A Survey of Methods for Genome Functional Analysis in Comparative Genomics
-
2018-07-20 https://doi.org/10.14419/ijet.v7i3.12.16454 -
Genome Correspondence, Gene Functional Analysis, Gene Expression, Protein-to-protein interaction, Gene Identification, Gene sequence extraction. -
Abstract
In biomedical technologies, Gene functional analysis is an emerging concept in understands the DNA sequence and gene product analysis and gene interaction in different real time medical applications. Finding data sequences of gene functionalities. There are many techniques have been used to progress functionality of functionality of genome analysis. In this paper, we present algorithmic, calculation oriented and mathematical comparison under analysis of genome. We develop techniques for dynamic and automatic calculation of Genome relations; these relations are enabled in automatic identification of orthodox for Genome from redundant Genes in yeast Genome. We present a method to identify automatic protein to protein interaction Based on related patterns related to specific presentations, we observe understand frame of functional proteins were developed to find Gene identification with accurate and reliable formations like sensitivity & specificity. We also present methods for systematic “denovo†identification of motifs. The techniques do not depend on previous information of gene operate and in that way stand out from the present literary works on computational design finding. Based on the genome-wide preservation styles of known elements, we designed three preservation requirements that we used to discover novel motifs. Our comparative results give comparative genomic to process our outstanding of any pieces. Our proposed techniques are flexible to verify comprehensive data genes and provide reliable research on complicated genomes on human specifications.
Â
Â
-
References
[1] Evelina Gasperskaja,Vaidutis KuÄinskas, “ The most common technologies and tools for functional genome analysisâ€, ACTA MEDICA LITUANICA. 2017. Vol. 24. No. 1. P. 1–11.
[2] The ENCODE Project Consortium. An integratÂed encyclopedia of DNA elements in the human genome. Nature. 2012; 489(7414): 57–74.
[3] The 1000 Genomes Project Consortium. A map of human genome variation from population-scale sequencing. Nature. 2010; 467 (7319): 1061–73.
[4] Cooper DN, Krawczak M, Polychronakos C, Tyler-Smith C, Kehrer-Sawatzki H. Where genotype is not predictive of phenotype: toÂwards an understanding of the molecular baÂsis of reduced penetrance in human inheritÂed disease. Hum Genet. 2013; 132: 1077–30.
[5] Shen H, McHale CM, Smith MT, Zhang L. Functional genomic screening approaches in mechanistic toxicology and potential future applications of CRISPR-Cas9. Mutat Res Rev Mutat Res. 2015; 764: 31–42.
[6] Barkholt L, Flory E, Jekerle V, Lucas-Samuel S, Ahnert P, Bisset L, et al. Risk of tumorigenicÂity in mesenchymal stromal cell-based theraÂpies-bridging scientific observations and regÂulatory viewpoints. Cytotherapy. 2013; 15(7): 753–9.
[7] Bishop R. Applications of fluorescence in situ hybridization (FISH) in detecting genetic abÂerrations of medical significance. Bioscience Horizons. 2010; 3(1): 85–95.
[8] Ihle MA, Fassunke J, König K, Grünewald I, Schlaak M, Kreuzberg N, et al. Comparison of high resolution melting analysis, pyroseÂquencing, next generation sequencing and imÂmunohistochemistry to conventional Sanger sequencing for the detection of p.V600E and non-p.V600E BRAFmutations. BMC Cancer. 2014; 14: 13.
[9] Nakazato T, Ohta T, Bono H. Experimental design-based functional mining and characÂterization of high-throughput sequencing data in the sequence read archive. PLoS One. 2013; 8(10): e77910.
[10] Shendurel J, Aiden EL. The expanding scope of DNA sequencing 2012. Nat Biotechnol. 2012; 30(11): 1084–94.
[11] Ko YA, Susztak K. Epigenomics: The science of no-longer-“junk†DNA. Why study it in chronic kidney disease? Semin Nephrol. 2013; 33(4): 1–15.
[12] Kurdyukov S, Bullock M. DNA methylation analysis: choosing the right method. Biology. 2016; 5(1): 1–21.
[13] Bannister AJ, Kouzarides T. Regulation of chromatin by histone modifications. Cell Res. 2011; 21(3): 381–95.
[14] Kimura H. Histone modifications for human epigenome analysis. J Hum Genet. 2013; 58(7): 439–45.
