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Nucleotides

Nucleotides are the fundamental building blocks of nucleic acids, such as DNA and RNA.
These small molecules consist of a nitrogenous base, a sugar, and one or more phosphate groups.
Nucleotides play crucial roles in various biological processes, including energy production, cell signaling, and genetic information storage and transfer.
Understanding the structure, function, and interactions of nucleotides is essential for advancinag research in fields like molecular biology, genetics, and biotechnology.
Optimizing the accuracy and efficiency of nucleotie research can be achieved through the use of AI-driven platforms like PubCompare.ai, which can help identify the most reliable and reproducible experimental protocols from the scientific literature, preprints, and patents.

Most cited protocols related to «Nucleotides»

Non-A/C/G/T bases on reads are simply treated as mismatches, which is implicit in the algorithm (Fig. 3). Non-A/C/G/T bases on the reference genome are converted to random nucleotides. Doing so may lead to false hits to regions full of ambiguous bases. Fortunately, the chance that this may happen is very small given relatively long reads. We tried 2 million 32 bp reads and did not see any reads mapped to poly-N regions by chance.
Publication 2009
Genome Nucleotides Poly A
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

fastp supports automatic adapter trimming for both single-end and paired-end Illumina data and uses different algorithms for each of these tasks. For single-end data, adapter sequences are detected by assembling the high-frequency read tails; for paired-end data, adapter sequences are detected by finding the overlap of each pair.
The adapter-sequence detection algorithm is based on two assumptions: the first is that only one adapter exists in the data; the second is that adapter sequences exist only in the read tails. These two assumptions are valid for major next-generation sequencers like Illumina HiSeq series, NextSeq series and NovaSeq series. We compute the k-mer (k = 10) of first N reads (N = 1 M). From this k-mer, the sequences with high occurrence frequencies (>0.0001) are considered as adapter seeds. Low-complexity sequences are removed because they are usually caused by sequencing artifacts. The adapter seeds are sorted by its occurrence frequencies. A tree-based algorithm is applied to extend the adapter seeds to find the real complete adapter, which is described by the pseudo code in Algorithm 1.
In Algorithm 1, the function build_nucleotide_tree() is used to convert a set of sequences to a tree, in which each node is a nucleotide and each path of root to leaf is a sequence. A node’s dominant child is defined as its major child with a dominant percentage (>90%). This algorithm tries to extend an adapter seed in the forward direction to check its validity since a valid adapter can always be extended to the read tails. And if this adapter seed is valid, a backward extension is applied to obtain the complete adapter sequence. The process of extending an adapter seed in forward and backward directions is given in Figure 2.
For paired-end data, fastp seeks the overlap of each pair and considers the bases that fall out of the overlapped regions as adapter contents. The overlapping detection algorithm was derived from our previous work, AfterQC. Compared to sequence-matching-based adapter-trimming tools like Cutadapt and Trimmomatic, a clear advantage of the overlap-analysis-based method is that it can trim adapters with few bases in the read tail. For example, most sequence-matching-based tools require a hatchment of at least three bases and cannot trim adapters with only one or two bases. In contrast, fastp can trim adapters with even only one base in the tail.
Although fastp can detect adapter sequences automatically, it also provides interfaces to set specific adapter sequences for trimming. For SE data, if an adapter sequence is given, then automatic adapter-sequence detection will be disabled. For PE data, the adapter sequence will be used for sequence-matching-based adapter trimming only when fastp fails to detect a good overlap in the pair.
Publication 2018
Child Nucleotides Plant Leaves Plant Roots Tail Trees
