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Genetic Code

The genetic code is the set of rules used by living cells to translate information encoded within genetic material (DNA or RNA sequences) into proteins.
This fundamental biological process involves the conversion of nucleotide sequences into the amino acid sequences that form the structure of proteins.
Understainding the genetic code is crucial for advanciing research in fields such as genomics, molecular biology, and bioinformatics.
Accurate mapping of the genetic code enables scientists to decipher the functons of genes, engineer novel proteins, and uncover the underlying causes of genetic disorders.
Leveraging cutting-edge AI-driven technologies like PubComparae.ai can streamline this critical research, enhancing reproducibility and maximizing the impact of genetic code optimization studies.

Most cited protocols related to «Genetic Code»

1
Identifying RNA Editing Sites Using PREP
As mentioned, the PREP suite of programs identifies potential sites of RNA editing based on the evolutionary principle that editing increases protein conservation among species. This is a fundamental quality of RNA editing in plants that was noticed upon its discovery in 1989 (19–21 ) and has been repeatedly observed in nearly all subsequent studies. Full details of the PREP-Mt methodology have been published previously (16 (link)). Essentially, all three programs perform the same series of steps: (i) an input sequence is translated using the standard genetic code; (ii) the translated sequence is aligned to a set of homologous proteins; (iii) the alignment is examined column-by-column to determine if an editing event could increase the similarity of the input sequence to the sequences in the pre-defined alignment. An edit site is predicted if a C-to-U change in a codon causes it to produce an amino acid that is found in more of the homologous proteins than the amino acid coded for by the unedited codon. If a cutoff value is specified by the user, the score of the edited version of the codon must also be >C.
The major difference between each server is in the set of homologous proteins used for comparison to the input sequence. For PREP-Aln, the protein homologs derive from the RNA-tagged sequences in the input file provided by the user. PREP-Aln pulls out all of the DNA sequences from the input alignment, and then builds the homologous protein alignment by translating the RNA sequences remaining in the input alignment. PREP-Aln then compares each of the pulled DNA sequences to the translated RNA alignment. For PREP-Mt and PREP-Cp, the set of homologous proteins is determined by the user when the gene name parameter is specified. These alignments of known mitochondrial or chloroplast proteins have been pre-generated from data available in GenBank and literature sources. The mitochondrial alignments were described previously and consist predominantly of six species with widespread transcriptomic sequence data (Figure 2A), and three species (Marchantia polymorpha, Chara vulgaris, Chaetosphaeridium globosum) that lack RNA editing (16 (link)). To create the chloroplast alignments, chloroplast genomes from seed plants whose transcriptomes have been extensively examined for editing (Figure 2B) were downloaded from GenBank. The known positions of edit sites were used to reconstruct mature, edited RNA sequences and these sequences were translated using the standard genetic code. Homologous proteins were aligned with ClustalW and manually adjusted when necessary to produce a collection of 35 alignments representing all chloroplast genes with evidence for editing in at least one of the seed plants in this study (Figure 2B).

Seed plants with extensive editing data for (A) mitochondrial genes and (B) chloroplast genes. For each species is listed the number of genes with editing information, along with the number of edited (Pos) and unedited (Neg) cytidines found in those genes. The chronogram shows evolutionary relationships and approximate divergence times for species. Divergence times are listed in millions of years (MYA) and were taken from published analyses (22 ,23 (link)). Species in black were used to generate the sets of homologous protein alignments and to optimize the cutoff value. Species in red were used for the unseen tests only.