[15] Pabinger S, Rodiger S, Kriegner A, VierÂlinger K, Weinhausel A. A survey of tools for the analysis of quantitative PCR (qPCR) data. Biomolecular Detection and Quantification. 2014; 1(1): 23–33.
[16] Filion M. Quantitative Real-time PCR in ApÂplied Microbiology. Norfolk: Caister Academic Press; 2012.
[17] Rao X, Huang X, Zhou Z, Lin X. An improveÂment of the 2ˆ(–delta delta CT) method for quantitative real-time polymerase chain reÂaction data analysis. Biostat Bioinforma BioÂmath. 2013; 3(3): 71–85.
[18] Malone JH, Oliver B. Microarrays, deep seÂquencing and the true measure of the tranÂscriptome. BMC biology. 2011; 9: 34.
[19] Williams AG, Thomas S, Wyman SK, HolloÂway AK. RNA-seq data: challenges in and recÂommendations for experimental design and analysis. Curr Protoc Hum Genet. 2014; 83: 11.13.1–20.
[20] Prieto JM, Balseiro A, Casais R, Abendano N, Fitzgerald LE, Garrido JM, et al. Sensitive and specific enzyme-linked immunosorbent assay for detecting serum antibodies against MyÂcobacterium avium subsp. paratuberculosis in fallow deer. Clin Vaccine Immunol. 2014; 21(8): 1077–85.
[21] Stanczyk FZ, Clarke NJ. Advantages and chalÂlenges of mass spectrometry assays for steroid hormones. J Steroid Biochem Mol Biol. 2010; 121(3–5): 491–5.
[22] Ascano M, Gerstberger S, Tuschl T. Multi-disÂciplinary methods to define RNA-protein interactions and regulatory networks. Curr Opin Genet Dev. 2013; 23(1): 20–8.
[23] Helwa R, Hoheisel JD. Analysis of DNA-proÂtein interactions: from nitrocellulose filter binding assays to microarray studies. Anal BiÂoanal Chem. 2010; 398(6): 2551–61.
[24] MacArthur DG, Manolio TA, Dimmock DP, Rehm HL, Shendure J, Abecasis GR, et al. GuideÂlines for investigating causality of sequence variants in human disease. Nature. 2014; 508(7497): 469–76.
[25] Meneely P. Genetic Analysis: Genes, GeÂnomes, and Networks in Eukaryotes. 1st ed. Oxford: Oxford University Press; 2014.
[26] Van der Oost J, Westra ER, Jackson RN, WiedÂenheft B. Unravelling the structural and mechÂanistic basis of CRISPR-Cas systems. Nat Rev Microbiol. 2014; 12(7): 479–92.
[27] Rath D, Amlinger L, Rath A, Lundgren M. The CRISPR-Cas immune system: biology, mechanisms and applications. Biochimie. 2015; 117: 119–28.
[28] Gasiunas G, Barrangou R, Horvdath P, SiksÂnys V. Cas9-crRNA ribonucleoprotein comÂplex mediates specific DNA cleavage for adapÂtive immunity in bacteria. Proc Natl Acad Sci U S A. 2012; 109(39): E2579–86.
[29] Mali P, Yang L, Esvelt KM, Aach J, Guell M, DiCarlo JE, et al. RNA-guided human geÂnome engineering via Cas9. Science. 2013; 339(6121): 823–6.
[30] Karen Y. He 1, Dongliang Ge 2,* and Max M. He, “Big Data Analytics for Genomic Medicineâ€, Int. J. Mol. Sci. 2017, 18, 412; doi: 10.3390/ijms18020412.
[31] Manolis Kellis1,2, Nick Patterson1 , Bruce Birren1 , Bonnie Berger2,3,5, Eric S. Lander, “Methods in comparative genomics: genome correspondence, gene identification and motif discoveryâ€, Re-annotation of the Saccaromyces cerevisiae Genome. Comparative and Functional Genomics 2: 143-154.
[32] Lee, T.I., N.J. Rinaldi, F. Robert, D.T. Odom, Z. Bar-Joseph, G.K. Gerber, N.M. Hannett, C.T. Harbison, C.M. Thompson, I. Simon, J. Zeitlinger, E.G. Jennings, H.L. Murray, D.B. Gordon, B. Ren, J.J. Wyrick, J.B. Tagne, T.L. Volkert, E. Fraenkel, D.K. Gifford, and R.A. Young. 2002. Transcriptional regulatory networks in Saccharomyces cerevisiae. Science 298: 799-804.