Unassembled sequence reads from both SSU rRNA gene PCR amplicons (pyrotags) and metagenome sequencing were preprocessed (quality control and alignment) by the bioinformatics pipeline of the SILVA project (20 (link)). Briefly, reads shorter than 200 nt or with more than 2% of ambiguities or more than 2% of homopolymers were removed. Remaining reads from amplicons and metagenomes were aligned against the SSU rDNA seed of the SILVA database release 108 (www.arb-silva.de/documentation/background/release-108/) (20 (link)) using SINA (26 (link)). Unaligned reads were not considered in downstream analysis to eliminate non 16S rDNA sequences.
Remaining PCR amplicons were separated based on the presence of aligned nucleotides at E. coli positions of the respective primer binding sites instead of searching for the primer sequences itself. This strategy is robust against sequencing errors within the primer signatures or incomplete primer signatures. This separation strategy works because the amplicon size of one primer pair is significant longer, with overhangs on both 3′ and 5′ site, compared with the amplicon of the second primer pair. With this approach the need for barcoding during combined sequencing of 16S pyrotags derived from different PCR reactions on the same PTP lane was avoided. FASTA files for each primer pair of the separated samples are available online at www.arb-silva.de/download/archive/primer_evaluation.
Reads of the filtered and separated 16S pyrotag datasets as well as metagenomes were dereplicated, clustered and classified on a sample by sample basis. Dereplication (identification of identical reads ignoring overhangs) was done with cd-hit-est of the cd-hit package 3.1.2 (www.bioinformatics.org/cd-hit) using an identity criterion of 1.00 and a wordsize of 8. Remaining sequences were clustered again with cd-hit-est using an identity criterion of 0.98 (wordsize 8). The longest read of each cluster was used as a reference for taxonomic classification, which was done using a local BLAST search against the SILVA SSURef 108 NR dataset (www.arb-silva.de/projects/ssu-ref-nr/) using blast-2.2.22+ (http://blast.ncbi.nlm.nih.gov/Blast.cgi) with default settings. The full SILVA taxonomic path of the best BLAST hit was assigned to the reads if the value for (percentage of sequence identity + percentage of alignment coverage)/2 was at least 93. In the final step, the taxonomic path of each cluster reference read was mapped to the additional reads within the corresponding cluster plus the corresponding replicates (as identified in the previous analysis step) to finally obtain (semi-) quantitative information (number of individual reads representing a taxonomic path). Raw output data are available in the Supplementary Material in Supplementary Tables S48–S50.
Publication 2012
Binding Sites DNA, Ribosomal Escherichia coli FCER2 protein, human Metagenome Nucleotides Oligonucleotide Primers Ribosomal RNA Genes Sequence Alignment SULT1E1 protein, human
The SILVA release cycle and numbering corresponds to that of the EMBL database, a member of the International Nucleotide Sequence Database Collaboration (http://www.insdc.org). Thus, the ribosomal RNA sequences used to build version 91 of the SILVA databases, which is referred to in this paper, were retrieved from release 91 (June 2007) of EMBL. A complex combination of keywords including all permutations of 16S/18S, 23S/28S, SSU, LSU, ribosomal and RNA was used to retrieve a comprehensive subset of all available small and large subunit ribosomal RNA sequences. All candidate rRNA sequences extracted from the EMBL database were stored locally in a relational database system (MySQL). The specificity of the SILVA databases for rRNA is assured by the subsequent processing of the primary sequence information.
The source database providing the seed alignment, required for the incremental alignment process, included a representative set of 51 601 aligned rRNA sequences from Bacteria, Archaea and Eukarya with 46 000 alignment positions. The SSU alignment positions are currently kept identical with the ssu_jan04.arb database which has officially been released by the ARB project (http://www.arb-home.de) in 2004. For the large subunit RNA databases, an in-house, aligned database was used as the seed. It encompasses a representative set of 2868 sequences from all three domains (150 000 alignment positions). Since the quality of the final datasets critically depends on the quality of the seed alignments both datasets were iteratively cross-checked by expert curators during database build-up. Within this process, all sequences that could not be unambiguously aligned were removed from the seed.
Publication 2007
A 601 Archaea Bacteria Base Sequence Eukaryota Nucleotides Protein Subunits Ribosomal RNA Ribosomes