, & Mower J.P. (2009). The PREP suite: predictive RNA editors for plant mitochondrial genes, chloroplast genes and user-defined alignments. Nucleic Acids Research, 37(Web Server issue), W253-W259.
Publication 2009
Amino Acids Biological Evolution Chara Chloroplast Proteins Chloroplasts Codon Cytidine Gene Expression Profiling Genes Genes, Chloroplast Genes, Mitochondrial Genetic Code Genome, Chloroplast Marchantia Mitochondria Plant Embryos Plants Proteins RNA Sequence SET protein, human Transcriptome
2
Genome Annotation Pipeline (PGAP) at NCBI
The PGAP pipeline is designed to annotate both complete genomes and draft genomes comprising multiple contigs. PGAP is deeply integrated into NCBI infrastructure and processes, and uses a modular software framework, GPipe, developed at NCBI for execution of all annotation tasks, from fetching of raw and curated data from public repositories (the Sequence and Assembly databases) through sequence alignment and model-based gene prediction, to submission of annotated genomic data to public NCBI databases.
On input, PGAP accepts an assembly (either draft or complete) with a predefined NCBI Taxonomy ID that defines the genetic code of the organism. PGAP also accepts a predetermined clade identifier, matching the genome in question to a species-specific clade. Clade IDs are computed using a series of 23 universal ribosomal protein markers and are independent of taxonomy. In the absence of a clade ID, we can infer the ID from taxonomy in the majority of cases. The clade ID determines the realm of core proteins used as the target protein set. PGAP annotation of a new genomic sequence can be requested at the time of submission to GenBank. Taxonomic and clade identifiers are determined outside of the annotation pipeline, and are influenced by GenBank curatorial decisions. The clade-dependent sets of protein clusters as well as sets of curated structural ribosomal RNAs (5S, 16S and 23S) are generated and maintained outside of PGAP. More details on the PGAP workflow are provided below.
Tatusova T., DiCuccio M., Badretdin A., Chetvernin V., Nawrocki E.P., Zaslavsky L., Lomsadze A., Pruitt K.D., Borodovsky M, & Ostell J. (2016). NCBI prokaryotic genome annotation pipeline. Nucleic Acids Research, 44(14), 6614-6624.
Publication 2016
Genes Genetic Code Genome Proteins Protein Targeting, Cellular ribosomal A-protein Ribosomal RNA Sequence Alignment SET protein, human
3
Validating de novo Mutation Calls
From the list of predicted de novo mutation calls obtained from 15 parent-offspring trios for which consent was obtained to use the sequencing data for research purposes, we designed PCR primers with Primer3 (refs. 42 (link),43 (link); version 2.3.5) using Tm 63, primer length 25–30 bp and product size ranging from 350–700 bp and avoiding common SNPs. We could not design primers for variants in difficult regions, such as those that completely lay within a medium-size repeat, for example, long interspersed nuclear elements (LINEs). For other difficult cases where variants were in repeats but flanking sequences showed overlap with unique sequences, we manually designed primers so that at least one primer (forward or reverse) overlapped with the unique sequences. All primer pairs were then checked manually using In-Silico PCR (see URLs) for targeted sequences.
We performed Sanger sequencing on PCR products amplified from trio DNA using BigDye Terminator mix version 3.1 (Applied Biosystems) and the ABI PRISM 3730 DNA sequencer. Sequence chromatogram traces were manually inspected to verify the de novo mutation pattern within the trio using Sequencher (Gene Codes). See Supplementary Figure 5 for Sanger sequencing traces of the validation experiments.
Rimmer A., Phan H., Mathieson I., Iqbal Z., Twigg S.R., Wilkie A.O., McVean G, & Lunter G. (2014). Integrating mapping-, assembly- and haplotype-based approaches for calling variants in clinical sequencing applications. Nature genetics, 46(8), 912-918.
Publication 2014
Genetic Code Long Interspersed DNA Sequence Elements Mutation Oligonucleotide Primers Parent prisma Single Nucleotide Polymorphism TRIO protein, human
4
Integrating ChEMBL and ZINC for Protein Targets
We loaded ChEMBL20 into ZINC as follows. We only used targets of type SINGLE PROTEIN and PROTEIN COMPLEX. We process activity annotations for molecular targets, not for whole organisms. We normalized pKi, IC50, EC50, AC50, pIC50 to a single standard pKi value, which we rounded to two decimal places.108 We filtered out data flagged with the data_validity_comment field indicating possible problems in the source document. We associate compounds annotated for protein complexes to each of the genes involved in that complex. Two common areas of biology where multi-gene complexes are observed is for the cell surface receptor integrins and the ligand-gated ion channels such as the nicotinic acetylcholine receptor. For instance, integrin VLA-1 is an alpha-1/beta-1 heterodimer of two genes, ITGA1 and ITGB1, respectively. Likewise, nAChRs such as (alpha-3)2(beta-4)3 is a heteropentamer of two genes CHRNA3 and CHRNB4 respectively. In such cases, compounds annotated for the complex are associated with each of the constituent genes. For single proteins, we used Uniprot gene symbols109 (link) based on the Uniprot accession codes in ChEMBL. Orthologs in the TrEMBL part of Uniprot often did not have assigned gene symbols, in which case we used the Uniprot accession code as a provisional gene name.
Sterling T, & Irwin J.J. (2015). ZINC 15 – Ligand Discovery for Everyone. Journal of Chemical Information and Modeling, 55(11), 2324-2337.
Publication 2015
Genes Genes, vif Genetic Code Integrin alpha1beta1 Integrins Ligand-Gated Ion Channels Multiple Birth Offspring Nicotinic Receptors Proteins Protein Targeting, Cellular Receptors, Cell Surface Zinc
5
Comparative Gene Identification in Phage Genomes
We compared gene identification between PHANOTATE and the three most popular gene callers used to identify genes in phages (Supplementary Table S1): GeneMarkS, Glimmer, and Prodigal using a set of 2133 complete phage genomes, which were downloaded from the GenBank FTP server (Benson et al., 2017 (link)). We did not include nine Mycoplasma and Spiroplasma phages, which use an alternative genetic code. We ran PHANOTATE and each of the three alternative gene callers with default (or ‘phage’ if available) parameters on each phage genome, as is done for most phage genome annotation projects (Supplementary Table S1). In addition, the ‘meta’ option was used to allow Prodigal to run on genomes smaller than 20 kb.
worf=-1∏c=1codons(P(not stop)GCFPmaxmaxGCframecGCFPminminGCframec)* RBS * START
To mask out functional, but non-protein-coding regions of the genomes, we used the program tRNAscan-SE to find the tRNA genes in each genome. To compare the algorithms, we counted the number of ORFs predicted by each respective algorithm and compared those predictions to the corresponding genes in GenBank.
McNair K., Zhou C., Dinsdale E.A., Souza B, & Edwards R.A. (2019). PHANOTATE: a novel approach to gene identification in phage genomes. Bioinformatics, 35(22), 4537-4542.
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Publication 2019
Bacteriophages Genes Genetic Code Genome Mycoplasma Open Reading Frames Spiroplasma Transfer RNA