[33] McCue, L., W. Thompson, C. Carmack, M.P. Ryan, J.S. Liu, V. Derbyshire, and C.E. Lawrence. 2001. Phylogenetic footprinting of transcription factor binding sites in proteobacterial genomes. Nucleic Acids Res 29: 774-782.
[34] Tatusov, R.L., D.A. Natale, I.V. Garkavtsev, T.A. Tatusova, U.T. Shankavaram, B.S. Rao, B. Kiryutin, M.Y. Galperin, N.D. Fedorova, and E.V.Koonin. 2001. The COG database: new developments in phylogenetic classification of proteins from complete genomes. Nucleic Acids Res 29: 22-28.
[35] Ashburner, M., C.A. Ball, J.A. Blake, and G. Sherlock. 2000. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 25: 25-29.
[36] Avinash Yadlapati, Dr. Hari Kishore Kakarla, “An Advanced AXI Protocol Verification using Verilog HDLâ€, Wulfenia Journal, ISSN: 1561-882X, Volume 22, Number 4, pp. 307-314, April 2015
[37] P Ramakrishna, K. Hari Kishore, “Design of Low Power 10GS/s 6-Bit DAC using CMOS Technology “International Journal of Engineering and Technology(UAE), ISSN No: 2227-524X, Vol No: 7, Issue No: 1.5, Page No: 226-229, January 2018.
[38] A Murali, K. Hari Kishore, “Efficient and High Speed Key Independent AES Based Authenticated Encryption Architecture using FPGAs “International Journal of Engineering and Technology(UAE), ISSN No: 2227-524X, Vol No: 7, Issue No: 1.5, Page No: 230-233, January 2018.
[39] G.S.Spandana,K Hari Kishore “A Contemporary Approach For Fault Diagnosis In Testable Reversible Circuits By Employing The CNT Gate Library†International Journal of Pure and Applied Mathematics, ISSN No: 1314-3395, Vol No: 115, Issue No: 7, Page No: 537-542, September 2017.
[40] K Hari Kishore, CVRN Aswin Kumar, T Vijay Srinivas, GV Govardhan, Ch Naga Pavan Kumar, R Venkatesh “Design and Analysis of High Efficient UART on Spartran-6 and Virtex-7 Devicesâ€, International Journal of Applied Engineering Research, ISSN 0973-4562, Volume 10, Number 09 , pp. 23043-23052, June 2015
[41] K Bindu Bhargavi, K Hari Kishore “Low Power BIST on Memory Interface Logicâ€, International Journal of Applied Engineering Research, ISSN 0973-4562, Volume 10, Number 08 , pp. 21079-21090, May 2015.
[42] Korraprolu Brahma Reddy, K Hari Kishore, “A Mixed Approach for Power Dissipation Reduction in Nanometer CMOS VLSI circuitsâ€, International Journal of Applied Engineering Research, ISSN 0973-4562 Volume 9, Number 18 , pp. 5141-5148, July 2014.
[43] Nidamanuri Sai Charan, Kakarla Hari Kishore "Reorganization of Delay Faults in Cluster Based FPGA Using BIST†Indian Journal of Science and Technology, ISSN No: 0974-6846, Vol No.9, Issue No.28, page: 1-7, July 2016.
[44] Sravya Kante, Hari Kishore Kakarla, Avinash Yadlapati,"Design and Verification of AMBA AHB-Lite protocol using Verilog HDL" International Journal of Engineering and Technology, E-ISSN No: 0975-4024, Vol No.8, Issue No.2, Page:734-741, May 2016.
[45] Bandlamoodi Sravani, K Hari Kishore, “An FPGA Implementation of Phase Locked Loop (PLL)â€, International Journal of Applied Engineering Research, ISSN 0973-4562, Volume 10, Number 14 , pp. 34137-34139, August 2015
[46] Meka Bharadwaj, Hari Kishore "Enhanced Launch-Off-Capture Testing Using BIST Designs†Journal of Engineering and Applied Sciences, ISSN No: 1816-949X, Vol No.12, Issue No.3, page: 636-643, April 2017.
-
Downloads
-
How to Cite
Udayaraju, P., B. Siva Varma, P., & Jeevana Sujitha, M. (2018). A Survey of Methods for Genome Functional Analysis in Comparative Genomics. International Journal of Engineering & Technology, 7(3.12), 681-688. https://doi.org/10.14419/ijet.v7i3.12.16454Received date: 2018-07-28
Accepted date: 2018-07-28
Published date: 2018-07-20