Most recents protocols related to «Nucleotides»

Example 3

We tested the ability of mouse embryonic stem cells to tolerate s4U metabolic RNA-labeling after 12 h or 24 h at varying s4U concentrations (FIG. 6A). As reported previously, high concentrations of s4U compromised cell viability with an EC50 of 3.1 mM or 380 μM after 12 h or 24 h labeling, respectively (FIG. 6A). Hence, we employed labeling conditions of 100 μM s4U, which did not severely affect cell viability. Under these conditions, we detected a steady increase in s4U-incorporation in total RNA preparation 3 h, 6 h, 12 h, and 24 h post labeling, as well as a steady decrease 3 h, 6 h, 12 h, and 24 h after uridine chase (FIG. 6B). As expected, the incorporation follows a single exponential kinetics, with a maximum average incorporation of 1.78% s4U, corresponding to one s4U incorporation in every 56 uridines in total RNA (FIG. 6C). These experiments establish s4U-labeling conditions in mES cells, which can be employed to measure RNA biogenesis and turnover rates under unperturbed conditions.

To test the ability of the method to uncover s4U incorporation events in high throughput sequencing datasets we generated mRNA 3′ end libraries (employing Lexogen's QuantSeq, 3′ mRNA-sequencing library preparation kit) using total RNA prepared from cultured cells following s4U-metabolic RNA labeling for 24 h (FIG. 7) (Moll et al., supra). Quant-seq 3′ mRNA-Seq Library Prep Kit generates Illumina-compatible libraries of the sequences close to the 3′end of the polyadenylated RNA, as exemplified for the gene Trim28 (FIG. 8A). In contrast to other mRNA-sequencing protocols, only one fragment per transcript is generated and therefore no normalization of reads to gene length is needed. This results in accurate gene expression values with high strand-specificity.

Furthermore, sequencing-ready libraries can be generated within only 4.5 h, with ˜2 h hands-on time. When combined with the invention, Quant-seq facilitates the accurate determination of mutation rates across transcript-specific regions because libraries exhibit a low degree of sequence-heterogeneity. Indeed, upon generating libraries of U-modified RNA through the Quant-seq protocol from total RNA of mES cells 24 h after s4U metabolic labeling we observed a strong accumulation of T>C conversions when compared to libraries prepared from total RNA of unlabeled mES cells (FIG. 8B). In order to confirm this observation transcriptome-wide, we aligned reads to annotated 3′ UTRs and inspected the occurrence of any given mutation per UTR (FIG. 9). In the absence of s4U metabolic labeling, we observed a median mutation rate of 0.1% or less for any given mutation, a rate that is consistent with Illumina-reported sequencing error rates. After 24 h of s4U metabolic labeling, we observed a statistically significant (p<10−4, Mann-Whitney test), 25-fold increase in T>C mutation rates, while all other mutations rates remained below expected sequencing error rates (FIG. 9). More specifically, we measured a median s4U-incorporation of 2.56% after 24 h labeling, corresponding to one s4U incorporation in every 39 uridines. (Note, that median incorporation frequency for mRNA are higher than estimated by HPLC in total RNA [FIG. 6C], most certainly because stable non-coding RNA species, such as rRNA, are strongly overrepresented in total RNA.) These analyses confirm that the new method uncovers s4U-incorporation events in mRNA following s4U-metabolic RNA labeling in cultured cells.

We expect the same incorporation results of other modified nucleotides, such as s6G or 5-ethynyluridine, as reported previously (Eidinoff et al., Science. 129, 1550-1551 (1959); Jao et al. PNAS 105, 15779-15784 (2008); Melvin et al. Eur. J. Biochem. 92, 373-379 (1978); Woodford et al. Anal. Biochem. 171, 166-172 (1988)).

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Patent 2024
Anabolism Anus Cells Cell Survival Cultured Cells DNA Library Gene Expression Genes Genetic Heterogeneity High-Performance Liquid Chromatographies Kinetics Mouse Embryonic Stem Cells Mutation Nucleotides Peptide Nucleic Acids Preparation H Ribosomal RNA RNA, Messenger RNA, Polyadenylated RNA, Untranslated Transcriptome TRIM28 protein, human Uridine Whole Transcriptome Sequencing

Example 57

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1H NMR (400 MHz, CDCl3) δ 1.14-1.29 (m, 6H), 1.31-1.43 (m, 3H), 3.83-4.07 (m, 2H), 4.15-4.54 (m, 3H), 4.91-5.11 (m, 1H), 5.61-5.74 (m, 1H), 5.81-5.97 (m, 1H), 7.14-7.24 (m, 3H), 7.27-7.44 (m, 211), 7.48-7.51 (m, 1H), 7.80 (t, J=7.96, 7.96 Hz, OH), 9.30 (s, 1H). LC-MS m/z 516.3 (M+1+)

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Patent 2024
1H NMR Nucleosides Nucleotides Pharmaceutical Preparations Virus Diseases

Example 122

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To a stirred solution of crude 325 in MeCN (100 mL) under nitrogen at rt, was added dropwise a solution of 2,6-dimethylphenol (1.22 g, 10.0 mmol), triethylamine (4.18 mL, 30.0 mmol), and DABCO (0.112 g, 1.00 mmol) in MeCN over 30 min. The mixture immediately turned deep red at the beginning of addition, and was stirred an additional 90 min after addition was completed. The reaction mixture was concentrated by rotary evaporation, and the residue was redissolved in CHCl3 (300 mL). The solution was washed sequentially with sat. aq. NaHCO3 (1×300 mL) and brine (2×300 mL), dried over Na2SO4, filtered, and concentrated by rotary evaporation to give a crude red oil. Flash chromatography on the Combiflash (330 g column, 5 to 20% EtOAc in hexanes gradient), gave 326 (5.02 g, 85% yield over 2 steps) as an off-white solid foam.

