modelling errors and give estimates on expected model accuracy, SWISS-MODEL relies on the QMEAN scoring function (31 (link)). QMEAN uses statistical potentials of mean force to generate global and per residue quality estimates. The local quality estimates are enhanced by pairwise distance constraints that represent ensemble information from all template structures found. For more information on quality estimation we refer to a dedicated section in Results.

SWISS-MODEL allows for further customization of steps 1 and 3. Expert users can directly upload custom target-template sequence alignments, template structures or DeepView project files (26 (link)) in separate input forms.
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Publication 2018
Amino Acids Amino Acid Sequence Biological Evolution cDNA Library Hypersensitivity INDEL Mutation Protein Domain Proteins Protein Subunits Sequence Alignment
In multivariate analyses such as PCA, large differences in variances between columns are corrected by standardizing each column; i.e. dividing each column by its standard deviation. Thus each column will have the same weight in the multivariate analysis. For OTU abundance tables, such a procedure is inappropriate as the disparities in column sums can be 100-fold. Methods based on chi-squared distances rather than variances deal with this by comparing weighted column profiles [62] , computed as relative abundances for each OTU within a column, with the overall column sum retained as a weighting factor. However, chi-square distances are sums of squares and can be overly sensitive to outliers and sequencing “jackpot” effects such as those occurring in pyrosequencing data [63] (link). Bray-Curtis distances can be a useful alternative, as it is based on the distance between profiles, as long as the differences in actual column sums are also accounted for in the final study. The other approach to the problem of disparities between column sums has been to subsample the over-abundant columns down to the same number as the smaller ones. However this results in a loss of information, rarely an optimal procedure in statistical contexts. This subsampling procedure is inspired by the popular idea of rarefaction in coverage studies first invented by Sanders [64] , but has yet to be proved beneficial for all microbial community structures. The parallels between gene expression microarray analyses and microbial abundance analyses was mentioned in [65] (link), which proposed several expression-inspired strategies for robustifying abundance measurements. The main points were that rankings and thresholding are important in the presence of noise and high variability in sequence depths. As in gene expression analysis filtering the OTUs is beneficial, especially in the latter multiple testing adjustments. The phyloseq package enables easy filtering and rank transformations in the same vein as robust multi-array averaging (rma) [66] (link). We provide further details in (McMurdie and Holmes, [67] ).
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Publication 2013
factor A Gene Expression Microarray Analysis Gene Expression Profiling Microbial Community Structure Strains Veins
The VEP’s caches are built for each of Ensembl’s primary species (70 species as of Ensembl version 84); the files are updated in concert with Ensembl’s release cycle, ensuring access to the latest annotation data. Cache files for all previous releases remain available on Ensembl’s FTP archive site [91 ] to facilitate reproducibility. For 15 of these species there are three types of cache files: one with the Ensembl transcripts, a “refseq” one with the RefSeq transcripts, and a “merged” one that contains both. Caches for both the latest GRCh38 and previous GRCh37 (hg19) human genome builds are maintained. The human GRCh38 cache file is around 5 gigabytes in size, including transcript, regulatory, and variant annotations as well as pathogenicity algorithm predictions. Performance using the cache is substantially faster than using the database; analyzing a small VCF file of 175 variants takes 5 seconds using the cache versus 40 seconds using the public Ensembl variation database over a local network (performance can be expected to be slower when using a remote database connection).
The VEP can use FASTA format files of genomic sequence for sequence retrieval. This functionality is needed to generate HGVS notations and to quality check input variants against the reference genome. The VEP uses either an htslib-based indexer [92 ] or BioPerl’s FASTA DB interface to provide fast random access to a whole genome FASTA file. Sequence may alternatively be retrieved from an Ensembl core database, with corresponding performance penalties.
Cache and FASTA files are automatically downloaded and set up using the VEP package’s installer script, which utilizes checksums to ensure the integrity of downloaded files. The installer script can also download plugins by consulting a registry. The VEP package also includes a script, gtf2vep.pl, to build custom cache files. This requires a local GFF or general transfer format (GTF) file that describes transcript structures and a FASTA file of the genomic sequence.
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Publication 2016
GB virus C Genome Genome, Human Homo sapiens Neoplasm Metastasis Pathogenicity Patient Discharge

Most recents protocols related to «Base Sequence»

Example 2

As discussed herein above, the disclosed methods improve the antiseptic properties of a dental implant without using charged metallic ions via conversion of the nitrogen moieties in titanium nitride surface to a positively charged quaternary ammonium via a Menschutkin reaction.