Most recents protocols related to «Genetic Code»

1
Mitochondrial Genome Assembly from Peronospora
Contigs from a de novo genomic assembly in CLC Genomics Workbench (v9; Qiagen, Redwood City, CA) were identified as mitochondrial due to sequence similarity with P. tabacina mitochondrial sequences (KT893455) by BLAST analysis (Derevnina et al. 2015 (link)). These were used as templates for further assembly with SeqMan NGen (v16.0.0, DNASTAR, Madison, WI, USA). The resulting assemblies were evaluated for uniformity and depth of coverage. Contigs were broken when gaps/low coverage or inconsistencies were observed and the set of smaller contigs reassembled using the reference-guided assembly—special workflows assembly option of SeqMan NGen to extend the ends of the contigs and close the gaps. Open reading frames (ORFs) were predicted and annotated with Geneious v9.1.8 (Biomatters, New Zealand) using the universal genetic code. Encoded products of genes were identified using BLAST (Altschul et al. 1990 (link)) analysis against mitochondrial genome sequences published for Peronospora tabacina (Derevnina et al. 2015 (link)). Genes encoding tRNAs were identified using tRNAscan-SE v1.3.1 (Lowe and Eddy 1997 (link)).
Fletcher K., Martin F., Isakeit T., Cavanaugh K., Magill C, & Michelmore R. (2023). The genome of the oomycete Peronosclerospora sorghi, a cosmopolitan pathogen of maize and sorghum, is inflated with dispersed pseudogenes. G3: Genes|Genomes|Genetics, 13(3), jkac340.
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Publication 2023
Genes Genetic Code Genome Genome, Mitochondrial Mitochondrial Inheritance Open Reading Frames Peronospora Proteins Redwood Sequence Analysis Transfer RNA
2
Modeling Organelle Machinery Gene Encoding
The setup of the model is for a generic gene encoding organelle machinery. The gene may be encoded in the organelle, or in the nucleus of the cell, in which case it must be transported to the organelle through the cytosol. We assume that there is no systematic difference in the intrinsic properties of this representative gene between the two possible encoding compartments, neglecting, for example, differences in genetic code that may be required for the different locations [7 (link)]. Working at a coarse-grained level, we will use this general picture to describe both mitochondria and plastids. The model has two dynamic variables: xm(t) is the available amount of functional gene product in the organelle at time t (for example, the various protein subunits of electron transport chain complexes) and xc(t) is the available amount of gene product in the cytosol at time t.
The parameters of the model for the cellular processes include the following non-negative constant rates: λ is the baseline synthesis rate of gene product (which will be scaled by the cell’s response to the environment, see below), D is the import or transport rate of gene product from the cytosol to the organelle, νm, νc are the degradation rates of gene product in the organelle and in the cytosol, respectively. We also include a parameter p, the proportion of wild-type oDNA which allows the synthesis of functional gene product in the organelle. This parameter is required to capture the potential for oDNA damage, which occurs on longer (evolutionary) time scales than the cell biological processes above. Instead of modelling both gene expression and mutation time scales explicitly, which would require a rather more involved simulation setup, we coarse-grain the effect of mutation into this expected oDNA damage load which stays constant over a cell lifetime. This damage can be pictured as arising from mutation in the history of the lineage of the cell; the purpose of our study here is to characterize the balance between this potential accumulation of oDNA damage and the selective pressure favouring oDNA encoding. Lower p corresponds to more oDNA damage, compromising the expression of functional organelle machinery; p = 1 corresponds to perfect oDNA and hence the maximum possible expression capacity. The propensity for oDNA damage in an organism, both within a lifetime and across generations, will act to reduce p. The cell processes and properties described by these parameters are illustrated in figure 1a.
The parameters D and νc describe the ability of a nuclear-encoded gene product to translocate to its required position in the organelle. These can be used to model different mechanisms proposed for the hydrophobicity hypothesis. One mechanism is that hydrophobic gene products cannot readily be unpacked to import into the organelle [1 (link),22 (link)], corresponding to a high value of the degradation rate in the cytosol νc, as the gene product is merely lost and hence can be considered as degraded. Another mechanism is that the gene product is mistargeted, usually to the endoplasmic reticulum [17 (link),23 (link)], so that the import to the organelle takes a much longer time: corresponding to a low value of the transport rate D.
García Pascual B., Nordbotten J.M, & Johnston I.G. (2023). Cellular and environmental dynamics influence species-specific extents of organelle gene retention. Proceedings of the Royal Society B: Biological Sciences, 290(1994), 20222140.
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Publication 2023
Anabolism Biological Evolution Biological Processes Cell Nucleus Cells Cereals Cytosol Electron Transport Endoplasmic Reticulum Gene Expression Generic Drugs Genes Genetic Code Mitochondria Mutation Nuclear Protein Organelles Physiology, Cell Plastids Pressure Protein Biosynthesis Proteins Protein Subunits
3
Detecting Borrelia in Tick, Rodent, and Human Samples
