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Review on DSP based dynamic gene encoding schemes for the detection of protein coding region
Published in Arun Kumar Sinha, John Pradeep Darsy, Computer-Aided Developments: Electronics and Communication, 2019
M Raman Kumar, Vaegae Naveen Kumar
The heredity information of any living species lies in gene. The gene is made up of DNA (Deoxyribo Nucleic Acid). The bio-molecule called DNA is built from four nucleic acids. They are Adenine (A), Thymine (T), Cytosine (C) and Guanine (G). The gene has two major regions called protein coding regions and non-coding regions. The protein coding region involves in protein formation and these are also called as “exons”. In most of the species, non-coding regions do not participate in protein formation, however few species contain control information associated to protein formation and treated as “junk DNA”. The analysis of protein coding region gives biological information which helps to predict the primary structure of protein, subsequently secondary and tertiary structures. It is also used to develop drug design, DNA repeats [1] and agricultural applications [2]
Introduction and Background
Published in Jay L. Nadeau, Introduction to Experimental Biophysics, 2017
The generation of a single-stranded RNA molecule from its DNA complement is called transcription. One generally speaks of transcription of genes, since as a general rule, each sequence of DNA that encodes for a specific protein is called a gene. (However, the original idea of “one gene, one protein” is an oversimplification, as a single gene can encode for multiple proteins.) The parts of DNA that are directly transcribed into complementary RNA are called coding regions. Noncoding regions include promoters, enhancers, repressors, and many others, some still being discovered. (Identifying all of the regulatory regions for specific genes is a major challenge in the biology of eukaryotes.) Regulatory regions are usually upstream of the coding region, or toward the 5’ end of the DNA molecule, and are involved in controlling the activity of coding regions.
Applications in Genomics
Published in Sylvia Frühwirth-Schnatter, Gilles Celeux, Christian P. Robert, Handbook of Mixture Analysis, 2019
Stéphane Robin, Christophe Ambroise
accounts for the frequency of the (m + 1)-nucleotides in state g. Such a model is denoted M1-Mm in Muri (1998) as the hidden states (zt) follow an M1 model and the observed sequence (yt) conditionally arises from an Mm model. As an example, coding regions are composed of triplets of nucleotides (codons) that are ultimately translated into amino acids, which constitute the building block of a protein. An M1-M2 model can typically account for this triplet structure (Nicolas et al., 2002).
The polymorphisms in cGAS-STING pathway are associated with mitochondrial DNA copy number in coke oven workers
Published in International Journal of Environmental Health Research, 2022
Xiaohua Liu, Xinling Li, Wan Wei, Yahui Fan, Zhifeng Guo, Xiaoran Duan, Xiaoshan Zhou, Yongli Yang, Wei Wang
The rs 610913 locates in the coding region of the cGAS gene, with changes from proline to histidine upon C to A conversion of the DNA sequence at position 261. Meanwhile, the structure analysis showed that there are structural conformation changes owing to base mutations (Thada et al. 2019). The rs 11554776, located on chromosome 5, is a missense mutant of arginine to histidine. The rs 7380824 belongs to the missense variant of arginine to glutamine, and rs 78233829 is a missense mutant of glycine to alanine. Through covariance analysis, we did not find significant differences in mtDNAcn among these polymorphisms, the rs610913 CA+AA had significant interaction effects with STING rs11554776 GG+GA, rs7380824 CC+CT, and rs78233829 GC+CC on mtDNAcn. The research evidenced that PAHs-exposed workers had the risk of decreasing mtDNAcn of leukocytes, which may be the combined effect of genetic variation and PAHs exposure. It is necessary to take effective intervention measures to reduce exposure to PAHs and protect vulnerable populations.
Production, characterization, and powder preparation of quorum quenching acylase AiiO for pathogen control
Published in Preparative Biochemistry and Biotechnology, 2019
Yue Li, Jing Zhao, Chunshan Quan, Liming Jin, Yongbin Xu, Ming Chen
The AiiO gene coding region of 810 bp was amplified from sequence GU581165 (GenBank) in genomic DNA of O. tritici using the following primers: AiiO-F: 5′–GGGAATTCCATATGATGAAATCCC ATGAAAT (NdeI site underlined); AiiO-R: 5′–CGGAATTCTTAAGCCGTGCAGTC (EcoR I site underlined). PCR amplification was performed with initial denaturation at 98 °C for 5 min, followed by 30 cycles including 98 °C for 10 s, 58 °C for 15 s, and 72 °C for 1 min, and then 72 °C for 10 min. The PCR product was gel purified, digested by restriction enzymes and subcloned into the expression vector pET-28a. The correctly cloned plasmid pET-28a-aiiO was transformed into E. coli BL21 (DE3) to form the recombinant strain.
In vitro functional analysis of human cytochrome P450 2A13 genetic variants: P450 2A13*2, *3, *4, and *10
Published in Journal of Toxicology and Environmental Health, Part A, 2018
Vitchan Kim, Sora Yeom, Yejin Lee, Hyoung-Goo Park, Myung-A Cho, Harim Kim, Donghak Kim
Genetic variations in the P450 enzymes may exert the greatest impact on the fate of therapeutic drugs and carcinogenic chemicals (Lee and Kim 2011; Zhou, Liu, and Chowbay 2009). Toxicogenetics has the potential to predict the precise outcomes of metabolically transformed chemicals and help to manage their adverse effects in advance (Ciliao et al. 2017; Desaulniers et al. 2017; Lukas et al. 2017). To date, at least nine nonsynonymous single nucleotide polymorphisms (SNP) in the coding region of P450 2A13 gene were identified (http://www.PharmVar.org).