To prepare the antibacterial quaternized TiN surface, an implant which has been coated with TiN was used. The implant was cleaned to improve yield. The implant was washed with two solvents in sequence, acetone and isopropanol, to remove any dust particulate and other residue. The native oxide layer was removed by sonicating in 1:10 HCl:deionized water for 1 minute. This treatment additionally removes any residue that may not have been removed by the solvents. Acetonitrile was used as the solvent; however, any solvent may be used with preference for polar solvents giving improved reaction times (Stanger K., et al. J Org Chem. 2007 72(25):9663-8; Harfenist M., et al. J Am Chem Soc 1957 79(16):4356-4358). An excess of allyl bromide was added to the solvent and continuously stirred. The sample was then submerged in the solution, and full reaction of the surface occurred within about 60 minutes, as confirmed by contact angle measurement. A reference was also measured by submerging in solvent for the duration with no reactant to ensure any changes in surface properties was due to the quaternization.

TABLE 2
SampleContact Angle (°)
As-deposited TiN<6
In solvent 2 hrs (no reaction)16 ± 2
Allyl bromide 30 minutes67 ± 1
Allyl bromide 60 minutes72 ± 3
Allyl bromide 120 minutes71 ± 2

Without wishing to be bound by a particular theory, the increased hydrophobicity of the treated surfaces can be due to the presence of the allyl groups on the surface which will impart some hydrophobicity. The contact angle measurements provide information on whether or not a reaction has occurred and whether it has saturated.

The biocidal activity was tested using live bacteria cultures from a patient's mouth, which provides the full flora to act against rather than targeting an individual strain of bacteria. The bacteria was incubated on the sample surface using several bacteria film thicknesses. The thickness is defined by keeping the same interaction surface area while varying the volume of bacteria solution added. Across two separate patients and several separate growths, within 4 hours 40-50% reduction in bacteria unit counts were observed for quaternized TiN as compared to traditional Titanium implants, outperforming traditional TiN coatings. FIG. 4 shows for two separate patients a set of typical bacteria growth result of the quaternized samples. The exact efficiency varies, as each patient has different flora which varies depending on environmental factors such as hygiene, diet, and familial history.

Unless defined otherwise, all technical and scientific terms used herein have the same meanings as commonly understood by one of skill in the art to which the disclosed invention belongs. Publications cited herein and the materials for which they are cited are specifically incorporated by reference.

It will be apparent to those skilled in the art that various modifications and variations can be made in the present disclosure without departing from the scope or spirit of the disclosure. Other aspects of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.

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Patent 2024
Acetone acetonitrile allyl bromide Ammonium Anti-Bacterial Agents Anti-Infective Agents, Local Bacteria Diet Implant, Dental Ions Isopropyl Alcohol Metals Nitrogen Oral Cavity Oxides Patients Solvents Strains Surface Properties Titanium titanium nitride

Example 3

Amino acids sequences alignment shows 67% identity between OR5A1 and OR5A2, 58% with OR5AN1 and 41% with OR11A1 (FIGS. 2A-B). To further address the question of how well paralogy predicts functionality and selectivity, we compared the response of these ORs to beta-ionone and 2-ethyl fenchol, the two well-known agonists of OR5A1 and OR11A1 respectively (Jaeger et al., 2013; Adipietro et al., 2012). These compounds were tested in concentration-response analysis in luciferase assays, as described previously. In each experiment, an empty vector was used as negative control (pEFIBRHO). Representative concentration-response curves are given in FIGS. 3A-B.

It was observed that OR5A1, the closest paralog of OR5A2, and OR11A1 are both activated by their own cognate agonist. On the contrary, in these experimental conditions, OR of the invention (namely OR5A2) as well as OR5AN1 are stimulated neither by beta-ionone nor by 2 ethyl-fenchol, both showing concentration-response curves similar to the empty vector.

Altogether, these results indicate that OR5A1 and OR5A2, although members of the same subfamily, show different agonist specificity (beta-ionone vs musk) indicating that amino acids similarity doesn't robustly predict OR selectivity and functionality among paralogs.

