Nucleotides
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»
After the alignment, BWA decodes the color read sequences to the nucleotide sequences using dynamic programming. Given a nucleotide reference subsequence b1b2…bl+1 and a color read sequence c1c2…cl 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.
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
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.
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
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 (
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 (
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 (
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 (
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 (
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)).
Example 57
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+)
Example 122
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).
Example 109
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.
Example 119
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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More about "Nucleotides"
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.