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Kevan M.A. Gartland, Munis Dundar, Tommaso Beccari, Mariapia Viola Magni and Jill S. Gartland

References 1. Fleischmann RD, Adams MD, White O, Clayton RA. Whole-genome random sequencing and assembly of Haemophilus influenzae Rd. Science 1995; 269: 496-512. 2. C. elegans Sequencing Consortium. Genome sequence of the nematode C. elegans: a platform for investigating biology. Science 1998; 282: 2012-2018. 3. Lander ES, Linton LM, Birren B, Nusbaum C, Zody MC, Baldwin J, Devon K, Dewar K, Doyle M, FitzHugh W, Funke R. Initial sequencing and analysis of the human genome. Nature, 2001; 409: 860

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Zelimir Kurtanjek

-46. 8. Clare A, “Machine learning and data mining for yeast functional genomics”, PhD Thesis, 2003, University of Wales, Aberystwyth, UK. 9. Huttenhower C., Mutungu K.M., Indik N., Yang W., Schroeder M., Forman J.J., Troyanskaya O.G., Coller H. Detailing regulatory networks through large scale data integration. Bioinformatics 2009; 25(24): 3267-3274. 10. Taymaz-Nikerel H, Cankorur-Cetinkaya A, Kirdar B. Genome-Wide Transcriptional Response of Saccharomyces cerevisiae to Stress-Induced Perturbations. Front Bioeng Biotechnol 2016; 4

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Edo D’Agaro

: 2001). 36. Yip KY, Cheng C, Gerstein M. Machine learning and genome annotation: a match meant to be? Genome biol 2013; 14:205. 37. Day N, Hemmaplardh A, Thurman RE, Stamatoyannopoulos JA, Noble WS. Unsupervised segmentation of continuous genomic data. Bioinformatics. 2007; 23: 1424–1426. 38. Boser BE, Guyon IM, Vapnik VN. A training algorithm for optimal margin classifiers. (Pittsburgh, PA: ACM Press: 1992). 39. Noble WS. What is a support vector machine? Nature Biotech 2006; 24: 1565–1567. 40. Hastie T, Tibshirani R, Friedman J

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Hanna Grajek

-342. Lin C. & Todo T. (2005). The cryptochromes. Genome Biol , 6 (5), 220-226. Liu B. Liu H., Zhong D. & Lin. C. (2010). Searching for a photocycle of the cryptochrome photoreceptors. Current Option in Plant Biuol. , 13 , 578-586. Löser G. & Schäfer F. (1986). Are there several photoreceptors involved in phototropism of Phyomyces blakesleeanus? Kinetic studies of dichromatic irradiation, Photochem. Photobiol.   43 (2), 195-204. Lu C. Y. & Liu Y. Y. (2002). Electron transfer oxidation of

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Open access

Kevan M.A. Gartland and Jill S. Gartland

://www.simplot.com/plant_sciences 24. Halterman D, Guenthner J, Collinge S et al. Biotech crops in the 21st century: 20 years since the first biotech potato. Am. J. Potato Res. 2016; 93: 1-20. 25. Armen, J. Arctic apples: Leading the ‘next wave’ of biotech foods with consumer benefits. Australasian Biotechnology, 2015; 25: 50. No. 2, http://search.informit.com.au/documentSummary;dn=296007511823496;res=IELHEAISSN:1036-7128. 26. Smyth SJ. Canadian regulatory perspectives on genome engineered crops. GM Crops and Food 2017; 8: 35-43. 27. Silva KJP

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Guillermo de Anda-Jáuregui, Cristobal Fresno, Diana García-Cortés, Jesús Espinal Enríquez and Enrique Hernández-Lemus

1 Introduction Behind every biological system, there is a program governing the expression of different genes in the genome. In normal phenotypes, these programs will lead to a state of homoeostasis. Nevertheless, alterations to the regulatory mechanisms may lead to pathological conditions. One example of these pathologies is breast cancer. Gene regulatory programs (GRPs) are composed of several genomic and epigenomic mechanisms interacting in a complex, nonlinear fashion. In recent years, genomic technologies have allowed the study of these systems. Such

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Munis Dundar, Satya Prakash, Ratnesh Lal and Donald K. Martin

References 1. Biotechnology Industry Organisation, https://www.bio.org/what-biotechnology 2. Moraes F, Goes A. A decade of human genome project conclusion: scientific diffusion about our genome knowledge. Biochemistry and Molecular Biology Education 2016; 44(3): 215-223. 3. Wilson BJ, Nicholls SG. The Human genome project, and recent advances in personalized genomics. Risk Management and Healthcare Policy; 2015; 8: 9-20. 4. Ulrich CM, Robien K, McLeod HL. Cancer pharmacogenetıcs: polymorphısms, pathways and beyond, Nature Reviews Cancer

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Alena Anatolievna Famina, Sergey Victorovich Malyshev and Oksana Yurievna Urbanovich

Abstract

Grain yield is closely associated with kernel weight. Cell wall invertase (CWI) and sucrose synthase (SUS) are one of the most important enzymes for sink tissue development and carbon partition, and has a high association with kernel weight. Allellic composition of the TaCwi-A1 and TaSus2-2В loci was tested in 79 winter wheat cultivars using a co-dominant markers CWI21- CWI22, which amplified 404 or 402-bp and Sus2-185/589H2- Sus2-227/589L2, which amplified 423 or 381-bp fragments in different wheat accessions respectively. Some samples carried the mutation in the TaCwi-A1 locus that negatively affects thousand-kernel weight (TKW) were shown to have TKW higher than the cultivars and lines that do not have this mutation in their genomes and despite the significant differences in TKW (from 39,4 to 59,8 g), all investigated varieties possess Hap- L haplotype. It can be attributed to the fact that the TaCwi-A1 and TaSus2-2В are only two of the genes associated with kernel weight and its allelic composition analysis cannot explain all phenotypic variances.

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Hanna Aljaksandrauna Bareika, Anatasija Vyachaslavauna Sidarenka and Galina Ivanovna Novik

References 1. Makinen OE, Wanhalinna V, Zannini E, Arendt EK. Foods for special dietary needs: Non-dairy plant based milk substitutes and fermented dairy type products. Crit Rev Food Sci Nutr 2016; 56(3):339-49. 2. Siezen RJ, Starrenburg MJ, Boekhorst J, Renckens B, Molenaar D, van Hylckama Vlieg JE. Genome-scale genotype-phenotype matching of two Lactococcus lactis isolates from plants identifies mechanisms of adaptation to the plant niche. Appl Environ Microbiol 2008; 74: 424-436. 3. Knosaug EP, Ahlgren JA