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Patent 2024
agonists Amino Acids beta-ionone Biological Assay Cloning Vectors fenchol Figs Genetic Selection Ligands Luciferases musk

Example 16

The instant study was designed to test the efficacy in cotton rats of hMPV vaccines against a lethal challenge. mRNA vaccines encoding hMPV fusion protein were used. The mRNA polynucleotide encodes a full-length fusion protein and comprises the wild-type nucleotide sequence obtained from the hMPV A2a strain.

Cotton rats were immunized intramuscularly (IM) at week 0 and week 3 with the mRNA vaccines encoding hMPV fusion protein with either 2 μg or 10 μg doses for each immunization. The animals were then challenged with a lethal dose of hMPV in week 7 post initial immunization via IV, IM or ID. The endpoint was day 13 post infection, death or euthanasia. Viral titers in the noses and lungs of the cotton rats were measured. The results (FIGS. 9A and 9B) show that a 10 μg dose of mRNA vaccine protected the cotton mice 100% in the lung and drastically reduced the viral titer in the nose after challenge (˜2 log reduction). Moreover, a 2 μg dose of mRNA vaccine showed a 1 log reduction in lung viral titer in the cotton mice challenged.

Further, the histopathology of the lungs of the cotton mice immunized and challenged showed no pathology associated with vaccine-enhanced disease (FIG. 10).

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Patent 2024
Animals Base Sequence Euthanasia Gossypium Human Metapneumovirus Immunization Infection Lung mRNA Vaccine mRNA Vaccines Mus Nose Pneumonia, Viral Polynucleotides Proteins Rats, Cotton RNA, Messenger Rodent Strains vaccin Vaccines
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Example 1

Expression cassettes for expressing a precursor protein were synthesized. Each cassette contained a CMV promotor, followed by the sequence for the precursor protein. The synthesized precursor protein expression cassettes were cloned into the ROVV using the methods described by Law el al. (A New Approach to Assessing HSV-1 Recomination during Intracellular Spread. Viruses 2018, 10, 220), the disclosure of which is incorporated herein by reference.

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Patent 2024
Human Herpesvirus 1 Protein Precursors Protoplasm Virus
Not available on PMC !

Example 1

Antibodies to CD59 and K41 Amadori-modified GCD59 were each generated using CD59 fragments. For anti-K41 Amadori-modified GCD59 antibody preparation, CD59 fragments having Amadori-modified K41 were used. Antibodies were prepared by mouse immunization and development of hybridoma cells from animals exhibiting successful expression of antibodies with high affinity and specificity. Clone H9 was developed as a capture antibody, binding to both glycated and non-glycated CD59. Clones D2 and D3 were developed as detection antibodies, binding to K41 Amadori-modified GCD59. Antibodies were sequenced and analyzed to identify antibody regions. Resulting sequences are provided in Tables 1-7. Antibodies D2 and D3 were found to have heavy and light chains with identical amino acid sequences, but with heavy chain nucleotide sequences differing by a single nucleotide.

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Patent 2024
Amino Acid Sequence Animals Antibodies Antibodies, Anti-Idiotypic Base Sequence CD59 protein, human Clone Cells Hybridomas Immunization Immunoglobulins Light Mice, House Nucleotides

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The PGL3-basic vector is a plasmid designed for routine cloning and reporter gene expression experiments. It provides a basic backbone for inserting genes of interest and evaluating their expression in various cell lines. The vector contains a multiple cloning site for gene insertion and a luciferase reporter gene for downstream analysis.

More about "Base Sequence"

Nucleic acid sequence, DNA sequence, RNA sequence, nucleotide order, genetic code, genomic structure, transcript structure, genetic information, molecular biology, bioinformatics, sequencing technologies, Sanger sequencing, next-generation sequencing, quantitative PCR, reporter assays, transfection reagents, RNA extraction kits.
The order of nucleotides in a nucleic acid molecule, such as DNA or RNA, is known as the base sequence.
This primary structure is a fundamental aspect of molecular biology, as it determines the biological activities and functions of nucleic acids.
Researchers often utilize techniques like Dual-Luciferase Reporter Assay System, Lipofectamine 2000, TRIzol reagent, Lipofectamine 3000, BigDye Terminator v3.1 Cycle Sequencing Kit, PsiCHECK-2 vector, PmirGLO vector, and RNeasy Mini Kit to study and manipulate nucleic acid sequences.
These tools enable the analysis, quantification, and modification of gene expression, as well as the investigation of regulatory elements and interactions.
Understanding base sequence is crucial for a wide range of applications, including genetic engineering, diagnostics, evolutionary biology, and personalized medicine.