Genomic DNA extracted from pooled ticks, rodent tissue, and patient’s whole blood were screened for the presence of Borrelia using a genus-based TaqMan real-time PCR assay targeting the Borrelia 16S rRNA gene [28 (link)]. Borrelia-positive samples were further characterized by sequencing the flagellin (flaB) and 16S rRNA genes. The primer and probe sequences and conditions used in this study has been described previously by Takhampunya et al. [18 (link)]. The PCR products were purified using ExoSap-IT PCR Product Cleanup Reagent (Applied Biosystems, Foster City, CA) and sequenced using a SeqStudio genetic analyzer (Applied Biosystems). Raw sequences were edited and assembled using DNA Sequencher ver. 5.1 (Gene Code Corporation, Ann Arbor, MI).
Takhampunya R., Longkunan A., Somchaimongkol S., Youngdech N., Chanarat N., Sakolvaree J., Tippayachai B., Promsathaporn S., Phanpheuch B., Poole-Smith B.K., McCardle P.W, & Lindroth E.J. (2023). Borrelia miyamotoi a neglected tick-borne relapsing fever spirochete in Thailand. PLOS Neglected Tropical Diseases, 17(2), e0011159.
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Publication 2023
Biological Assay BLOOD Borrelia Flagellin Genes Genetic Code Genome Oligonucleotide Primers Patients Real-Time Polymerase Chain Reaction Reproduction Ribosomal RNA Genes RNA, Ribosomal, 16S Rodent Ticks Tissues
4
Integrated Genetic Demultiplexing of Single-Nuclei RNA-Seq
To filter low quality nuclei, only those with greater than 200 and less than 10,000 features and less than 5% of reads that map to the mitochondrial genes were used in downstream analyses. Pooled nuclei were demultiplexed by hashtag oligonucleotides using HTODemux function in Seurat v458 (link)-61 (link). Pooled samples were also demultiplexed using Vireo, a genotype based demultiplexing method62 (link). We performed genetic demultiplexing analysis using genotype data following the methods described in Weber et al.63 (link), implemented in a Nextflow workflow64 (link). Briefly, bulk RNA-seq reads from each sample were mapped to the reference genome (GRCh38.p13) using STAR52 (link). Pooled single-nuclei RNA-seq reads were mapped to the reference genome using STARsolo65 (link). Variants among the samples within each pool were identified and genotyped with bcftools mpileup66 (link) using the mapped bulk reads. Individual cells were then genotyped only at the sites identified using the bulk RNA using cellsnp-lite (mode 1a)67 . Cell genotypes were used to identify the sample of origin for each cell using Vireo62 (link). Code for the genetic demultiplexing workflow can be found at https://github.com/AlexsLemonade/alsf-scpca/tree/main/workflows/genetic-demux.
To integrate the methods, we first used sample identity assigned from the hashtag oligonucleotides. If the nuclei were confidently assigned a sample, it was compared to the genotype-based sample assignment. Those that did not match the same sample were filtered out. If the nuclei were assigned as a doublet or to none of the samples, the nuclei were assigned to a sample based on the genotype-based approach. 84,700 nuclei with confident sample assignment were used in analysis.
As our dataset included a very large number of nuclei to be integrated and was expected to have certain cell types only present in certain samples, we used the reciprocal PCA integration approach on the 2,000 most variable features to combine the nuclei from each sample. We first found the integration anchors with the FindIntegrationAnchors function then used the IntegrateData function in Seurat v4 to integrate all our filtered nuclei59 (link)-61 (link).
Lee M.K., Azizgolshani N., Shapiro J.A., Nguyen L.N., Kolling F.W., Zanazzi G.J., Frost H.R, & Christensen B.C. (2023). Tumor type and cell type-specific gene expression alterations in diverse pediatric central nervous system tumors identified using single nuclei RNA-seq. Research Square.
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Publication Preprint 2023
Cell Nucleus Cells Dietary Fiber Genes, Mitochondrial Genetic Code Genotype Nucleus Solitarius Oligonucleotides Reproduction RNA-Seq Self Confidence Trees
5
Mapping E. coli Genes to COG Functional Codes
The COG function codes for E. coli K-12 MG1655 genes are extracted from the NCBI COG database [57 (link)–59 (link)]. In total, there are 3542 gene IDs that can be mapped to a total of 2150 COG IDs relevant to the E. coli K-12 MG1655 strain in the database. The functional code for each COG ID is determined from the file “cog-20.def.tab” as downloaded from NCBI COG database. When a COG entry has multiple functional codes, the first functional code for this COG ID was used.
To investigate the COG functional distributions of the genes in each FPE score range, we mapped the genes in each FPE score range to their COG IDs from NCBI COG reference database and identified how many of them remain unmapped. If a gene is mapped to multiple COG IDs, the weightage of the COG ID will be assigned as a fraction of the totally mapped COG IDs, accordingly. For example, if one gene is mapped to two COG IDs, then, the weightage for each COG ID of the gene will be 0.5, respectively. Finally, the functional codes for each mapped COG ID are identified and assigned the weightage as defined previously. There are 26 functional codes altogether, but only 23 functional codes are relevant to E. coli K-12 MG1655 (letters A and C through X). There is no gene mapped to the other 3 functional codes (i.e., B, Y and Z). Any of the unmapped genes to COG ID will be assigned “unmapped”. The assignments for the functional codes can be found in Additional file 1: Table S7.
Tantoso E., Eisenhaber B., Sinha S., Jensen L.J, & Eisenhaber F. (2023). About the dark corners in the gene function space of Escherichia coli remaining without illumination by scientific literature. Biology Direct, 18, 7.
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Publication 2023
Escherichia coli Genes Genetic Code Strains