1H NMR (400 MHz, CDCl3) δ 8.20 (d, J=7.4 Hz, 1H), 7.06 (s, 3H), 6.08 (d, J=7.4 Hz, 1H), 5.94 (d, J=15.9 Hz, 1H), 5.02 (dd, J=52.1 Hz, 3.1 Hz, 1H), 4.31 (d, J=13.8 Hz, 1H), 4.32-4.18 (m, 2H), 4.03 (dd, J=13.6 Hz, 2.0 Hz, 1H), 2.13 (s, 6H), 1.15-0.97 (m, 28H).

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Patent 2024
1H NMR Bicarbonate, Sodium brine Chloroform Chromatography Hexanes Nitrogen Nucleosides Nucleotides Petroleum Pharmaceutical Preparations triethylamine triethylenediamine Virus Diseases
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Example 109

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The alkene (2.91 mmol) was dissolved in MeOH (0.1 M) and Pd(OH)2/C (0.146 mmol) was added. A Parr Hydrogenator was used at 40 psi. The palladium catalyst was carefully filtered off through celite and rinsed with EtOAc. The crude material was used in the next step and provided quantitative yield.

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Patent 2024
Alkenes Celite Nucleosides Nucleotides Palladium Pharmaceutical Preparations Virus Diseases

Example 119

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To a stirred solution of 322 (5.30 g, 10.58 mmol) in THF (106 mL) under nitrogen at 0° C., was added a solution of TBAF (1.0 M in THF, 21.17 mL) dropwise via syringe. The mixture was brought to rt and stirred 2 h. Volatiles were removed by rotary evaporation to give a crude yellow oil. The material was taken up in EtOAc and flash chromatography on the Combiflash (330 g column, 0 to 5% MeOH in DCM gradient) gave 2.8 g of mostly purified material as a white solid. This material was dissolved in methanol and immobilized on Celite, then loaded on top of a 10% w/w KF/silica column. Flash chromatography (10% MeOH in EtOAc) gave 323 (1.96 g, 63% yield over 3 steps) as a white solid. 1H NMR analysis showed a 13:1 β:α dr at the C2′position (integration of methyl doublet).

Major isomer 1H NMR (400 MHz, MeOH-d4) δ 8.18 (d, J=8.1 Hz, 1H), 7.04 (d, J=7.6 Hz, 1H), 5.95 (d, J=8.2 Hz, 1H), 3.93 (dd, J=12.2 Hz, 2.1 Hz, 1H), 3.89 (t, J=8.2 Hz, 1H), 2.64 (m, 1H), 0.99 (d, 7.1 Hz, 3H). ES+APCI (70 eV) m/z: [M+HCO2]302.9.

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Patent 2024
1H NMR Celite Chromatography Isomerism Methanol Nitrogen Nucleosides Nucleotides Petroleum Pharmaceutical Preparations Silicon Dioxide Syringes Virus Diseases

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More about "Nucleotides"

Nucleotides are the fundamental building blocks of nucleic acids, such as DNA and RNA.
These small molecules consist of a nitrogenous base, a sugar, and one or more phosphate groups.
Nucleotides play crucial roles in various biological processes, including energy production, cell signaling, and genetic information storage and transfer.
Understanding the structure, function, and interactions of nucleotides is essential for advancing research in fields like molecular biology, genetics, and biotechnology.
Nucleic acids, like DNA and RNA, are made up of long chains of nucleotides.
The nitrogenous bases in these nucleotides include adenine (A), guanine (G), cytosine (C), and thymine (T) in DNA, or uracil (U) in RNA.
The sugar component is typically ribose or deoxyribose, and the phosphate groups provide the backbone of the nucleic acid molecule.
Nucleotides are involved in a variety of important biological processes.
They are the building blocks for DNA and RNA, which store and transmit genetic information.
Adenosine triphosphate (ATP), a nucleotide, is the primary energy currency of cells, powering many cellular processes.
Cyclic nucleotides, such as cAMP and cGMP, act as important signaling molecules, regulating various cellular functions.
Advancing nucleotide research requires robust and reliable experimental protocols.
Techniques like HiSeq 2000, HiSeq 2500, and NovaSeq 6000 are used for high-throughput DNA sequencing, while methods like TRIzol reagent, RNeasy Mini Kit, and QIAquick PCR Purification Kit are employed for RNA extraction and purification.
The Agilent 2100 Bioanalyzer and MiSeq platform are used for quality control and analysis of nucleic acid samples.
To optimize the accuracy and efficiency of nucleotide research, AI-driven platforms like PubCompare.ai can be utilized.
This tool can help identify the most reliable and reproducible experimental protocols from the scientific literature, preprints, and patents, ensuring that researchers can access the best practices and enhance the reproducibility of their studies.