Jul 26, 2020
Protocol: Phylogenetics analysis of TP53 gene in humans and its use in biosensors for breast cancer diagnosis
- Sara Da Silva Nascimento1,
- Pierre PhD Teodosio1
- 1LaBECom
External link: https://univisa.edu.br/labecom/
Protocol Citation: Sara Da Silva Nascimento, Pierre PhD Teodosio 2020. Protocol: Phylogenetics analysis of TP53 gene in humans and its use in biosensors for breast cancer diagnosis. protocols.io https://dx.doi.org/10.17504/protocols.io.biz9kf96
License: This is an open access protocol distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Protocol status: In development
We are still developing and optimizing this protocol
Created: July 26, 2020
Last Modified: July 26, 2020
Protocol Integer ID: 39713
Keywords: TP53, Biosensors, Diagnosis of Breast Cancer, Phylogeny, AMOVA,
Abstract
Biosensors are small devices that use biological reactions to detect target analytes. Such devices combine a biological component with a physical transducer, which converts bio-recognition processes into measurable signals. Its use brings a number of advantages, as they are highly sensitive and selective, relatively easy in terms of development, as well as accessible and ready to use. Biosensors can be of direct detection, using a non-catalytic ligand, such as cell receptors and antibodies, or indirect detection, in which there is the use of fluorescently marked antibodies or catalytic elements, such as enzymes. They also appear as bio-affinity devices, depending only on the selective binding of the target analyte to the ligative attached to the surface (e.g., oligonucleotide probe).
METHODOLOGY
METHODOLOGY
Protocol: Phylogenetic analysis of TP53 gene in humans and its use in biosensors for breast cancer diagnosis
Dataset: Initially, 301 sequences of a fragment of the human TP53 gene recovered from GENBANK (https://www.ncbi.nlm.nih.gov/popset/430765060) and participated in a PopSet made available by Hao, X.D and collaborators in 2013 (PopSet: 430765060).
Phylogeny
For the visualization of variable sites: Logos will be generated through the Weblogo3 program (Crooks, 2004). The analysis of the number of populations will be performed with the Structure 2.3 program (Pritchard, 2000) and two different methods are tested: a posteriori probability and ad hoc (k). The “a posteriori” probability will be calculated using an ancestry model with mixed alleles for 20,000 interactions in the burn-in period, followed by 200,000 Monte Carlo interactions via Markov Chain, increasing only the K value (number of populations), which will be from 1 to 10 according to Pritchard's methodology (2000).
For determine the most appropriate number of populations: The Evanno method (2005) will be used, using an ad hoc amount based on the second-order rate of the likelihood function between the successive values of K. Posteriori and k probability tests will initially be applied to the dataset in isolation.
Molecular variance
For the analysis of genetic variability: A project will be created with the Arlequin Software 3.1 (Excoffier et al., 2005). which aims to measure molecular diversity using standard estimators such as Theta (Hom, S, k, Pi), Tajima Neutrality test, paired and individual FST values, in addition to temporal divergence and demographic expansion indices (mismatch and Tau values) by molecular variance analysis (AMOVA) (Excoffier, 1992).
For the evolutionary molecular signal test: In this method, the distance matrix between all haplotype pairs will be used in a hierarchical variance analysis scheme producing estimates of variance components analogous to Wright's F statistics involving nonlinear transformations of the original information in estimates of genetic diversity. Mantel's Z statistic will be used to represent the divergence between possible microhabitats using the MULTIVAR (Mantel for Windows) program (Mantel, 1967).
References
CROOKS G.E., HON G, CHANDONIA JM, BRENNER SE WEBLOGO: A sequence logo generator,Genome Research, 14:1188-1190, (2004). EXCOFFIER, L. AND H.E. L. LISCHER (2010) Arlequin suite ver 3.5: A new series of programs to perform population genetics analyses under Linux and Windows. Molecular Ecology Resources. 10: 564-567. EXCOFFIER, L; SMOUSE, P; QUATTRO, J (1992) Analysis of molecular variance inferred from metric distances among DNA haplotypes: Application to human mitochondrial DNA restriction data. Genetics 131, 479-491. G. EVANNO, S. REGNAUT et. al. Blackwell Publishing, Ltd. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Department of Ecology and Evolution, Biology building, University of Lausanne, CH 1015 Lausanne, Switzerland 2005. HAO XD, YANG Y, SONG X, et al. Correlation of telomere length shortening with TP53 somatic mutations, polymorphisms and allelic loss in breast tumors and esophageal cancer. Oncol Rep. 2013;29(1):226-236. doi:10.3892/or.2012.2098. MANTEL, N. (1967). The detection of disease clustering and a generalized regression approach. Cancer Res. 27, 209-220. PRITCHARD, JK; STEPHENS, P; DONNELLY, P (2000) Inference of population structure using multilocus genotype data. Genetics 155, 945–959.