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More about "Genetic Code"

The genetic code is the fundamental set of rules used by living cells to translate the information encoded within genetic material (DNA or RNA sequences) into functional proteins.
This critical biological process involves the conversion of nucleotide sequences into the amino acid sequences that form the structure and determine the function of proteins.
Understanding the genetic code is crucial for advancing research in fields such as genomics, molecular biology, and bioinformatics.
Accurate mapping of the genetic code enables scientists to decipher the functions of genes, engineer novel proteins, and uncover the underlying causes of genetic disorders.
Leveraging cutting-edge AI-driven technologies like PubCompare.ai can streamline this essential research, enhancing reproducibility and maximizing the impact of genetic code optimization studies.
PubCompare.ai's platform allows researchers to locate the best protocols from literature, pre-prints, and patents through intelligent comparisons, helping to improve the accuracy and efficiency of their work.
Tools like Sequencher 5.1 software, Sequencher 4.8, BigDye Terminator v3.1 Cycle Sequencing Kit, and the 3730 DNA Analyzer from ABI are commonly used in genetic research to analyze DNA sequences and uncover the secrets of the genetic code.
By combining these powerful software and hardware solutions with the insights from PubCompare.ai, researchers can unlock new discoveries and drive breakthroughs in fields ranging from personalized medicine to sustainable biofuel production.
Weather you're a seasoned bioinformatician or a budding molecular biologist, understanding the genetic code and leveraging the right tools and technologies is crucial for advancing your research and making a meaningful impact on the world.